Advice for Picking a Graduate Program

(This post is authored by Eleazar Eskin.)

For over a decade, I’ve been involved in graduate admissions for both the UCLA Bioinformatics Ph.D. Program and the UCLA Computer Science Ph.D. Program. Each year, we admit a group of prospective students; some come to UCLA, and some go elsewhere. Students admitted to multiple programs face difficult decisions when choosing where to begin graduate studies. The factors involved in their decision are varied and often independent of academic considerations. For example, when I was an undergraduate, I chose to attend the Computer Science Ph.D. Program at Columbia University—mostly because I wanted to live in New York City!

However, when considering academic issues, determining which graduate program is best for you is not so straightforward. In this blog post, I provide some advice on how to choose a graduate program. While my focus is on Bioinformatics, the general concept applies to any program in the sciences. In particular, you should consider the following questions.

Whose lab can I join?  By far the most important factor affecting your Ph.D. education will be the lab you join. A great advisor at a lower-ranked institution will lead to much better student outcome than a crappy advisor at Stanford, Harvard, or MIT. Great advisors will, among other things, develop expectations for their graduate students that are in line with the students’ career goals and provide sufficient structure and resources for the students to work toward achieving these goals.

However, choosing a graduate program and institution for a single advisor is not a great idea, because most students do not end up in the lab they thought they would join when they applied to the program. An ideal graduate program will have multiple faculty that could be a great fit for you. Insights on determining which faculty could be a great fit for you are in my blog post on how to choose a graduate advisor.

Take-home point: Choosing your lab is much more important than choosing your graduate program—or even the name-recognition value of the institution you will attend.

How do I get an advisor, and is it easy to switch advisors?  Graduate programs relevant to Bioinformatics are usually set up in one of two ways. In both cases, the host faculty (or principle investigator) of the lab in which you perform research is also your advisor. One approach is a rotation system, where students initially try three labs and ultimately join one for research during their final years in the program. An advantage of the rotation approach is that you get to try out three faculty before making a decision on which lab to join while you complete the degree.

Other programs, such as the UCLA Computer Science Ph.D. Program, have direct admissions. Here, students join their primary research lab beginning in their first term. In direct admissions programs, it is important for you to know how easy or hard switching advisors would be if the lab turns out to be a poor match. For example, you would want to know if program funding is tied to the individual host faculty—or if it is tied to the department. You can typically change advisors easily if your funding is tied to the department. When funding is tied to the PI, it can be more difficult to switch advisors.

Take-home point: Finding out the advisor selection process at your prospective programs can help you decide which program would offer you the most flexibility.

What training will I get, and what will I learn?  In addition to working with a faculty on research, every graduate program has a substantial training component. During your first year, you will spend most of your time completing coursework in addition to your research. For some Bioinformatics programs, the primary courses are “seminar” type courses in which multiple faculty present their research each week. Seminars provide a useful, important survey of research on campus, but they have little educational value. Other programs feature an integrated curriculum with specific pedagogical goals. These types of courses will help you learn a lot more skills and applications.

Some institutions may provide additional training opportunities through coursework offered by other departments. For example, at UCLA, Bioinformatics Ph.D. students can gain skills in computer languages and data analysis from the Departments of Computer Science, Biomathematics, and Ecology and Evolutionary Biology, among others. You should find out if extra-departmental courses, such as advanced courses in statistics and computer science, would be available to you on the prospective campuses. These types of opportunities are often not available in graduate programs that are a part of medical schools and lack access to a basic campus.

Take-home point: Campus-wide course options and quality—both within and outside of the home department—are important considerations when comparing prospective programs.

How much activity in Bioinformatics is happening on campus?  Part of your education as a Ph.D. student comes from attending seminars held by visiting scholars and guest speakers. Some department have a very active speaker series and organize workshops for faculty and students, while others may hold seminars and workshops relevant to Bioinformatics less frequently. Bringing in scholars from other institutions is important, because these events expand your training and expose you to ideas and methods not represented at your campus. If you are planning on attending an institution with a limited number of faculty involved in Bioinformatics, you will likely have fewer relevant seminars and activities available to you.

Take-home point: Current graduate students will be able to tell you how many relevant seminars, workshops, and other events can be expected from your prospective programs.

How collaborative is the Bioinformatics community?  Collaboration with other groups on campus exposes you to other faculty and students. Multiple groups rarely collaborate at some institutions, while intra-lab collaboration is extremely common on other campuses. Intra-lab collaboration will greatly broaden your experience as a graduate student. When you visit your prospective program, you may not know which lab you will join, but you can get a feel of whether or not the institution and community is collaborative.

