Divyat Mahajan

  • Ph.D. Candidate, Mila

  • Visiting Researcher, Meta FAIR
I am a final year Ph.D. candidate at Mila & Université de Montréal, advised by Ioannis Mitliagkas. Currently, I am a visiting researcher at Meta FAIR working on pretraining large language models with the memory and generalization team. My research interests can be outlined broadly as follows.
  • Learning algorithms for efficient adaptation under distribution shifts and robustness to spurious correlations.
  • Disentangled (causal) representation learning for sample efficient generalization and interpretability.
  • In-context learning and prior-fitted networks for probabilistic (causal) inference.
For further details, please refer to my thesis proposal (report, slides). My research is supported by the FRQNT doctoral fellowship, and I am deeply grateful for the amazing collaborations that have enrinched my Ph.D. journey. I am advised by Kartik Ahuja and Pascal Vincent under the Meta AIM Program, where I work on compositional generalization and multi-token prediction for large language models. I also did a summer internship at Microsoft Research Cambridge, where I worked on amortized inference and in-context learning with Cheng Zhang and Meyer Scetbon. Further, in the initial years of my Ph.D. I worked with Vasilis Syrgkanis at Stanford on causal inference.

Prior to starting my Ph.D., I was a research fellow at Microsoft Research India, where I worked with Amit Sharma on trustworthy machine Learning from the lens of causality, specifically on out-of-distribution generalization, privacy, and explainable machine learning. Earlier, I completed my undergraduate in Mathematics and Computer Science from the Indian Institute of Technology, Kanpur.

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