I am a Ph.D. candidate in Computer Science at the Max Planck Institute for Software Systems and Saarland University, Germany. I am jointly advised by Prof. Isabel Valera and Prof. Krishna Gummadi.

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).

  • Machine Learning
  • Fairness
  • Explainability
  • Causality
  • Accountability
  • Ph.D. in Computer Science, (ongoing)

    Saarland University and 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

Recent News

  • We will be presenting CARMA as a poster at the European Workshop on Algorithmic Fairness in Mainz!
  • Our latest work on scaling up the generation of causal algorithmic recourse recommendations was accepted at ACM FAccT 2024!
  • Recently reviewed for ICML 2024, ICLR 2024, FAccT 2024, and JMLR!



5+ years experience in coding


Practical and research experience

Fair/Explainable/Robust ML

Coursework, research projects


Experience in coursework and applied research projects

Analytics & Visualization

Experience in performing basic data analysis on large datasets


Extensive experience in working on industrial and academic projects

Recent Publications

(2023). Do Invariances in Deep Neural Networks Align with Human Perception?. AAAI 2023.

PDF Cite Code DOI

(2022). Don't Throw it Away! The Utility of Unlabeled Data in Fair Decision Making. ACM FAccT 2022.

PDF Cite Code Project Slides DOI


Ph.D. in Computer Science
May 2021 – Present Saarbrücken, DE
Doctoral candidate in computer science focused on fair and trustworthy machine learning systems for applications in decision making and high-stakes scenarios.
M.Sc. in Computer Science
Oct 2017 – Apr 2021 Saarbrücken, DE

Graduate student in computer science with a strong focus on machine learning/artificial intelligence.

  • Grade: 1.2 / 1.0 (German scale)
  • Core courses: Artificial Intelligence, Information Retrieval and Data Mining, Machine Learning
  • Advanced courses: Statistical Natural Language Processing, Neural Networks: Implementation and Application, High-level Computer Vision, Methods of Mathematical Analysis, Statistics with R, Human-centered Machine Learning, Machine Learning in Cybersecurity, Information Extraction
  • Seminars: Machine Learning
B.Tech. in Electronics and Communication Engineering
Aug 2011 – Jul 2015 Kolkata, IN

Undergraduate student of electronics engineering.

  • Grade: 8.8 / 10
  • Core courses: Signals and Systems, Digital Electronic and Integrated Circuits, Analog circuits, Control System, Digital Communications, Analog Communications, Digital Signal Processing
  • Elective courses: Microprocessor and Microcontrollers, Data Structures and C, Information Theory and Coding, Object Oriented Programming, Microelectronics and VLSI Design, Embedded Systems, Database Management Systems


Research assistant and Master’s thesis student
Jun 2019 – Mar 2021 Saarbrücken, DE

Research student working in the topics of fairness in generative models, with a particular focus on variational autoencoders.

  • Worked on estimating, quantifying and mitigating bias in variational generative models.
  • Additionally explored the potential of using such models in a range of applications: anomaly detection, adversarial example detection and defense, classifier calibration.
  • Designed a system based on variational autoencoders to generate counterfactual data for fairness scenarios under minimal causal assumptions.
  • Supervisors: Prof. Krishna Gummadi, Prof. Isabel Valera
Graduate Assistant
Apr 2018 – Mar 2019 Saarbrücken, DE

Worked as a part of the project on Mutual Intelligibility and Surprisal in Slavic Intercomprehension.

  • Data collection and cleaning through web crawling and multi-sentence alignment for multilingual NLP experiments.
  • Worked on developing the web-based application of the linguistic experiment to assist in user studies.
  • Supervisor: Prof. Dietrich Klakow
Systems Engineer
Jul 2015 – Aug 2017 Bengaluru, IN

Worked as part of the Engineering Services Communication Products group. Responsibilities.

  • Development and maintenance of the flagship Session Border Controller (SBC) for a reputed US client.
  • Develop core functionalities of the system using the knowledge of SIP (Session Initiation Protocol) and VoIP (Voice over IP).
Research assistant
Jan 2015 – Jun 2015 Shibpur, IN

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.

  • Supervisor: Prof. Tamaghna Acharya

Related Projects

CARMA: Causal Algorithmic Recourse with (Neural) Model-based Amortization

CARMA: Causal Algorithmic Recourse with (Neural) Model-based Amortization

We explore improving the practicality of providing causal recourse explanations through a novel neural network model-based automation framework.

FairAll: Fair Decisions With Unlabeled Data

FairAll: Fair Decisions With Unlabeled Data

We explore the helpfulness of unlabeled data for fair, optimal and stable decision-making in societal settings.

Generating Counterfactuals for Causal Fairness

Generating Counterfactuals for Causal Fairness

Project that was conducted as part of my Master’s thesis to explore the application of generative models to compute counterfactuals for fairness.

Bias in Generative Models

Bias in Generative Models

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.

Using VAE for Robustness

Using VAE for Robustness

Exploring potential use cases of variational autoencoders in the context of robustness of ML systems.

Debiasing Word Embeddings

Debiasing Word Embeddings

Mini-project that looks at potential bias in pre-trained word embeddings and methods on how to remove such bias.

Temporal Point Processes and Smart Broadcasting

Temporal Point Processes and Smart Broadcasting

Project that explores various algorithms in temporal point processes. Also explores one potential use-case in smart broadcasting of messages.

Predicting Vulnerability of Windows Machines to Malware

Predicting Vulnerability of Windows Machines to Malware

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.

Adversarial Attacks and Defense for CNN

Adversarial Attacks and Defense for CNN

The project explores CNN classification models and their vulnerability to various adversarial attacks. Also explores a defence mechanism for it.

Neural Machine Translation

Neural Machine Translation

A mini-project that looks at the task of neural machine translation using sequential models and attention mechanism.

Exploring Personalized Image Captioning

Exploring Personalized Image Captioning

This project explores personalization of generating image captions. We explore different architecture choices of Attend2u and also analyze personalization of the captions.

Word2Mat: A New Word Representation

Word2Mat: A New Word Representation

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.

Recent & Upcoming Talks

Leveraging Unlabeled Data for Fair Decision Making
On Computing Counterfactuals for Causal Fairness
Generating counterfactuals using VAEs


Deep Learning: Sequence Models
Studied sequence models and applications in NLP.
See certificate
Algorithms on Strings
Studied basic algorithms applied to strings.
See certificate
Front-end Web UI Frameworks and Tools
Studied Front-end web design using Bootstrap.
See certificate
Machine Learning: Regression
Studied in depth regression models in machine learning.
See certificate
Data Structures
Studied basics of data structures.
See certificate
Usable Security
Studied basics of HCI and Privacy from user’s perspective.
See certificate
Algorithmic Toolbox
Studied basics of algorithms.
See certificate
Algorithms on Graphs
Studied basic algorithms applied to graphs.
See certificate


Core Lecture on Machine Learning
Teaching assistant tasked with setting up the course project, assignment sheets, and the final exams.
Seminar on Machine-Assisted Decision-Making
Tutor for the seminar tasked with grading students' presentations and assignments, guiding doubt-clearing sessions.
Advanced Lecture on Statistical Natural Language Processing
Tutor tasked with grading assignments, exams, and leading tutorial sessions.

Awards & Achievements