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2014 December 23

A long PhD is not a bad thing

In response to, where she argued in favor of 5-year PhDs, and producing many papers as a grad student, I commented

I spent 8 years on my PhD (of course, I changed fields from pure math to computer science to computer engineering in that time). I only had a few papers when I was done, but I was in a hot new field and got a tenure-track position immediately. Unfortunately, it was not a good fit, and I ended up moving to another institution after 4 years, where it took me 7 more years to get tenure. So my BS-to-tenure time was 19 years. (The second job was a good fit, and I’m still at that university, though in a different field and in a different department.)

I find it difficult to advise students to race through grad school or to write huge numbers of crappy papers. I think that it is more important for students (and researchers in general) to write one or two high-quality papers that might actually make a difference.

Of the papers I wrote in grad school, one has never been cited (probably only one other person ever read it), one is my 6th most-cited paper (350 citations in Google Scholar and 86,600 hits with Google), and one has had very modest citations (85). My thesis itself was one-year throwaway work (only cited 9 times).

Note: I had fellowships for most of grad school, so only worked as an RA for 2 quarters and a TA for one. The highly cited paper was one that was not the result of any funded project, but an idea that another fellowship student came up with on his homemade computer and that we played with for a few years. The idea made over $100,000 in license fees for the campus and is what got me into the hot field that I was later hired for. I think that a lot has been lost by pushing students to be “hands in the lab” for senior researchers.

I’ve been sitting on this comment since March, with the idea of turning it into a full blog post.  I’ve seen a lot of different attitudes on the part of both grad students and faculty about how long a PhD should take and how much should be done for it.

My personal take is that a PhD education should be both broad and deep—one should have enough breadth of knowledge to teach several different undergrad courses and enough depth in one subject to have contributed original work to the field.

Research faculty generally want students to stick around for a fairly long time, so that they get payback in terms of co-authored papers for investment they have made (usually with Federal money) in the students’ initial training. A lot of them see no value to breadth, though, and just want someone to do the tough work in their lab.  They want students to start in research labs right away and see any time spent in coursework as wasted. These faculty often value research much more highly than teaching, doing the bare minimum teaching that the university lets them get away with—they also don’t pursue further education themselves, not attending any research seminars unless the seminar topics are directly tied to their current research projects.  The students they turn out are often very narrow researchers—good in one field, but not adaptable to changes in technology or research funding fads. Although these faculty often have impressive research teams, I’m not impressed with them as professors, as they have too narrow a view of what the role entails—they should be working in a private or national research lab rather than as professors at a university.

A more balanced professorial view sees the role of grad students primarily as students, learning how to be researchers and teachers, rather than as hired hands in the research lab.  As students, they should be continually learning new things, not just getting lab results in a narrow specialty.

Some grad students want to get the PhD certification as quickly as possible with as little effort as possible.  They generally end up in jobs that don’t require a PhD, so I don’t know why they bother—they’d be better off in most cases getting an MS degree (which is much faster) and going to work in industry.

Other grad students end up getting in a rut: not making much progress on their research, not taking any classes, not working on other research projects—basically just marking time.

Others start many projects, but don’t bring any of them to the state of completion needed for a thesis (that was me as a grad student—always busy, always learning, but not wrapping things up). Both the students in a rut and the students flitting from project to project may need to have their funding cut off, to motivate them either to finish theses quickly or give up—my thesis was written in a year after I was told I had only one year of funding left.  I think that there is some benefit to letting productive students have a free rein for a while, though—forcing students into a narrow niche too soon results in narrow researchers.

Some students try to turn their PhD thesis into a life work—as if the thesis is the best thing they’ll ever do.  This is a serious mistake that results in their staying a grad student for much too long. The point of a PhD thesis is to get the student a PhD—it is to establish that the student is capable of original work that contributes to the field and of writing that work up, no more. My own thesis was basically a throw-away research product.  By the time I was done with it, I realized that it was the wrong approach for tackling the design problem.  The only interesting part was a cute NP-completeness proof for a routing problem, all in pictures, but that was a time when new NP-completeness results were basically unpublishable, so I never bothered publishing it anywhere other than my thesis.

Having students do original work is not enough—the check that students can write things up is an important one. I’ve seen more students fail to get PhDs because they couldn’t write up their work than because they couldn’t do the research—that is one reason why our advancement to candidacy requirement consists mostly of writing a long, detailed research proposal, essentially a first draft of the thesis.  Students who can’t write either need to get help or find a job that does not require as much writing as most jobs that require PhDs.  (Incidentally, the problem of writer’s block often hits hardest those students whose writing is the best, when they can get it out—the problem is often one of perfectionism. So the strategy for addressing the problem has to be primarily psychological, not just instruction in writing.)

