Mentoring and Being Mentored: Strategies For Effective Mentoring
King-Wai Yau, Ph.D.
Department of Neuroscience, Johns Hopkins University School of Medicine
It is a great pleasure to be invited to give a talk at this meeting. It is especially an honor for me because, unlike many of you here, I was actually a dropout from medical school. I began as a medical student at the University of Hong Kong after high school, but soon decided to further my education in the US. Upon graduation from college in this country, I briefly considered an MD-PhD program, but in the end decided to go for a straight Ph.D. I have not looked back since.
Bigger versus smaller labs
With that caveat, let me dive into the topic of mentoring, which is what I was asked to talk about today. Let me begin by saying that mentoring is a rather personal affair, and there are – to borrow some of Simon and Garfunkel’s words – 50 ways to mentor somebody. Most of you are probably too young to know this, but there was a famous song by Simon and Garfunkel entitled “50 ways to love somebody”. Also, my track is 100% basic-research-oriented, so it may not completely apply to physicians or physician-scientists, which I presume many of you are. Finally, I do wonder whether I am that great a mentor myself. I presume a good mentor is defined as someone whose disciples are happy and well-trained, and they blossom into good independent scientists down the road. Based on this criterion, I guess some people in my lab would probably consider me a good mentor, but certainly not everyone. In fact, learning to mentor has been a long hard road for me, and still is. One reason is that the practice of science has changed so much over the years. Gone are the days when labs were small and the PIs were hands-on scientists – at least this was the case for the labs I was associated with both as a student and a postdoc. My Ph.D. was done in a lab with just 4 people, and I did both my first and second round of postdoctoral trainings in labs where I worked essentially side by side with the PI, day in and day out, doing experiments or analyzing the data. Those were the good old days. Nowadays, labs tend to be much larger, with the PI often reduced – mind you, I used the word “reduced” rather than “elevated” – to the role of a manager. I blame it on the institutionalization, or conglomerization, of science. Call this lab enlargement progress, or a necessary evil, if you like, but I do miss the cottage-industry atmosphere that was more typical of the past. My second postdoctoral advisor, Alan Hodgkin, was one of the greatest physiologists of the last century. He received the Nobel Prize at age 49 for solving the mechanism of the action potential. However, at the age of 65, he would still come in the lab on weekends and help me make solutions or digitize data, both boring affairs. My first postdoctoral advisor, Denis Baylor, was himself a postdoc, a great admirer and in some ways a copy of Hodgkin, so you have to judge what I am going to say in the light of my lineage. Indeed, looking at myself and others who have gone through the same labs, I can readily detect common traits, for better or for worse, inherited from the mentors. So, please take it as a civic responsibility to be a good mentor, because, whether you like it or not, you will leave behind your footprints in the scientific world through your disciples.
I see several reasons why labs are getting bigger. One is the exponential growth of knowledge in biomedical science, which opens up more fertile ground for research. The second is the rapid emergence of biotechnology, no less propelled by its successful commercialization. This biotech revolution brings forth more techniques to master and utilize, thus prompting labs to get bigger in order to accommodate them. Third, there is the waxing and waning of government grant support. When time is good, it is easy for PIs to obtain more grant money and so labs have to expand in order to justify the support. When time is bad, oddly enough, many PIs continue to keep relatively large labs and sizeable personnel, presumably to use the age-old strategy of averaging for safeguarding against occasional unproductive projects and grant terminations. We all know it is easy to expand, but far more difficult to contract. Finally, the fact that more people are doing science means more competition for the same grant money and scientific problems, so the perception is that a bigger army is less vulnerable than a smaller one.
