Principal AI Research Manager
Visiting Researcher · Wellcome Sanger Institute
PhD (ECE) · Cornell University
Research Interests
Sequential Decision-Making (RL, Bandits and Bayesian Optimisation) · Foundation and Generative Models · AI for Science and Intelligent Systems
Latest
Jan 2026: A Finite Time Analysis of Thompson Sampling for Bayesian Optimization with Preferential Feedback is accepted at AISTATS 2026.
Jan 2026: Reinforcement Learning Using Known Invariances is accepted at AISTATS 2026.
Oct 2025: No-Regret Thompson Sampling for Finite-Horizon Markov Decision Processes with Gaussian Processes is accepted at NeurIPS 2025.
Oct 2025: Invited talk on Decision-Making Under Uncertainty: AI with Human-in-the-Loop Perspective at the Workshop on Causal AI in Healthcare Policy & Practice, Oxford University.
Aug 2025: Mapping and reprogramming human tissue microenvironments with MintFlow is under review at Nature.
Jun 2025: Reinforcement Learning with Thompson Sampling: No-Regret Performance over Finite Horizons is accepted at the ICML 2025 workshop Exploration in AI Today.
Jun 2025: Towards a Foundation Model for Communication Systems is accepted at the ICML 2025 workshop ML for wireless communication and networks.
May 2025: Bayesian Optimization from Human Feedback: Near-Optimal Regret Bounds is accepted at ICML 2025.
May 2025: Will be giving a tutorial on Foundation Models for Communication Systems at IEEE GLOBECOM, December 2025, Taipei.
Mar 2025: Giving a Tech Talk on AI and Communication at Cambridge University, Mar 10th.
Feb 2025: Giving a presentation at the Computational Statistics and Machine Learning (OxCSML) seminar series, Oxford University, Feb 21st.
Jan 2025: Near-Optimal Sample Complexity in Reward-Free Reinforcement Learning is accepted at AISTATS 2025.
Oct 2024: Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm is accepted at NeurIPS 2024.
Oct 2024: Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow is accepted at NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty.
Sep 2024: Adversarial Contextual Bandits Go Kernelized will be presented at European Workshop on Reinforcement Learning (EWRL 2024).
Sep 2024: Kernel-Based Function Approximation for Average Reward Reinforcement Learning will be presented at European Workshop on Reinforcement Learning (EWRL 2024).
Jul 2024: Giving a tutorial on Recent Advances of Statistical Reinforcement Learning at UAI 2024, July 15th in Barcelona. [slides]
Jun 2024: Involved in organizing a local ICML meetup on July 12th in London.
Jun 2024: Open Problem: Order Optimal Regret Bounds for Kernel-Based Reinforcement Learning is accepted at COLT 2024.
Jun 2024: Reward-Free Kernel-Based Reinforcement Learning is accepted at ICML 2024.
Jun 2024: Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency is accepted at ICML 2024.
Apr 2024: Giving a tutorial on Recent Advances of Statistical Reinforcement Learning at UAI 2024, July 15th in Barcelona.
Dec 2023: Optimal Regret Bounds for Collaborative Learning in Bandits is accepted at Algorithmic Learning Theory (ALT) 2024.
Dec 2023: Adversarial Contextual Bandits Go Kernelized is accepted at Algorithmic Learning Theory (ALT) 2024.
Earlier news (2021–2023)
Oct 2023: Collaborative Learning in Kernel-based Bandits for Distributed Users is accepted at IEEE Transactions on Signal Processing.
Oct 2023: Giving a talk at the Inria Scool seminar series at University of Lille.
Oct 2023: Adversarial Contextual Bandits Go Kernelized is available on arXiv.
Sep 2023: Kernelized Reinforcement Learning with Order Optimal Regret Bounds is accepted at NeurIPS 2023.
Aug 2023: Generative Diffusion Models for Radio Wireless Channel Modelling and Sampling is accepted at GLOBECOM 2023.
Aug 2023: Kernelized Reinforcement Learning with Order Optimal Regret Bounds is accepted at EWRL 2023.
Aug 2023: Check out Tor Lattimore's response to the open problem on online confidence intervals for RKHS elements.
Jul 2023: Giving an online lecture at FeDucation seminar series (Florida International University).
Jun 2023: Giving a seminar on kernel-based reinforcement learning at Deepmind/Ellis CSML seminar series.
Jun 2023: Presenting Information Gain and Uniform Generalization Bounds for Neural Kernel Models at ISIT 2023, Taipei.
May 2023: Giving an invited talk on kernel-based RL at the London Symposium on Information Theory.
Apr 2023: Delayed Feedback in Kernel Bandits is accepted at ICML 2023.
Apr 2023: Provably and Practically Efficient Neural Contextual Bandits is accepted at ICML 2023.
Apr 2023: Image generation with shortest path diffusion is accepted at ICML 2023.
Feb 2023: Delayed Feedback in Kernel Bandits is available on arXiv.
Jan 2023: Sample Complexity of Kernel-Based Q-Learning is accepted at AISTATS 2023.
Jan 2023: Fisher-Legendre (FishLeg) optimization of deep neural networks is accepted at ICLR 2023.
Dec 2022: Presenting Gradient Descent: Robustness to Adversarial Corruption at OPT2022 workshop at NeurIPS 2022, New Orleans.
Oct 2022: Near-Optimal Collaborative Learning in Bandits has been designated as an Oral presentation at NeurIPS 2022.
Jul 2022: Presenting an open problem on noise-free kernel-based bandit at COLT 2022, London.
May 2022: Provably and Practically Efficient Neural Contextual Bandits is available on arXiv.
May 2022: Near-Optimal Collaborative Learning in Bandits is available on arXiv.
May 2022: Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning is accepted at ICML 2022 for a Spotlight presentation.
Oct 2021: Scalable Thompson Sampling using Sparse Gaussian Process Models is accepted at NeurIPS 2021.
Oct 2021: A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance is accepted at NeurIPS 2021.
Oct 2021: Optimal Order Simple Regret for Gaussian Process Bandits is accepted at NeurIPS 2021.
Aug 2021: Moderating the "Bandits, RL and Control" session at COLT 2021.
Aug 2021: Presenting Tight Online Confidence Intervals for RKHS Elements at COLT 2021.
Jan 2021: On Information Gain and Regret Bounds in Gaussian Process Bandits is accepted at AISTATS 2021.