When I first visited Duke University more than 10 years ago as a faculty candidate, our current Chair, Larry Carin, was the host for my visit. I met several of our faculty members who have been at Duke for some time, as well as senior faculty members that had just joined the department (then-Chair April Brown and then-Director of the Fitzpatrick Center David Brady, among others). What impressed me the most was the enthusiasm that prevailed in the department, about the effort under way to establish a great academic unit with the core values of outstanding education and prominent research. As the recruiting process progressed, Larry recommended a book to me, Good to Great by Jim Collins, which was a very popular title at the time in business management. The book summarizes a research effort analyzing the case of 11 companies that have done extremely well compared to their peers and the general stock market for a period of over 15 years, trying to identify the critical elements that led to building such successful organizations. It was clear to me why Larry was interested in such a study, as this topic was in the minds of everyone in the ECE department at that period in time.

While it is impossible to tell the true predictive value of these types of postmortem analysis, there were some interesting and compelling points that were worth discussing. I certainly was intrigued by the prospect of building up a strong research effort and new educational initiatives in the department, and decided to come join the excitement. Having 10 years to look back upon our department that has gained substantial momentum along the way, it is an interesting exercise to see what aspect of those analyses were relevant to our department’s current path to success. The book describes six characteristics of these success stories, but I will ponder upon three or four that might be directly relevant to academic institution like ours.

Before I put together my random thoughts on these issues, I would like to share the insight some of our colleagues have shared in the “ECE Corner Office” section: I most strongly resonate with Richard Fair’s story on “Change, or Die”. All of our operating environment changes over time, either fast or slow, and the thriving organizations derive their success from how well they adapt to these changing conditions. Such changes often pose threats to successful organizations, yet provide opportunities for upcoming organizations to compete. All of these factors of success cannot be considered unless in the context of constant change: the Roman Empire did not last forever, and neither will the era of those dominant Engineering research/educational institutions of today, no matter how strong you feel their grip is in the current environment. Everything changes, and there lies our opportunity to build a great organization.

The first topic is the type of leadership that is common to these organizations with success. The authors term the type of leadership “Level-5”, characterized by individuals with extreme personal humility and intense professional will. They are the most modest people who never let her/his own ego get in the way of the success of the team/organization, nor quibble about trivial credit taking for personal gains (as the authors quote Harry Truman, “you can accomplish anything in life, provided that you do not mind who gets the credit”). At the same time, they move with incredible intensity when it comes to achieving success for the team or organization. I have personally been blessed with having two mentors I can confidently identify as “Level-5 leaders” throughout my professional training process prior to Duke (one is my thesis advisor, and the other is my manager of three years at Bell Labs). While they do not strive to attract the flashes of stardom, they focus on building the core ingredients of success with ferocious will, eventually achieving outstanding success. Many Nobel Laureates I know in person also feature such a leadership: throughout their career, they do not explicitly pursue the glories of Nobel Prize, but work tenaciously to achieve scientific progress which eventually lead them to such an honor. Look around our department, and you will see many such leaders quietly building up incredible momentum, whether it is the novelty in their cutting-edge research efforts or means to educate the future leaders of our society: I am sure these efforts will eventually lead to outcomes that will have a huge impact in our world.

The second topic is about the process of building the organization by attracting outstanding talent, which many of us can see as an important procedure towards a great organization. We typically struggle with the role of strategic planning to think about where the organization (department) should go, and finding adequate people with the right talent to achieve those strategic goals. One of their findings is somewhat striking (and counter-intuitive): they find in these success stories, that instead of defining a strategic direction and then getting people to get them there, these leaders have done things in the exactly opposite order. Making analogies to people getting on a bus to go to a great place, “they first got the right people on the bus (and the wrong people off the bus), and then figured out where to drive it”. They always begin with “who” rather than “what”, which make it easier to change directions and adapt to the changes that one will inevitably face down the road (remember the “Change or Die” comment from Richard!!). What is an important field of research will inevitably change in ten years, and we want people who can be successful in those future fields. More desirably, we want to have people who can define what will be an important field of research ten years from now, and naturally be the leaders in those fields by driving those changes. I believe we have done exceptionally well on this front. Looking at our faculty members that have joined the department since I came in 2004, we have assembled an outstanding group of people who have become leaders in the field since. We certainly got the right people on the bus without necessarily worrying about exactly where the bus will go (of course, that does not mean that we want the bus to be driven in random Brownian path!!): the classic example is myself, in the sense that I absolutely did not foresee that I will be working so heavily in quantum information science, nor gigapixel cameras, when I accepted the offer to come to Duke. After 10 years, we indeed have a pretty clear direction that our people should be driving towards: such direction as the big data and information analytics and system integration research (current developments in “Future Technologies” initiative). These are some new directions that have become clear opportunities for our department based on the talent and expertise we have accumulated in the past decade, that was not obvious when the these faculty members were brought to Duke. We face an exciting prospect of becoming leaders in these rapidly emerging fields by driving the cutting edge of change in these research fields. These days, I get into interesting discussions with my colleagues that I have the most respect for, on whether we should hire pure talent or strategically in certain areas of research. We are in a bit of a different place compared to 10 years ago: we have some directions along which we want to “drive the bus” now, defined by some great people that have come aboard our bus in the past decade and a half. At the same time, we cannot lose the sight of bringing the “right kind of talent” on board: those that do the most rigorous research yet are capable of pursuing new directions. One cannot build a leading organization by following others. The fact that everything changes works greatly in our favor: if we have people who take some risks and strives to be at the leading edge of those changes, that is the recipe of success for building a leading organization in the future.

The next topic is termed “Confront the brutal facts, yet never lose faith”. This topic indeed is about how organizations face the issue of “change”, especially if it is of the nature and magnitude that completely changes the landscape the operation of their organization stands on. Many organizations face this issue as the world is changing, and again, as our colleague Richard points out, one will die if they do not face the challenge and change. The successful organizations have recognized the nature of the changes that is impacting their existence, and “developed a simple yet deeply insightful frame of reference” to base all of their decisions on in confronting these changes. The changes do not go away if you turn your head the other way. When confronted properly, these changes that are potential threats to one’s existence can turn into the greatest opportunities for success. We know of many great companies of the past, for example Kodak, Zerox and Nortel Networks, who have failed to see the change in the landscape (in these cases, shift to digital imaging and internet-based communication technologies) and disappear. Big changes of similar magnitude might be coming our direction, such as on-line education, federal funding environment for research, and higher education in developing countries, that might change the way Duke operates the education and research enterprise. We have to accept these changes as the new norm, and figure out an effective way to transform ourselves to not just survive but thrive in the new environment. I do not claim to know all the answers at this time, but feel that these crises might indeed turn out to be an opportunity for us to shine and rise to be the leadership status if we can learn to seize the changes and use them to our advantage.

The final topic that is of relevance is developing “simple yet deeply insightful frame of reference” to base all of the organization’s important decisions on. This insight must effectively answer the following three questions: “What can you be the best in the world at? What drives your economic engine? What are you deeply passionate about?” Among these three, the second one is highly relevant for the companies they analyzed (pursuing economic profit), but might be less relevant for an academic institution like ours. Although we cannot ignore the cost of operating an academic institution, I believe neither education nor research should be driven with economic profit as a metric. Our success should be measured by the success of people we train, whether they are undergraduate or graduate students, based on the impact they make in the society. So, we should paraphrase the questions as follows: What can we be the best in the world at, in which we are deeply passionate about, that will make our graduates the most successful once they leave Duke? If we can collectively come up with an answer to these questions and base all of our decisions based on that answer (faculty hiring, how to structure our educational programs, our research portfolio, and strategic initiatives), we should be able to make consistent decisions that will allow us to build an organization that best addresses these goals.

They describe the process of “good to great” transition as a slow yet steady momentum-building process, just like turning a large flywheel. The initial efforts to get the flywheel turning are enormous yet the results are not visible: but they are essential in building up the momentum to get things going. Such build-up process can be painful as the progress will necessarily seem very slow, but once the momentum builds up, spectacular results will follow. We have a lot of institutional experience on this front at Duke: Coach K had 10 years of build-up period before he could establish one of the greatest college basketball programs in the history of the game. While the long-term sustainability is yet to be seen, it took Coach Cutcliff over six years to achieve the success we witnessed this season. Duke’s highly visible educational programs, such as our professional schools and the signature undergraduate program, took a few decades to build up momentum and emerge into national eminence. We have definitely been building up a lot of momentum that is starting to show up in objective metrics (as Larry pointed out in the past few years). Our progress in undergraduate education is based on a long track record of our excellent teaching, as well as important initiatives such as the curriculum reform from 10 years ago that has led to our current educational structure. Based on these momentum-building measures, we have recently cracked the top 20 in undergraduate education in electrical engineering. The rate at which we have been expanding our research is equally stunning: the systematic expansion in research has led to a very high level of success in faculty productivity in various metrics, and our Ph.Ds are making much larger impact in both academic and industry environments of late. There is no question we have built up huge momentum in the past 15 years, and the trend is likely to continue. We are indeed about to make a huge leap, and it is exciting to note that it came as a result of the successful drive that has built up in the past 15 years.

On a closing note, it is important to again point out the message from Richard’s note: Change or Die. After an additional decade, we have the benefit of seeing where the 11 examples of Good-to-Great companies studied by Jim Collins’ team have landed at this point. While some of the companies continue to excel (such as Abbott Laboratories and Nucor Corporation), many companies have fallen victims to the changes that have caught them by surprise. Circuit City has gone out of business after the electronics retail industry largely moved to on-line marketplace. Fannie Mae deviated substantially from their core business values during the housing crisis, and needed huge infusion of public funds just to survive and has not recovered since. These examples show that even the most successful organizations are vulnerable and can lose their prominence in this changing world: a great opportunity for department like ours as we try to build up more momentum and rise in the ranks, but reminds us that the core values of the institution must be maintained for continued success. The essential driving force to adapt to the changes is the ability of our members to take some risk and move into new areas to lead this change: some of our most successful colleagues have done exactly that, whether it’s metamaterials, computational imaging, or machine learning. We should create a departmental environment where our young faculty members are encouraged to take some risks in their research directions. I am convinced that with adequate mentoring and guidance, the types of people we hire will be able to make progress in new areas, eventually growing into leaders of their fields. When we have more people like that, we will naturally become a great department in the leadership position.