In any case, labs are getting bigger, and the trend is here to stay. There are advantages and disadvantages to a large lab, and I have personally experienced both. Up till the early 1990s, my lab only had a handful of people. In those days, I worked exclusively in electrophysiology and there were only one or two projects to tackle at a given time. I was able to think comfortably about the problems and to get myself involved in the experiments. By the time it was a few years ago, my lab reached a peak of about 14 people – by no means a megalab but compared to my past still really huge. The plus side was that we had a whole host of scientific expertise within the lab, including gene cloning, antibody production and immunochemistry, biochemistry, and, finally, gene-targeted mouse production and doing single-cell or whole-animal physiology on the mice. The self-sufficiency was great. At the same time, people in the lab were able to learn and interact with each other. The downside, however, was that it became much more difficult for me to interact sufficiently with anybody, certainly not in any deep way. This may not be a big issue with molecular biology, where a lot of the techniques are recipe-driven and results can take weeks or months to come out. However, it is difficult to run an electrophysiology lab with more than a few people, because at its core physiology is a very personal kind of science. When experiments are not working, one has to improvise in order to coax them to work. Then, when experiments are finally humming along, data come out so fast that one needs to think about them constantly in order to design the next experiment.
By trial and error, I am now settling toward a size of 7-9 people, still large enough to keep the lab sufficiently diverse and vibrant, but not so large as to make it too impersonal. On the money side, a group larger than about 10 people will also require more than 2 or 3 modular NIH grants. Unless you would not mind turning yourself into one grant-writing machine – and there are PIs who wouldn’t mind that – you might find this overwhelming. So, you have to decide whether getting research money is the means or the end for you. More research money will get you more people and space, and perhaps more fame and power, but not necessarily better science. Both routes are viable, depending on what you are going after. And there is room in the scientific community for both.
Obviously, different PIs have different tastes and talents. Ultimately, you have to know yourself. In fact, I consider getting-to-know-oneself to be singularly the most important ingredient of success – not just in science but in any endeavor. While there are numerous ways to do something well, you need to know the one way that makes you tick. This may seem trivial or obvious, but it is by no means easy. In fact, I have seen in my career many brilliant individuals who have under-achieved simply because they did not match their talent to their task. If you are a laboratory person, stay as close to research as possible. If you are a good manager, stay that way, but make sure that you pick the right people for the lab. I often tell my lab people that, if they know their own strengths and weaknesses by the end of their postdoctoral training, they are already doing very well. Certainly one does not want to wait till tenure time to find out.
I am fortunate in that I tend to look inward. I watch myself enough and worry constantly, so that, thank goodness, I have not yet taken a major misstep in my career so far, and hope this will never happen. I happen to belong to the camp that believes in perfection, and that “less” often means “more” in terms of the number of publications. I might call this the high road, and it is not necessarily the best road to success. In any case, if you have the same obsession as I do, then the undertaking of every project and the writing of every paper becomes a major task. How, then, do I deal with more than one project? Well, even with a lab now of under 10 people, it is far from easy. I rotate through projects. I would work intensely on one project for an extended period, for weeks or longer, and try to bring it to some reasonable state before moving on to the next project. From the point of view of the project, this way of working offers a better chance for depth and rigor. On the other hand, if you are the student or postdoc waiting in line to get detailed input from the PI, you will have to be very patient. And often projects cannot wait. I don’t have any good answers to this quagmire. Having independent people in the lab would help. Avoiding bandwagon projects would also help, so that experiments, and even discoveries, can wait. There is a fundamental dilemma in deciding between breadth and depth, between programmatic research that sails with the wind and truly original advancements that do not come by readily. Some of you may have heard the old Chinese saying “An era can create a hero, or a hero can create an era”. The first situation is certainly a hundred times easier.
Naturally, a PI tends to add more people to the lab when things are going well. The only concern is that research, in particularly research at a high level, is to some degree a random walk, so that productivity is not necessarily in phase with the number of people in the lab. This is not unlike the supply-demand situation in the marketplace. Unfortunately, a lab cannot function like an automobile plant, where workers can be furloughed and rehired at will depending on market demand. So, it is sometimes better to pass up research opportunities by keeping the lab manageably small, rather than having a whole crew idling when the dry spell hits. In the final analysis, you have to know and listen to yourself, and adjust accordingly. It suffices to say that the greatest discoveries in science so far have invariably been made by single individuals or very small teams. On the other hand, it is also true that, for every early-day Bill Gates or Steve Jobs, there are a thousand others out there with similar dreams who have long faded into oblivion. So it boils down to a matter of personal philosophy. There is no one truth. The important thing is that, if you enjoy the process of doing and thinking about science, rather than just go for the glory, then, no matter how the chips fall in the end, the endeavor is still worthwhile and rewarding.
