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I am a PhD student in the Machine Learning Group at the University of Cambridge, supervised by Dr. Jośe Miguel Hernández-Lobato. My research interests lie in massively scalable Bayesian inference and probabilistic machine learning, uncertainty estimation and out-of-distribution detection, and diffusion models and sampling. Previously, I was an AI Resident in the Google Brain team at Cambridge, MA, where I worked with Jasper Snoek and Balaji Lakshminarayanan on probabilistic machine learning methods. I received my Masters in Biomedical Engineering from Johns Hopkins University, where I worked with Dr, Michael I. Miller on using machine learning and computational anatomy to understand the pathology of neurodegenerative diseases such as Alzheimers and schizophrenia using diffeomorphic mapping of brain MRI scans. I received my Bachelors in Engineering Physics at IIT Delhi, where I worked with Dr. Uday Khankhoje on stochastic solutions to forward and inverse imaging. |
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June 2024: I will be a Research Scientist intern in Meta Reality Labs in Burlingame! June 2024: We have some really exciting SOTA inverse problem results with our new method DEFT, a way to finetune diffusion models for conditional generation [paper] [code] June 2024: We show that you can speed up marginal likelihood optimization of GPs by over 30x using warm starts and pathwise sampling [paper] May 2024: I presented a poster at ICLR 2024 in Vienna on Controlled Monte Carlo Diffusions [paper] [code] May 2024: I presented a poster at ICLR 2024 in Vienna on Improved Stochastic Gradient Descent for GPs [paper] [code] February 2024: I gave an invited talk at the SIAM UQ Conference in Trieste on SGD for large-scale Bayesian ML [event] [slides] February 2024: I gave an invited talk at Atinary Technologies in Lausanne on SGD for GPs [venue] [slides] February 2024: I gave a guest lecture on Conditioning in SDEs [slides] December 2023: I gave an invited talk at the NeurIPS@Cambridge meetup on our NeurIPS Oral [event] [paper] |
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Transport meets Variational Inference: Controlled Monte Carlo Diffusions Stochastic Gradient Descent for Gaussian Processes Done Right Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent Sampling-based inference for large linear models, with application to linearised
Laplace Simple and principled uncertainty estimation with deterministic deep learning via
distance awareness DEFT: Efficient Finetuning of Conditional Diffusion Models by Learning the
Generalised h-transform Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian
Processes Kernel Regression with Infinite-Width Neural Networks on Millions of Examples A Simple Approach to Improve Single-Model Deep Uncertainty via
Distance-Awareness Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution
Detection in Neural
Networks A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection Evaluating prediction-time batch normalization for robustness under covariate
shift Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep
Learning |