In recent years there has been considerable pressure on universities to pump students through faster, at both the undergraduate and graduate level. The effect has often been to deny students the chance to explore things outside a very narrow field—once undergrads have completed major requirements and university-mandated general education, there is no time left for other interests (and general-education requirements rarely are satisfied by other interests—they are usually mandated to be a bunch of low-level courses distributed across the curriculum to ensure butts in seats for various departments). Grad school pressure to reduce time-to-degree has often resulted in reducing the coursework requirements and getting students into research labs sooner, again reducing the breadth of student education.

Personally, I like “honors” programs, where at least the top students get released from the rigid bureaucratic requirements of general education and are free to shape idiosyncratic programs that get breadth and depth by following multiple interests, rather than by taking large numbers of survey courses.  I had such a program as an undergrad (the Honors College at Michigan State) and my son is currently in such a program (the College of Creative Studies at UCSB). It may not work for all students, but it is a good way to handle the students who are actually interested in learning things, not just in getting a degree.

In addition to my math degree, as an undergraduate I took a variety of other courses, some of which were interesting, some of which turned out to be duds. As a grad student, I continued this practice, and some of the just-for-fun courses turned out to be crucial to my future success.  For example, the computer music class lead to my taking the VLSI design class, in order to make a single-chip implementation of the plucked-string algorithm that Alex Strong and I had developed.  I ended up teaching VLSI design for over a decade, and the plucked-string paper is my 6th most-cited paper (365 citations on Google Scholar). Neither the plucked-string algorithm nor the VLSI design would have happened if Alex and I had followed the more conventional route of joining a professor’s lab and working on the problems that professor was funded for.  I would have finished my degree sooner, but would have developed a much narrower view of what research is worthwhile.  Although I took a long time as a grad student and a long time as an assistant professor, I still made tenure when I was 38, which is (just barely) below the average age for scientists getting tenure (over 39 according to Physics Today).

My son plans currently to take a lot of courses in his major (computer science), in his other academic interests (math, maybe physics and linguistics, maybe computer engineering), and in his recreational interests (acting)—it looks like he’ll only be required to take one or two classes that are of no interest to him.  He has taken more time in his pre-college schooling than I did, so he’ll probably not get his BS until he is 22 (I finished mine at 19), but he probably won’t need as long in grad school as me, because he’ll have had more time and opportunity to explore his interests earlier. (I certainly wasn’t ready to found a company at age 18!) For that matter, he might decide to go into full-time engineering with just a BS, and not go the academic route at all—his entrepreneurial spirit is more like his uncle than like his father.

Perhaps he’ll do what a lot of the students I teach have done: work for several years (or decades) in industry, then come back to grad school when bored with that, wanting a more interesting challenge.  The re-entry grad students generally do not take a long time to the PhD, because they are focused on their research, though they don’t seem to be much better than other grad students on planning what comes after the PhD.

2014 March 13

Suggestions for changes to biomed training

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Yesterday I attended a a discussion lead by Henry Bourne (retired from UCSF) about problems in the training system for biologists in the US.  His points are summarized fairly well in his article A fair deal for PhD students and postdocs and the two articles it cites that preceded it:

In a recent essay I drew attention to five axioms that have helped to make the biomedical research enterprise unsustainable in the US (Bourne, 2013a). This essay tackles, in detail, the dangerous consequences of one of these axioms: that the biomedical laboratory workforce should be largely made up of PhD students and postdoctoral researchers, mostly supported by research project grants, with a relatively small number of principal investigators leading ever larger research groups. This axiom—trainees equal research workforce—drives a powerful feedback loop that undermines the sustainability of both training and research. Indeed, unless biomedical scientists, research institutions and the National Institutes of Health (NIH) act boldly to reform the biomedical research enterprise in the US, it is likely to destroy itself (Bourne, 2013b).

I’m basically in agreement with him that very long PhD+postdoc training current in biology in the US is fundamentally broken, and that the postdoc “holding tank” is not a sustainable system.

I also agree with him that one of the biggest problems in the system is paying for education through research grants. Grad student support should be provided directly, either as fellowships or training grants (I prefer individual fellowships like the NSF fellowships, he prefers training grants). By separating support for PhD training from research support, we can effectively eliminate the conflict of interest in which students are kept as cheap labor rather than being properly trained to become independent scientists (or encouraged to find a field that better fits their talents). By limiting the number of PhD students we can stop pumping more people into the postdoc holding tank faster than we can drain the tank by finding the postdocs real jobs.

I disagreed with one of his suggestions, though. He wants to see the PhD shrunk to an average of 4.5 years, followed by a 2–4-year postdoc. I’d rather keep the PhD at 6.5 years and eliminate the postdoc holding tank entirely. In engineering fields, researchers are hired into permanent positions immediately after their PhDs—postdoc positions are rare.  It is mainly because NIH makes hiring postdocs so very, very “cost-effective” that the huge postdoc holding tank has grown. If NIH changed their policies to eliminate support for postdocs on research grants, allowing only permanent staff to be paid, that would help quite a bit.

