Hi! I recently started as an Assistant Professor in the Computer Science and Engineering department at the Indian Institute of Technology, Delhi. My research focuses on addressing problems related to data and tech ethics, algorithmic fairness, tech alignment, and community-focused AI development. Broadly, I am interested in the interactions between society and automation and in addressing various questions around the potentially harmful impacts of AI applications.
Earlier, I was a Postdoctoral Associate, working with Walter Sinnott-Armstrong and Jana Schaich Borg at Duke University, and Hoda Heidari and Vincent Conitzer at Carnegie Mellon University. For this postdoc, I was affiliated with the Social Science Research Institute at Duke, the Duke Philosophy department, and the Kenan Institute of Ethics.
I received my Ph.D. from the Department of Statistics and Data Science at Yale University, where I worked with Elisa Celis. While at Yale, I was a Resident Fellow at the Information Society Project (ISP) at Yale Law School from 2022-2023 and a 2022 Policy Fellow at the Yale Institute for Social and Policy Studies (ISPS). Prior to that, I spent time at École Polytechnique Fédérale de Lausanne (EPFL) and the Indian Institute of Technology, Kanpur.
If you are a student at IIT Delhi, happy to chat about potential projects; but do check out the themes covered in my courses below first to get a sense of the topics I am interested in. For non-IITD students reaching out with internship or assistantship requests, it’s unlikely I will be able to reply, given the volume of such emails. I would recommend checking out the IITD PG programs if you’re interested in research and you can mention my name in your MS/PhD application if you wish to work with me.
For folks outside academia, in non-profits, industry, or other organizations working on AI ethics, please feel free to reach out directly to chat or collaborate on any problems related to responsible AI.
Vijay Keswani, Breanna Nguyen, Cyrus Cousins, Vincent Conitzer, Jana Schaich Borg, Walter Sinnott-Armstrong
We Need Practical AI Alignment Methods to Mirror Human Reasoning
ICML 2026. openreview
Cyrus Cousins, Vijay Keswani, Vincent Conitzer, Hoda Heidari, Jana Schaich Borg, Walter Sinnott-Armstrong
Towards Cognitively-Faithful Decision-Making Models to Improve AI Alignment
ICLR 2026. arxiv
Vijay Keswani, Cyrus Cousins, Breanna Nguyen, Vincent Conitzer, Hoda Heidari, Jana Schaich Borg, Walter Sinnott-Armstrong
Moral Change or Noise? On Problems of Aligning AI With Temporally Unstable Human Feedback
AAAI 2026 (Alignment Track). arxiv
Vijay Keswani, Vincent Conitzer, Walter Sinnott-Armstrong, Breanna K. Nguyen, Hoda Heidari, Jana Schaich Borg
Can AI Model the Complexities of Human Moral Decision-Making? A Qualitative Study of Kidney Allocation Decisions
CHI 2025. arxiv
Vijay Keswani, Anay Mehrotra, L. Elisa Celis
Fair Classification with Partial Feedback: An Exploration-Based Data-Collection Approach
ICML 2024. arxiv
Vijay Keswani, Vincent Conitzer, Hoda Heidari, Jana Schaich Borg, Walter Sinnott-Armstrong
On the Pros and Cons of Active Learning for Moral Preference Elicitation
AIES 2024. arxiv
Kyle Boerstler, Vijay Keswani, Lok Chan, Jana Schaich Borg, Vincent Conitzer, Hoda Heidari, Walter Sinnott-Armstrong
On The Stability of Moral Preferences: A Problem with Computational Elicitation Methods
AIES 2024. arxiv
Vijay Keswani, L. Elisa Celis
Algorithmic Fairness From the Perspective of Legal Anti-discrimination Principles
AIES 2024 (An earlier non-archival version of this paper was presented at EAAMO 2022). ssrn
Vijay Keswani, L. Elisa Celis
Addressing Strategic Manipulation Disparities in Fair Classification
EAAMO 2023. arxiv
Vijay Keswani
Social Media Platform Structures and Their Implications
EcAI Workshop 2023. pdf
Vijay Keswani, L. Elisa Celis, Matthew Lease, Krishnaram Kenthapadi
Designing Closed-Loop Models for Task Allocation
HHAI 2023. arxiv
Florian Evéquoz, Johan Rochel, Vijay Keswani, L. Elisa Celis
Diverse Representation via Computational Participatory Elections – Lessons from a Case Study
EAAMO 2022. arxiv
Vijay Keswani, Oren Mangoubi, Sushant Sachdeva, Nisheeth K. Vishnoi
A Convergent and Dimension-Independent Min-Max Optimization Algorithm
ICML 2022. arxiv
Vijay Keswani, Matthew Lease, Krishnaram Kenthapadi
Designing human-in-the-loop approaches for closed deferral pipelines
BHCC 2021 (Non-archival). arxiv
Vijay Keswani, L. Elisa Celis
Auditing for Diversity using Representative Examples
ACM SIGKDD 2021. arxiv
Vijay Keswani, Matthew Lease, Krishnaram Kenthapadi
Towards Unbiased and Accurate Deferral to Multiple Experts
AIES 2021. arxiv
L. Elisa Celis, Lingxiao Huang, Vijay Keswani, Nisheeth K. Vishnoi
Fair Classification with Noisy Protected Attributes: A Framework with Provable Guarantees
ICML 2021. arxiv
Vijay Keswani, L. Elisa Celis
Dialect Diversity in Text Summarization on Twitter
The Web Conference 2021. arxiv
L. Elisa Celis, Vijay Keswani
Implicit Diversity in Image Summarization
CSCW 2020. arxiv
L. Elisa Celis, Vijay Keswani, Nisheeth K. Vishnoi
Data preprocessing to mitigate bias: A maximum entropy based approach
ICML 2020. arxiv
L. Elisa Celis, Vijay Keswani
Improved Adversarial Learning for Fair Classification
arxiv
L. Elisa Celis, Sayash Kapoor, Farnood Salehi, Vijay Keswani, Nisheeth K. Vishnoi
A Dashboard for Controlling Polarization in Personalization
Invited paper in AI Communications 2019
(Previously in IJCAI-ECAI 2018)
L. Elisa Celis, Lingxiao Huang, Vijay Keswani, Nisheeth K. Vishnoi
Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees
ACM-FAT* 2019. arxiv
L. Elisa Celis, Vijay Keswani, Damian Straszak, Amit Deshpande, Tarun Kathuria, Nisheeth K. Vishnoi
Fair and Diverse DPP-based Data Summarization
ICML 2018. arxiv