Biological Data Science Meeting

Visitor of the Week: Zhou "Jason" Shi


Meet Zhou “Jason” Shi of the Gladstone Institutes at UCSF. Working as a postdoc in the lab of Katherine Pollard, who is among the 47 Chan Zuckerberg Investigator selected in 2017, Jason is also a junior research scholar at the Chan Zuckerberg Biohub. He is on campus for the third iteration of Biological Data Science. He marked his first CSHL meeting with a talk titled “Comprehensive and ultra-rapid identification of genetic variants in human gut microbiome”.

What are your research interests? What are you working on?
My general research interest is to better address biological problems by developing computational methods and bioinformatics tools. My current project involves developing a computational method to quickly identify specific microbes from any huge, messy pool of microbes in the human gut.

How did you decide to make this the focus of your research?
Due to having an interest in broad research topic, I only recently centralized the focused my research: microbiome. Microbiome is something I am passionate about and the more time I spend in studying microbiomes, the more I am amazed by how they impact human health, especially in "surprising" or unexpected ways.

How did your scientific journey begin?
I come from a software engineering background. During a software process class in my senior year, my instructor shared a translated version of a quote from a great computer scientist, Donald Knuth: “I can’t be as confident about computer science as I can about biology. Biology easily has 500 years of exciting problems to work on.” At that time, I was in working on an unchallenging project so was easily convinced by the quote and then applied to a microbiology graduate program.

Was there something specific about Biological Data Science meeting that drew you to attend?
I chose to attend this meeting mainly because it is one of the most exciting meetings with a clear focus on data science for biology. The meeting this year included multiple topics I am personally and highly interested in; e.g., algorithms and deep learning.

What is your key takeaway from the meeting?
One key takeaway I found very inspiring is that transfer learning allows the use patterns learnt from developing retina cells to recognize cell types in adult mouse.

What did you pick up or learn from the meeting that you plan to apply to your work?
I learned a technique that further breaks down k-mers to l-mers then only indexes minimizers of l-mers for efficient algorithms. I found this technique very intriguing because I may be able to apply it to my project to improve the data structure behind the bioinformatics tool I am building.

If someone curious in attending a future iteration of this meeting asked you for feedback or advice on it, what would you tell him/her?
I would be happy to tell him/her of my personal great experience at this meeting. Also, for anyone who enjoy method intensive talks and want to take back to their lab a great variety of cool ideas handling biological data, then this is a meeting they should consider and definitely enjoy.

What do you like most about your time at CSHL?
The ease of communicating with anyone during the meeting was truly amazing.

Thank you to Jason for being this week's featured visitor. To meet other featured scientists - and discover the wide range of science that takes part in a CSHL meeting or course - go here.