Draining the postdoc holding tank would probably take a decade or more even with rational policies, but current policies of universities and industry (only hiring people in bio after 6 years or more of postdoc) and of the NIH (providing generous funding for postdocs but little for permanent researchers) make the postdoc holding tank likely to grow rather than shrink.

He pointed out that NIH used to spend a much larger fraction of their funding on training students than they do now—they’ve practically abandoned education, in favor of a low-pay, no-job-security research workforce (grad students and postdocs).

A big part of the problem is that research groups have changed from being a professor working with a handful of students to huge groups with one PI and dozens of postdocs and grad students. Under the huge-group model, one PI needs to have many grants to keep the group going, so competition for research grant money is much fiercer, and there is much less diversity of research than under a small-group model.

The large-group model necessitates few PIs and many underlings, making it difficult for postdocs to move up to becoming independent scientists (there are few PI positions around), as well as making it difficult for new faculty to compete with grant-writing machines maintained by the large groups.

A simple solution would be for NIH to institute a policy that they will not fund any PI with more than 3 grants at time, and study sections should be told how much funding each PI has from grants, so that they can compare productivity to cost (they should also be told when grants expire, so that they can help PIs avoid gaps in funding that can shut down research).  The large groups would dissolve in a few years, as universities raced to create more PIs to keep the overhead money coming in.  The new positions would help drain the postdoc holding tank and increase the diversity of research being pursued.

Of course, the new positions would have to be real ones, not “soft-money” positions that have no more job security than a postdoc. NIH could help there too, by refusing to pay more than 30% of a PI’s salary out of Federal funds.

Of course, any rational way of spending the no-longer-growing NIH budget will result in some of the bloated research groups collapsing (mainly in med schools, which have become addicted to easy money and have built empires on “soft-money” positions).

I think that biology has been over-producing PhDs for decades—more than there are permanent positions for in industry and academia combined. That combined with the dubious quality of much of the PhD training (which has often been just indentured servitude in one lab, with no training in teaching or in subjects outside a very narrow focus on the needs of the PhD adviser’s lab), has resulted in a situation where a PhD in biology is not worth much—necessitating further training before the scientist is employable and providing a huge pool of postdoc “trainees”, many of whom will never become independent scientists.

Tightening the standards for admission to PhD programs and providing more rigorous coursework in the first two years of PhD training (rather than immediately shoving them into some PI’s lab) would help a lot in increasing the value of the PhD.

Unfortunately, I see our department going in the opposite direction—moving away from the engineering model of training people to be independent immediately after the PhD and towards a model where they are little more than hands in the PI’s labs (decreasing the required coursework, shrinking the lab rotations, and getting people into PI labs after only 2 quarters). I gave up being grad director for our department, because I was not willing to supervise this damage to the program, nor could I explain to students policies that I did not agree with.

One thing we are trying to do that I think is good is increasing the MS program, so that there is a pool of trained individuals able to take on important research tasks as permanent employees, rather than as long-term PhDs or postdocs. Again, the engineering fields have developed a much better model than the biomedical fields, with the working degree for most positions being the BS or MS, with only a few PhDs needed for academic positions and cutting-edge industrial research. Note that a PhD often has less actual coursework than an MS—PhD students have been expected to learn by floundering around in someone’s lab for an extra 5 years taking no courses and often not even going to research seminars, which is a rather slow way of developing skills and deadly to gaining a breadth of knowledge. Biotech companies would probably do well to stop hiring PhDs and postdocs for routine positions, and start hiring those with an MS in bioengineering instead.

2013 February 21

Foreign national Ph.Ds in engineering

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I just got some mail from the American Society for Engineering Education that gave some information about what colleges are awarding high and low percentages of PhDs in engineering fields to foreign nationals:

Doctorates Awarded to Foreign Nationals Remain the Same

In 2011, 54.2 percent of doctoral degrees in engineering were awarded to foreign nationals. This was the same percentage the year before in 2010. It is a slight retreat from the highpoint of 61.6 percent that was held for the 2006 and 2007 academic years.

Schools with the Highest Percentage of Engineering Doctorates Being Awarded to Foreign Nationals (Minimum of 25 doctoral degrees awarded, 105 schools fit this criterion.)

