We design, build and evaluate systems for comprehending and interacting with population-level structure and trends in large code and data corpora.
These systems augment human intelligence by giving users a “useful degree of comprehension in a situation that previously was too complex.” -D. C. Engelbart
What success looks like
A vibrant, supportive community of undergraduates, masters, and PhD students who are creating new tools for thought and action that augment human intelligence and agency.
How we’re getting there
- Practicing project planning and replanning
- Finding the quickest way(s), e.g., interviews, observations, surveys, paper prototypes, simulations, minimal deployments, ..., to get the data we need for the riskiest risks in our project
- Frequent feedback from each other
- Working to create, publish, and publicize research we’re proud of
- at top-tier HCI research conferences, i.e., CHI, UIST, and CSCW, and journals, i.e., TOCHI
- Replanning rather than submitting unfinished, poorly thought-out, and/or hastily written work
- Reflecting on our personal and community practices
- Getting enough sleep so we can bring our best selves to our work
Text compression across multiple documents
Janet Sung, Jamie Lee, Yuri Vishnevsky, Kathy Qian, Amy Zhang
Intelligent creativity augmentation
Software at scale
Tianyi Zhang, Björn Hartmann, and Miryung Kim
Perception of & trust in AI
Zana Bucinca, Phoebe Lin, Krzysztof Gajos, Berk Ustun
Rebecca Hao, Jake Cui
Interfaces for exploring emergent structure in medical record corpora
Tianyi Zhang, Finale Doshi-Velez & collaborators
Program synthesis for data at scale
Programming + Linguistics
- Accepted for publication: Approaching polyglot programming: what can we learn from bilingualism studies?
Rebecca Hao and Elena Glassman. PLATEAU 2019.
HCI + Software Engineering
- Visualizing API Usage Examples at Scale.
Elena Glassman*, Tianyi Zhang*, Björn Hartmann, and Miryung Kim. CHI 2018.
*indicates equal contributions (supplemental info)
HCI + Program Synthesis
- Accepted for publication: Characterizing Developer Use of Automatically Generated Patches.
José Pablo Cambronero, Jiasi Shen, Jürgen Cito, Elena Glassman, and Martin Rinard. IEEE VL/HCC 2019.
- Writing Reusable Code Feedback at Scale with Mixed-Initiative Program Synthesis.
Andrew Head*, Elena Glassman*, Gustavo Soares*, Ryo Suzuki, Lucas Figueredo, Loris D'Antoni and Björn Hartmann. ACM Learning @ Scale 2017.
*indicates equal contributions
Asaro-Biggar ('92) Family Assistant Professor of Computer Science, University of Rochester
Stanley A. Marks and William H. Marks Assistant Professor of Computer Science
specializing in human-computer interaction
Postdoctoral Scholar, UCLA PhD in Software Engineering '19
Masters student, Graduate School of Design
CS & Art, Film, and Visual Studies
Undergraduate researcher, CS
CS & Philosophy
CS & Linguistics
CS & Linguistics
Now at Twitter
Applying to college!