One of the most rewarding aspects of being a professor is guiding students as they identify and achieve their educational, research, and professional goals. This mentorship role is often buried within the faculty mission of research, teaching, and service, but it is a core component of our daily activities and responsibilities. As such, faculty mentorship is one of the strongest resources for creating successful outcomes for graduate students…the challenge is to incorporate such mentorship earlier in the graduate experience.

Socialization is defined as “…the processes through which individuals gain the knowledge, skills, and values necessary for successful entry into a professional career…” [1]. In the traditional Ph.D. program, the faculty research advisor imparts this knowledge to the Ph.D. student, most often in the context of being an independent researcher. Typically, this mentorship occurs once the student has completed all coursework and qualifying exams, and is fully integrated into the research group. Yet, for most Ph.D. students, the foundation for completing the degree and much of their future success is rooted in the mentoring provided during the first two years.

One of the greatest dangers for any graduate student after matriculation, and especially for members of underrepresented groups, is the feeling of isolation or the concern that you do not fit into the culture of your research group, department, and/or chosen field of study. As an African-American woman and member of a group that is sorely underrepresented in science and engineering, I am acutely aware of the perils posed during this critical, initial period of graduate study. From the time of matriculation, Ph.D. students require socialization to their research group, department, and institution; yet, the relationship with the faculty advisor is new and under development, and may present its own set of challenges. Therefore, alternative forms of mentorship are an essential approach to the successful socialization of graduate students.

As I reflect on my experiences as a Ph.D. student at the University of Michigan, I find that in many ways, it was representative of the traditional graduate program structure. I was very fortunate to have had an excellent faculty advisor and mentor; but, this relationship did not fully blossom until well after the qualifying exam in my second year. During the first two years of study, like many other graduate students, my academic life was dominated by coursework, interactions with peers and faculty instructors, and preparation for the qualifying exam. Fortunately, my first two years of graduate study were also characterized by peer mentoring, which provided socialization and a sense of community. Peer mentorship occurred spontaneously because the Applied Physics Program at UofM consisted of a common physics curriculum for all students in the first year and required weekly seminars for the first two years.

These requirements created an atmosphere in which cohorts of Applied Physics students often took courses together and formed study groups, and encouraged first-year and second-year students to interact and share knowledge about navigating the program, selecting research advisors, and passing the qualifying exam. Another important aspect of my experience at UofM was that the student body and faculty in science and engineering included a critical mass of underrepresented minorities that provided role models and successful examples for emulation.

Of course, my graduate school experiences are not unique, and the peer mentoring that was instrumental to my success as a graduate student is not the only model for promoting better socialization through mentorship. The growing recognition of the importance of socialization and mentorship in graduate education is well documented. The 2001 ASHE-ERIC Higher Education Report, “Socialization of Graduate and Professional Students in Higher Education: A Perilous Passage?,” identifies the following trends that necessitate reform of the traditional Ph.D. program structure [1]:
Diversity – One aspect of graduate student socialization is that the “backgrounds and predispositions of prospective graduate students” influence their perceptions of belonging to a potential graduate program and their decisions of whether to attend a given institution or not. The impact of the program structure on the ability to recruit diverse student populations (i.e., underrepresented groups, women & minorities) should be considered.
International Graduate Students – As the graduate student population continues to comprise large percentages of international Ph.D. students, it is important to ensure these students receive professional socialization beyond the routine attention to academics and research.
Professionalism – A successful Ph.D. program should not only prepare students for completion of coursework and the research dissertation, but it should instill in students the qualities and sensibilities of professionalism that they will be expected to display after graduation.
Professionalization – A successful Ph.D. program should also provide some guidance and training on issues that have historically been learned through on-the-job training, such as grant-writing or teaching in the case of academia.
Ethics – While ethics are not always explicitly taught in the context of Ph.D. education, the expectation exists that students will be well-versed in ethics issues during their professional careers.
Technology and Distance Learning – As technology continues to enable distance learning and on-line education, it is important to determine how this will impact socialization and transfer of knowledge, especially with respect to Master’s education.

We can see examples of some of these trends being addressed in our current Ph.D. graduate program, such as the Responsible Conduct of Research (RCR) training required by the graduate school, the technical writing and presentation courses required for international graduate students, and the Ph.D.+ Program offered by the Pratt School of Engineering. Less obvious are the actions we can take as faculty and as a department to better support our Ph.D. students and to promote socialization early in our graduate program. Some reforms have been suggested in higher education literature.

In the same 2001 ASHE-ERIC Higher Education Report, the following suggestions for improvement were made [1]:
Modifying the program – “More collaborative, holistic approaches to learning necessitate systemic change that challenges most existing approaches to graduate and professional study.”
Increasing diversity – “Developing greater flexibility and more options for students so that graduates are more versatile, attracting more women and minority group members, and providing better information about careers continue to be among the major areas of improvement advocated by major national commissions.”
Offering support for students – “Graduate programs will have not only to create more supportive and collaborative environments in the face of increasing diversity but also to sustain them over time.”
Modifying faculty and administrative roles – “The relationship of faculty to students should be interactive, collaborative, open, and mutually evaluative. Relationships need not be power based but should be more interactive with faculty-student, teaching, and research relationships more cooperative.”

Austin provides student recommendations for how graduate programs can provide better socialization, especially in the context of preparing future faculty [2]. The following two suggestions are of particular interest:
More attention to regular mentoring, advising, and feedback – “…faculty members should provide regular, ongoing advising and thorough, periodic feedback and assessment. Assessment should help students determine their progress as scholars and future faculty members. Such advising requires department chairs and graduate deans to work with faculty members to develop effective, mutually respectful, efficient advising relationships. In addition to effective advising, reducing the conflicts between faculty members and graduate students is important.”
Regular and guided reflection – “…graduate students should be encouraged to engage in ongoing, systematic self-reflection. Socialization for doctoral students is largely about making sense of graduate school and the academic career, developing one’s interests and areas of strength, determining how one’s values and commitments relate to those in the profession, and developing one’s own sense of place and competence within that profession. The time and support for reflection are important ingredients in the socialization process.”

Austin synthesizes the student recommendations to propose a modest revision of the typical PhD program [2]:
“A revised doctoral program could begin with an opportunity for entering students to discuss with faculty members their intellectual and professional goals. Though students’ goals often change as they gain experience and learn more about the questions of their fields, the initial assessment could be used to begin focused planning and decision-making. A planning session at time of entry could be followed by annual discussions with a faculty advisor about how the student’s goals are changing and how courses, research, teaching, and other experiences are contributing to progress toward the goals.”

Austin also notes [2]:
“Without a plan these recommendations might appear to add more time to a doctoral program or to the work of already busy faculty members. Yet many of these suggestions involve reorganizing, not adding time.”

Chesler and Chesler describe alternative, gender-informed, mentoring models for women engineering scholars [3]:
“Model 3: Collective Mentoring – Collective mentoring is an evolution of the multiple mentor/single mentee model whereby senior colleagues and the department take responsibility for constructing and maintaining a mentoring team. The entire department or organization must establish and ensure the effective mentoring and performance of graduate students and young professionals. In this way, senior colleagues and the department itself send the message that their progress is a priority concern and may create a departmental climate that overcomes some of the obstacles not only to effective mentoring of women, but also to their effective performance, retention, and advancement.”

Davidson and Foster-Johnson suggest specific actions that departments can take to improve the mentorship of graduate researchers of color [4]. Of particular interest is the suggestion that departments create formal mentoring programs in which faculty are rewarded for participation, which should be voluntary, and that choice should be involved in the matching process with mentors.

It is important to note that one of the positive outcomes of being sensitive to diversity is that the community and departmental culture can improve for everyone when the programmatic structure accounts for vulnerable members of underrepresented groups. As Chesler and Chesler state [3], “Organizational change that creates more egalitarian and caring communities will benefit men as well as women.”

I believe that a stronger sense of community, as well as a stronger faculty presence in and commitment to the development of PhD students during the early years of graduate study, are the best ways to improve departmental culture, to combat attrition, to increase diversity of our graduate student population (specifically students from underrepresented groups), to better serve diverse student populations, and to continue to attract and matriculate the very best PhD students. So, to conclude, I pose the following questions:
1) What structural changes can/should we make to our Ph.D. program to promote peer mentoring and to foster a sense of community?
2) How can we as faculty members be active participants, as mentors and advocates, in the socialization of Ph.D. students after matriculation?

With modest effort, creative thinking, and flexibility, I am certain that we can channel our existing mentorship functions into a force for improvement within our department.

References
[1] J. C. Weidman, D. J. Twale, and E. L. Stein, “Socialization of Graduate and Professional Students in Higher Education: A Perilous Passage?,” San Francisco, CA 2001.
[2] A. E. Austin, “Preparing the Next Generation of Faculty: Graduate School as Socialization to the Academic Career,” The Journal of Higher Education, vol. 73, pp. 94-122, 2002.
[3] N. C. Chesler and M. A. Chesler, “Gender-informed mentoring strategies for women engineering scholars: On establishing a caring community,” Journal of Engineering Education, vol. 91, 2002.
[4] M. N. Davidson and L. Foster-Johnson, “Mentoring in the Preparation of Graduate Researchers of Color,” Review of Educational Research, vol. 71, pp. 549-574, 2001.

The recent acquisition of Advanced Liquid Logic (ALL) (http://www.liquid-logic.com/) by Illumina, Inc. of San Diego (http://www.illumina.com/) on July 23, 2013 capped the translation of Duke University research and inventions to a major player in the gene sequencing market place. According to a press release (http://investor.illumina.com/phoenix. zhtml?c=121127&p=irol-newsArticle&ID=1840193&highlight= ) “ALL… has developed a proprietary “digital microfluidics” technology based on electrowetting that precisely manipulates small droplets within a sealed disposable cartridge to perform complex laboratory protocols. This proven technology will enable Illumina to deliver the simplest and most efficient sample-to-answer next-generation sequencing (NGS) workflow.” Whereas gene sequencing is currently done primarily in research institutions, ALL’s technology conceivably could open access to big clinical markets by providing an automated sample preparation front end to Illumina’s awesome sequencing tools. Who would have thought…?

