Dean Lee Speaker Event
- Austin Esparza
- Sep 20
- 5 min read
Dean Lee LinkedIn: https://www.linkedin.com/in/deanslee/
Why Every Biologist Needs Computational Skills
Written by: Austin Esparza, CSULB Biotechnology Club
Dean Lee joined the CSULB Biotechnology Club to share his path from neuroscience at UCLA and Harvard to computational biology in industry. His talk emphasized a simple theme: biology is data-driven. The students who move fastest learn to connect biological questions with clear, reproducible analysis.
What motivated your shift from neuroscience research to computational biology?
Dean’s background gave him strong domain knowledge, but to answer questions he was interested in, he built new skills by recreating figures from published papers and then expanded on them by testing his own hypotheses on the same datasets.
Why are computational skills so important for biologists today?
Coding and statistics are baseline requirements. Even lab-focused roles expect computational literacy. Dean encouraged students to begin learning on their own terms, whether or not they pursue a background in biology, computer science, or statistics, and to treat all of these skills as part of core training. Computational biology is also a fantastic option for students primarily studying computer science.
What academic and career path do you recommend for students interested in computational biology?
Dean emphasized starting early. Build a foundation in statistics, pair it with biology coursework, and gain technical experience from year one. He pointed to tuition-supported research roles, such as those at The Scripps Research Institute, as one way to make advanced study affordable.
For students targeting industry, he highlighted Georgia Tech’s online Master’s in Computer Science (~$7,000) as an affordable option that can provide strong technical depth. For those considering a PhD, he recommended assistant positions at research institutes, which often lead to first-author publications and make applicants more competitive for top programs.
How should students approach learning programming and analysis tools?
Projects drive mastery. Courses provide exposure, but projects force you to integrate coding, statistics, and biological context. Dean shared that personal projects in R became central to his profile and directly useful in interviews and on the job. To get started, he recommended practical resources like Bioconductor and the Galaxy Training Network.
Should students focus on R or Python?
Both matter. R remains popular for RNA-seq and statistics. Python is strong in machine learning. Start with whichever tool solves a problem you care about. Add the other to expand your range.
What role can AI tools play in learning computational biology?
AI can accelerate learning and make code more readable. ChatGPT can help unblock progress and speed iteration. Dean noted that while GitHub Copilot can be useful, he personally discontinued his subscription. The takeaway was that AI can help, but consistency and project-based learning matter more.
How should students prepare for graduate school?
Top programs increasingly expect first-author publications. Focus on producing tangible outputs while weighing program fit.
What advice do you have for building confidence in computational skills?
Be consistent! Four to six hours a week for six months is enough to build competence. Start by automating small tasks. Ship something you can show and then start to scale up to larger projects.
Recommended resources
Figure One Lab GitHub: re-enact Figure 1 from modern papers to build end-to-end analysis experience.
Bioconductor: R based packages and workflows for genomics.
Galaxy Training Network: tutorials and workflows for common analysis tasks.
Rosalind: problem driven bioinformatics practice.
AI tools: ChatGPT for coding support. Copilot optional, not essential.
From Dean’s recent LinkedIn post
Dean Lee, September 18, 2025
“For someone just starting out, what entry-level skills or projects do you think stand out the most for downstream analysis computational biology roles in industry?”
There are several angles to consider.
First, because there are almost no truly entry-level computational biology jobs right now in industry, any talk about gaining entry-level skills can be misleading. To compete in this job market, you'll have to be ready to compete with scientists who have been doing this for years.
So, how can you still have a shot? I think one viable path forward is to learn to analyze the most commonly used data types and string them together into a single coherent story.
Right now if you go to any biology conference, you'll see a lot of people combining scRNA-seq, spatial transcriptomics, imaging data, and potentially real-world evidence to answer one particular question related to patient health. And when you take a look at the Nature papers that have come out in the past couple years, they follow the same trend. This signals that using all of these different data types together creatively is now considered the most sophisticated kind of data analysis, and there are still jobs that hire for people who can do this. But because this is not an easy skill to learn at all, all of these roles tend to be senior to principal scientist roles, not entry-level.
So, back to the original question up top. For someone just starting out, maybe you have 2-5 years before you plan to apply for jobs. The most straightforward way I can think of to acquire these skills is to replicate the results of one of the Nature papers I mentioned above.
This is no small feat, but if you are still a student in school, you have the greatest advantage: time.
You have enough time to acquire the experience you need to be competitive, but there’s no time to waste."
Additional insights from Dean on LinkedIn
Dean is very active on LinkedIn, where he shares frequent updates on industry trends and practical advice for students and professionals. If you are serious about building a career in computational biology or biotechnology, following his posts is an easy way to keep learning beyond the classroom.
It would be impractical for me to pull everything useful he says and place it into one post. That would be better served in a book or a lecture series. As a sample of the education you stand to gain by following him, I’ve pulled some of the things I have found most useful from the last month or two:
Anatomy of a Computational Biology Project: Study, Apply, Communicate. Projects beat certificates because they show the full cycle from fundamentals to deliverables. https://lnkd.in/p/gqpUcEuJ
Small Biotech vs Big Pharma: Startups reward doers who wear many hats. Big pharma rewards knowledge of the org map and avoiding redundant work. Use both perspectives to shape expectations.
Resume Workshop Takeaways: Skip the professional summary. Use sparse bolding to mirror job requirements. Treat the resume as marketing to get the first interview. https://lnkd.in/p/gYURmigM
Blueprint your path: Find five recent hires in roles you want. Trace publications to job responsibilities. Reproduce the skills that got them hired.
Unconventional job search strategies: Target directors at posters, leverage state internship funding, pitch a guest blog, and reach out to newly hired directors before roles are posted. Try with care.
LinkedIn as your storefront: Transfer your strongest bullets to your profile. Lead with concise keywords, and do your best to represent your professional presence. https://lnkd.in/p/guAH5TmX
De-risk your PhD series:
Design projects with marketable elements.
Join labs with strong industry ties.
Choose labs sitting on lots of data.
Track lab alumni outcomes to see what actually gets people hired. https://lnkd.in/p/gDTpuXJc
Choose a lab with money.
Soft skills for comp bio: Listening for biological and organizational context is a force multiplier. Clarify the request before you code.
Meetings that matter: Replace update meetings with decision meetings. Do background reading before the call to protect team time.
Get Unstuck: Learning can be a challenge, computational work is no different. Break learning into manageable calendar chunks.
There are no fixed paths: There are so many paths in life and your career. Look to the market and to those already thriving in roles you wish to pursue. Look at job descriptions and ask questions.
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