Every week we peel one hard-working grad student away from the lab for a quick Q&A to learn a little bit more about the people
crazy passionate enough to work in one of the UC San Francisco labs while they get their PhD in Bioengineering through the UC Berkeley/UC San Francisco Graduate Program in Bioengineering. Today we have Jeffrey Yunes, a graduate student in the Babbitt Lab where he researches informatics-based approaches to the computational prediction of protein function. Jeffrey received his BS in Computer Science in 2006 from the Georgia Institute of Technology and then went on to write warehouse optimization software for Amazon.com for three years before returning to academia to pursue his PhD. Read on to see how dinosaurs, space, and math tricks got Jeffrey to where he is today. Thanks, Jeffrey!
When did you first realize you loved science?
I grew up loving dinosaurs, space, math tricks, Capsela, and computers. (I don’t remember which one was introduced to me first, but thank my parents for doing so.) As I got older, I realized that the more math and science I learned, the better I was at making stuff. In college, I grew excited by the prospects of applying computer science to biological data.
What brought you to UC San Francisco?
Many factors led me to the Joint Graduate Group: the access to faculty across both campuses; the high caliber classes in bioinformatics, computer science, and statistics; the flexible curriculum; and lots of potential advisors working in bioinformatics- all in a place I wanted to be!
When you’re not hard at work in the lab, how do you spend your time?
The entrepreneurial spirit and accompanying resources in the San Francisco Bay Area are unparalleled. There are biotech hackerspaces, prototyping studios, technical workshops, and release parties. That being said, I like to work on side projects; I just released a mobile app, and have been dabbling in decentralized cryptocurrencies. I also like to exercise and participate in one-off events in the area.
What’s the coolest thing about your current research in bioengineering?
Sometimes, I’m exploring how to model relationships between protein sequence and function. Other times, I’m mustering weak signals in homologous protein sequences to identify how a protein became pathogenic. I get to learn the latest machine learning algorithms and take part in interdisciplinary collaborations. The work changes every day, and I’m always learning new stuff and becoming a better scientist.
If you could give one piece of advice to someone thinking about a graduate program in bioengineering or a related field, what would it be?
Take the time to find a problem you think is important. The process will be unsettling; at first, it will seem that everything has been investigated. When you suddenly discover a treasure trove of unexplored problems, you’ll know that you’ve matured scientifically.