معرفی
Dr. James Grant is a Lecturer in Statistics at Lancaster University's School of Mathematical Sciences since 2021, where he also serves as the EDI Lead for both the School and the ProbAI Research Hub. He is active in several roles at the Royal Statistical Society and serves as an Associate Editor for the ACM Transactions on Probabilistic Machine Learning.
Grant completed his PhD in 2019 through the STOR-i Centre for Doctoral Training at Lancaster University under the supervision of Professors David Leslie, Kevin Glazebrook, and Roberto Szechtman. Prior to his PhD, he worked as a Machine Learning Research Student at Secondmind.ai and as a Research Associate within the STOR-i Centre for Doctoral Training.
Dr. Grant's research focuses on online sequential decision making for problems with complex data structures, particularly in bandit learning and reinforcement learning. He specializes in multi-armed bandits, online optimization, and recommender systems where algorithms learn optimal decisions through iterative processes of decision-making, data observation, and model updating. His research emphasizes collaboration with industry, having worked on the Next-Generation Converged Digital Infrastructure project with BT.
His publication record demonstrates strong expertise in bandit algorithms with applications spanning federated learning systems, anomaly detection in time series data, and phylogenetic tree analysis. Grant's work consistently bridges theoretical foundations with practical applications across telecommunications, surveillance systems, and decision-making frameworks.
- Notable Paper Award Winner at AISTATS 2020 for "On Thompson Sampling for Smoother-than-Lipschitz Bandits"
- Associate Editor for ACM Transactions on Probabilistic Machine Learning
- Active participant in Royal Statistical Society initiatives
Dr. Grant currently supervises six PhD students working on diverse research topics including bandit learning, tropical statistics, anomaly detection, sequential simulation methods, safety in reinforcement learning, and Bayesian optimization. His former research associate, Changjiang He, has progressed to become a Lecturer in Computer Science at the University of Roehampton. As an EDI Lead, Grant actively promotes equality, diversity, and inclusion within the academic community while maintaining a robust research program that bridges theoretical statistics with real-world applications.