Teach by example
Probably nothing is more important in mentoring than teaching by example, I think – whether it be spending long hours at work, be focused and meticulous about one’s work, be honest, open-minded and dispassionate about one’s data, or just be serious about what one does. When it comes to lab duties, experiments or subsequent data analysis, I try not to draw a line between teacher and disciple. If I am the best person for a certain procedure or task, I don’t hesitate to do it, no matter how menial or boring. I also do not keep strict work versus home hours. Obviously, for this to be possible, one needs the understanding and support of the family. Fortunately, I do. On the other hand, is it really necessary for a mentor to operate in such a compulsive manner? The answer is No, nor is it the most efficient way to do things. Nonetheless, I do it for the sake of spirit, and of principle. I think it is really important, for the good of both yourself and your associates, that you are in the trenches with them, all the way.
I also try to be transparent to my people, so they really see how I think about a scientific problem and deal with it. In particular, I want them to see my scientific strengths and weaknesses. It is actually encouraging for the student and postdoc to see that the professor is just as human as they are, thus putting them at ease. Regular small-group brain-storm sessions are also helpful, during which I would let my egos drop. I would be the first to laugh or curse at myself, if I was wrong or stupid.
From my own experience, all of these qualities will come much more naturally if one genuinely falls in love with a scientific problem, not any different from falling in love with an individual. Thus, I strongly encourage you to pick your scientific problems carefully. Without the true passion, the pursuit will fall apart sooner or later. Most of the time, your supervisees will only be as eager as you are about a scientific problem. And, believe me, they are generally very perceptive.
How to pick lab members
You can only make a resonator resonate if you drive it at its natural frequency. So, the easiest way to mentor is to find people who have similar interests and inclinations as yourself. When this happens, all you need to do is to lead, and the people in the lab will naturally follow. I have learnt this the hard way. In my younger years, I could never figure out why perfectly sound advice sometimes would go nowhere. Although I am a neuroscientist, it still took me a long time to figure out that we are all different, that behavior and cognition are dictated by the mostly hard-wired nervous system. Yes, there is neural plasticity. But, believe me, the plastic part of the brain is much much smaller than the non-plastic part. You cannot easily change yourself, much harder others.
This brings us to the issue of picking people. Well, the good news is that there are some useful guidelines. The bad news is that, no matter how hard you try, your hit rate is not likely to go much beyond 50%. For graduate students, all one can go by is the academic record. Academic records don’t necessarily reveal experimental ability or creativity. The up side is that the students are generally young and moldable, so they can be influenced, at least up to a degree. For postdocs, there is the benefit of several more years of research track record about the individual. In getting comments from a reference for the applicant, one first has to calibrate the reference, because everything is relative. Thus, preferably, the applicant’s advisor is a scientist you know or know about. When contacting a reference, I don’t usually write. Instead, I would call the person up on the phone without warning, in order to catch his or her spontaneous answers to my questions. I have a list of questions that I would clearly spell out to the reference, rather than just let the reference develop the conversation. These pointed questions include ratings on the applicant’s intellectual ability, experimental ability, scientific honesty, dedication, independence, career ambition, receptiveness to criticism, work habits, communication skills, personality, interpersonal skills, capability of teamwork and overall promise as a scientific leader. In soliciting answers, I have learnt to stay away from accepting at face value such adjectives as “good”, “very good”, “hard-working” etc., which are not terribly meaningful. Instead, I would use a scale of 1 to 10, or percentiles, in order to make the ratings as quantitative as possible. If the comments are all positive, I would always ask in the end “On balance, could you say something negative about this individual, because no one is perfect”. Almost invariably, you are going to elicit very useful information from this question. The point is: references generally tend to be nice and try not to speak ill of someone voluntarily. If the reference has indeed been avoiding certain negative issues, the question I just mentioned will open a flood dam.
