- [Aug 2019] Graduated from IIT Bombay with a Bachelors in Computer Science and Engineering with Honors.
- [Aug 2019] Received the Research Excellence Award for outstanding research work done during undergraduate studies.
- [May 2019] Awarded Travel Grant from the C'1992 Legacy Project Funds to present at IPMI 2019, Hong Kong.
- [May 2019] paper on perfect sampling for Bayesian MRFs accepted at Medical Image Analysis journal.
- [Feb 2019] paper on uncertainty estimation selected for oral presentation and opening talk of IPMI 2019.
- [Feb 2019] paper on uncertainty estimation and calibration for neural networks accepted at IPMI 2019, Hong Kong.
I'm interested in computer vision, machine learning, and medical image analysis. Some of my work has focused on uncertainty estimation in the context of perfect sampling for deep MRFs, and fast aleatoric uncertainty estimation and calibration for neural networks.
A Bayesian Neural Net to Segment Images with Uncertainty Estimates and Good Calibration
Suyash P. Awate
International conference on Information Processing in Medical Imaging (IPMI) 2019
Oral presentation, opening talk of the conference, acceptance rate ~10%
We propose a novel Bayesian decision theoretic deep-neural-network (DNN) framework for image segmentation, enabling us to define a principled measure of uncertainty associated with label probabilities. Moreover, our framework leads to a novel Bayesian interpretation of the softmax layer. We propose a novel method to improve DNN calibration.
Estimating uncertainty in MRF-based image segmentation: A perfect-MCMC approach
Suyash P. Awate,
Medical Image Analysis (MedIA) 2019, 55:181-196, Elsevier
We propose the modern paradigm of perfect MCMC sampling to sample multi-label segmentations from generic Bayesian Markov random field (MRF) models, in finite time for exact inference. Furthermore, for exact sampling in generic Bayesian MRFs, we extend the theory underlying Fill's algorithm to generic MRF models by proposing a novel bounding-chain algorithm.
For a complete list of projects, please refer to my CV.
Thanks to Jon Barron. for sharing this awesome template!!