1. University of North Texas 85.3%
2. University of Cincinnati 83.7%
3. SUNY, Buffalo 81.8%
4. University of California, Riverside 81.5%
5. University of Texas, Arlington 79.2%
6. University of Connecticut 78.7%
7. Louisiana State University 78.6%
8. Stony Brook University 77.9%
9. Lehigh University 76.2%
10. Brown University 75.0%
10. Iowa State University 75.0%
10. Northeastern University 75.0%
13. Illinois Institute of Technology 74.4%
14. Florida International University 73.8%
15. New Jersey Institute of Technology 73.5%
15. Univ. of Massachusetts, Amherst 73.5%
17. University of Houston 73.1%
18. Syracuse University 72.2%
19. FAMU-FSU College of Engineering 72.0%
20. Washington State University 71.9%

Schools with the Lowest Percentage of Engineering Doctorates Being Awarded to Foreign Nationals

1. University of Colorado, Boulder 21.5%
2. University of Notre Dame 29.1%
3. University of Pennsylvania 31.8%
4. University of California, Berkeley 34.1%
5. Wayne State University 37.0%
6. University of Iowa 37.1%
7. University of California, San Diego 40.7%
8. University of California, Santa Cruz 41.2%
9. Colorado School of Mines 41.7%
10. University of Washington 41.8%
11. Vanderbilt University 42.9%
12. University of Maryland, College Park 43.0%
13. University of Wisconsin, Madison 43.1%
13. Duke University 43.1%
15. University of Virginia 43.3%
16. George Mason University 44.0%
16. Southern Methodist University 44.0%
18. University of Missouri 44.2%
19. University of Utah 44.6%
20. Cornell University 45.5%

Source: ASEE Other data trends can be viewed at

I notice a few interesting things:

  • UC Riverside seems unable (or unwilling) to have California residents as grad students in engineering.  Given that many of the other schools with a very high foreign fraction are not notable engineering schools, I suspect that the reason is that UCR can’t attract Californians to their engineering program.  (There are some good engineering schools on the list, though, so there must be other reasons also.)
  • UC Berkeley, which gets a lot of flak for the number of foreign students, actually has one of the lowest rates in the country (though one can argue that 34% foreign is still too high for the good of the country, unless a large fraction of them immigrate after grad school).
  • UCSC, where I’m on the engineering faculty, also has a relatively low fraction of foreign PhD students. I think that our department pulls down this number, since we have generally less than 10% foreign students—they cost us more in grant funding, so we have a much higher standard for admission for them.  That might change as UC keeps jacking up the in-state tuition, though, as the differential is getting less significant.  An in-state student costs us $12.7k per quarter (not including overhead on the grant), an out-of-state student costs us $17.7k per quarter for the first year and $12.7k thereafter, and a foreign student $17.7k per quarter until they advance to candidacy.  Back when in-state tuition was nominal, the ratios were more like 2 to 1, rather than only 40% more for foreign students.  The difference is more like 33%, since we pay all grad students the same $7k during the summer, when there is no tuition (so $60k cost per year for foreign, $45k per year for California resident, plus overhead).
  • A foreign student costs about the same as a postdoc and is generally less productive for research, so Federal funding really encourages faculty to hire postdocs rather than train grad students.  I think that we need to get away from funding grad students through research grants, and switch over to a model more like the NSF fellowships, where the students are directly funded for their education, rather than as a byproduct of research funding.
  • I now see why UC Boulder struck me as so monochromatic—even their engineering school (which is usually the hotbed of internationalism on any campus) is only 21% foreign, and UC Boulder is not noted for domestic racial diversity either.
  • I think that one would get a very different picture if we looked at MS degrees in engineering, since the MS is the real working degree for most engineers.  The PhD is primarily for doing engineering research and college-level teaching, rather than for working engineers.  Foreign markets may assign a higher value to the PhD than the US labor market does, which would definitely skew the PhD pool more towards foreign students.

2012 February 26

Thoughts for revising a grad curriculum, part 3

Our department is thinking of doing a major overhaul of our graduate curriculum. This is the third in a series of posts on my thoughts about curricular design (earlier ones were Thoughts for revising a grad curriculum, part 1 and Thoughts for revising a grad curriculum, part 2).  The first post was applicable to almost any field, and the second post got more specific about some of the history, goals, and constraints of our department,but neither got down to the level of making specific proposals.

I’ll try to outline my ideas for a revised grad curriculum in this post, though I probably not get down to the level of detail needed for new catalog copy. I am still hoping to get pushback from colleagues around the world, former grad students, and even current grad students, so that our new curriculum design is the best we can manage.

I’ll organize this post as a series of questions, and try to provide my answers (which are not, of course the only possible answers).

How much research?

All PhD students have to do a thesis: an original piece of research.  MS students do not need to do original research, but do need to show high level of competence with the skills needed for  doing research.

Note the University of California grad requirements do not allow a “courses-only” masters program.  There must be some sort of capstone activity.  When we tried having this be a Master’s thesis, the projects grew to be too huge, and students were not completing the MS in several years, so we need to make sure that the capstone remains an appropriate size.  Our current requirement, that students do a one-quarter project with a written report and public oral presentation at the end, seems to be about the right size for the MS capstone.

I believe that PhD students need to develop skills in several areas and need to investigate several research questions before they start their theses.  The lab rotation system of biology programs, where students spend 3 quarters during their first year, each in a different lab,  is an excellent way to develop these skills, as well as providing a low-commitment way for faculty and students to explore the possibility of and adviser-advisee relationship.


