I'm deeply interested in models that can learn to reliably perform and verify high-level mathematical reasoning, motivated and grounded by applications in computer-assisted education. This ends up touching on problems in AI as well as in cognitive science.
|Phil. Trans. of the Royal Society A 2023||
Peano: Learning Formal Mathematical Reasoning
Parsel: A Unified Natural Language Framework for Algorithmic Reasoning
|NeurIPS Math-AI 2022||
Lemma: Bootstrapping High-Level Mathematical Reasoning with Learned Symbolic Abstractions
Left to the Reader: Abstracting Solutions in Mathematical Reasoning
Synchromesh: Reliable Code Generation from Pre-trained Language Models
Contrastive Reinforcement Learning of Symbolic Reasoning Domains
Open-domain clarification question generation without question examples
Pragmatic Code Autocomplete
Dynamic Dispatch of Context-Sensitive Optimizations
Static Placement of Computation on Heterogeneous Devices
A Lossless Data Reduction for Mining Constrained Patterns in n-ary Relations
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!