Shreyas Padhy
Email: sp2058 (at) cam.ac.uk
     

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 Bayesian inference and probabilistic machine learning, uncertainty estimation and out-of-distribution detection, and applications in biomedicine.

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 Bahelors in Engineering Physics at IIT Delhi, where I worked with Dr. Uday Khankhoje on stochastic solutions to forward and inverse imaging.

Recent Updates

September 2022: I presented a poster on our recent preprint on Spectral-Normalized Gaussian Proces (SNGP) at the ELLIS Doctoral Symposium 2022. [code] [preprint] [poster]

February 2022: I gave a talk on Optimal Transport Metrics at the Cambridge Machine Learning Reading Group. [talk] [slides]

22 October 2020:We released a preprint on Spectral-Normalized Gaussian Proces (SNGP) that scales up our methodology to ImageNet and validates SNGP as an uncertainty building block. [code] [preprint]

22 October 2020: Our work on developing Spectral-Normalized Gaussian Proces (SNGP) was accepted at NeurIPS 2020 for poster presentation. [code] [preprint]

30 July 2020: We have open-sourced Uncertainty Metrics, a Python library to provide an easy-to-use interface for computing various uncertainty and calibration metrics. [code]

16 July 2020: We released Uncertainty Baselines, a Python and Tensorflow library that provides templates and high-quality implementations of standard and state-of-the-art uncertainty methods. [code]

10 July 2020: Our work analyzing loss functions for calibration and OOD robustness was accepted as a poster at the Uncertainty & Robustness in Deep Learning Workshop at ICML 2020. [code] [paper]

Publications
Preprints

A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
Jeremiah Zhe Liu*, Shreyas Padhy*, Jie Ren*, Zi Lin, Yeming Wen, Ghassen Jerfel, Zack Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan
[code] [preprint] [poster]

Conferences

Simple and principled uncertainty estimation with deterministic deep learning via distance awareness
Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, and Balaji Lakshminarayanan.
Advances in Neural Information Processing Systems 2020.
[code] [preprint]

Journals

Using deep Siamese neural networks for detection of brain asymmetries associated with Alzheimer's Disease and Mild Cognitive Impairment
C.F. Liu*, Shreyas Padhy*, S. Ramachandran, V.X. Wang, A. Efimov, A. Bernal, L. Shi, M. Vaillant, J.T Ratnanather, A.V Faria, B. Caffo, M. Albert, M.I Miller.
Magnetic resonance imaging 64 (2019): 190-199., 2019.
[paper]

Stochastic Solutions to Rough Surface Scattering using the finite element method
Uday K. Khankhoje, Shreyas Padhy.
IEEE Transactions on Antennas and Propagation, (Vol 65, No 08), 2017.
[website] [paper]

Workshops

Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks
Shreyas Padhy, Zachary Nado, Jie Ren, Jeremiah Liu, Jasper Snoek, and Balaji Lakshminarayanan.
ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning.
[code] [paper]

A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection
Jie Ren, Stanislav Fort, Jeremiah Liu, Abhijit Guha Roy, Shreyas Padhy, and Balaji Lakshminarayanan
ICML 2021 Workshop on Uncertainty and Robustness in Deep Learning.
[paper]

“Evaluating prediction-time batch normalization for robustness under covariate shift
Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D'Amour, Balaji Lakshminarayanan, and Jasper Snoek.
ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning.
[code] [paper]

Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning
Zachary Nado at al.
Bayesian Deep Learning Workshop, 2021.
[code] [paper]

Graduate Thesis

“Analyzing shape and residual pose of subcortical structures in brains of subjects with schizophrenia
Shreyas Padhy
Masters Thesis, Master of Science in Engineering, Department of Biomedical Engineering, Johns Hopkins University.
[paper]

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