  • MS: 1 1-quarter research project (equivalent to a lab rotation) as a capstone.
  • PhD: 3 1-quarter research projects (lab rotations) in the first year.

How many courses?

Before figuring what topics we need to cover and how to organize them into courses, we need to know how much room we have for making requirements.  This is constrained by how much student time we can dedicate to course work and by how much our faculty can teach.  Although the curricular redesign was prompted in large part by constraints on teaching time, I’m going to look at this problem primarily in terms of student time.

Based on my earlier discussion about the desire to have both MS and PhD programs, and to allow students to switch between them,I think that we want to have very similar course requirements for the MS and PhD programs. Ideally, a self-funded MS student doing courses full-time should be able to finish in a year, and a more typical MS student with teaching assistantships and a small research project should be able to finish in two.  We would also like most PhD students to choose an adviser at the end of their first year and declare candidacy by the end of their second year. These constraints puts an upper bound of 9 courses on the requirements for the MS,with the capstone project counting as one of the courses in the 9-course limit. The PhD students can be required do do a bit more, but not by much, or transition between the programs is too difficult.

Given that undergrad training in bioinformatics is still rare and our students come in with often quite lopsided training (heavy in only one of biology, computer science, and statistics), I don’t think that we can copy some PhD programs that have no required course work, only thesis research. Our filed changes very rapidly and our students are not likely to be prepared for the different fields and different problems they’ll be facing 5–10 years from now unless the required coursework forces them out of their comfort zone. Quite frankly, I don’t trust some of our faculty to advise students well on taking enough breadth—they  are so narrowly focused on students preparing for their current research projects that they will end up producing specialists who are only employable by their own research group.  There is certainly room for disagreement about the minimum number of courses to ensure this breadth, but I feel that the PhD students should be taking at least as many courses as the MS students.

There are a number of less-than-course-sized educational requirements I think are needed, like attending research seminars, getting lab safety training, and learning things like advanced library search techniques, voice projection, preparation of posters and slides, training to be a TA, … .  These are often packaged as 1-unit classes (much smaller than the usual 5-unit course here), so that we can keep track of the requirements with the usual student records.  It is much easier for staff to check a transcript than to have lots of extra requirements that need extra record keeping.

Proposed course numbers:

  • MS:  8 “lecture” courses plus one project course. Also 3 quarters of attending research seminars and 1 how-to-be-a-grad-student mini-course covering a lot of the useful skills that are not tied to any specific subject matter.  Students with TAships would not be able to complete this program in a year, since a TAship is a half-time job and only allows time for 2 courses, not 3, in addition.
  • PhD: 8–9 “lecture” courses, plus 3 project courses (lab rotations). Also 9 quarters of attending research seminars and 1 how-to-be-a-grad-student mini-course covering a lot of the useful skills that are not tied to any specific subject matter.  Students should take 5–6 lecture courses and 3 lab rotations their first year (which means at most one TAship in their first year), and 3–4 courses in their second year, with adviser-sponsored research filling up the rest of their schedule (except for one or two TAships).  I’m not sure whether the “lecture” course requirements should be 8 (to match the MS) or 9.

Note: this requirements are very similar to what we currently have—I think that the size of our current program is not far off from what is needed, and it is mainly the content and organization that needs changing.

How many different tracks?

Our department now covers several different fields, and we’ve already moved away from a one-size-fits-all curriculum. We can go in a couple of different directions: either very generic “number of courses” requirements with students crafting their own boutique programs to match what they need, or several specific “tracks” that point students to courses that are appropriate for different goals.

When I was a student, I liked being able to take whatever courses I wanted, and I used that freedom to take some challenging courses that I would not have been able to fit into a more structured program.  I also ducked some courses as an undergrad that I probably should have taken (like calculus-based physics), and I was never advised to take statistics, which I now see as an essential for many science disciplines (especially bioinformatics).  So while the flexibility was good for me in some ways, I did not get the intensive advising I should have gotten to optimize my education.

A really dedicated student who knows what they want to do and scours the course catalog for the entire university, looking for appropriate courses to patch weaknesses and build strengths, can put together a really great program if the constraints are as generic as possible.  A less dedicated student could use the same system to avoid challenging material, stay in their comfort zone, and get a truly inferior education.  Based on what I’ve seen of the past 25 years, we get students across the spectrum in dedication and ability to design their own curricula, from ones who could do as good a job at curricular design as any of the faculty to ones who have to be told every course to take, because they can’t figure out what they need to know or where to find it.  Typically (though not always), the PhD students have a better sense of what they need than the MS students, who need more specific direction.

This decision is, to a large extent, one of the cost and quality of advising. We could craft a custom program for each student, carefully matching their needs to the available courses.  This takes 2–4 hours of faculty time per student per year (in addition to any other advising), and adds a couple of hours to the staff record-keeping and graduation checking time.  With about 40 grad students in the program, half of whom need course advising, we’re talking about 80 hours of faculty time and 40 hours of staff time a year added for a “boutique”  degree program, contrasted to a checklist program.

