Hello, I recently graduated with a Master's degree from the David R. Cheriton School of Computer Science at the University of Waterloo, where I was advised by Prof. Shai Ben-David. I am broadly interested in Theoretical Computer Science, particularly in Theoretical Machine Learning. I have recently been working on ethical issues (Fairness, Interpretability, Privacy, Robustness) in Machine Learning (and algorithms in general). I have also dabbled in some research projects related to Game Theory, Optimization, Graph Algorithms, and Clustering Theory. Overall, I am motivated by problems that are theoretically challenging, while also being practically relevant. Previously, I did my Bachelor's in Mathematics and Theoretical Computer Science from Chennai Mathematical Institute.

Fun fact: My Erdos number is 3 :)

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Papers

• On Learnability with Computable Learners
  Sushant Agarwal, Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner
  Appeared in ALT 2020. [pdf]
  
  

• Trade-offs Between Fairness, Interpretability, and Privacy in Machine Learning
  Sushant Agarwal
  Master's Thesis [pdf], Accepted as workshop poster in ALT 2020, in submission. [pdf]
  
  

• Trade-offs Between Fairness and Privacy in Machine Learning
  Sushant Agarwal
  Accepted in IJCAI 2020 Workshop on AI for Social Good. [pdf]
  
  

• Trade-offs Between Fairness and Interpretability in Machine Learning
  Sushant Agarwal
  Accepted in IJCAI 2020 Workshop on AI for Social Good. [pdf]
  
  

• A Different View of Fair Data Representation
  Tosca Lechner, Nivasini Ananthakrishnan, Sushant Agarwal, Shai Ben-David
  Workshop poster in ALT 2020, in submission. [pdf]
  
  

• A Critical Survey of Post-hoc Interpretability Tools Based on Feature Attributions
  Sushant Agarwal, Shai Ben-David
  Working paper. [pdf]
  
  

• Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
  Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Zhiwei Steven Wu, Himabindu Lakkaraju
  In Submission. [pdf]
  
  

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Projects

• Locality Sensitive Hashing for Probabilistic Clustering [pdf]
  Theoretical Foundations of Clustering, 2019.
  

• Tractable Variants of Envy-Free Cake Cutting [pdf]
  Game Theory, 2018.
  

• Improving Local-Search Methods Using Deep Neural Networks [pdf]
  Deep Learning in Computational Discrete Optimization, 2018.
  

• Google is Watching You
  Artificial Intelligence: Law, Ethics & Policy, 2017.
  

• Efficient Algorithms for Bounded Clique-Width Graphs
  Topics in Graph Algorithms, 2017.