Vedant Nanda

Vedant Nanda

I am an AI Scientist at Mistral. My research focuses on efficient language models and how we can pretrain them to be more inference-friendly.

Previously, I worked as a researcher at Aleph Alpha in Germany where I worked on inference optimizations for LLMs (see this talk) and pretraining optimizations of our tokenizer-free models (HuggingFace collection).

I got my PhD in Computer Science from University of Maryland, College Park, as part of the Maryland-Max Planck joint program, where I was advised by Krishna P. Gummadi and John P. Dickerson, and collaborated with Adrian Weller at Cambridge. During my PhD, I studied trustworthy deep learning, working on fairness (ICML2019 AAAI2020 AAAI2023), robustness (ICML2022 & AAAI2023), and connections between them (FAccT2021). Driven by important role of scale in deep learning, I also explored efficient representation learning (NeurIPS2023) and taught a seminar on Systems for LLMs. I also interned twice at AWS, working on counterfactual explanations with AWS Clarify and fairness in generative AI with AWS Bedrock.

I am fortunate to have worked with some amazing mentors (in alphabetical order): Soheil Feizi (UMD), Tom Goldstein (UMD), Hoda Heidari (ETH Zürich), Ponnurangam Kumaraguru (IIIT Delhi), Bradley C. Love (UCL), Rijurekha Sen (IIT Delhi), Pushpendra Singh (IIIT Delhi), Mariya Toneva (MPI-SWS), and Muhammad Bilal Zafar (Amazon).

For more, check out my CV.

Email: vnanda [at] mpi-sws [dot] org

Publications

Conference Papers

Pre-prints

Workshop Papers/Posters

  • An Empirical Analysis of Facebook's Free Basics
  • S. Singh *, Vedant Nanda *, R. Sen, S. Ahmad, S. Sengupta, A. Phokeer, Z.A. Farooq, T.A. Khan, P. Kumaragaguru, I.A. Qazi, D. Choffnes, K.P. Gummadi
  • ACM SIGMETRICS Performance Evaluation Review 2017 (Poster)
  • * joint first author