Take-home point: Checking if there are papers authored jointly between multiple groups of interest will help you assess the level of collaboration at your prospective programs.

How big is the community of students and faculty?  Informally interacting with other scholars in your research area is very important. Some of my closest colleagues today are individuals whom I met while I was a graduate student. You will get to know many of the students in your graduate program and in related programs; programs that have larger numbers of faculty and students, more departments of interest, and more workshops and other relevant events will provide more opportunity for you to establish these important relationships.

Take-home point: A large cohort of colleagues spanning multiple departments will give you more opportunities to interact (and have fun!) while in graduate school.

How likely am I to have funding for all my years as a graduate student?  The vast majority of programs in the physical and life sciences fund their graduate students for the entire duration of their doctoral education. Full funding is critically important, because university tuition nowadays is very high. It is up to the program to provide these assurances to admitted graduate students, but make sure to ask questions about this if the admission offer letter you receive is not clear. Some programs require students to work teaching and/or research assistant jobs for a salary and tuition waiver, while others provide fellowship stipends. Most graduate programs offer a combination of fellowships and student jobs in their funding packages.

Take-home point:  Make sure you ask about funding details when you are deciding among multiple prospective graduate programs.

These questions aren’t the only factors you should consider when deciding which graduate program to join. There are a host of personal issues that affect these choices, including what city you want to live for the next four to six years.

While this blog post focuses on the general issue of picking a graduate program, we at UCLA feel that our program compares very favorably along these lines. Our program has a tremendous number of new faculty who are at all career stages and invested in mentoring incoming graduate students. In general, students have a lot of agency in picking their advisor, even if they come to UCLA through a direct-admit program such as the Computer Science Ph.D. Program. Many students are co-advised between multiple faculty spanning different departments, an arrangement that increases a student’s breadth of research and engagement.

Our Bioinformatics Ph.D. curriculum tightly integrates computational and biological knowledge and provides students with training in methods development (Link to Grad Philosophy Blog Post). In addition, the Bioinformatics graduate curriculum is available to both Bioinformatics Ph.D. Program students and Computer Science Ph.D. Program students. Many Bioinformatics-relevant training camps, seminars, and workshops take place throughout the year at UCLA (for more information, read our blog post on UCLA Bioinformatics training environment).

This collaborative spirit is a hallmark of the UCLA campus, and many Bioinformatics faculty have co-authored with each other and myriad colleagues from other departments on campus. Each year, we welcome a cohort of nearly 20 new computational genomics students from the Bioinformatics Ph.D. Program, the Genetics and Genomics Ph.D. Program, and the Computer Science Ph.D. Program. These students typically take courses together and create a cohesive network of junior researchers with shared interests.

Our graduate students’ collaboration and comradery is evident. The Bioinformatics students organize an annual student-led retreat where they share research and build relationships in settings such as Catalina Island and Big Bear Lake. As for student life at UCLA, we have five (!) outdoor pools, perfect year-round weather, amazing global food culture, and beaches within a few miles of campus. The undergraduate students have caught on to how great a place it is to be. This year, UCLA became the first university to receive more than 100,000 applications for freshman admission!

Advice for Picking a Graduate Advisor

(This post is authored by Eleazar Eskin.)

Picking an advisor has major implications for one’s graduate career and is a decision that one makes very early in graduate school. (In some cases, even before a student starts a graduate program.) Unfortunately, these decisions must be made when a student is early in their career and has the least experience and perspective on managing academic relationships. Several issues are relevant to the choices you may face in terms of lab style. Is it best to join a big lab or a small lab?  An experienced faculty or a new faculty? An established research area or a new area?

In addition to research, many interpersonal factors are important to ensuring a productive advisor-advisee relationship that will ultimately promote your successful career. How well you get along with a faculty advisor may improve the quality of mentorship you receive. Further, how well you get along with other lab members may expand your opportunities for collaborative research and publication—during graduate school and beyond. The purpose of this blog post is to give advice to new students on which factors to consider when picking an advisor. When comparing offers of admissions from different schools, I suggest considering the following questions.

What types of papers will you write?  A Ph.D. program is focused on research, and you will spend the vast majority of time in the program doing research projects that result in publications. It is critical that you enjoy writing these papers in order to maintain stamina and avoid burnout. Each lab tends to have a specific publishing culture; some specialize in writing methods papers, and others produce more results papers. Often, high-level descriptions of a lab’s research interests do not capture the flavor of the papers. To develop a better understanding of the type of work performed by your prospective lab, first read recent papers published by its members. When reading these papers, ask yourself if you would enjoy writing these kinds of papers.  Ask yourself whether or not you find the papers exciting enough to spend the next four years producing more of them.