I say this because while our research in microfluidics had good potential for performing miniaturized, complex laboratory procedures, two things would make the events of July 23 rather improbable: 1) a poor track record by microfluidics companies worldwide in commercializing devices, and 2) a poor track record and infrastructure at Duke that would encourage university faculty entrepreneurs and assist the translation of patents and research ideas into spinoff companies.

Poor Commercialization Track Record
The commercialization track record of microfluidics technology in the US was poor – a surprisingly small number of practical commercial devices had been successfully introduced into the market place. Over the past 30 years microfluidic companies have been near-do-wells. Except for ink jet printer cartridges, few microfluidic products had hit a home run! An exception was the iSTAT handheld point-of-care system, launched in the mid 1990’s after burning through about $50M in R&D funding over 10 years. The device used passive capillary flow and embedded sensors to measure blood chemistries. iSTAT’s exit was a successful acquisition by Abbott in 2004 for $392M, and the product is still being sold.

While iSTAT’s acquisition encouraged would-be entrepreneurs in the field, few microfluidic companies have flourished. This is largely due to three things: 1) lack of killer apps, 2) high R&D and manufacturing costs of microfluidic devices to be sold into a relatively small market, and 3) competition from traditional robotic liquid handling systems. (H. Becker, Lab Chip, 9, 2759 (2009)) Thus, in 2000 the microfluidics market was only $34M, excluding print heads and microarrays.

Despite the lack of commercial success, DARPA saw a big opportunity in microfluidics in defense applications and pumped millions of dollars into R&D programs during1997-2002 (Microflumes, Microflips). DARPA’s model was to fund universities who would generate intellectual property, which then would be licensed to companies and presto, translation would happen! Only it didn’t. The costs of getting research out of a university and performing product development were too high. As a result, DARPA would eventually cut funding for microfluidics when no new usable technology emerged to assist the warfighter in the battlefield. DARPA has not funded any new purely microfluidics programs since. Microfluidics had its chance to demonstrate killer applications and translation, and the outcome was disappointing.

No Academic Infrastructure for Translation
Universities, like Duke, had set up licensing entities in the 1990s, like the Office of Science and Technology (OST), to attract royalty revenues and research contracts from companies. Generally, in my experience, this approach in licensing was to hold the line and protect the interests of the university at all costs, rather than to “do the deal.” In addition, it was very difficult for faculty to get patents on. First, Duke’s basic science focus did not encourage patents. Patents didn’t count for much in the university’s appointments, promotion and tenure committee (APT), even though patents were peer reviewed by the PTO. Patents just were not perceived as evidence of scholarship! Second, while OST had been set up to apply for and license patents, unless your Duke patent application came with a prospective licensee who was willing to pay for patent prosecution, your application would likely be rejected by OST. The other disincentive was Duke’s uninspiring patent royalty sharing policy.

It should be evident that in view of the realities in 1998, we didn’t enter microfluidics research with visions of ever spinning out a company from Duke with licensed patents in hand. In the space below I have highlighted some key lessons learned from the 15 year journey of research translation that I believe will be beneficial to others at Duke who have an entrepreneurial yearning. These recollections are my own, and do not represent an official view of ALL or Duke. I start with us going in the wrong direction in 1998.

The Early Duke Work
The late Prof. Allen Dewey at Duke had a vision in 1997 that microfluidic chips could be designed with high integration density using principles learned from computer architecture and VLSI silicon chip technology. He sold this vision to DARPA who funded MONARCH (Microfluidic Operations and Network Architecture Characterizations) during 1998-2002. The goal of this project was to design and evaluate architecture and technology for a reconfigurable microliquid handling system with biomedical applications. As the co-PI my job was to deal with a fundamental problem: how to scale up a microfluidic system in which liquids were driven by micropumps and switched with microvalves, all of which really were not very “micro”. Oh yes, I also was charged with thinking up some biomedical applications, even though my last biology class was in 1958! It was hard work for a semiconductor guy.

What we saw was that workers in microfluidics were trying to implement biomedical operations, like capillary electrophoresis and PCR, on glass or plastic substrates that had etched channels for routing liquids and MEMS (microelectromechanical systems) fabricated pumps and valves assembled on the substrates. Also, electrokinetic flow was starting to become commercialized (Caliper Life Sciences), where liquids were actuated in response to large voltages applied through the liquid along a channel. Crude fluorescent sensing was done through a microscope hovering over the chip.

Designing chips by hand
Microfluidic technology was such that chips needed to be designed by hand – full custom design by specialists. The number of such specialists amounted to maybe 200 designers in the world. However, the tens of thousands of workers who had applications for microfluidic devices had no chip design skills and, thus, no access to chips. This “applications bottleneck” was limiting commercialization.
Our EE perspective saw an opportunity for hierarchical design tools to enable those with applications to gain access to microfluidic chip design by entering the design process at the applications level, or perhaps the programming level. This access could unlock the bottleneck for applications looking for microfluidic solutions. Top-down design rather than bottom-up design! We also envisioned microfluidic chips with “data buses” and processors as used in computer architecture. This is what VLSI chip used – so why not microfluidic chips?

However, as EEs we were in the awkward position of being in a field dominated by chemists and mechanical engineers who understood both the applications and microfluidic technology. Research directions already had been set in motion. In 1998 we were just novices with an EE perspective, which didn’t amount to much at the time. However, taking a different direction that was informed by our experience in a vastly different world (computers, microelectronics, computer-aided design, etc.) would make for an entertaining ride.

At a DARPA PI meeting in 1998 I presented our ideas on hierarchical design and architecture to a skeptical audience that included about 150 of the 200 targeted chip design specialists I mentioned above. What we EE’s viewed as an opportunity was viewed by these chip designers as threatening and unrealistic. First, we were treading on their domain! They argued that the problem of microfluidic chip design was too hard to be captured in a computational scheme with software interfaces for non-specialists. One MIT chemical engineering professor yelled that we should consider architectures based on chemical processing plants, not computers.

Considering the complexities of microfluidics, as it was known at the time, the design of continuous flow chips using valves, pumps and channels was, indeed, too difficult to capture in efficient computational models in computer-like architectures. But a programmable microfluidic processor would be really cool! What was needed, however, was the creation of a new branch of microfluidics that enabled such a radical approach to happen.

Listening to sage advice
Following this PI meeting we were trying to keep from embarrassing our DARPA program manager further, so we kept an open mind to the advice of wiser folks in the field. There was outspoken advice everywhere on how microfluidics chips should be designed. Kurt Petersen, who had founded the most advanced microfluidics company, Cepheid, said that one needs to get away from today’s diagnostic procedures based on fluid boluses. The bolus approach involved placing samples in a reaction tube, adding reagents, mixing, etc, as was done ion a lab. Petersen believed that the bolus approach was too complicated and didn’t scale. “Instead of blindly automating the traditional, inefficient, laborious bolus approach, sample and reagent biochemical processing should be synchronized by a continuous-flow approach in which the biomedical fluids continuously flow through channels, reaction sites, processing sites, and measurement sites.” (K.E. Petersen, et al, Kluwer Academic Pubs., Boston, pp. 71-79, 1998)

Not willing to argue with the likes of K.E. Petersen, we started down the continuous flow approach and burned a year on it. After the first student dropped out and after suffering with architectural ideas that couldn’t be made to work, we determined that the maligned bolus approach was actually a better basis for programmable microfluidics. We simply lacked a way to implement it!

First lesson learned: Struggling with failed research may be a good opportunity for invention.

The dancing droplets
If you are working in a lab, the bolus approach to doing chemistry requires test tubes with people shuttling the tubes from one operation to the next. The chemistry lab is a completely reconfigurable architecture in which any given resource (hot plates, centrifuges, beakers, etc) can be used to perform multiple tasks in any sequence. By contrast, continuous flow microfluidics requires that liquid in a channel flows only sequentially from one resource location to another in one direction. Thus, resource use is fixed and hardwired for a given application. If you make a mistake, there is no going back for rework. If this were the case for microelectronics, we would not have computers!
Thus, the research problem was framed. Since people and test tubes don’t scale to the chip level, you need to be able to actuate discrete volumes of liquids (boluses) and route them through shared resources in a reconfigurable architecture. In computer chips, discrete packets of charge are switched and routed with transistors. But, in 1999 there was no microfluidic equivalent to a transistor that could switch and route discrete volumes of liquids.

The idea for a microfluidic transistor came from a Russian instrument maker who had been a post doc in Cell Biology at Duke. He had a notion of how to move droplets (boluses) on a hydrophobic surface under voltage control. Alex Shenderov was referred to me by a colleague in Biology as someone who knew about liquid actuation using electrowetting and who had filed a patent application on the topic. We had never heard of electrowetting.

The most knowledgeable treatises at the time described electrowetting as “troublesome”, since subtle uncontrolled changes in the liquid/surface interface made this actuation principle difficult to control and too dependent on the liquid’s properties. Also, most of the work in the field was 10-20 years old. Optimistic with our knowledge of modern microfabrication and how to control surfaces, we embarked on building electrowetting-based chips. I hired Alex as a consultant in 1999, and together he and my student, Michael Pollack, built the first working electrowetting microfluidic devices in early 2000. The videos of water droplets moving under voltage control were captivating. DARPA called them the “dancing droplets”, which even got the attention of DARPA’s Director. In late 2000 we put up a web site showing the dancing droplets and had tens of thousands of hits in the first days (http://microfluidics.ee.duke.edu/). Digital microfluidics had been demonstrated. Within a year, over 35 labs worldwide also were working on electrowetting microfluidics.

Second lesson learned: We knew we could get mileage out of cool videos for a while, but the technology had to be understood and applied to real problems. We were the wrong ones to find uses for our technology.

Looking for the killer app
From the beginning we focused on technology qualification by demonstrating microfluidic functions that could be performed on a chip, such as droplet dispensing, droplet splitting and merging, mixing the contents of two droplets, and how fast the droplets could be moved. Would you believe 20cm/sec! Nevertheless, our early focus was on basic understanding.

