Experience

University of Maryland - A. James Clark School of Engineering

• United States
• Higher Education
• 100 - 200 Employee

• Assistant Professor

  • Aug 2022 - Present



J.P. Morgan

• United States
• Financial Services
• 700 & Above Employee

• Senior AI Research Associate

  • Jul 2021 - Aug 2022

  Working in the Fairness and Explainability Team Working in the Fairness and Explainability Team



Carnegie Mellon University

• United States
• Higher Education
• 700 & Above Employee

• Doctoral Candidate

  • Aug 2015 - May 2021

  Thesis Title: Strategies for Fair, Reliable, and Trustworthy Machine Learning Thesis Committee: Prof. Pulkit Grover (advisor), Prof. Anupam Datta, Prof. Alexandra Chouldechova, Prof. Jose Moura, Dr. Kush Varshney Abstract: With Big Data comes Big Responsibility. The goal of my thesis is to make machine learning more reliable and trustworthy, by responsibly addressing the computational challenges of large-scale machine learning as well as the emerging trust issues concerning fairness and explainability through novel algorithmic strategies. Show less



Dataminr

• United States
• Technology, Information and Internet
• 700 & Above Employee

• Research Intern (AI, NLP)

  • Jun 2020 - Aug 2020

  Project Title: Event Extraction for Natural Language Processing with Graph Neural Networks Project Manager(s): Alejandro Jaimes, Joel Tetreault Project Mentors: Liang Ma, Tanay Saha Abstract: As part of this project, I developed a novel technique for event extraction that leverages the syntactic relations and dependencies among words in a sentence using graph neural networks. Project Title: Event Extraction for Natural Language Processing with Graph Neural Networks Project Manager(s): Alejandro Jaimes, Joel Tetreault Project Mentors: Liang Ma, Tanay Saha Abstract: As part of this project, I developed a novel technique for event extraction that leverages the syntactic relations and dependencies among words in a sentence using graph neural networks.



IBM

• United States
• IT Services and IT Consulting
• 700 & Above Employee

• Research Intern (AI for Social Good)

  • May 2019 - Aug 2019

  Project Title: Is there a trade-off between Fairness and Accuracy Project Manager: Kush Varshney Project Mentors: Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu Abstract: The goal of my project was to quantify and derive fundamental limits on the accuracy-fairness trade-off in machine learning using tools from information theory and understand the role of explainability in alleviating this trade-off (paper accepted at ICML'20). Project Title: Is there a trade-off between Fairness and Accuracy Project Manager: Kush Varshney Project Mentors: Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu Abstract: The goal of my project was to quantify and derive fundamental limits on the accuracy-fairness trade-off in machine learning using tools from information theory and understand the role of explainability in alleviating this trade-off (paper accepted at ICML'20).



IBM

• United States
• IT Services and IT Consulting
• 700 & Above Employee

• Research Intern

  • May 2017 - Aug 2017

  Project Title: Accuracy Runtime Trade-offs for Distributed Machine Learning Project Manager: Priya Nagpurkar Project Mentors: Gauri Joshi, Parijat Dube, Soumyadip Ghosh Abstract: As part of this project, I derived novel analytical trade-offs for characterizing Synchronous and Asynchronous Stochastic Gradient Descent (SGD) using tools from optimization and performance modeling (paper accepted at AISTATS 2018). Project Title: Accuracy Runtime Trade-offs for Distributed Machine Learning Project Manager: Priya Nagpurkar Project Mentors: Gauri Joshi, Parijat Dube, Soumyadip Ghosh Abstract: As part of this project, I derived novel analytical trade-offs for characterizing Synchronous and Asynchronous Stochastic Gradient Descent (SGD) using tools from optimization and performance modeling (paper accepted at AISTATS 2018).



University of Alberta

• Canada
• Higher Education
• 700 & Above Employee

• Summer Research Intern

  • May 2014 - Jul 2014

  Project Title: Improving Buried Object Imaging using Ultra Wide Band (UWB) Radar Project Advisors: Prof. Mrinal Mandal, Prof. Karumudi Rambabu Project Title: Improving Buried Object Imaging using Ultra Wide Band (UWB) Radar Project Advisors: Prof. Mrinal Mandal, Prof. Karumudi Rambabu



Indian Statistical Institute, Kolkata

• India
• Higher Education
• 100 - 200 Employee

• Winter Intern

  • Dec 2013 - Jan 2014



Indian Institute of Technology, Kharagpur

• India
• Higher Education
• 700 & Above Employee

• Research Intern

  • May 2013 - Dec 2013