Emtiyaz Khan

Emtiyaz Khan is a Research Professor and Team Leader of the Adaptive Bayesian Intelligence team at the RIKEN Center for Advanced Intelligence Project (AIP) in Japan. His research focuses on developing AI systems that can autonomously learn to perceive, act, and reason throughout their lives, bridging the gap between how living beings and computers learn.

Dr. Khan's research interests span multiple areas of machine learning including:

• Approximate inference and Bayesian statistics
• Deep learning and neural network optimization
• Reinforcement learning and active learning
• Information geometry and signal processing
• Online learning and continual adaptation

His recent work has focused on developing theoretically grounded Bayesian methods for deep learning, with applications in computer vision and decision-making systems. Dr. Khan has made significant contributions to variational inference, Bayesian optimization, and continual learning frameworks that enable AI systems to retain knowledge while learning new tasks.

Dr. Khan has received substantial research funding including:

• (2021-2026, USD 2.23 Million) JST-CREST and French-ANR's grant, The Bayes-Duality Project
• (2020-2023, USD 167,000) KAKENHI Grant-in-Aid for scientific Research (B), Life-Long Deep Learning using Bayesian Principles
• (2020-2023, USD 11,000) KAKENHI Grant-in-Aid for Chellenging Research (Exploratory), Linear algebra for continuous learning of large neural networks
• (2019-2022, USD 237,000) External funding through companies for several Bayes related projects

Dr. Khan is actively involved in the machine learning community, having served as General Chair for AISTATS 2026 and given keynotes at major conferences including Bayes Comp 2025 and the 1st EurIPS conference. His work has resulted in multiple papers accepted at top-tier conferences like NeurIPS, with some receiving spotlight presentations.