My broad interests lie in understanding the limits of learning by trying to answer questions such as when is learning possible and when is it not under certain model assumptions? Such questions are particularly interesting when posed in constrained settings, for instance, constraints on computational complexity or adversarial robustness. Understanding such fundamental limits foster the design of provably efficient and reliable Machine learning methods.

More recently, I have been fascinated with statistical and causal properties of interpolating estimators in overparameterized model classes. You can find our recent work on this topic here. For a general overview of my work, please see my publications.

Brief Bio:

I am a Ph.D. candidate in the International Max Planck Research School for Intelligent Systems. I am jointly supervised by Prof.Debarghya Ghoshdastidar at the Theoretical Foundations of Artificial Intelligence group, Technical University of Munich and Prof.Dr.Ulrike von Luxburg at the Theory of Machine learning group, University of Tuebingen.

Research interests

  • Theory of over-parameterized learning,
  • Causality
  • Kernel machines,
  • Concentration inequalities,
  • Statistical vs computational limits of learning.

Education

  • PhD in Statistical learning theory, since December 2018.

    University of Tuebingen, International Max Planck Research School for Intelligent Systems.

  • Masters in Intelligent Adaptive Systems

    University of Hamburg.

  • Masters in Mathematics, Bachelors in Mechanical Engineering

    Birla Institute of Technology and Sciences, Pilani.

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