I am Ph.D. candidate at IMPRS-IS Program, Tübingen, where I am part of EML and MIDAS group. I am fascinated by interdisciplinary R&D happening at the intersection of Computer Vision, Machine Learning, Biomedical and Healthcare Informatics. These days I am exploring Bayesian Deep Learning, Uncertainty Estimation, Generative models, and Explainable AI.
I was an undergrad at Computer Science and Engineering@IIT-Bombay.
Earlier, I spent some time as a research intern at Amazon Science, Microsoft Research, Honda Research Institute, and Nanyang Technological University-Singapore. Besides, I've also spent time working with medical imaging startups and as a quantitative researcher for algorithmic trading firm.
For more details about me, please check my homepage.
Full list of publication @ Google Scholar
- "USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution", Vikrant Rangnekar*, Uddeshya Upadhyay*, Zeynep Akata, Biplab Banerjee, The Conference on Uncertainty in Artificial Intelligence (UAI), 2023
- "The Manifold Hypothesis for Gradient-Based Explanations",Sebastian Bordt, Uddeshya Upadhyay, Zeynep Akata, Urlike von Luxburg, Explainable AI for Computer Vision (XAI4CV) Workshop - The IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023
- "BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural - Networks", Uddeshya Upadhyay*, Shyamgopal Karthik*, Yanbei Chen, Massimiliano Mancini, Zeynep Akata, European Conference on Computer Vision, ECCV 2022
- "Robustness via Uncertainty-aware Cycle Consistency", Uddeshya Upadhyay, Yanbei Chen, Zeynep Akata, Neural Information Processing Systems, NeurIPS 2021
- "Uncertainty-Guided Progressive GANs for Medical Image Translation", Uddeshya Upadhyay, Yanbei Chen, Tobias Hepp, Sergios Gatidis, Zeynep Akata, Medical Image Computing and Computer Assisted Interventions, MICCAI 2021