From this work we demonstrated a microfluidic “toolkit”, which could be used in performing chemical operations on boluses (droplets) of liquid. The new droplet platform drew interest from Prof. Krish Chakrabarty, who saw possibilities in new microfluidic systems and architectures that could be reconfigured on the fly. The concept of programmable microfluidics emerged from Duke in 2001. (J. Ding, K. Chakrabarty, and R.B. Fair, “Scheduling of microfluidic operations for reconfigurable two-dimensional electrowetting arrays,” IEEE Trans. CAD of Integ. Ckts. And Sys., 20, 1463 (2001)) Droplets were akin to bits of data that could be transported over busses and processed under computer control. Microfluidic chips with computer architectures were finally feasible.

Michael Pollack’s dissertation in 2001 would become a “must read” for all future graduate students in my lab. He demonstrated that the technology was robust, but we still didn’t know what it was good for. All we could do was think of the possibilities. One possibility was multiplexed assays. Vijay Srinivasan developed and characterized enzymatic assays on an electrowetting chip. The work was promising enough that Glaxo Smith Kline funded a project to explore electrowetting of simple biological fluids for the development of novel microfluidic assays of protein function, such as kinease assays. Since GSK’s main interest was drug discovery with small molecules, they concluded that our platform would not be useful to them. They also rejected several commercial platforms as well. Microfluidics was still a loser. We continued to scratch our heads for appropriate uses of a cool technology.

The Spinoff Years

Third lesson learned: You can think up uses for your technology, but it’s like pushing on a string.

It is not uncommon for researchers and technologists to try and conjure up applications for their ideas. This is like pushing on a string (supply-side tech transfer). And often times we come up with the wrong ideas. Having someone pull on your string from the demand side will more likely lead to success.

Alex Shenderov would form Nanolytics in 2001 to commercialize digital microfluidics. The company grew to about 30 employees, but eventually failed after trying supply-side ideas underpinned with fickle government funding. Nanolytics would be acquired by ALL in 2007.

Fourth lesson learned: Transferring technology out of a university lab is facilitated by transferring the students as well as the patents.

Michael Pollack and Vamsee Pamula, who in 2002 were post docs in my lab, really believed in droplet technology, so when the DARPA funding cut came, they had much incentive to continue with developing the technology …and eating. Having won the first Duke Start-up Challenge around 2000 (with a wireless mouse product), Michael and Vamsee definitely had the entrepreneurial bug, but were like deer in headlights. It wasn’t until 2004 that Advanced Liquid Logic was founded with two NIH grants in hand. Federal contracts provided R&D funding and responding to BAAs established capability targets for the technology. At this time, ALL’s biggest asset was four former Duke Ph.D. students who themselves transferred electrowetting technology.

With great difficulty, ALL licensed then-issued Duke patents on electrowetting around 2006. Attempts to reach a licensing agreement directly with Duke failed after trying for 1.5 years, so ALL went through Southeast Techventures (STI). STI had been set up during former Pratt Dean Kristina Johnson’s time at Duke with authority to license Duke patents to third parties. Thus STI got the nice licensing fee rather than Duke. Thereafter, ALL would be very aggressive in both acquiring patents and filing new applications, which would give them about 100 issued patents in 2013. Over 50% of those issued patents are jointly held with Duke. ALL essentially bottled up the entire field to become the only commercial player in the US. These joint patents will likely provide a continuing royalty stream to Duke, since they are now owned by Illumina.

While continuing to stay viable with federal funding, ALL also worked to turn prototype devices into stable, cheap, manufacturable systems. It was important to have a robust control box and reliable microfluidic chips in the hands of end users who wanted to try out their own applications. With success, end users might provide the demand-side pull of a killer app. It appears that having a stable, cheap microfluidics platform with good fluidic and electronic interfaces was a key to ALL’s successful acquisition by one of their end users.

Fifth lesson learned: Commercial success depends on a reasonably good idea and a great management team.

Perhaps the most important advice I gave to Michael and Vamsee was: “get a real CEO who has done it before.” As an advisor to the Aurora Funds since 1995, I saw many university spinoffs with great ideas, but with poor management. Often times founding CEOs can do more harm than good. Rich West joined ALL in 2005. He had been founder and CEO of TriVirix Inc. He also was a Duke engineering graduate. With Rich, ALL obtained investor funding without share dilution, set up a Board of Directors and a Scientific Advisory Board, and held down costs. ALL was starting to look like a real company.

Sixth lesson learned: Be prepared to be viewed as a competitor by your spinoff company and with suspicion by your university.

Acquiring patents is an important aspect of commercial success and attracting investors. ALL’s exclusive rights to Duke’s patents on electrowetting meant that our lab at Duke couldn’t do research with other commercial ventures in electrowetting that would “enable” the competition. In fact, in the beginning, our lab was the competition! This competition showed up in applying for federal grants, where Duke and ALL would be applying for the same funding.

The solution was to join forces. In 2005 Duke, ALL and Stanford were funded together by NIH to do a joint program in DNA sequencing on a chip. This type of joint research was the appropriate model for peaceful coexistence. And, it raised Duke’s awareness of what we were doing, although not in a way I foresaw.

As PI on the 2005 NIH grant and co-inventor on patents licensed to a co-PI’s company, the conflict-of-interest alarms sounded at Duke. Oh yes, I also chaired ALL’s Scientific Advisory Board and had options.

Finally, Duke was starting to pay attention to our entrepreneurial activities! In short order a plan was established to “manage” me to be sure I did nothing that would embarrass the university. Whereas I understand Duke’s position, it turns out that a conflict-of-interest plan (COI) would be the extent of Duke’s proactive involvement in our venture. This takes me to the next lesson learned.

Seventh lesson learned: Duke has no evident infrastructure to facilitate the translation of its university research to spinoff companies.

Duke University’s official response to the acquisition of ALL by Illumina for a huge sum of money has been … deafening silence!! For sure, Dean Katsuoleas and my colleagues in Pratt have been excited and congratulatory, and for that I am grateful. But other than worrying about a COI plan, there has been no official recognition that successful commercialization of Duke’s research ever happened on July 23, 2013 at an unprecedented scale for Duke. And Duke even received a check from the deal, as did STI!

I have since learned that Duke’s Office of Licensing and Ventures (OLV) was set up with the express intent to “…translate academic discoveries into commercial products…” by “…direct, daily interaction with faculty, small and large businesses…” (http://olv.duke.edu/index) To their credit, OST and eventually OLV would be helpful in interfacing with ALL’s patent attorneys on issues associated with joint Duke/ALL patents and assignment. Henry Berger was particularly helpful in this regard. But translation of our academic discoveries into commercial products otherwise was done independently of Duke and OLV. It’s seems that ours is the preferred model. Who can argue with success?

While there may be an expressed interest in supporting the growing number of entrepreneurs in the university, the biggest translation in the history of Pratt just occurred without those “daily interactions” with OLV. But, we had a really good conflict-of-interest plan!

Eighth lesson learned: More entrepreneurial opportunities are emerging out of the university, which may fail if Duke intends to continue being agnostic to commercialization by continuing to run a mere licensing shop (OLV) with an uninspiring patent royalty plan for Duke inventors and no recognition by APT.

Keys to Success
Over time, ALL grew in size to about 80 employees. They were very successful in securing federal funding, which paid for R&D and operations. We at Duke were able to join in on funded research that would have been difficult to do without ALL’s stable microfluidic platform. At the same time ALL pursued product development – a user-friendly platform that allowed users to develop applications in their own labs. The key to product development was a reliable microfluidic platform made in a cheap manufacturing technology. Also, ALL aggressively pursued prosecution of intellectual property. They found strategic partners who helped them find applications for their technology. And they were well managed. The acquisition by Illumina will facilitate ALL’s transition from federal funding to a product company driven by one of the hottest apps in town.

I feel like all of those involved in the early Duke research in microfluidics and those involved in more recent technology development efforts at ALL have collectively experienced the ultimate peer review! Granted we all experienced peer review milestones along the way with DARPA, NIH, and NSF as well as the United States Patent Office, but when someone is willing to pay in the range of $100M for access to your ideas, people and technology, well that’s something else.

Our students often ask us for recommendations and feedback about their career path and related topics. I find such types of conversations among the most important part of our job as educators. I would not dare to give advice in this letter, after all, as Oscar Wilde said, “I am not young enough to know everything.” Others are certainly much more qualified than me to advise, for example see President Richard H. Brodhead’s recent convocation speech for the class of 2017 (http://today.duke.edu/2013/08/rhbconvocation13). I simply want to share some of my personal experience and thoughts and invite the interested reader to go for a coffee (or tea in my case) to continue these important and fun conversations. For me academia has been the best job possible, and I will try to convey why.

Let me start by a brief biography since my life experience has without a doubt shaped me into who I am today. I left my country, Uruguay, at the age of 17. Leaving everything behind at such early age is a challenge, one that we as educators must understand since a large population of our students left their homes at a similar age. I first arrived at a Kibbutz in Israel, where I spent 6 marvelous months. Coming from a very socialist background, and after living the majority of my life under a military dictatorship, being in a Kibbutz was not only an incredible way to adapt to a new country, but also a way to experience what I have been reading about and discussing with my youth friends. After that unique experience, I moved to Haifa, and applied to the Technion, Israel Institute of Technology. Like many, I didn’t know what to study; after all, asking 17-18 years old what they want to do when they grow up is in the majority of the cases, a pointless question (I am now 47 and still don’t know what I want to do when I grow up). I knew I loved math (still do), and I thought about applying to the math department. One of my roommates explained to me that it was a mistake, since to get into the math department I had to only get about 75% in the admission exam, and that I should apply to electrical engineering where I needed to get about a perfect score (a simple consequence of supply and demand). So I did, got admitted, and from then, my academic life has been a dream! I spent 4 years working very hard, harder than ever, and got my degree. I loved it so much that I decided to continue to graduate school (or maybe I was running away from the responsibility of a real job?). I was fortunate to be accepted by David Malah to his team, and that was probably my first experience on what a great mentor is and how much he/she can influence the life of a student. Half of what I am today in academia is thanks to David, and the other half to my PhD advisor, Allen Tannenbaum. While I learned a lot about science, engineering and research from them, I learned as much about life. We are still very good friends, and they are the role models for me for the way I want to be with my own students. A good teacher, mentor, and advisor can help you to love what you do.

