Aniket Didolkar

I am a Ph.D. student at Mila and The University of Montreal, advised by Prof. Yoshua Bengio, Dr. Anirudh Goyal, and Prof. Michael Mozer. I am also a visiting researcher at Meta, where I work with Dr. Nicolas Ballas.

My research is rooted in building cognitive science-inspired deep learning techniques. Broadly, I am interested in designing models that learn and reason like humans. Most recently, I have been exploring the metacognitive abilities of large language models (LLMs) in the context of mathematical problem solving (1). In earlier work, I developed hybrid architectures that integrate recurrent networks with transformers to effectively handle long-context modeling (2). Another major thread of my Ph.D. has focused on object-centric learning, where I have worked on building general-purpose visual representations that capture compositional structure and enable downstream reasoning (3, 4, 5).

Going forward, I am particularly interested in:

• Equipping LLMs with good thinking frameworks: While reinforcement learning has dramatically improved LLM reasoning, there remains a gap in how these models acquire and reuse knowledge. I am excited by the possibility of enabling LLMs to convert past reasoning traces into procedural habits, and to adopt structured thinking frameworks that maximize the utility of their context window.
• Multi-agent collaboration: Much of human progress comes from collaboration. To use LLMs to unlock progress on challenging scientific endeavors it would be crucial to develop frameworks which allow various LLM agents to collaborate with each other in a scalable manner as opposed to one LLM thinking for an extremely long time.

Prior to my current role, I gained valuable research experience across academia and industry through several internships. I was a research intern at Valence Labs, where I worked with Dr. Jason Hartford on experimental design strategies for estimating the effects of gene knockouts in cells. Before that, I interned at Microsoft Research NYC with Dr. Alex Lamb on reinforcement learning. Prior to starting my Ph.D., I spent a year at Mila working with Dr. Anirudh Goyal and Prof. Yoshua Bengio on cognitive science-inspired deep learning projects, now published at NeurIPS 2021 and ICLR 2022. During my undergraduate studies, I was a Google Summer of Code student developer with Preferred Networks, where I contributed CUDA-optimized implementations of RNNs, GRUs, and LSTMs to the ChainerX deep learning library. I also collaborated with Prof. Rajiv Ratn Shah at IIIT Delhi on applied NLP projects, and with Prof. Aditya Gopalan at IISc Bangalore on time-series forecasting models for urban pollution.