[talks] Reminder: Colloquium Speaker-Elena Glassman, Tomorrow at 12:30

[Emily C. Lawrence]

CS Department Colloquium Speaker
Elena Glassman, University of California, Berkeley 
Monday, February 26, 2018 - 12:30pm 
Computer Science - Room 105
Host: Prof. Adam Finkelstein

"Interactive systems for code and data demography "

Programming-the means by which we tell computers what to do-has changed a
lot over time. Programming today means programming alongside hundreds of
fellow students, thousands of fellow professional software engineers at a
particular company, or millions of fellow developers in the open-source
community sharing their code online. In this talk, I will describe several
interactive systems I have built that exploit the structure within large
volumes of peer-produced code to help individual programmers learn how to
write more correct, readable code.

These systems are made possible by code demography, which I define as
statistics, algorithms, and visualizations that help people comprehend and
interact with population-level structure and trends in large code corpora.
The key to my approach is designing or inferring abstractions that capture
critical features and abstract away variation that is irrelevant to the
user. Code demography can reveal strategically diverse sets of aligned
code examples which, according to theories of human concept learning, help
people learn, i.e., construct mental abstractions that generalize well.

I will focus this talk on two families of systems that use program
analysis, program synthesis, and visualization to either power active
data-driven teaching in large programming classrooms or passive knowledge
sharing within developer communities. Some of these systems have been
integrated into UC Berkeley's largest introductory programming class,
which regularly enrolls over 1500 students. I will conclude with my vision
for how the techniques of code demography can be generalized to more types
of messy, structured, complex data corpora in order to help data
scientists and enable new data-driven programming paradigms.
 
Bio:
Elena Glassman is an EECS postdoctoral researcher at UC Berkeley, in the
Berkeley Institute of Design, funded by the NSF ExCAPE Expeditions in
Computer Augmented Program Engineering grant and the Moore/Sloan Data
Science Fellowship from the UC Berkeley Institute for Data Science (BIDS).
She earned her PhD in EECS at the MIT CS & AI Lab in August 2016, where
she created scalable systems that analyze, visualize, and provide insight
into the code of thousands of programming students. She has been a summer
research intern at both Google and Microsoft Research, working on systems
that help people teach and learn. She recently joined the program
committees of ACM CHI, ACM Learning at Scale, and two SPLASH workshops on
programming usability. She was awarded the 2003 Intel Foundation Young
Scientist Award, both the NSF and NDSEG graduate fellowships, the MIT EECS
Oral Master's Thesis Presentation Award, a Best of CHI Honorable Mention,
and the MIT Amar Bose Teaching Fellowship for innovation in teaching
methods. 

Prior to entering the field of human-computer interaction (HCI), she
earned her MEng in the MIT CSAIL Robot Locomotion Group and was a visiting
researcher at Stanford in the Stanford Biomimetics and Dextrous
Manipulation Lab.