Gabriel Poesia
Hello! I'm a 3rd year Computer Science PhD student at Stanford University, affiliated with the NLP Group and advised by Noah Goodman in the Computation and Cognition Lab.
Research-wise, I've been pursuing two intersecting avenues:
- I take inspiration from how human communication to think about how we could interact with computers with more flexibility and efficiency, and
- I take the field of education as a source of challenging fundamental AI problems, touching everywhere from reasoning to explainability and language understanding, as well as a target for important applications.
Publications
| Under review at ICLR 2022 |
Synchromesh: Reliable Code Generation from Pre-trained Language Models [PDF] |
| NeurIPS 2021 |
Contrastive Reinforcement Learning of Symbolic Reasoning Domains (to appear) [arXiv] |
| EMNLP 2021 |
Open-domain clarification question generation without question examples (to appear) |
| AAAI 2021 |
Pragmatic Code Autocomplete [PDF] [Code] |
| OOPSLA 2020 |
Dynamic Dispatch of Context-Sensitive Optimizations [Link] [PDF] [Code] |
| OOPSLA 2017 |
Static Placement of Computation on Heterogeneous Devices [Link] [PDF] |
| ECML/PKDD 2014 |
A Lossless Data Reduction for Mining Constrained Patterns in n-ary Relations [Link] [PDF] |
Other interests
Programming contests. I used to be an ACM-ICPC competitor (world finalist in 2015), and generally involved in programming contests in various ways. In particular, I authored 3 problems for the official ACM-ICPC Latin American regionals, 2 in 2017 and one in 2020. I've also coached several teams, taught at training camps in Latin America, and co-authored the problems that selected high schoolers to represent Brazil in the International Olympiad of Informatics in 2018.
Data musicalization. I've been having a lot of fun in creating music from data, as a powerful way to have people subjectively experience information. I have an ongoing side-project using COVID-related data that will be released this September 2021. Stay tuned!
Invited Talks
- Synchromesh: Reliable Code Generation from Pre-trained Models @ Microsoft Research, Oct 2021.
- Pragmatic Code Autocomplete: Resolving User Input Ambiguity in Context @ Microsoft Research Cambridge, Aug 2021.
- Contrastive Reinforcement Learning of Symbolic Reasoning Domains @ Google Research, Jul 2021.
- Pragmatic Code Autocomplete @ Microsoft Data+AI, Nov 2020.