Some of the faculty have not shown any interest in knowing about any courses other than their own, much less across the 7 or 8 departments that our students take classes from.  That means that custom programs would end up putting a heavy advising load on just two or three faculty, who would (in the way the university assigns “credit” for work) not being getting much, if any, reward for this advising time.

So I’d like to put together a system that allows the really dedicated students to challenge themselves without too many required courses filling up their time, but which forces students to move out of their comfort zones and get the breadth of education they should be getting.  I think that the MS programs should have very specific tracks, to reduce the faculty advising load and because the MS students generally need more specific advice.  The PhD program can be a bit more “boutique”, but students who don’t have the willingness or ability to design their own curricula should be able to use one of the MS tracks.

Suggested tracks:

  • MS: emphasis on bioinformatics tool building:  Requires tool-using and tool-building courses in the department, Bayesian statistics and machine learning from outside the department.  This program may end up looking a lot like our current one, though we don’t currently have a tool-using grad-level course.
  • MS: emphasis on computational biology: Requires tool-using and statistical inference, but only minimal programming.  Requires more biology than the tool-building track.  This track will require some new courses or refocusing of existing courses.
  • MS: emphasis on biomolecular engineering: Requires minimal programming and tool-using, requires courses we may not currently have in molecular biology and bioengineering.  This track will require careful design, as I don’t believe we currently have a coherent program at the masters level in biomolecular engineering—we have an assortment of courses but the core that ties them together is not really working.  We may be doing ok for PhD students, though our offerings in biomolecular engineering are not as extensive or as polished as our bioinformatics, but the program is too scattered right now for a good MS degree.
  • PhD: every student has to prepare a detailed curriculum, together with justification for each course and for the overall balance of courses, and provide a graduation checklist for the staff to use.  Any of the MS tracks can be used and will be provided in the required format to the students, so that those who do want to customize have detailed models to follow, and those who want to just do something standard don’t need to do anything.

What topics do students need to learn?

Obviously, there are a lot of topics that could be taught and we can’t regard them all as essential.  What is essential for one student doing one research project may be just breadth for another, or may be totally irrelevant.  Also, the “essential” material will change over time, sometimes quite rapidly as the underlying technology that generates the data we use changes.

I’m not expert enough in biomolecular engineering to list core topics there, and I know too much bioinformatics to want to get down to the level of specific topics.  So for this post I’m going to try to group things into pretty broad categories, and give an idea of the overall balance I think the program should strive for.

  • How to be a grad student: Students need to make a transition from education consumers to information generators. This is an on-going process that can take several years, but it is greatly facilitated by explicit teaching of presentation skills, library research skills, citation skills, co-authorship, research ethics, sharing of data, critical reading of papers, and so forth.  A mini-course (like the current BME 200) can start the process, but it needs to be supported by requirements for frequent (at least annual) oral and poster presentation and by lab-group journal clubs, required written documents (lab rotation reports, thesis proposal, and so forth), and research seminar attendance outside the lab group.
  • Programming: all of our students will need to be able to do some programming.  The tool-builders will need to be expert programmers in two or three different languages, but even the tool users and biomolecular engineers will need to be able to write small programs in a scripting language (like Python) to be able to get tools to work together and to be able to do simple data analysis and presentation.  We should requires some training in programming (even if just a community college intro course) as a prerequisite for entering the program. Few of our students (even those with strong programming backgrounds) come in with much skill at Python programming, so one course with a strong emphasis on programming is useful, but it should not be the sole focus of any of our grad courses.
  • Statistics: all of our students will need some statistics.  The tool-builders will need a strong background in Bayesian statistics, and everyone will need some classical statistical inference (so that they can understand an properly use notions like E-value).
  • Biology: everyone will need some biology, probably to the level of an undergrad course in genetics and an undergrad course in cell biology.  The biomolecular engineers will need a lot more, both in terms of lab techniques and fundamental understanding of the field they are working in (which may be stem cells, viruses, protein nanopores, DNA replication, or any of a number of other things).  Again, we should require that our students come in with some training in biology, but some of the subjects we’ll expect students to know are not readily available at community colleges, so some students may have to make up missing course work after becoming grad students.
  • Biochemistry: Because so much of our work is based on DNA and RNA sequence data and engineering of DNA and RNA molecules, students should have a thorough understanding of the biochemistry of the machinery for the fundamental dogma (transcription, RNA editing, reverse transcription, and translation).  Most of our students will not need enzyme mechanics and the biochemistry of metabolism or protein folding, but perhaps some will. There is some question about how much biochem should be prerequisite to entering the program and how much should be required after entering.  Given the fit between BIOC 100A and what our students need to know, we may want to require that course explicitly.
  • Bioethics: Since its inception, our program has required that students take a full course in bioethics—not just the “be a good boy or girl” research ethics class that NSF and NIH expect, but a serious look at the potential consequences of new technologies and ways to reason about the ethical dilemmas they may pose, rather than having mainly emotional “new=good” or “new=bad” reactions.  I believe that we should continue such a requirement, though we may want to reconsider the various ways this can be met.
  • Machine Learning: Many of our tools use classifiers and other tools from machine learning, and so tool-builders need to learn about various machine learning techniques—how they work, what their limitations are, and how to implement them.  Computational biology tool users need to know a little about the limitations of the tools, but do not need as detailed an understanding of how to build machine-learning tools.  It is not clear that biomolecular engineering students have any need for understanding machine learning.
  • Bioinformatics tools: What tools and databases currently exist and how can we use them effectively to answer a variety of biological questions.  We have an intro course at the undergrad level for this, which may be able to share lectures with an intro grad course, but we probably also need some more advanced tool user courses, if we are to have a computational biology track for the M.S.
  • Bioinformatics algorithms: We currently have a core course (BME 205) teaching models and algorithms, and several more advanced courses teaching a mixture of tool use and algorithms.  We may want to refactor these courses so that the tool use can be offered without the algorithms component to grad students from other departments, and perhaps even to computational-biology track MS students.  That will leave scattered bits of algorithm material that may need to be swept together into a more advanced algorithms course for tool builders.  The core course itself may need to be reorganized (with material from other courses) to be two course: one on the understanding of tools (with a lot of the background on stochastic modeling) and simpler Python scripting, and another on more advanced programming and more sophisticated algorithms (not just the dynamic programming of the current course, but some graph algorithms and some machine learning, perhaps).
  • Biomolecular engineering: I’m not sure what topics we need to cover in our department to prepare students for research in the field.  There are all kinds of lab skills and engineering skills that may be needed, but I don’t know which ones are core and which ones are specific to one research lab.