Take-home point: Reading the most recent 5 to 10 papers published by a prospective research lab is a worthwhile investment.

What kind of publication record can you expect?  A set of publications will be the tangible outcome of your Ph.D. career. The extent and impact these publications have—in terms of impact factor, journal name recognition, number of citations, etc.—will help define the next steps of your career.  It is very important to consider what your expected publication record will be when you finish your Ph.D. A mind-boggling mystery to me is why promising graduate students join labs where they are unlikely to publish frequently enough to be competitive at the next level.

Consider the average publication record of recent lab alumni (at the time that they graduated) in order to reasonably estimate what your publication record will be. This data can be hard to obtain, so another way to estimate this is to consider the per capita publication rate. This figure can be computed by considering the number of publications published per year times the number of lab members per publication divided by the total number of lab members.  This statistic is clearly inaccurate, because some labs are comprised of staff, students, post-docs, and undergrads who each contribute differently to projects and publish at different rates. However, computing this number is still useful to get a rough estimate of what you can expect.

Very promising students often join extremely prestigious and well-funded labs. On the surface, these labs seem like a great option because many publish multiple papers per year in high impact journals like Science and Nature.  However, if the lab has more than 30 members, the per capita publication rate is low and the chances of gaining authorship on these papers is relatively small. In some high-profile labs, many students still finish their Ph.D.’s with relatively poor publication records.

Take-home point: Examining the expected publication record will give insight on the types and frequencies of authorship you may expect to gain in a prospective lab.

Where do alumni of the lab end up?  If you have a clear goal in mind for your post-Ph.D.  career, consider joining a lab that produced alumni who have accomplished similar goals. If your objective is to be a professor, it may not be the best idea to join an established lab where no past student has remained in academia. If your goal is to end up in industry, it is likely a good fit to join a lab where many alumni have placed well in industry. If your post-grad goals are unclear, it is still useful to find out where a prospective lab’s students end up after finishing their graduate degrees. In particular, it is important to know how much mentoring the advisor provides to help students figure out their future directions and achieve their goals.

Take-home point: Exploring the post-graduate career trajectories of the lab’s past graduate students will give you an idea of the type of mentorship the prospective advisor provides.

How often will you see your advisor?  There is large variance in terms of how much total time you will spend with your advisor, and the quality of your first meeting with the prospective advisor may not predict the amount of future mentorship you can expect to receive. You may have a great initial talk with your potential advisor, but you may hear from lab members that they have a sit down meeting with their advisor only once every 3 months. Advising this infrequent may not be enough for you to develop research interests, carry projects to completion, and prepare a successful post-grad career.

If you think weekly individual meetings are necessary for your success, ask current lab members if this arrangement would be possible. Current lab members will know if, for example, their advisor allows drop-in meetings to answer your questions. In addition, you should find out how hands-on the advisor is with paper writing and how timely they are with helping push projects along. In today’s world, faculty often have tremendous amounts of pressure on their schedules due to grants, administrative obligations, teaching, and other responsibilities. Commonly, lab projects will be delayed for weeks because the advisor didn’t have time to make edits on the manuscript. However, many faculty prioritize writing and return edits very quickly.

Take-home point: Current lab members will be able to tell you how much hands-on guidance in research and publishing can be expected from your prospective advisor.

What kind of training will you get and who will train you?  In addition to completing research projects, one important aspect of a graduate program is how much you will improve as a researcher and what skills you will master (and to what extent you will master them). Ph.D. programs are inherently apprenticeship programs; much of the training you receive will come from your lab responsibilities. It is important to know how much the other students have learned, and how they learned these skills. Some labs have journal clubs and other structures for additional, more comprehensive training. In some labs, the faculty directly train graduate students in skills such as analyzing and interpreting data. In other labs, you may be primarily mentored by a post-doc or other senior member of the lab. While the latter example is not necessarily a bad option, you should know in advance what you are signing up for.

Take-home point: Current and recent lab members can tell you who taught them which specific skills, and you can take into account how the lab structure would fit your learning style.

