I am currently a Machine Learning Engineer at Cerebras Systems. Prior to that, I was graduate student in computer science at the University of Toronto and Vector Institute. I am grateful to be supervised by Nisarg Shah (CS Theory group) and Frank Rudzicz (Vector Institute). My research explores strategic behaviour, fairness, and incentives in machine learning using techniques from algorithmic game theory, economics and mechanism design (I still continue to work on these topics). Prior to graduate school, I completed my bachelors degree in electrical and computer engineering at the University of Toronto.


  • September 2020
    • Our paper, Analyzing Text Specific vs Blackbox Fairness Algorithms in Multimodal Clinical NLP, was accepted at the 3rd Clinical Natural Language Processing Workshop at EMNLP paper!
    • UPDATE: This paper was selected as the best short paper at the 3rd Clinical Natural Language Processing Workshop at EMNLP!
  • August 2020
    • I have joined Cerebras Systems as a Machine Learning Engineer! Cerebras builds the world’s most powerful chip using wafer-scale integration to accelerate artificial intelligence by orders of magnitude beyond the current state. I will be initially working on the ML Frameworks team to provide integration with libraries like pyTorch and Tensorflow.
  • June 2020
    • Our new paper on fair algorithms for multi-armed bandits using Nash Welfare is on arxiv! paper
  • January 2020
    • Our paper, The Effect of Strategic Noise on Linear Regression, was accepted at AAMAS 2020! paper
    • Our paper, Designing Fairly Fair Classifiers via Economic Fairness Notions, was accepted at WWW 2020 with Oral! paper
  • November 2019
    • Our paper, The surprising power of hiding information in facility location, was accepted at AAAI 2020! paper
    • Began my internship at Xanadu, a quantum computing research firm, as part of a Mitacs internship. I am investigating the potential of multiple quantum circuits connected classically for machine learning applications
  • June 2019
    • Our paper, Generative Adversarial Networks for text using word2vec intermediaries was accepted at the 4th Workshop on Representation Learning at ACL 2019!
  • May 2019
    • Our paper, JacNet: Learning Functions with Structured Jacobians was accepted at the Invertible Neural Nets and Normalizing Flows workshop at ICML 2019!