What topics do we need to teach?

This question is a bit different from the previous one, as we hope that courses offered by other departments can serve a number of our students’ needs.  Because we have a tiny department and we can’t dedicate all of our teaching resources to the grad program (we have bioinformatics and bioengineering undergrad courses to teach also!), we have to have out students taking some of their grad education from other departments. Many of the topics in the previous section are best handled by other departments (statistics by AMS, machine learning by computer science, biology by MCD bio, biochem by chemistry, …).

We will probably also have to rely heavily on other departments (like electrical engineering, computer engineering, microbiology, and chemistry) for some of the more technical training the biomolecular engineers will need.

We will have to teach bioethics, bioinformatics tool usage, and bioinformatics algorithms. We’ll probably have to teach some of the more specific biomolecular engineering techniques (whatever those turn out to be).  We may even have to teach a little programming, or at least provide occasions in classes where students are forced to practice their programming.

Questions for the part 2 post that I still haven’t addressed:

  • How do we integrate the biomolecular engineering students grad students fully into the department?  It would be a real shame to have them disappear into faculty advisers’ labs for 4 years, and not interact with the other grad students. (This is already a serious problem with our 10 faculty currently spread over 4 buildings, and soon to be spread over 5 buildings.)
  • Are there any core subjects that all our students should take, regardless of their eventual research? Having a common core helps develop camaraderie between the grad students and leads to fruitful collaborations between labs (grad students are often the vectors for the infection of collaboration).
  • Are there further subjects that are core to different tracks?
  • What do we do about subjects that we see as essential, but that do not fit the campus’s one-size-fits-all grad-course size (35 lecture hours, with about 100 hours outside class)?  Should we create mini-courses?  Portmanteau classes that have multiple, nearly independent topics?
  • How will the revised graduate curriculum support the (fairly small) number of bioinformatics undergrads, who currently are expected to take 2 or 3 of the first-year grad courses?
  • Is there a way to get any synergy between the graduate biomolecular engineering courses and the (fairly large) number of bioengineering undergrads?

I’ve also not yet addressed the question of how the things we need to teach are broken up into courses and how we write the requirements so that they don’t need to be rewritten every couple of years as other departments tweak their curricula.

2012 February 9

Thoughts for revising a grad curriculum, part 2

Our department is thinking of doing a major overhaul of our graduate curriculum. This is the second in a series of posts on my thoughts about curricular design (the first was Thoughts for revising a grad curriculum, part 1).  I am hoping to get pushback from colleagues around the world, former grad students, and even current grad students, so that our new curriculum design is the best we can do within existing constraints.

The first post was pretty generic, and could be applied to almost any field.  In this post I want to concentrate a bit more on things specific to the fields our department covers.  We started out just doing bioinformatics, with the intent of eventually branching out into biomolecular engineering, and having two different grad programs in the department.  Because of various constraints put on our department, our department grew much slower than originally planned. (One of the biggest constraints was imposed by a former dean: we had to hire a big-name chair before we were allowed to do any junior recruitments.  He didn’t provide resources big enough to actually land a big-name chair, though we had serial 2-year negotiations with each of a few promising candidates, before each decided that the dean was not serious about providing any support for growing the department, so we had an effective hiring freeze for several years.)

