VLDB 2026 Research / reviewers in the wild / expert
Rayna Bhattacharyya
dblp:409/9947
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Theoretical computer science
1 paper |
Automated reasoning and model checking · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computing education · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Automated reasoning and model checking › theorem proving
interactive theorem proving |
1.0 | 1 | 2026 | LeanTutor: Towards a Verified AI Mathematical Proof Tutor · AAAI 2026 |
Automated reasoning and model checking
theorem proving |
1.0 | 1 | 2026 | LeanTutor: Towards a Verified AI Mathematical Proof Tutor · AAAI 2026 |
Computing education
intelligent tutoring systems |
0.3 | 1 | 2026 | LeanTutor: Towards a Verified AI Mathematical Proof Tutor · AAAI 2026 |
Methods — techniques the papers use, named apart from their topics
lean theorem prover · 2.0large language model · 2.0autoformalization · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | LeanTutor: Towards a Verified AI Mathematical Proof TutorabstractThis paper considers the development of an AI-based provably-correct mathematical proof tutor. While Large Language Models (LLMs) allow seamless communication in natural language, they are error prone. Theorem provers such as Lean allow for provable-correctness, but these are hard for students to learn. We present a proof-of-concept system (LeanTutor) by combining the complementary strengths of LLMs and theorem provers. LeanTutor is composed of three modules: (i) an autoformalizer/proof-checker, (ii) a next-step generator, and (iii) a natural language feedback generator. To evaluate the system, we introduce PeanoBench, a dataset of 371 Peano Arithmetic proofs in human-written natural language and formal language, derived from the Natural Numbers Game. Manooshree Patel, Rayna Bhattacharyya, Thomas Lu, Arnav Mehta, Niels Voss, Narges Norouzi, Gireeja Ranade |
AAAI | 2 |