What about new faculty?  New faculty are often the best option for a graduate student, yet opportunities to work with junior scholars are very under-appreciated. Recently-hired faculty are often a great choice, because they have an extremely exciting research program that is rapidly expanding. Students who join these labs are often involved in finishing the ongoing projects that helped the faculty land their job at the current institution. When considering the expected publication record, you can consider their own publication record as factoring into your expected publication record (if the faculty is willing to include you in their projects).

New faculty often are heavily involved in training the group and spend a tremendous amount of time with their students. Compared to senior faculty, they often have many fewer responsibilities. Finally, joining a new faculty’s lab is exciting. The life of a new faculty is an adventure with many ups and downs. Publishing the lab’s first big papers, getting the first grants, setting up the lab, recruiting the first students, and teaching the first classes are all exciting (in addition to stressful) times for a new professor. Being a student of an advisor early in their career is joining this wild—and potentially very rewarding—ride.

Take-home point: Many early students of now-senior faculty have done very well. For example, out of the first 5 students that I supervised for their Ph.D.’s, 4 are currently in faculty positions.

Are these considerations the only considerations for picking an advisor? Of course not. Other important—if not more important—factors to consider include personal connection and feeling of a “good fit”. However, we hope the points above provide some foundational insight into what should be considered when picking an advisor.

UCLA Launches CGSI with Inaugural Summer Programs

In 2015, Profs. Eleazar Eskin (UCLA), Eran Halperin (UCLA), John Novembre (The University of Chicago), and Ben Raphael (Brown University) created the Computational Genomics Summer Institute (CGSI). A collaboration with the Institute for Pure and Applied Mathematics (IPAM) led by Russ Caflisch, CGSI aims to develop a flexible program for improving education and enhancing collaboration in Bioinformatics research. In summer 2016, the inaugural program included a five-day short course (July 18-22) followed by a three-week long course (July 22 to August 12).

Over the past two decades, technological developments have substantially changed research in Bioinformatics. New methods in DNA sequencing technologies are capable of performing large-scale measurements of cellular states with a lower cost and higher efficiency of computing time. These improvements have revolutionized the potential application of genomic studies toward clinical research and development of novel diagnostic tools and treatments for human disease.

Modern genomic data collection creates an enormous need for mathematical and computational infrastructures capable of analyzing datasets that are increasingly larger in scale and resolution. This poses several unique challenges to researchers in Bioinformatics, an interdisciplinary field that cuts across traditional academic fields of math, statistics, computer science, and biology—and includes private-industry sequence technology developers. Innovation depends on seamless collaboration among scientists with different skill sets, communication styles, and institution-driven career goals. Therefore, impactful Bioinformatics research requires an original framework for doing science that bridges traditional discipline-based academic structures.

The summer 2016 courses combined formal research talks and tutorials with informal interaction and mentorship in order to facilitate exchange among international researchers. Participants in the short program attended five full days packed with lectures, tutorials, and journal clubs covering a variety of cutting-edge techniques. Senior trainees, including advanced graduate students and post-docs, underwent additional training through the long program’s residence program. The extended program enabled these scientists to interact with leading researchers through a mix of structured training programs and flexible time for collaboration with fellow participants and other program faculty.

Collaboration on a wide variety of problem types and research themes facilitated cross-disciplinary communication and networking. During both courses, CGSI participants shared technical skills in coding and data analysis relevant to genetic and epigenetic imputation, fine-mapping of complex traits, linear mixed models, and Bayesian statistics in human, canine, mouse, and bacteria datasets. Scholars at different stages of their careers explored application of these methods, among others, to emerging themes such as cancer, neuropsychiatric disorders, evolutionary adaptation, early human origins, and data privacy.

CGSI instructors and participants established mentor-mentee relationships in computational genomics labs at UCLA, including the ZarLab and Bogdan Lab, while tackling practical problems and laying groundwork for future publications. In addition, participants developed comradery and professional connections while enjoying a full schedule of social activities, including dinners at classic Los Angeles area restaurants, volleyball tournaments in Santa Monica, bike rides along the beach, morning runs around UCLA campus, and even an excursion to see a live production of “West Side Story” at the Hollywood Bowl.

CGSI organizers thank the National Institutes of Health grant GM112625, UCLA Clinical and Translational Science Institute grant UL1TR000124, and IPAM for making this unique program possible. We look forward to fostering more collaboration between mathematicians, computer scientists, biologists, and sequencing technology developers in both industry and academia with future CGSI programs.

Visit the CGSI website for an up-to-date archive of program videos, slides, papers, and more:
http://computationalgenomics.bioinformatics.ucla.edu/

Enrollment in 2017 CGSI programs opens this fall with a registration deadline of February 1.

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