Vedant Nanda
I am an AI Researcher at Aleph Alpha in Germany where I work on efficient and steerable LLMs. For an overview of some of my work at Aleph Alpha, check out this short(-ish) talk.
I got my PhD in Computer Science from University of Maryland, College Park. I was part of the Maryland-Max Planck joint program through which I also spent time at Max Planck Institute for Software Systems (MPI-SWS). Throughout my PhD, I was fortunate to have the guidance of Krishna P. Gummadi (MPI-SWS) and
John P. Dickerson (University of Maryland) as my advisors, and collaborated closely with Adrian Weller at the University of Cambridge.
My research focuses on building a better understanding of trustworthy deep learning through an empirical lens.
I've contributed to research in fairness (ICML2019 AAAI2020 AAAI2023),
robustness (ICML2022 & AAAI2023), and my work was one of the first to establish a connection between the two fields (FAccT2021).
Lately, in the era of large models, I have been thinking about efficient learning (NeurIPS2023) and taught a seminar on Systems for LLMs.
During my PhD, I had the pleasure of interning twice at Amazon where I first worked on counterfactual explanations with AWS Clarify and then on fairness aspects of generative AI with AWS Bedrock.
I obtained my undergrad from IIIT Delhi (2015 - 2019) majoring in Computer Science and Engineering, where I was associated to Precog. During my undergrad I worked on topics related to Social Computing, Computational Social Science and ICT4D.
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). I spent the wonderful summer of 2018 interning at MPI-SWS where I was advised by Krishna P. Gummadi.
For more, check out my CV.
vnanda [at] mpi-sws [dot] org