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 Bioinformatics: The Philosophy of the Training Environment and Programs

(This post is jointly authored with Alexander Hoffmann, Hilary Coller, Matteo Pellegrini, and Nelson Freimer.)

UCLA has a rich training environment for Bioinformatics that extends beyond the core academic programs.  For structured academic learning, UCLA offers an Undergraduate Bioinformatics Minor and a Bioinformatics Ph.D. Program.  In addition, UCLA coordinates multiple training programs, several of which are open to researchers from other institutions who are at all stages of their careers.  Many of these programs are either hosted or jointly sponsored by the Institute for Quantitative and Computational Biology (QCB) at UCLA, which is directed by Alexander Hoffmann (UCLA).

Over the past 10 years, driven by the ubiquity of genomics throughout the field, biology has become a data science. Every biomedical research institution has been challenged with supporting the analysis of genomic data generated by groups who traditionally have not cultivated substantial computational expertise. Many of our peer institutions delegate genomic data analyses to a specific Bioinformatics core group that operates on a “fee-for-service” model.

The Bioinformatics core “fee-for-service” model poses many problems.  First, complex issues that arise during analysis of genomic data are difficult to predict in advance.  Projects often require much more effort than anticipated by research groups, leading core groups to struggle with insufficient funds to cover the actual time spent on analysis.  Second, research groups utilizing the core often want to move the project in different directions than what was originally proposed.  In the long term, exploring additional aspects of data can be inefficient when data analysis is delegated to a core group on an as-needed basis.

At UCLA we follow a different approach.  We believe that research groups should receive the training and resources to analyze the genomic data that they generate.  This “training and collaboration” model is the best solution for efficiently completing projects and advancing skills in a research group.  Over the past ten years, UCLA has significantly invested in this training and collaboration model.  For example, UCLA’s Bioinformatics programs are explicitly organized to connect research groups with core groups across campus and provide infrastructure and training to students, faculty, and staff working in many different fields.

Bioinformatics training programs held at UCLA include:

    1. The Collaboratory. The Collaboratory of postdoctoral fellows, directed by Matteo Pellegrini (UCLA), provides an experimental and empirical research environment for bioscientists and computational scientists to collaboratively design and conduct experiments. Most bioscience laboratories have limited capabilities in large-scale data analysis. The Collaboratory’s main mission is to advance genomic data analysis by connecting UCLA bioscience faculty with QCB faculty and fellows.  The Collaboratory fellows are a select group of postdocs funded by the Collaboratory to engage in collaborative projects that leverage their specific expertise.

      The Collaboratory fellows are also responsible for organizing intensive tutorials designed to train UCLA students and postdocs in the latest next-generation sequence analysis techniques. In addition to providing computational expertise to bioscience researchers at UCLA, the Collaboratory also sets up and maintains a next-generation sequence data analysis server, and participants develop methodologies to process new types of data. The Collaboratory has a year-round schedule of workshops open to the Bioinformatics community.

 

    1. Bruins in Genomics Undergraduate Summer Research Program (B.I.G. Summer). B.I.G. Summer is an integrated undergraduate training and research program in genomics and bioinformatics at UCLA. Participants gain an intensive, practical experience in integrating quantitative and biological knowledge while learning how to pursue graduate degrees in the biological, biomedical or health sciences.  The program begins with two weeks of hands-on tutorial workshops that cover fundamental concepts in genomics critical to participation in today’s research.  The remaining weeks are focused on research.  Students work in pairs under the supervision of UCLA faculty mentors and QCB postdoctoral fellows.

      B.I.G. Summer offers unique opportunities that are often not available to undergraduates, including next generation sequencing analysis workshops, weekly science talks by senior researchers, a weekly journal club, professional development seminars, social activities, concluding poster sessions, and a GRE test prep course.  In addition, a special NIH-funded curriculum in neurogenomics, directed by Nelson Freimer and Eleazar Eskin, provides B.I.G. Summer participants with an intensive exposure to this rapidly growing field, in which UCLA is among the leading centers worldwide. B.I.G. Summer is organized by Alexander Hoffmann, Hilary Coller, Tracy Johnson, and Eleazar Eskin. This year, B.I.G. Summer is held from June 19th to August 11th, 2017.  The B.I.G. Summer Program is sponsored by the following generous institutions:

      UCOP for a UC-HBCU partnership Program in Genomics and Systems
      NIH NIBIB for NGS Data Analysis Skills for the Biosciences Pipeline R25EB022364
      NIH NIMH for Undergraduate Research Experience in Neuropsychiatric Genomics R25MH109172

 

    1. Undergraduate and MS Research Program. One of the best ways for faculty to provide training to undergraduate and graduate students is through mentorship in research labs. A substantial challenge to this approach is the increasing number of undergraduate students who want to get involved in research.  For example, there are many more Computer Science majors interested in research than can be absorbed by the number of faculty presently in the Department of Computer Science.  In order to meet rising undergraduate demand for research opportunities, we created an Undergraduate and Master’s student research program.

      This program connects researchers across campus with interested students from a variety of majors.  In doing so, we leverage UCLA’s strength in Bioinformatics to offer a greater number of research opportunities available to undergraduates with and outside of the Department of Computer Science.  Each research opportunity posted on the webpage has a list of requirements, ranging from “one course in Bioinformatics or programming” to “a full year of coursework in programming.”  For students who have completed relevant coursework or are planning their academic schedule, this program provides a clearly defined path to become involved in research projects on campus.

 

    1. Informatics Center for Neurogenetics and Neurogenomics (ICNN). As with other areas of biomedical science, the post-genome era raises the prospect of transformational advances in neuroscience research. However, neuroscience faces special challenges in analysis, interpretation, and management of the vast quantities of information generated by genetic and genomic technologies. The phenotypic and organizational complexity of the nervous system calls for distinct analytical and informatics strategies and expertise.