I loved the Technion, still do; I consider it the best university in the world (sorry Duke, but being second to the Technion is nothing to be ashamed of). I left to MIT for a year, already having a faculty position waiting for me back at the Technion: a dream job. That year was 20 years ago. How I ended up for 3 years at HP Labs in Palo Alto instead of going back to Israel is a long story for another occasion, but this was an industrial experience I recommend to everybody in our field. After that the possibility of moving to the University of Minnesota appeared, and I took it, I don’t regret it at all. I spent 15 great years in Minnesota before coming to Duke in 2012. I had and still have a blast in my job.

I was interviewed a few years ago by the main newspaper in Uruguay on the occasion of receiving the PECASE from the White House (yes, you get to visit the White House, unescorted!), and I was asked why I moved from industry back to academia. I gave many motives, but one (I will give a few more down below) was that I find universities a place where we don’t get old: we keep innovating, meeting new fabulous people, and new and naïve students with great ideas and questions. Students… they can make us so proud!

Let’s continue this line of thought. Every year we teach new students, and hopefully we positively shape their future, like my teachers and mentors helped me to shape mine. Even when we engage in innovative ways of teaching, like MOOCS, we meet new students. While I got engaged with Coursera for many motives, one being the fact that in science and in all new experiments we have to be players and not spectators, without any doubt for me the best part of my Coursera experience were the forums, and to see how naturally students help each other and how we can communicate with people otherwise we would have never had the chance to interact. Knowledge, and in particular the thirst for knowledge, transcends languages, political views, religion, and everything in between.

Academia also permits us to keep innovating scientifically. While all my research in the past 20 years is connected, what I am doing today is very different from what I did 20 years ago, and that is the case for most of my preferred colleagues. I am lucky that I am involved in very challenging research that can be life transformative as well, like our activities in deep brain stimulation (neurosurgery), HIV, and early child behavioral analysis. What can be more fun and rewarding than engaging in challenging activities that have the potential, one step at a time, of helping others?

This brings me to another key reason why I consider this the best job in the world. It allowed me to meet incredible colleagues and human beings. My closer collaborators are also among my best friends, often we first become friends and then collaborators, but sometimes the other way around also happens. I have so much fun, and have learned so much from my collaborators, that is sometimes incredible we get paid to do this (though don’t believe our salaries are so high of course). I don’t want to name all my close friends and collaborators here, we can do that when we get together for that coffee I mentioned above, but let me name one that very sadly, just passed away a few weeks ago: Vicent Caselles. The great friendship and academic collaboration we had is an example of how much I enjoy this work.

I find academia the perfect place to have a great balance between professional and family life. I feel privileged that the flexible time allowed me to pick my kids every early afternoon from school, for over 12 years, while my wife worked. It also allowed me to be at virtually every school event they have, including 9 consecutive years of camping with their school class. My family is my life, and my job allowed me to exercise this. A good friend once told me “the only free people in the world are very rich people and professors, since I can’t be in the first group, I chose to be in the second.” While we work very hard in academia, I believe that much harder than any other job we could do, we work in our own time, and that is priceless.

Yes, there are other great jobs out there, some as exciting as academia, and the world is very different today than it was when I became a professor. Yes, I was very privileged to have outstanding collaborators and students, outstanding program managers for my grants, and incredible support from the administration, and this certainly contributed to my happy life in academia. I always look for other challenges to combine with my current academic activities, other places to contribute and to become excited with. But academia is in my heart, and I hope everybody can enjoy their job as much as I enjoy mine.

I spent a lot of time thinking about what I could offer in a corner office piece that would be distinct from all of the excellent perspectives that we’ve seen so far.  An advantage in coming later in that sequence is I can carve out a pretty focused topic or two, as I’ve tried to do below.

Administration: Sometimes Good

As I’m just about to step down from what is really my first long-term administrative position with day-to-day responsibilities, I have been debriefing myself about what was good, what was bad, and what might I do differently if I find myself in a similar position in the future.  For what it’s worth, here are my some of thoughts on that experience and things that I will consider with respect to any future opportunities.

Many aspects of being DGS were very rewarding.  That sounds a bit fake, I know, but it’s the truth.  It was a new experience that called on different skills than those exercised in the research and teaching parts of academia. I had the privilege of working with some dedicated and talented people (Samantha Morton and Stacy Tantum in particular).  When things are firing on all cylinders, and everyone brings complementary skills and ideas to the issues at hand, the amount that can be accomplished is impressive.  Being part of that kind of team is enormously satisfying.

As DGS, students really appreciate you and the attention and help you can give them.  There are a surprising number of student issues and conflicts that require a neutral viewpoint beyond the parties involved.  Being able to provide that perspective to help find a solution, and more generally assisting students to navigate a sometimes-tricky path towards a Ph.D. is also very satisfying.  Faculty colleagues are also appreciative of your effort, but there’s always an element of thank-goodness-I’m-not-you about that appreciation.  I too had that feeling before I was DGS, and I look forward to feeling it again (sorry Jeff and Krish!).

Another benefit of serving in a position like DGS is that you learn a lot—way more than I imagined I would. I wish I’d read Jeff Glass’s corner office early in my DGS term.  It contains many, many nuggets of useful advice on the soft skills needed for administration, some of which I have learned the hard way.  In my view, the most important of those is this.  Like all academics, I always know best (or think I do).  And there are times when you simply cannot believe the stupidity (from your perspective, of course) of what others are doing or decisions that have been made.  Through this, it is absolutely essential to remember that, deep down, you are usually all on the same team.  You really have many of the same high-level goals, but the boundary conditions that exist for or are perceived by different people in different positions are different.  Keep it rational, try to orient the problem so that it can be defined in terms of a common goal, and don’t let it get personal.  Easier said than done, but with thought and care usually problems can be solved to everyone’s equal satisfaction (or more truthfully, dissatisfaction). This is how you know when you’ve found a good solution.

Administration: Sometimes Challenging

One of the biggest challenges I faced as DGS was what I think is a mismatch between both the quantity and the types of administrative duties the job requires, and the human resources available to dispatch those duties.  Everything I’m about to say is probably obvious to those who have had administrative responsibilities before.  In fact, if I traveled back in time to visit myself to pass along this advice, the younger me would have told the older me that this is all obvious and not to waste his time.  But the degree to which these things matter is much more than I would have guessed. 

Any biggish organization (and I think one that is responsible for around 200 students, as ECE is, qualifies) needs enough people to keep an eye on things all the time.  Something that size can go off the rails very quickly without constant vigilance (especially when students are involved!).  But just as important as the quantity of effort available is the availability of different kinds effort to address the distribution of complexity of the things that need to get done.  In a way, this is the universal impedance matching problem.  The ability to transmit power depends not just on the amount of power available but the distribution of that power within its component parts (V and I, E and H, etc.).  A bad match means a lot of wasted power.

The need to have the right balance of available effort is particularly true when a significant fraction of the job involves interfacing with an organization (the Graduate School) whose administrative procedures have been structured around on departments with grad programs that are far smaller than ECE. These procedures do not scale up in a sensible way and can create a massive and not-always-necessary time sink for the one person with the official authority to handle them (the DGS).  Nan Jokerst mentioned this to me once, long ago, and I had no idea how right she was.

It’s hard to know what is the right distribution of authority and personnel to successful run an organization (although I can say that placing essentially all authority and responsibility on the DGS, as the Graduate School requires, is probably not optimal).  A useful strategy to find a reference point might come from looking at a comparable administrative structure that definitely is successful on many levels.  Having worked closely with them for several years, I consider the Pratt Professional Masters Program to be an extremely well-run group, top to bottom.  They react quickly and sensibly to deal with new issues that arise, and solve problems in a way that leaves a process in place to deal with them when they appear again.  And the program provides great service to its students. 

Admittedly there are structural differences between the ECE grad program and the Professional Masters Program.  But the number of students in each is comparable, and many of the administrative responsibilities scale with student population.  The Professional Masters Program has two full-time, senior-level administrators, and 6 full-time academic, admissions, and support staff (I’m excluding career services which also supports the regular Pratt grad programs). 

In contrast, the ECE grad program has one full-time, mid-level administrator (and I admit I’ve been fortunate to work with two excellent ones), a high-level faculty administrator who devotes whatever time can be cobbled together from that which research and teaching do not demand, and a lot of additional (also excellent) support from research staff who devote far more time and energy to helping with the grad program than they perhaps should.  You may be asking yourself, why didn’t I try to change the amount and distribution of administrative support available?  Ah, but I did, and that ended in one of the most unbelievably Kafkaesque scenarios that I’ve ever witnessed.  Suffice it to say, it didn’t work.

This is definitely not to say that it can’t be rewarding to be responsible for an organization that is administratively lean, especially when it involves working with great colleagues and coworkers as I have had the chance to do.  We were able to grow the program and leave it on solid (maybe too solid!) financial footing, and we also managed to improve a few internal structures and processes (such as the distribution of grad school money among the Pratt departments).  But leading a lean organization has left me a little unsatisfied in the sense that I really wasn’t able to accomplish or improve all of the things that I would have liked.  The sheer volume of routine but time-consuming tasks that probably don’t all need to be attended to by a faculty member (but must be) made that tough.  And should I consider taking on administrative responsibilities in the future, I will take a close look at the distribution of tasks, staff, and authority before saying yes. 

I think much of the above might come across as more negative than I intend, so let me end this part with a prediction.  Right now I’m acutely aware of all of the challenges of administration, and the thought of doing this again is pretty far from my mind.  But as I get farther removed from it, I’m going to start missing some of the positive and even fun things about it.  I bet I’ll say yes to some future opportunity, and I may even seek one out.  But later, not now.

