Sandrine Dudoit's slides on Individualized Career development plans

1. Junior Principal Investigator Sessions – WK01
Sandrine Dudoit
Division of Biostatistics and Department of Statistics
University of California, Berkeley
www.stat.berkeley.edu/~sandrine
ISMB 2016 – Intelligent Systems for Molecular Biology
Orlando, FL
July 10, 2016
Version: 10/07/2016, 06:34
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2. Agenda
How to outline an individualized career development plan to
become a successful PI.
• This session will cover the concept of having an active role on
your own career development and empowering trainees to
have a strategy when picking mentors and pursuing training.
• This encompasses making very conscious decisions about
what your niche or “brand” will be when looking for jobs
(academic or not).
• Having an “IDP” is a requirement for all NIH grants on the
“training” phases and it is also being requested by private
foundations.
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3. Caveat Emptor
• Giving advice.
“Le plus excellent et divin conseil est d’apprendre `a s’´etudier
et `a se connaˆıtre soi-mˆeme.”
“The most excellent and divine of advice is to learn to study
and know yourself.”
(Pierre Charron, Le trait´e de la sagesse, 1601)
• Everyone is different/One size does not fit all.
• Things rarely (never!) go according to plans.
• I’ve never had a career plan.
My studies and career have been guided by encounters, with
persons and research questions.
My research is oriented by problems that I am excited about
and believe are important and interesting.
I have been lucky in that these problems have also been timely
and of great interest to the community.
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4. Caveat Emptor
But I also work hard. And although I didn’t have a career plan,
I tackle these problems in a principled, rigorous, and honest
manner.
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5. Outline
1 My Journey
2 What Is an IDP?
3 A Career in Interdisciplinary Research
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6. My Journey
• I am a Professor in the Division of Biostatistics and
Department of Statistics at UC Berkeley.
• My research and teaching activities concern the development
and application of statistical methods and software for the
analysis of biomedical and genomic data.
• Statistical methodology. My methodological research interests
regard high-dimensional inference and include exploratory data
analysis (EDA), visualization, loss-based estimation with
cross-validation (e.g., density estimation, regression, model
selection), and multiple hypothesis testing.
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7. My Journey
• Applications to biomedical and genomic research. Much of my
methodological work is motivated by statistical inference
questions arising in biological research and, in particular, the
design and analysis of high-throughput microarray and
sequencing gene expression experiments, e.g., RNA-Seq for
transcriptome analysis and genome annotation and ChIP-Seq
for DNA-protein interaction profiling (e.g., transcription factor
binding). My contributions include: exploratory data analysis,
normalization and expression quantitation, differential
expression analysis, class discovery, prediction, integration of
biological annotation metadata (e.g., Gene Ontology (GO)
annotation).
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8. My Journey
• Statistical computing. I am also interested in statistical
computing and, in particular, reproducible research. I am a
founding core developer of the Bioconductor Project
(www.bioconductor.org), an open-source and
open-development software project for the analysis of
biomedical and genomic data.
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9. My Journey
• 1988–1992: BSc in Mathematics, Carleton University, Ottawa,
Canada.
• 1992–1994: MSc in Probability Theory, Carleton University,
Ottawa, Canada. Advisor: Don Dawson.
• 1994–1999: PhD in Statistics, University of California,
Berkeley. Advisor: Terry Speed.
• 1999–2001: Postdoc in Biochemistry, Stanford University.
Advisor: Pat Brown.
• 2001–: Professor of Biostatistics and Statistics, University of
California, Berkeley.
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10. My Journey
Aspects of my doctoral and postdoctoral education that I have
found particularly useful.
• Statistical consulting course. Department of Statistics, UC
Berkeley.
• Internship in wet-lab. Walter and Eliza Hall Institute (WEHI),
Melbourne, Australia.
• Doctoral fellowship for interdisciplinary research. Program in
Mathematics and Molecular Biology (PMMB).
• Postdoctoral fellowship in wet-lab. Brown Lab, Department of
Biochemistry, Stanford University.
• Practice with grant proposal writing. Assist PI with RO1
proposals, apply for own fellowships.
• Participation in small, focused conferences, workshops, and
short-courses. Latest research developments, open questions,
networking.
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11. My Journey
• Bioconductor Project. Software development, reproducible
research.
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12. What Is an IDP?
What is an Individual Development Plan (IDP)?
Quoted from NIH Training Center (NIHTC).
• “An Individual Training & Development Plan (IDP) is a tool
to help support, plan, and track your career development and
learning opportunities.”
• “With IDP services from NIHTC, you can learn how to build
an IDP based on your greatest strengths, address areas for
growth, and confidently discuss your IDP with your
supervisor.”
• “An experienced consultant, versed in mentoring and coaching
Management Interns and Presidential Management Fellows,
will work with you from a holistic perspective. Taking account
of all considerations, you can develop a plan to serve as a
unique contributor aligned with the NIH mission.”
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13. What Is an IDP?
• “The relationship with the IDP consultant is confidential and
impartial.”
• “NIHTC can also bring IDP best practices on-site to your
team or office.”
• Two 50-minute sessions: $299.
https://trainingcenter.nih.gov/idp consulting.html
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14. What Is an IDP?
What are the benefits of IDP Consulting?
Quoted from NIH Training Center (NIHTC).
• Develop an Individual Development Plan customized to your
needs.