We added biomolecular engineering eventually anyway, but with fewer than half the faculty that we wanted in either bioinformatics or biomolecular engineering.  To avoid having to go through the 5-year-process of creating a new grad program, we shifted our original grad program from “Bioinformatics” to “Biomolecular Engineering and Bioinformatics”.  So one design constraint on our program is that the graduate program has to accommodate both biomolecular engineering grad students and bioinformatics grad students in the same program.  We can (and currently do) have different tracks through the program for different concentrations, but they are not independent programs as they were originally envisioned.

I like to distinguish (mildly) between “bioinformatics” and “computational biology”.  The tools used are the same, but the emphasis is slightly different.  In computational biology, the emphasis is on the biology, with the bioinformatic tools being just a means to analyze data for a biological question.  In bioinformatics, the tool is the central object of study, with the biological questions it can answer being proof of its value.  (One gets the same sort of distinction in other fields, like microscopy, where the emphasis can be on improving the state of the art or on using microscopes and microscopy techniques developed by others to look at biologically interesting things.)

When we started, as a very tiny department whose initial faculty were all engineers, we concentrated on bioinformatics.  As we have grown (from a tiny department to a small one), we’ve added some biologists, which has changed the culture of the department somewhat.  (Engineering grad programs and biology grad programs have very different view on the right ways to choose and train grad students.)

Each of our biomolecular engineering faculty has rather different needs for the training of their students, since they are in such disparate fields (stem cells, vaccine design, sequencing technologies, biosensors). Our initial attempts at creating a core curriculum for the biomolecular engineers has not been as successful as our core curriculum for bioinformatics, and we are seeing a need to redo the bioinformatics core, so the biomolecular core must be in even more need of fixing.

One decision we made when we were just starting out was to concentrate on “tool-building” rather than “tool-using” as the core of our program—to do the original research of engineering new tools, rather than the mere application of them to scientific questions (see my post Engineering vs science).  But students from the science departments (particularly biology departments, but also ocean sciences and chemistry) are now interested in learning how to use bioinformatics tools, and there is now enough demand for that education that we should provide more of it than we currently do.  We could get larger enrollment in our grad courses if we found ways to attract and successfully teach students from other departments—students who may not have either the background or the mindset that we expect of the students in our program. As the number of available tools in our field has grown, it has also become more important for us to teach even tool-builders about the existing tools, so that they don’t end up re-inventing the wheel (a serious problem in many bioinformatics programs based in computer-science departments, based on the papers I’m sometimes called on to referee).

So some questions we need to address in our redesign of the grad curriculum include

  • Do we wish to retain a focus on bioinformatics (where we have been very strong)? or branch out into computational biology (where we’ve had some good papers, but generally as spinoffs of bioinformatics development)?
  • Do we add a computational biology track for our MS students? (That is, a mainly-courses approach for learning how to use existing tools to answer a variety of biologically interesting questions.)
  • Do we add a computational biology track for our PhD students?  I think that some of our students are already doing theses that mainly use other people’s tools, rather than developing their own tools, though we are not providing initial training towards this as a goal.
  • If we add computational biology courses, will they be primarily service courses for other departments or training for our students?
  • How do we integrate the biomolecular engineering students grad students fully into the department?  It would be a real shame to have them disappear into faculty advisers’ labs for 4 years, and not interact with the other grad students. (This is already a serious problem with our 10 faculty currently spread over 4 buildings, and soon to be spread over 5 buildings.)
  • How many different tracks through the program do we want to describe and maintain?
  • Do we even want tracks? Is our advising strong enough (and our students disciplined enough) to allow students to craft their own programs out of courses we offer, or do we need to provide very clear requirements?  (For that matter, would some advisers take advantage of students having too much freedom, to advise them take only a very narrow set of courses of use to the one adviser’s research, rather than getting a broader education that would prepare them better for their future careers?)
  • Are there any core subjects that all our students should take, regardless of their eventual research? Having a common core helps develop camaraderie between the grad students and leads to fruitful collaborations between labs (grad students are often the vectors for the infection of collaboration).
  • Are there further subjects that are core to different tracks?
  • What do we do about subjects that we see as essential, but that do not fit the campus’s one-size-fits-all grad-course size (35 lecture hours, with about 100 hours outside class)?  Should we create mini-courses?  Portmanteau classes that have multiple, nearly independent topics?
  • How will the revised graduate curriculum support the (fairly small) number of bioinformatics undergrads, who currently are expected to take 2 or 3 of the first-year grad courses?
  • Is there a way to get any synergy between the graduate biomolecular engineering courses and the (fairly large) number of bioengineering undergrads?

As you can see, I still have more questions than answers at this stage of the curricular redesign.

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