Challenges and Pitfalls of Bayesian Unlearning
- Ambrish Rawat
- James Requeima
- et al.
- 2022
- ICML 2022
Ambrish Rawat is a Senior Research Scientist in the AI Security & Privacy team at IBM. His research interests are at the cross-sections of security, privacy and Artificial Intelligence (AI). Most recently, he has worked on Adversarial AI, AI Governance and Privacy Enhancing Technologies (PETs). He is passionate about building trustworthy AI systems with security and privacy guarantees within the regulatory demands of GDPR as well as EU AI and Digital Acts.
In addition to publishing at top-tier conference like AISTATS, ESORICS, NeurIPS and ACL, his research has been showcased at events like BlackHat USA 2021 and he has led teams to winning positions in several competitions including US-UK Government's PETs Prize Challenge, Federated Tumor Segmentation Challenge (FeTS). He has also contributed to open-source projects including Linux Foundation's Adversarial Robustness Toolbox and IBM Federated Learning.
He holds a Master of Philosophy in Machine Learning and Machine Intelligence from the University of Cambridge, UK, and a Master of Technology in Mathematics and Computing from the Indian Institute of Technology, Delhi (IIT Delhi). He joined IBM in 2016 and has since been leading and contributing to numerous efforts in AI and ML at the Dublin Research Lab.
His work has been published at top AI conferences and he's an active contributor to open source software projects. He have been recognised as Master Inventor at IBM for his contributions to IBM patent portfolio and has also received Research Division Award and several Outstanding Technical Accomplishment Awards for the contributions to the vast array of cutting-edge research at IBM.