Starting January 2017, I will be an Assistant Professor at
Carnegie Mellon University's
Heinz College of Public Policy
and Information Systems, focusing on machine learning for solving big
societal problems! I will be looking for PhD students!
I'm currently a postdoc at MIT primarily working on online education
with Polina Golland.
I teach, develop, study, and apply machine learning and data analysis
Prior to postdoc'ing and after five outrageous years in grad school at
MIT, I staged a successful Ph.D. thesis offense on
Star Wars Day
2015. My thesis was on
machine learning for analyzing social data and medical images, and it
won the George M. Sprowls award for best thesis in Computer Science at
MIT! My advisors were
Polina Golland and
Before finding myself stranded in Massachusetts, I spent my childhood
frolicking in the warmer climates of sunny California and completed my
undergraduate studies at UC
Berkeley in May 2010.
I enjoy teaching! Check out my
teaching page for a list of classes
I've taught at MIT, UC Berkeley, and in Jerusalem at a summer program
MEET that brings together Israeli
and Palestinian high school students. There are also videos of me
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George Chen is a postdoc at MIT in Electrical Engineering and Computer Science. In January 2017, he will join the faculty of Carnegie Mellon University's Heinz College of Public Policy and Information Systems. He teaches, develops, studies, and applies machine learning and data analysis tools. His work has spanned a diverse range of applications such as forecasting trends on Twitter, recommending products to people in systems like Netflix, finding human organs in medical images, and detecting buildings and villages in massive satellite images to help plan infrastructure development projects. George obtained his S.M. (2012), E.E. (2014), and Ph.D. (2015) degrees from the Electrical Engineering and Computer Science department at MIT, where he received the George M. Sprowls award for best Ph.D. thesis in Computer Science (2015). He previously completed his B.S. (2010) at UC Berkeley, dual majoring in Electrical Engineering and Computer Sciences, and Engineering Mathematics and Statistics. George enjoys teaching and has taught at the high school, undergraduate, and graduate levels, receiving teaching awards at UC Berkeley and MIT including the top graduate student teaching award at MIT, the Goodwin Medal (2015). His work with GridForm on analyzing satellite images to help bring electricity to rural India won the $10,000 grand prize at MIT's IDEAS Global Challenge 2014 for innovation and entrepreneurship in public service.
Here's what's on my plate:
New edX course on computational probability and inference
With faculty members
, as well
and recent graduate
I'm developing an upcoming free online
advanced-high-school/intro-college course on building computer
programs that do probabilistic reasoning. The course teaches basic
probability, probabilistic graphical models, and machine learning
— all heavily intertwined with how to code things up!
We're aiming to make this class accessible to anyone who knows
introductory Python programming and calculus. Stay tuned!
Nonparametric inference for analyzing social data and medical images
To forecast whether a news topic will go viral on Twitter, we can
compare it to past news topics with similar Tweet activity. More
generally, we can make a prediction based on an observation by
looking at similar past observations. My
develops theory to understand when, why, and how well such
so-called "nearest-neighbor" methods work in applications such as
trends on Twitter
products to people in systems like Netflix
, and finding human
organs in medical images. I'm doing some follow-up work during my
My papers and past projects can be found here.
As a grad student, I moonlighted in various gigs:
In the largest MIT grad dorm
Sidney-Pacific (housing 700
residents), I ran wacky competitive events for a year (such as a
clothing-drive-fashion-show mashup!) before I took up the mantle as
Vice President of Resources in the subsequent year 2012-2013,
managing 10 grad students and $140,000.
I pondered higher education decades down the line as part of the
Task Force on the Future of MIT
Education (2013-2014), for which I led discussions with grad
students, helped assemble student and faculty surveys, and realized
how immensely expensive operating MIT is.
I organized four semesters of talks for
Learning Tea, gathering people around MIT interested in machine
learning (Fall 2011 to Spring 2013).
Some of my other stints include racing for
MIT's cycling team during the
2015 road season, supporting the
Thirsty Ear student pub as a
member of its executive committee (2013-2015), and
handling a year of event publicity and
photography for the EECS Graduate
Student Association (2011).
I claim cycling, climbing, cooking, crafting libations, hitting the
cinemas, skiing, and sewing as hobbies.
Last updated May 6, 2016. Photo credit: Danica Chang.