      The ICNN, directed by Nelson Freimer and Giovanni Coppola, provides advanced analysis and informatics support to a highly interactive group of neuroscientists at UCLA who conduct basic, clinical, and translational research.  Generally, today’s lack of corresponding resources in analysis and informatics constitutes a bottleneck in their research; ICNN provides for these investigators access to excellent facilities for genetics and genomics experimentation.  ICNN faculty are experts in statistical genetics, gene expression analysis, and bioinformatics, and they oversee the activities of highly-trained staff members in  accomplishing three goals: (1) Providing expert consultation and analyses for neurogenetics and neurogenomics projects;  (2) Developing and maintaining a shared computing resource that is incorporated within the large campus-wide computational cluster for computation-intensive analyses, web-servers, and state of the art software tools for a wide range of applications (including user-friendly versions of public databases, as well as workstations on which ICNN users will be trained to employ these tools); (3) Providing hands-on training in analysis and informatics to group users.

 

  1. Computational Genomics Summer Institute (CGSI). In 2015, Profs. Eleazar Eskin (UCLA), Eran Halperin (UCLA), John Novembre (The University of Chicago), and Ben Raphael (Princeton University) created CGSI. A collaboration with the Institute for Pure and Applied Mathematics (IPAM), led by Russ Caflisch, CGSI is developing a flexible program for improving education and enhancing collaboration in Bioinformatics research. The goal of this summer research program is to bring together mathematical and computational scientists, sequencing technology developers in both industry and academia, and the biologists who use the instruments for particular research applications.

    CGSI is a unique opportunity for junior and senior scholars in Bioinformatics to foster collaborative relationships, accelerate problem-solving, and unleash the full potential of their projects.  The program facilitates interdisciplinary collaboration and training with a mix of formal and informal events. For example, senior scholars present traditional research talks and tutorials, while junior scholars present mini-presentations and organize journal clubs.  CGSI fosters interactions over an extended period of time and is laying crucial groundwork to advance the mathematical foundations of this exciting field.  This year, CGSI will be held from July 6th-26th, 2017. CGSI is made possible by National Institutes of Health grant GM112625.

 

“Give a Man a Fish, and You Feed Him for a Day. Teach a Man to Fish, and You Feed Him for a Lifetime.”

Register Now for UCLA Computational Genomics Summer Institute 2017

CGSI brings together mathematical and computational scientists, sequencing technology developers in both industry and academia, and the biologists who use the instruments for particular research applications. Research talks, workshops, journal clubs, and social events provide a unique opportunity to foster interactions between these three communities over an extended period of time and advance the mathematical foundations of this exciting field.

SHORT PROGRAM: July 10 – 14, 2017
LONG PROGRAM: July 6 – 26, 2017
@ UCLA Campus, Los Angeles

Visit our website to learn more:
http://computationalgenomics.bioinformatics.ucla.edu/

REGISTRATION IS OPEN

Register now for this upcoming summer’s Short and Long Courses:
http://bit.ly/2017CGSIapplication
The deadline to register for the 2017 programs is February 1, 2017.

Overview

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.

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.

Organizers

Eleazar Eskin
University of California, Los Angeles
CGSI Director

Eran Halperin
University of California, Los Angeles
CGSI Director

Russ Caflisch
University of California, Los Angeles
IPAM Director

John Novembre
University of Chicago

Ben Raphael
Brown University

Francesca Chiaromonte
Penn State University

2017 Faculty

Note: This is a list of confirmed faculty for the 2017 programs, as of Jan. 20. We will expand this list in coming weeks.

Kin Fai Au, University of Iowa domain-tiniest
Brian Browning, University of Washington domain-tiniest
Jason Ernst, UCLA domain-tiniest
Eleazar Eskin, UCLA domain-tiniest
Jonathan Flint, UCLA domain-tiniest
Ilan Gronau, Herzliya Interdisciplinary Center domain-tiniest
Eran Halperin, UCLA domain-tiniest
Jo Hardin, Pomona College domain-tiniest
Fereydoun Hormozdiari, University of California, Davis domain-tiniest
David Koslicki, Oregon State University domain-tiniest
Jessica (Jingyi) Li, UCLA + IPAM domain-tiniest
Jennifer Listgarten, Microsoft Research domain-tiniest
Kirk Lohmueller, UCLA domain-tiniest
John Novembre, University of Chicago domain-tiniest
Lior Pachter, University of California, Berkeley domain-tiniest
Bogdan Pasaniuc, UCLA domain-tiniest
Ben Raphael, Princeton University domain-tiniest
Gunnar Rätsch, Eidgenössische Technische Hochschule Zürich domain-tiniest
Saharon Rosset, Tel Aviv University domain-tiniest
Cenk Sahinalp, Simon Fraser University domain-tiniest
Sriram Sankararaman, UCLA domain-tiniest
Alexander Schönhuth, Centrum Wiskunde & Informatica, Amsterdam and UCLA IPAM domain-tiniest
Sagi Snir, University of Haifa domain-tiniest
Jae-Hoon Sul, UCLA domain-tiniest
Fabio Vandin, University of Padova domain-tiniest
Daniel Wegmann, Université de Fribourg domain-tiniest
William (Xiaoquan) Wen, University of Michigan domain-tiniest
Noah Zaitlen, University of California San Francisco domain-tiniest
Alex Zelikovsky, Georgia State University domain-tiniest
Or Zuk, Hebrew University of Jerusalem domain-tiniest

CGSI is made possible by National Institutes of Health grant GM112625.

Read more about CGSI’s 2016 programs at the ZarLab blog: http://www.zarlab.xyz/ucla-launches-cgsi-with-inaugural-summer-programs/.