In the end, a PI’s work is only as good as the people in the lab who will execute them, especially nowadays, when a PI is more likely to sit in an armchair most of the time. If there is any doubt about a prospective student or postdoc, my strong advice is not to take the person. Passing up a potential star is always better than recruiting a potential disaster. Moreover, it takes only one lab member to corrupt the atmosphere of a lab. I do understand the temptation of taking an admirer because we all want to feel good about ourselves. Also, when you are an upstart PI, you need warm bodies in the lab. Still, there are too many horror stories about bad apples. I have personally experienced such individuals in my own lab as well.
Once someone has entered the lab, I try to provide as many opportunities to the person as possible, whether it be research projects, learning new techniques off-site, mentoring junior people, etc. I also encourage intellectual freedom. I have an open-door policy throughout the day, even if it means that I shall be interrupted umpteen times by knocks at the door. With the screening I just mentioned, the chance of accepting the wrong person into the lab is reduced, but by no means nil. On the other hand, the fact that someone does not click with you scientifically doesn’t mean he or she does not have great promise. Hadyn was a great composer and genius, but Beethoven was supposed to have remarked that he did not learn much as a student from Haydn. When a mismatch does happen, it is good to think for the individual and suggest the best alternative for the person’s future. Generosity and understanding never fail in the long run.
I also try not to keep a student or postdoc much longer than is necessary for the person. If the person is ready for postdoctoral work or a faculty job, I encourage the person to move on. Some PIs like to hang on to good workers for as long as they can. Certainly, the temptation to do that is understandable. While this may seem beneficial to the lab in the short run, it often harms the lab in the long run. The reputation of a lab is amplified by the trainees who move on and become successful investigators themselves. Moreover, keeping the same people around for too long can make the lab stagnant. Even world-class collaborators part their ways after years of collaboration. If you genuinely care about the career future of your people, they will feel it and reciprocate.
One last point I would like to emphasize is that the training for someone consists of a whole package, encompassing bench skills, thinking, judgment, habits, and, last but not least, communicative skills. Through the years, I have spent many hours and days helping people write papers, often side by side with them in front of the computer. I also have spent untold hours helping them prepare and practice research talks or even just journal clubs. Even if it means returning to the lab on Sunday morning to listen to someone practice an important talk for the fifth time, I would not hesitate. This may seem excessive. But, believe me, when it comes to job talks, or even lesser presentations, the pros know all too well that this approach works wonders.
Look For a Mentor
By the same token, how would you go about looking for a mentor? Based on what I have said, you can probably guess my answer, so I won’t belabor on it. I just want to say one thing, which is, I think a mentor’s scientific style and standards are far more important than the person’s specific scientific interests. Picking up good habits and judgment will benefit you a lifetime, but picking up a nice technique or scientific problem may not last you very long, especially nowadays, when science advances so quickly.
Well, this is pretty much all I want to say. You have heard a lot of clichés in this talk, but they became clichés largely because they are genuinely words of wisdom – if you take them seriously, that is. For myself, I have learnt a lot about mentoring through my own blunders over the years. Had I learnt these skills much earlier in my career, I would have done much better myself. So I hope that, based on what I have said, you will avoid the same mistakes. Finally, do I now practice 100% of what I preach? Well, as much as I try, I don’t think I am able to do that, certainly not 100%. I guess I am like everyone else: I am only human. I am not infallible.
To sum up, I have described one way of mentoring, and there are 49 other ways. Diverse mentoring style and substance aside, however, the ultimate goal should be the same, namely, to bring out the best qualities from the young charge, whatever those qualities are, and not merely turn the person into another clone of yours. A great art or piano teacher is someone who teaches a student to successfully execute a piece with the student’s own heart and soul, not the heart and soul of the teacher – of course, we are assuming that the student is gifted to begin with. Science is very much like art.
Thank you very much.