One More Thing

I’d like use this platform to raise one more issue that I wrestle with a lot and definitely do not have solved.  If any of you do have this solved, please let me know and I’ll buy you a nice dinner while you tell me the answer.  I suppose it’s more of a confession: I wish working with Ph.D. students were more uniformly rewarding.  My words were carefully chosen.  In my experience, almost all students are rewarding to some degree.  But there are a lot of times when it is frustrating, especially when so much effort is spent to support them, and what comes back in return is clearly not anywhere near a comparable effort.

In many ways I feel like a coach, herding and organizing and pushing my team of researchers to try to get the most out of everyone’s skills.  That by itself can be a rewarding experience—just look at all of the collegiate and professional coaches who are in it for life.  But there’s a twist that I think applies to all of us faculty: we can still play the game better than most of those we coach. So, where is the right balance between playing and coaching duties?

I know people who have gone to extremes in both directions. I have a colleague who left a faculty position after more than a decade to go to research company because he was tired of students, not him, getting to do all the fun stuff.   But I also know have a colleague at NASA who been PI on more scientific rockets than almost anyone (more than a hundred, I think) who simply loved to manage.  The moment the money came in on a project, he was on telecons, pushing everyone to meet their obligations, and that’s what he wanted to do.

So, obviously, the sweet spot in this balance varies from person to person.  But it is easy to unintentionally slide into a state where you are more coach and less player than you want to be.

A Closing Thought

The things I’ve discussed here all boil down to the most precious commodity in life: time.  We all think about this in the context of work-life balance, among other things.  But within the normal faculty existence the issue of work-work balance is pretty important too.  The number of different hats we are asked to wear is staggering.  Without careful career management, it is very easy to slip into a state in which you have nearly zero time to spend on the things that are actually the most important to you or that are the most valued in academia.  The feeling of loss of control that comes from that imbalance can be very stressful.  Fortunately we do all have the ability to manage our work-work balance, at least to some degree.  But it takes real effort to do so.

Looking back at my publications, I can see that most of my research output during my time as DGS was built on ideas I had before becoming DGS.  Those ideas have by now been mined pretty well.  Now it’s time to spend more time hiding in my office, getting my hands dirty with research, and (hopefully) starting to replenish the bank of interesting new ideas to pursue.  Undoubtedly the best thing about life in academia is that I have that opportunity, and for that I’m grateful.

I have learned a lot from the Corner Office columns written by my colleagues during the past few months. In order to write about something that has not already been covered, I have decided to focus here on the themes of productivity in academia, industry relations, student mentoring, and international collaborations.

My research is at the intersection of Computer Engineering and micro/nano-systems. To be successful as a researcher in this field, significant collaboration with industry partners is necessary. Therefore, let me first elaborate on the recipes for successful university/industry collaboration.

How to Promote University/Industry Research Collaboration

Research collaboration between university researchers and industry experts requires a number of key ingredients. The first requirement for promoting collaboration is that university researchers work on the right problems. What is a right problem to work on? That depends on the expertise of the researchers, the challenges being faced in companies for which no solutions exist today, and on whether the problem is one of research or development of known technology in a company setting. For example, I have never attempted to collaborate with companies in the area of RF circuit design because my knowledge in this domain is very limited. Also, I have never approached industry partners with proposals for providing ready-to-use tools and software.

Early in my career, I was successful in developing significant industry collaboration in the area of system-on-chip (SOC) design and testing, thanks to the right timing (SOC was the new thing in the mid- and late-90s), research grants from NSF that offered matching funds and strong encouragement for industry collaboration, and interactions with many industry colleagues who could have been university professors with their research mindset. I would travel extensively and give talks at companies, and quite often I would get follow-up emails and phone calls for further discussions. NSF funding was a great facilitator and early career awards from Government funding agencies are always a credibility booster. There is clearly a “winner take all” scenario that prevails in these situations, an outcome no doubt of the emphasis on excellence and merit.

In contrast to the above examples of success, a few collaborations failed to take off when the companies insisted on IP agreements and the university lawyers could not agree with company lawyers. Other attempts at collaboration failed when key people in the companies left for other jobs. Finally, collaborative efforts failed when companies showed no appreciation for theoretical insights and dictated the intellectual content of the research, resulting in frustration and a lack of true collaboration.
In later years, most of my successful collaborations with companies have been a result of outstanding PhD students. Most companies view PhD students as a long-term investment and they appreciate the training provided to them by the university research environment. (Some industry sponsors have even told me with a fair bit of seriousness that they do not care so much for the research that professors do, but they greatly value the training of graduate students.) In my case, for example, my collaborations with companies in board/system-level diagnosis and 3D chip testing have been driven by the hard work of many top-rate students. It is of course incumbent on the Professors in such scenarios to ensure that the companies work with only the best students, those who are motivated, qualified, and willing to make the extra effort to understand industry challenges and practical issues.

Therefore, successful university/collaboration requires a research model that is somewhat different from the traditional university model of “blue sky” research, yet retains the characteristics of independence of thinking and a focus on longer-term problems of tomorrow.

University/Industry Collaboration Models

University researchers must sustain their research programs through external financial support, the right mix of research topics, and high-quality graduate students. In my field (and I believe this is true for many of my colleagues), there is the added burden of ensuring that research is directed towards problems that are relevant to medium- and long-term industry needs, and students are adequately trained for a career in the semiconductor industry. I am faced with a vibrant industry community that includes top-rate researchers and visionaries. Therefore, I have to always ask myself: “How should university researchers engage with this community, and conversely how should industry interact in a meaningful way with the university community?

Several engagement models come to mind as I think about this question:

The oblivious model: Each side deliberately ignores to a large extent the existence of the other, and limits engagement to occasional meetings at conferences and student hiring. This model allows freedom of thought in the most complete sense for a university researcher but deprives the researcher from early access to exciting developments in industry, and it deprives companies from the benefits of harnessing the intellectual prowess and creativity available at universities. Unfettered thinking can lead to major breakthroughs but success stories are rare amidst a large body of mediocre, low-impact research.

The consulting model: A university researcher provides expert opinions and insights on a specific but narrowly defined problem of interest to a company. While such short-term engagements are certainly more meaningful than the oblivious model, they often do not provide benefits to the larger community. Neither do they foster longer-term synergistic relationships. The company dictates the problem to be solved and a university professor completes a “homework assignment”.

The directed research model: Companies often provide research funding to universities in the form of contracts. Many of us in academia have been recipients of such contracts and we have undoubtedly benefited from the support and mentoring provided to graduate students, access to industry data, and guidance on research directions. However, it is often frustrating for university researchers to navigate the hurdles of IP agreements between universities and companies, and the barriers placed on publications and broader dissemination of research findings. In addition, identifying concrete deliverables a priori and meeting them in a timely manner can detract from the intellectual freedom necessary for high-quality research.

The true research collaboration and partnership model: A research collaboration can be most successful and lead to longer-term benefits when the partnership is between equals, research is truly open, problem areas and specific topics are discussed without excessive consideration of commercial benefits for the company, and where mutual respect reigns. Funding in the form of gifts with no IP barriers or burden of deliverables is surely the best way to sustain longer-term collaboration that is beneficial to all. University researchers have the luxury of being able to think in unconventional ways and “out of the box”. Indeed it is incumbent upon them to do so. They can identify and exploit theoretical methods from other fields, as well as form connections between problems that are not obvious to an industry practitioner. In addition to gift funding, such an interaction model is sustained and enriched by the participation of industry mentors in PhD theses committees, industry internships for students, and systematic transition of basic research into industry practice and automation tools. Quite often, such collaborative relationships transcend university/affiliations; collaborations continue in the form of new institutional partnerships when key personnel change jobs.

Irrespective of the collaboration model between university researchers and companies, constant vigilance on both sides is needed to ensure that added value is an outcome of the collaboration, industry sees more value than simply a pipeline for trained graduates, and university researchers find the collaboration to be an enriching intellectual experience. From a university perspective, successful industry collaboration requires a change in mindset—publications should no longer be the only yardstick for measuring success. Publications will always arise from a truly open and success research collaboration, hence the emphasis must be placed on professional relationships that build trust and mutual respect. Making unreasonable demands for funds, data, or resources is counter-productive for a university researcher while the industry collaborator must understand that university research is not expected to lead to ready-to-use design flows or tools. We must start modestly, take small steps, and then go on to bigger things. That is surely a recipe for success.

The Right Research Topics

Academic freedom is a great thing. I am free to work on research problems of my choice, but of course with the caveat that I have to be able to sustain the research program through external grants and the right graduate students. It is tempting to be opportunistic and target research topics that are in vogue, where there is a lot of federal funding, or where there are significant opportunities for collaboration. Such opportunism helps a faculty member in many situations, but the agility with which we change research focus or jump into new areas must be sustained through a genuine appreciation of the area at an intellectual level. When we choose a research topic, we have to ask ourselves whether we will be as excited by this topic after 5+ years, whether our liking for it borders on the obsessive (obsession is sometimes good for research!), and whether we can in reasonable time reach a sufficient level of excellence to compete successfully and be rated as among the very best in the field. I always remember the words of George Bernard Shaw: “Take care to get what you like or you will be forced to like what you get”.

As engineers, we often have to grapple with the need to link new ideas with applications. Conceptual advances in theory or new breakthroughs in technology are often reined in by the need to justify practical applications. Whenever in doubt in such situations, I remind myself of the words of Prof. Herbert Kroemer from the ECE department in UC-Santa Barbara, who won the Nobel Prize for Physics in 2000: “The principal applications of any sufficiently new and innovative technology have always been—and will continue to be—applications created by that technology.” This is also referred to as Kroemer’s Lemma of New Technology! The logic is circular, but so is research and so is the universe in a metaphysical sense.

So how do we start working on a new problem? Our PhD degrees have prepared us to learn new things, and to be prepared to change (as eloquently expressed by Richard Fair in his Corner Office column). First, we have to use our imagination and look beyond the short term. In the words of the French Nobel laureate Andre Gide, “One does not discover new lands without consenting to lose sight of the shore for a very long time“. Also, we have to start “doing” and not just “think” all the time. Finally, we have to be prepared to go against the general consensus and take risks judiciously. I like the pictures below, which express just these thoughts (adapted from originals by Rob Rutenbar, now at University of Illinois).