• Identify, clarify, and commit to goals based on your priorities
and professional goals.
• Create and develop strategies for goal achievement.
• Track progress toward your goals.
• Understand, evaluate, and strengthen your technical and
non-technical competencies.
• Practice confidently discussing strategies for aligning
expectations with those of your supervisor.
• Make the most out of a recent promotion, job opportunity, or
other developmental prospect.
• Analyze alternatives and solutions.
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15. Interdisciplinary Research
Beware of jack-of-all-trades-master-of-none one.
• What is a bioinformatician/computational biologist/data
scientist? Is it a biologist that is computer savvy or a
statistician that has dabbled with genomic data?
• Don’t try to do everything yourself.
• The best approach to interdisciplinary research is not just one
person that knows a little bit about everything, but rather a
team of experts in component disciplines, i.e., biological
subject-matter, statistics, computer science.
• This doesn’t mean statisticians should be ignorant of biology
and vice versa.
• On the contrary, we each need to learn a minimum about
other fields in order to have a common language,
communicate well, and be aware of the various aspects,
challenges, and pitfalls of an interdisciplinary research project.
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16. Niche/Brand
• Clearly define your area of expertise.
• Look for projects that you are excited about, that you have the
expertise to tackle, for which you have the potential to make
a contribution, for which you have the right environment, and
that are relevant and of interest to the community.
• Be specific about project goals, have well-defined and
self-contained scientific questions.
One of the hardest steps can be the translation of a biological
question into a statistical question, i.e., the specification of a
parameter of interest.
• Start from subject-matter question vs. hammer looking for
nail.
• Get involved early, at experimental design stages, know how
data are generated.
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17. Niche/Brand
• Decide on point of entry. Tackle pre-processing (e.g.,
normalization) or focus on higher-level analyses (e.g., class
discovery).
Cf. Feasibility.
• Don’t reinvent the wheel. Survey the literature in different
fields, consult with experts in other fields.
• Don’t loose the forest for the trees.
• Avoid jargon, hype, fluff.
• Communicate throughout the project. Joint group meetings,
interactions at faculty, postdoc, and graduate student levels.
• Practice makes perfect. Start early, do not get discouraged by
setbacks, focus on long/middle-term goal, learn from the
journey.
• Be excited about your work, makes a world of difference when
putting in long hours.
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18. Training
As graduate student, postdoc, or even junior faculty.
• Dry-lab and wet-lab co-mentors. Choose based on research,
but also mentoring skills and personal relationship.
• Interact with peers.
• One project, two dissertations.
• Courses. Enroll or sit in courses, e.g., biology and computing
for a statistician.
• Workshops. External, e.g., Bioconductor, or internal, e.g.,
Computational Genomics Resource Laboratory, UC Berkeley
(qb3.berkeley.edu/cgrl).
• Lab meetings, journal clubs.
• Publishing. Get involved in writing of articles.
• Grantsmanship. Practice with grant proposal writing, by
assisting PI with proposals and applying for own fellowships.
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19. Communication
• Communicate. Communicate regularly with the persons that
will evaluate your dissertation/case, make sure they know
what you are working on and are aware of your progress, so
that, if needed, you can revise your plan.
• Team of mentors. On the scientific front, dry-lab and wet-lab
co-mentors.
On the admin front, mentors that can help with navigating
university and funding agency bureaucracy, advise with job
application, tenure case, career decisions.
• Team of collaborators. Choose not only based on on-paper
academic record, but on ability to communicate and get along
with them.
I value more and more the personal aspects of a collaboration,
the ability to enjoy working with collaborators.
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20. Communication
• Publishing. Judicious choice of message and journal for each
publication; one project, two publications; don’t let “the best
be the enemy of the good”.
• Internal exposure. Give or attend seminars and short courses
within your own institution.
• External exposure. Editorial work, conferences, short courses.
Nowadays, blogs, Twitter?
• Curriculum vitae. Write a compelling CV based on your
expertise and accomplishments.
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21. The Plan
• Niche/Brand. Identify area of expertise and, within that area,
well-defined and self-contained scientific questions.
• Identify your strengths and weaknesses.
• Devise training program of courses, workshops, reading list,
co-mentors, and team of collaborators.
• Apply for suitable fellowships and grants, after surveying
government and private funding agencies and discussions with
mentors.
Cf. Lucia Peixoto’s presentation.
• Work hard ... and enjoy the journey!
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22. Les Devises Shadok
Figure 1: Les devises Shadok. “En essayant continuellement on finit par
r´eussir. Donc: Plus ¸ca rate, plus on a de chances que ¸ca marche.” “If
you keep trying you’ll eventually succeed. Therefore: The more you fail,
the more chances you have to succeed.”
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23. Les Devises Shadok
Figure 2: Les devises Shadok. “Si ¸ca fait mal c’est que ¸ca fait du bien!!”
“If it hurts it means it’s good for you!!”
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24. Les Devises Shadok
Figure 3: Les devises Shadok. “Pourquoi faire simple quand on peut faire
compliqu´e?!” “Why makes things simple when they can be
complicated?!”
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25. Les Devises Shadok
Figure 4: Les devises Shadok. “Quand on ne sait pas o`u l’on va, il faut y
aller!! ... ... et le plus vite possible.” “When you don’t know where
you’re going, you better go ... ... and the fastest possible.”
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