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Publications
Preprints
Tropical Logistic Regression Model on Space of Phylogenetic Trees G. Aliatimis, R. Yoshida, B. Boyaci, J.A. Grant. (In Submission) Apple Tasting Revisited: Bayesian Approaches to Partially Monitored Online Binary Classification J.A. Grant, D.S. Leslie. (In Submission)Published / In Press
Online Detection and Fuzzy Clustering of Anomalies in Non-Stationary Time Series C. He, D.S. Leslie, and J.A. Grant. (2024) Signals. Learning to Rank under Multinomial Logit Choice J.A. Grant, D.S. Leslie. (2023) Journal of Machine Learning Research. Sequential Simulation Optimization with Censoring: An Application to Bike Sharing Systems C. Gibbons, J.A. Grant, and R. Szechtman. (2023) In Proceedings of 2023 Winter Simulation Conference Filtered Poisson Process Bandit on a Continuum J.A. Grant, R. Szechtman. (2021) European Journal of Operational Research
On Thompson Sampling for Smoother-than-Lipschitz Bandits J.A. Grant, D.S. Leslie. (2020) AISTATS Notable Paper Award Winner
Adaptive Policies for Perimeter Surveillance Problems J.A. Grant, D.S. Leslie, K. Glazebrook, R. Szechtman, A. Letchford. (2020) European Journal of Operational Research Adaptive Sensor Placement For Continuous Spaces J.A. Grant, A. Boukouvalas, R. Griffiths, D.S. Leslie, S. Vakili, E. Munoz de Cote. (2019) ICMLOther
Poisson Process Bandits: Sequential Models and Algorithms for Maximising the Detection of Point Data J.A. Grant (2019, PhD Thesis)
Posterior Contraction Rates for Gaussian Cox Processes with Non-identically Distributed Data J.A. Grant, D.S. Leslie. (2019, Working Paper)
Combinatorial Bandits with Filtered Feedback J.A. Grant, D.S. Leslie, K. Glazebrook, R. Szechtman. (2017, Technical Report)
Bayesian Changepoint Detection in Solar Activity Data J.A. Grant (2014, Masters Thesis)-
Selected Talks
Technical Talks on Specific Papers
Apple Tasting Revisited - Cardiff University OR Department Seminar 2022 | Slides
Learning to Rank Under Multinomial Logit Choice - EcoSta 2022 | Slides
On Thompson Sampling for Smoother-than-Lipschitz Bandits - AISTATS 2020 | Recording | Slides
Online Learning: Applications in Surveillance and Quality Control - Naval Postgraduate School OR Department Seminar 2020 | SlidesOverviews and Lighter Introductions
Bandit Approaches to Balance Exploration and Exploitation - Nordic Personalisation Day 2023 (Keynote at Recommender Systems-focussed conference) | Recording (From 09:20) | Slides
Learning in Real-Time Fault Detection - NG-CDI Tech Talks 2022 (Research Talk for Broad Industrial Audience) | Recording | Slides
Intro to Multi-Armed Bandits - Tommy Flowers Network 2021 (Introductory Talk for Industry-Sponsored PhD Students)| Recording | Slides
Online Learning and Decision Making - Peak.ai Ensemble 2021 (Industry Webinar) | Recording | SlidesSupervision
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Group Members (Supervisees)
Current Students
Soheil Arabzadeh is a final year PhD student working with myself and David Leslie on bandits and online optimisation.
George Aliatimis is a second year PhD student working with myself, Burak Boyaci, and Ruriko Yoshida (Naval Postgraduate School) on tropical statistics.
Theo Crookes is a first year PhD student working with myself, Idris Eckley, and Lucy Gullon (BT) on bandits and anomaly detection.
Robert Lambert is a first year PhD student working with myself, Rob Shone, and Roberto Szechtman (Naval Postgraduate School) on sequential methods in simulation.
Adam Page is a first year PhD student working with myself, Anna-Lena Sachs, Chris Kirkbride, and Datasparq on safety in reinforcement learning.
Former Group Members
Changjiang He was a Senior Research Associate on the NG-CDI project working with myself and David Leslie on anomaly detection and clustering for telecommunications data. Changjiang now works as a Lecturer in Computer Science and the University of Roehampton.
Teaching
This year I am leading the module MATH330 Likelihood Inference, in weeks 1-5 of Michaelmas term. Current Lancaster students can find the MATH330 Moodle page here and access additional support via email, or office hours on Thursdays from 1200-1300 (appointments outside this time can be arranged on an individual basis).
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DR. JAMES A GRANT
Email: j.grant [at] lancaster.ac.uk
Pronouns: he/him
Twitter: @james_a_grant
Room B76
Postgraduate Statistics Centre
Lancaster University
Lancaster, UK
LA1 4YR
View on Campus Map
I am a Lecturer in Statistics in the Department of Mathematics and Statistics at Lancaster University.
I am also a Co-Director of the MSc in Data Science at Lancaster, a member of the committee of the Young Statisticians section of the Royal Statistical Society, and an Associate Editor for the ACM Transcations on Probabilistic Machine Learning.
In 2019, I completed my PhD under the supervision of Professors David Leslie, Kevin Glazebrook, and Roberto Szechtman. Previously, I have worked as a Machine Learning Research Student at Secondmind.ai, and a Research Associate within the STOR-i Centre for Doctoral Training, and Department of Mathematics at Statistics at Lancaster. My full CV is available here.
Research
My research considers online sequential decision making for problems with complex data structures. I am particularly interested in multi-armed bandits, online optimisation, and recommender systems. A key feature of this research is collaboration with and inspiration from industry. I am always open to discussing the prospect of new collaborations in theoretical or applied areas.