Meet P. Vadera

I am a Ph.D. candidate in Computer Science at the University of Massachusetts Amherst, where I am advised by Benjamin Marlin. My research interests include Bayesian deep learning and robustness in deep learning. In the past, I have also worked in the areas of mHealth, and medical imaging.

Prior to joining the Ph.D. program, I was working as a Member of Technical Staff at Innovaccer building products on big-data stack to help healthcare organizations deliver better care. I earned my Bachelor of Technology (B.Tech.) degree from Indian Institute of Technology (IIT) Gandhinagar, where I majored in Mechanical Engineering and had a minor in Computer Science and Engineering.

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1. Post-hoc loss-calibration for Bayesian Neural Networks
   Meet P. Vadera, Soumya Ghosh, Kenney Ng, Benjamin M. Marlin
   Conference on Uncertainty in Artificial Intelligence (UAI), 2021

2. Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks
   Meet P. Vadera, Brian Jalaian, Benjamin M. Marlin
   Conference on Uncertainty in Artificial Intelligence (UAI), 2020

3. URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks
   Meet P. Vadera, Adam D. Cobb, Brian Jalaian, Benjamin M. Marlin
   ICML Workshop on Uncertainty and Robustness in Deep Learning, 2020

4. Assessing the Adversarial Robustness of Monte Carlo and Distillation Methods for Deep Bayesian Neural Network Classification
   Meet P. Vadera*, Satya Narayan Shukla*, Brian Jalaian, Benjamin M. Marlin
   AAAI Workshop on Artificial Intelligence Safety (SafeAI), 2020

5. Investigating Fusion-Based Deep Learning Architectures for Smoking Puff Detection
   Meet P. Vadera, Benjamin M. Marlin
   IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), 2019 [Poster paper]

6. Towards Joint Segmentation and Active Learning for Block-Structured Data Streams
   Conrad Holtsclaw, Meet P. Vadera, Benjamin M. Marlin
   KDD Workshop on Data Collection, Curation, and Labeling (DCCL) for Mining and Learning, 2019 [Best paper award]

7. Assessing the Robustness of Bayesian Dark Knowledge to Posterior Uncertainty
   Meet P. Vadera, Benjamin M. Marlin
   ICML Workshop on Uncertainty and Robustness in Deep Learning, 2019

8. Multiclass Diagnosis of Neurodegenerative Diseases: A Neuroimaging Machine-Learning-Based Approach
   Gurpreet Singh, Meet P. Vadera, Lakshminarayanan Samavedham, Erle Chuen-Hian Lim
   ACS Journal of Industrial & Engineering Chemistry Research, 2019

9. Machine Learning-Based Framework for Multi-Class Diagnosis of Neurodegenerative Diseases: A study on Parkinson’s Disease
   Gurpreet Singh, Meet P. Vadera, Erle Chuen-Hian Lim, Lakshminarayanan Samavedham
   IFAC Symposium on Dynamics and Control of Process Systems including Biosystems DYCOPS-CAB, 2016

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