My research broadly aims to bridge the practicality gap of responsible AI/ML, focusing on methods that study and improve the fairness, explainability, accountability and long-term impact of machine learning systems in the context of consequential societal decision-making tasks.
I completed my Master’s thesis titled “On Computing Counterfactuals for Causal Fairness” with the Networked Systems Research Group, supervised by Prof. Krishna Gummadi and Prof. Isabel Valera. I have also been fortunate to have collaborated with several incredible mentors: Preethi Lahoti (Max Planck Institute for Informatics), Prof. Dietrich Klakow (Spoken Language Systems, Saarland University), Prof. Adrian Weller (University of Cambridge).
PhD in Computer Science, (ongoing)
Max Planck Institute for Software Systems, Saarbrücken, Germany
M.Sc. in Computer Science, 2021
Saarland University, Saarbrücken, Germany
B.Tech. in Electronics and Communication Engineering, 2015
Heritage Institute of Technology, Kolkata, India
5+ years experience in coding
Practical and research experience
Coursework, research projects
Experience in coursework and applied research projects
Experience in performing basic data analysis on large datasets
Extensive experience in working on industrial and academic projects
Graduate student in computer science with a strong focus on machine learning/artificial intelligence.
Undergraduate student of electronics engineering.
Research student working in the topics of fairness in generative models, with a particular focus on variational autoencoders.
Worked as a part of the project on Mutual Intelligibility and Surprisal in Slavic Intercomprehension.
Worked as part of the Engineering Services Communication Products group. Responsibilities.
Worked on developing, optimizing and testing a novel community-based routing algorithm usingsocial metrics for message transmission in delay-tolerant networks in post-disaster scenarios.
We explore the helpfulness of unlabeled data for fair, optimal and stable decision-making in societal settings.
Project that was conducted as part of my Master’s thesis to explore the application of generative models to compute counterfactuals for fairness.
This project explores the case for potential bias in generative models such as variational autoencoders. The project also briefly looks at ways to mitigate such bias.
Exploring potential use cases of variational autoencoders in the context of robustness of ML systems.
Mini-project that looks at potential bias in pre-trained word embeddings and methods on how to remove such bias.
Project that explores various algorithms in temporal point processes. Also explores one potential use-case in smart broadcasting of messages.
This is a data-science project that aims to predict which Windows machines are more prone to malware attacks. I show the application of various different methods for the task and give a comparative analysis between them.
The project explores CNN classification models and their vulnerability to various adversarial attacks. Also explores a defence mechanism for it.
A mini-project that looks at the task of neural machine translation using sequential models and attention mechanism.
This project explores personalization of generating image captions. We explore different architecture choices of Attend2u and also analyze personalization of the captions.
This project explores a novel embedding technique for words into matrices instead of vectors. We explore this novel embedding method and how it could improve contextual sense.