Leena C Vankadara

Bio

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? 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.

I joined Amazon Research as an Applied Scientist II, in January 2023. Previously, I was a Ph.D. candidate in the International Max Planck Research School for Intelligent Systems. I was jointly supervised by Prof.Debarghya Ghoshdastidar at the Theoretical Foundations of Artificial Intelligence, Technical University of Munich and Prof.Dr.Ulrike von Luxburg at the Theory of Machine learning group, University of Tuebingen.

Recent News

• [01/01/2023] Started a new position in the Causality lab at Amazon Research as an Applied Scientist II!.
• [21/12/2022] Submitted my PhD Thesis!
• [02/12/2022] Luca Rendsburg and I will be presenting our work on Interpolation and Regularization for Causal Learning. at NeurIPS 2022 in New Orleans, USA.
• [03/11/2022] New Preprint: A Consistent Estimator for Confounding Strength.
• [15/09/2022] Paper: Interpolation and Regularization for Causal Learning. accepted to NeurIPS 2022.
• [14/09/2022] Giving a talk on Causal properties of interpolating estimators in the Department of Mathematical Statistics, Technical University of Munich.
• [30/06/2022] Will be giving a talk on "Is Memorization Compatible with Causal Learning? The Case of High-Dimensional Linear Regression." at Center Inria Lille - Nord Europe.
• [27/05/2022] I will be in London for two weeks attending the annual meeting of the Institute of Mathematical Statistics and COLT 2022!
• [19/05/2022] Giving a talk on "Interpolation and Regularization for Causal Learning" at the Women in Data Science conference in Chemnitz.
• [27/04/2022] Mahalakshmi Sabanayagam will be presenting our work on Graphon based Clustering and Testing of Networks: Algorithms and Theory at ICLR 2022! .
• [24/03/2022] Giving a talk on "Kernels for High-Dimensional Large-Scale Clustering" at the SIAM Conference on Imaging Science.

Publications

Most recent publications on Google Scholar.
^‡ indicates equal contribution.

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Vitæ

Full Resume in PDF.

• Amazon Research January 2023 - now

  Applied Scientist II
  Causality Lab
• IMPRS-IS 2018 - 2022

  Ph.D. Candidate
  Theory of Machine Learning Group
• Amazon Research Fall 2020

  Applied Scientist, Intern
  Causality Lab
• Max Planck Institute (MPI-IS) June - December 2018

  Research Scientist, Intern
  Statistical Learning Theory Group
• University of Tuebingen April 2017 – June 2018

  Teaching Assistant
  Theory of Machine learning
  
• University of Tuebingen 2017

  Master's Thesis
  Theory of Machine learning group
• University of Hamburg 2014 - 2017

  Master's Student
  Masters in Machine learning

Productivity tools

I am an avid fan of productivity tools that enable me to make life more efficient, organized, and simply more enjoyable. There have been massive improvements in the productivity tools space in the past 5 years with tools like Notion that allow users to completely customize the tool according to their specific needs. BTW, if you have not heard of or used Notion yet, you are missing out. I rely on it every single day to manage every single aspect of my life.

Website Design

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