Chakrabarty_1bChakrabarty_1a

 

 

 

 

 

 

 

 

 

 

 

 

In my experience, I have found that it helps to have one core area (“home”) where I can maintain a leadership role over a long period of time. With the sure footing of a home base, it becomes easier to feel more secure, and to “roam” and explore newer areas. Research must lead to impact; of course, impact can be measured in different ways, but as a researcher, I need to know in advance how the impact will be measured when I start a new project. There is a certain of sense of speculation involved since we have to select research topics that are just emerging, so that we can ride the wave and be a leader. So this process is a lot like buying stocks.

When I started my academic career, a mentor advised me to always have three bags of ideas. The first bag should contain ideas that could be explained quickly to a practitioner, and through which I could get industry projects and make immediate impact. The second bag should contain ideas for longer-term projects, possibly leading to PhD theses, multi-year grants, and good publications. We should not underestimate the importance of the second bag and revisiting with a critical eye what we already know. Again, I quote Andre Gide: “Everything has been said before, but since nobody listens we have to keep going back and beginning all over again.” The third bag is the most interesting, since it should contain the most high-risk ideas, which might not even be appropriate for graduate student research, might require years of thinking and mulling, but which might one day lead to my magnum opus. I still remember this advice and I make a conscious effort to have all three bags with me!

Another way of looking at research topics is to imagine the figure shown here. My portfolio of research problems at any time includes several points in this space. This strategy has served me well and, again, it reminds me of how we invest money in stocks.

Chakrabarty_2How to Mentor Students

Students are arguably the most important “products” of any university. We do not manufacture artifacts for the marketplace. Instead, we train students for the workplace for a wide range of professions. Hence student mentoring is for me a key job requirement. Here let me dwell on my philosophy of working with graduate students.

Broadly speaking, my role as a mentor for graduate students aspect involves two important tasks: (1) Explain a big problem; (2) Give sound advice and be a trusted guide. I read somewhere long back that a good mentor must be a good listener (key to good communication), be a good problem-solver, and be a good observer (able to spot “problems”).

The first job of an advisor is to dispel fear from the minds of students. When a grad student starts research, we would expect the student to start with a clean slate, i.e., more like a blank sheet of paper on which nothing has been written yet. However, we often overlook the fear mentality that prevents the student from taking the first steps towards leaving his or her imprint on this sheet of paper. In my role as advisor, I take inspiration from India’s Nobel laureate poet Rabindranath Tagore, who penned these famous lines in Gitanjali (the poet’s own translation from Bengali):

Where the mind is without fear and the head is held high;
Where knowledge is free;
Where the world has not been broken up into fragments
By narrow domestic walls;
Where words come out from the depth of truth;
Where tireless striving stretches its arms towards perfection;
Where the clear stream of reason has not lost its way
Into the dreary desert sand of dead habit;
Where the mind is led forward by thee
Into ever-widening thought and action;
Into that heaven of freedom, my Father, let my country awake.

A fearless and proud student is almost always a productive student. All we have to do is to appeal to a student’s pride, and there is hardly a better motivator than the pride that comes from having achieved something notable. A fearless student is never afraid to ask why, question the status quo without being dismissive, and seek answers to difficult questions. In the words of Aristotle, “It is the mark of an educated mind to be able to entertain a thought without accepting it.” Furthermore, students have to understand that success in research requires patience and an inner calm that helps us focus our creative energies (yoga and meditation are of course highly recommended!). When I was new to Duke, Provost Peter Lange told me: “tenure is not an event, it is a process”. Likewise research is not a sprint, it is more akin to a marathon. Again, in the words of Aristotle, “We are what we repeatedly do. Excellence, then, is not an act, but a habit.”

Every student is different, and instead of insisting that every student adjust to my style or personality, I make efforts to understand the unique needs of students and meet them halfway. I have, at various times, provided gratis service (of course with the rider that I am not a professional in these matters!) as a marriage counselor, tax advisor, immigration consultant, mental health counselor, personal safety guide, etc.

International Collaborations

One of the most satisfying aspects of my job is the opportunity to collaborate with outstanding researchers in my field all over the world. The Duke brand name is a privilege, and it is an honor to be a Duke ambassador in these activities. International collaborations offer many benefitstangible and intangible. Tangible benefits include the opportunity to work first-hand with students who ultimately join Duke and work with me for their PhD degrees. This is an example of risk mitigation, whereby I already know a student through a joint project before he or she joins our PhD program. I regularly host visiting researchers from overseas and their presence at Duke for short- to medium-term visits enriches the graduate school experience for my PhD students. Other tangible benefits include participation in joint research proposals (such as EU projects, special programs at NSF, grants from EPRSC-UK and the National Science Council in Taiwan), and an understanding of cultural differences that help me to do a better job in teaching and student advising at Duke. Many companies today are globalized, and US students must understand the challenges and opportunities involved in such a work environment. Researchers from other countries often approach the same problem in different ways, and many of my most satisfying research accomplishments have been inspired by different ways of problem solving. In addition to my Duke family of graduate students (present and graduated), I have a large extended family worldwide that includes students from nearly all the countries that I have visited. Good working relationships with the best researchers all over the world (and an understanding of how they carry out their research) helps me to be a better Editor-in-Chief and Associate Editor of journals.

I have always valued efforts aimed at bridging different cultures, and additional benefits of international collaborations include the opportunity to see amazing places, enjoy exotic food, and learn about history, politics, languages, architecture, lifestyles, and religions. The list is endless.

My colleagues Dan Sorin and Chris Dwyer like to do some good-natured leg pulling. They calculated my average velocity based on all the international travel that I do. Of course, they were technically incorrect; my average velocity is zero since I always return home to Duke. What they actually calculated was my average speed, which admittedly is a really large number.

Final Words of Advice

In my experience in mentoring graduate students and working with colleagues from other countries and cultures, I have learned that I can make a point much more effectively using allegories. So here are three stories with their associated morals (told to me by a friend in Germany).

Lesson 1
A crow was sitting on a tree, doing nothing all day.
A small rabbit saw the crow, and asked him, “Can I also sit like you and do nothing all day long?”
The crow answered: “Sure, why not.”
So, the rabbit sat on the ground below the crow, and rested.
All of a sudden, a fox appeared,
Jumped on the rabbit… and ate it.

Moral of the story: To be sitting and doing nothing, you must be sitting very, very high up!

Lesson 2
A turkey was chatting with a bull.
“I would love to be able to get to the top of that tree,” sighed the turkey, “but I haven’t got the energy.”
Well, why don’t you nibble on some of my droppings?” replied the bull. “They’re packed with nutrients.”
The turkey pecked at a lump of dung and found that it gave him enough strength to reach the first branch of the tree.
The next day, after eating more dung, he reached the second branch.
Finally after a fortnight, there he was proudly perched at the top of the tree
Soon he was spotted by a farmer, who promptly shot the turkey out of the tree.

Moral of the story: Bull*#*@ might get you to the top, but it won’t keep you there.

Lesson 3
A little bird was flying south for the winter.
It was so cold, the bird froze and fell to the ground in a large field.
While it was lying there, a cow came by and dropped some dung on it.
As the frozen bird lay there in the pile of cow dung, it began to realize how warm it was. The dung was actually thawing him out!
He lay there all warm and happy, and soon began to sing for joy.
A passing cat heard the bird singing and came to investigate.
Following the sound, the cat discovered the bird under the pile of cow dung, and promptly dug him out and ate him!

Morals of the story:
1) Not everyone who drops s$*t on you is your enemy.
2) Not everyone who gets you out of s$*t is your friend.
3) And when you are in deep s$*t, keep your mouth shut.

The above lessons can be useful for all of us, as we navigate different roles at different times in our academic career.
In closing, I stress that productivity in an academic career is not possible without a supportive environment. I have been fortunate to have many excellent role models in Duke ECE and an administration that has always been appreciative of my efforts in research, mentoring, industry partnerships, and international collaborations. When I first arrived at Duke, I was under the impression that I am expected to work on a specific problem and in a specific area. I asked Loren Nolte (the department chair at that time) what problem I should work on. I can imagine how much amused Loren must have been, and he still chuckles and reminds me of that episode. I guess I have come a long way since then!

If it had taken me as long to figure out the first words of my first lecture as it has to figure out the first lines of this Corner Office, I am not entirely sure I would have ever made it to my first class sixteen years ago. I am faced now, as I was then, with a new experience, having been given a unique opportunity to talk about my experiences with people in my community. Then, it was all about the software tools I thought would be useful for our undergraduate students – but which were not covered in any one class. Now, it is all about what I think might be useful for teaching our undergraduate students.

I am going to focus on what happens after setting a schedule and getting the right space and teaching assistant support for a course and coming up with a schedule – mainly because I just discovered that writing about those processes took over 1400 words and didn’t really get to the heart of the questions I have been asked to answer! So, now assume you have a course to teach, a place to teach it in, an idea of what needs to happen with the syllabus, and a spherical cow radiating milk isotropically. The latter may not be relevant, but I hope it brought to mind a whimsical image.

The items I want to talk about are lectures, homework, laboratory experiences and assignments, and assessments.

Lectures

For an established class, deciding what to do with a lecture can either be very easy or very hard. Not quite as hard as deciding when it is time to change research directions – Nan Jokerst, Jeff Glass, and Richard Fair all discussed that particular conundrum in their posts – but still there is very much a sense of attachment to the material in a course and the way it might be conveyed. For example, I still mourn the loss of a lecture on Newton Polynomials that was removed from Computational Methods to make room for a more robust discussion of integration. But with only 35 hours to work with, and having to account for tests, and logistics, and everything else, there is only so much that can go into a course. I know for myself that I have to remain vigilant that I do not let something that is personally interesting – but which does not provide the students with an appreciably or proportionally greater understanding of the material – take time away from other things.

My notebooks for classes have day-by-day dividers in them so I can put the notes and references for a particular day in their own place. I also generally try to use a Sympodium or a slate PC for writing “on the board.” This does a number of things for me: I can use many, many colors; I can go back to previous slides if someone needs to see them; and I can print out what I wrote on a given day and put it in the notebook. I thus have a history of the semester’s material that I can use to improve things in the next cycle.

