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SUMMARY:IC Colloquium : Interactive systems for code and data demography
DTSTART:20180305T101500
DTEND:20180305T113000
DTSTAMP:20260408T051430Z
UID:c5fd6ae27df099179420e4ff295ba98d3cac81fb3305ba232f9ea7fd
CATEGORIES:Conferences - Seminars
DESCRIPTION:By: Elena Glassman - UC Berkeley\nIC Faculty candidate\n\nAbst
 ract:\nProgramming—the means by which we tell computers what to do—has
  changed a lot over time. Programming today means programming alongside hu
 ndreds of fellow students\, thousands of fellow professional software engi
 neers at a particular company\, or millions of fellow developers in the op
 en-source community sharing their code online. In this talk\, I will descr
 ibe several interactive systems I have built that exploit the structure wi
 thin large volumes of peer-produced code to help communities of programmer
 s learn about\, reflect on\, and teach how to write more correct\, readabl
 e code.\nThese systems are made possible by code demography\, which I defi
 ne as statistics\, algorithms\, and visualizations that help people compre
 hend and interact with population-level structure and trends in large code
  corpora. The key to my approach is designing or inferring abstractions th
 at capture critical features and abstract away variation that is irrelevan
 t to the user. Code demography can reveal strategically diverse sets of al
 igned code examples which\, according to theories of human concept learnin
 g\, help people learn\, i.e.\, construct mental abstractions that generali
 ze well.\nI will focus this talk on two families of systems that use progr
 am 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 int
 egrated into UC Berkeley’s largest introductory programming class\, whic
 h 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
  large complex data corpora and enable new data-driven programming paradig
 ms.\n\nBio:\nElena Glassman is an EECS postdoctoral researcher at UC Berke
 ley\, in the Berkeley Institute of Design\, funded by the NSF ExCAPE Exped
 itions 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 EE
 CS Oral Master’s Thesis Presentation Award\, a Best of CHI Honorable Men
 tion\, and the MIT Amar Bose Teaching Fellowship for innovation in teachin
 g methods.\nPrior 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 M
 anipulation Lab.\n\nMore information
LOCATION:BC 420 https://plan.epfl.ch/?room==BC%20420
STATUS:CONFIRMED
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