There are some disadvantages to using a Sympodium. First, you are stuck standing in one place – and if you are in Teer 203 or Schiciano, that one place may be relatively far from where the projection screen is. Second, you are beholden to the resolution of the screen for how much material you put up at once. Third, when you point to something and say, “look here,” no one has any idea what you are talking about unless you make some obvious motion with the cursor or use a laser pointer. Or, in Teer 203, come out from behind what I call the Wizard of Oz Box, walk on stage, and actually point to something.

For a new class, deciding what material to be included and how to parse it out into lectures can be very difficult. For Mechatronics, I made a list of the various topics I wanted to cover, wrote lecture notes on my tablet for them as one continuous file, and then began cutting and pasting the material I got through on a particular day. I then looked at how the course coverage was matching the specific items listed for each day on the syllabus and adjusted. As it happens, a few weeks into that class I realized that the pacing and content thus far were not working; I ended up taking a few lectures to go back over some territory and re-mapped the syllabus for the rest of the semester.

Regardless, I always try to work in a couple places where the students can work together on some aspect of the course we have discussed. For introductory classes, that might mean finding equations that will model a circuit or writing a bit of MATLAB code; for upper level classes, it might mean coming up with a plan to implement a particular controller. Whatever it is, the process of engaging the students with the material and then hearing back their answers will serve two purposes. First, it will get them talking – which always helps if a few of them are a bit sleep-deprived. Second, it will give you a little bit of instant information as to how the material is coming across. There may be groups who cannot come up with correct answers or who ask clarifying questions while they are working – finding that out during the lecture, when there is still time to provide some form of clarification to the whole class, can be very valuable.

I also try to stay on top of campus news to make sure I know what is going on with our students in general. A quick read of The Chronicle (Duke’s, not “of Higher Education”) can be very valuable in learning what events our students see as important. Including those items in a lecture can also help reinforce the notion that We Are Duke. This can take place in the minutes just before class starts as people are filing in or even during a lecture if it is relevant.
Mainly though, be sure to take Nan’s advice on teaching: “Be organized and enthusiastic, teach clearly, and treat the students with respect, and you will do well as a teacher.” (ECE Corner Office by Nan Jokerst, 02 Nov 2012)

Effectiveness of Homework Assignments

I have taught a fairly broad range of courses – everything from Introduction to Engineering Computer Programs to Seapower & Maritime Affairs – and for each I have had to determine the function of homework. There have been some common components:

-  Homework is most effective when the feedback loop is accurate and efficient. Students should get back thoroughly examined material in a reasonably short period of time. I have always worked to get the number of teaching assistants that will meet this need. For the classes I teach, I also provide TAs with a set of solutions and a grading rubric. If possible, I will have assignments split into parts and I will have one TA grade all submissions for a part. In ECE 110 I have two grader TAs, so students turn in a Part I and a Part II for each homework. All 40 people will have their Part I graded by the same person, which hopefully leads to more consistent grading. There is a bit of overhead involved – two entries in the gradebook and two piles to collect and redistribute – but the overall process has worked well.

-  Homework is most effective when there is a means of comparing the finished work against legitimate processes. This does not necessarily mean “a correct solution,” because oftentimes work done at home will and should provide open-ended opportunities for students to develop their answer in a way that is unique and is in keeping with the principles established by the course. Doubtless, there are some assignments where there will be A Right Answer – but generally there are multiple different paths to get there.

-  Homework is most effective when students have the tools to complete it. An assignment does not need to look exactly like an example from the book or from a lecture, but there should be some reasonable expectation that the underlying information and skills required to solve the problem are available to the student given a reasonable amount of work. Which, of course, means defining “reasonable.” Any takers?

One part of how I use homework which varies from class to class is the notion of collaboration. This is a complex issue. There is value in requiring that an individual, alone, gathers all the information necessary to develop a solution to a given prompt and develops that solution alone. There is value in having that same process be shared by a group with all the viewpoints that may be discussed along the way. What is critical, however, is giving clear guidance about collaboration policy. Gary Ybarra and Lisa Huettel, for example, have crafted the controlling document for ECE 110. The end result establishes what is and is not allowed, gives examples, and even informs the students of historical repercussions for failure to abide by the rules. It also notes that there is a “gray area” and encourages students to contact instructors in those cases.

Laboratory Experiences and Assignments

For those classes with hands-on labs, the experience and insight gained from performing the work, analyzing the results, and crafting the conclusions can be a very powerful component of the course. For the Computational Methods course in particular, labs are the primary means by which students will actually learn the material. For other courses, such as Fundamentals of ECE, labs are the avenue students take to become familiar with building blocks of ECE. The most effective laboratory experiences I have seen have some common components as well:

-  Laboratory experiences are most effective when they complement or clearly supplement lectures and readings. If a lab involves material that has not been covered yet, students can either miss the point or get frustrated at not being able to understand what is going on. If a lab is going to introduce some new concept, it is very important that the period during which the lab is performed by prefaced with a period of instruction on that material. That may involve a class lecture, an in-lab talk from a laboratory TA, or clear reading material – preferably with examples. If the lectures for a course start slipping behind for some reason, consider re-working the lab schedule or determine a means by which the students will be otherwise prepared to truly learn from the laboratory experience.

-  Laboratory experiences are most effective when there is an assignment requiring that the student takes information from the lectures, from the readings, and from the laboratory itself and combines them to demonstrate a broader understanding of the material as a result of having done the lab.

-  Finally, laboratory experiences are most effective when they work. Admittedly, learning the mechanisms by which an experiment will not succeed can be very important, but for early classes where students do not have sufficient troubleshooting experience or theoretical knowledge to “fix” a malfunctioning lab setup, care must be taken to create a laboratory experience that provides legitimate data and supports the theories discussed in class. Also, whomever runs the lab must be prepared as well.

Assessments

This section of my post will not be on the test. But it will be on tests. In many courses, especially early ones, a healthy portion of the assessment of a student’s learning in a course will be done via tests. And, depending on the course, of the 35 hours devoted to a standard 1-credit course, tests generally take between 0.83 hours and 1.875 hours total depending on if you have two or three tests and whether you teach in 50-minute or 75-minute sections. That either represents a small or large portion of the whole depending on your perspective. In my experience:

-  Tests are most effective when they can be completed in the time allotted. I know there will be some disagreement here, and my experience is biased by the nature of the courses I teach. I also want to emphasize the “can” here – there is certainly no requirement that every test be written such that every student in a course can complete it. But there does need to be a sense that a well-prepared student can complete the tasks at hand in the time allotted. For some of my classes, this has meant moving the test to the lab, where the students could use 170 minutes to work on the assessment. I will say that this is one area I continue to struggle with – as much as I try to account for my experiences as well as the fact that it will not take me any additional time to interpret a problem (since I wrote it), I still get this wrong.

-  Tests are most effective when they efficiently cover the breadth and depth of material. I will generally give a class a “road map” for an upcoming test. For Computational Methods, the first road map is a 5-page long outline of concepts and commands totally 23 topics with several sub-topics. The second is a 1-page long outline with 6 topics (including “Know everything from the first test”). Once I make that map, I work to have some aspect of each part show up somewhere on the test.

-  Tests are most effective when they ask valid questions. I have managed to give some test questions that were either impossible or trivial. Allow me to illustrate from ECE 61L Test III Fall of 2001:

Gus' Corner Office Image

Just before I went to copy the test, I decided to be clever and ask for the current through resistor R3 instead of the inductor current. Hilarity ensued. Almost everyone got a 0 for that problem, which means almost everyone got a perfect score on that problem.

Given the pressure that students feel during a test, and given the contribution of the testing process (studying for, taking, and getting back) to the learning process, it is crucial that tests be legitimate.

Lessons Learned

I’ll cap this off with what I think I have learned from the experiences I have had teaching so far.

-  Keeping things organized is essential, especially for larger classes. For me, that means having a spreadsheet with all my to-do items for each of my classes as well as structures in place for exchanging materials with the TAs and returning materials to the students. For my Mechatronics class, as an example, I have a rolling cart with 80 hanging folders – one per student. TAs return all graded material to each student’s folder, and then I roll the folder to class or lab for recovery. It greatly reduces the time it takes to have students find their stuff and gives me an easy visual way to see if people have been showing up to class.

-  Keeping track of “where we are” is crucial. I generally teach Monday and Friday, and a great deal goes on between lectures. Be sure to note for yourself where you actually stopped in a given lecture, not what the schedule said you were supposed to cover.

-  Knowing where things fit into the grand curricular scheme of things can really help. I have a head start here comprised of having gone through Duke as an undergraduate, of having had appointments in three departments, and of having taught classes for all four. Since I teach or run lab for all incoming engineers, knowing the different degree requirements for all of Pratt is a job requirement. Knowing what goes on for all of ECE on the undergraduate side – not just within on curricular group – can help you include connections between the material you are teaching and the material in other classes. That, in turn, can give the students a better sense of the big picture.

-  “Cooperation Without Compromise” – I first heard this as the motto of the Navy Chaplain Corps. As an aside – it is decidedly more appropriate here than the motto of the part of the Navy I was in, the Seabees, which is Construimus Batuimus (“We Build, We Fight”).

The idea is that there may be times you need to work to cooperate with a student or group of students without compromising the integrity of the course or your own integrity. For example, some of our varsity athletes have rigid travel schedules – working with them in advance to overcome those obstacles may involve moving deadlines or providing other accommodations. Other students will have life events that merit consideration. And that consideration takes time and effort on your part, but it will be worth it.

-  Be approachable. Come to class a little early and stay a little late if students want to bend your ear. For your office hours, emphasize that the main purpose is to discuss concepts from the course, but that conversations on other topics are certainly welcome too.

If any part of this has been useful, there are many members of the staff, faculty, alumni, and students who should get the credit. The rambling, however, is on me. Duke in general and Pratt specifically have been my home now for nearly 24 years, and I wouldn’t have it any other way. I consider it a very real privilege indeed to be a member of the ECE faculty, and I’d like to thank Larry for the opportunity to contribute this post. I have learned much from the posts that have been in the series so far and look forward to reading more.

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