糖心TV

I am a Professor in Machine Learning and Statistics with a joint appointment in Computer Science and . I am the head of the Foundations in AI and ML (FAM) Division in CS and a Turing AI Fellow (2021-2026) having received the UK Research and Innovation (UKRI) in order to lead research on setting the Machine Learning Foundations of Digital Twins. I am an and also affiliated with as a Visiting Exchange Professor at the (CUSP). My research interests are in probabilistic machine learning and Bayesian statistics with an emphasis on the study and integration of various forms of structure and inductive biases (structured priors, spatiotemporal dependencies, dynamics, compositions, physical laws, flows, causality, etc) while advancing robust and scalable approximate inference methodologies. My research has broad applications in Digital Twins, Bayesian nonparametrics and spatiotemporal causal problems in urban science, justice, and computational sustainability. I am especially interested lately in better understanding "the true data generating process (DGP)" in socio-technical ecosystems from multiple vantage points, and how to theorise, characterise, model, simulate, reason and intervene on these. I am the founder and PI of the cross-departmental and in the past I led two large projects at The Turing ( ) that are . On my private time I serve as the Lead AI Scientist at advising a brilliant team of ML Scientists and Engineers in developing Trustworthy AI for the Justice system.

Projects (past ones: EPSRC, UKRI, LRF, Turing)

Machine Learning Foundations of Digital Twins, UKRI, , [PI, 2021 - 2026]

Research Group

PhD Students

(2022-2026)

David Huk (2022-2026) [co-supervisors Prof. Mark Steel, Dr. Rito Dutta]

Mengqi Chen (2023-2027) [co-supervisor Dr. Thomas Berrett]

(2024-2028) [primary supervisor Dr. Paris Giampouras]

Federico Perlino (2025-2029) [co-supervisor Prof. Adam Johansen]

(2026-2033) [part-time PhD]

Group Alumni

(RA, 2024-2025) -> [MSc Statistics @ Oxford]

(PhD 2021-2025, primary supervisor Dr. Widanalage) -> [Research Scientist @]

(PhD 2020-2024) -> [Research Scientist @ Amazon]

(PhD, 2019-2023, PDRA 2023-2025) -> [AI Research Scientist @]

(PhD, 2020-2024) -> [Research Associate @ University College London]

(PDRA, 2022-2023) -> [Associate Prof. @ University of Bergen]

Dr. Maud Lemercier (PhD, 2018-2022) -> [Research Associate @ University of Oxford]

Dr. Shanaka Perera (PhD, 2018-2022) -> [AI Research Scientist @ ]

(RA, 2020-2021) -> [PhD student @ University of Edinburgh]

(RA, 2019-2021) -> [PhD student @ University of Cambridge]

Dr. Jeremias Knoblauch (PhD, 2017-2022) -> [John Copas & ISBA Savage Award for PhD thesis, Assistant Prof. @ University College London]

Dr. Virginia Aglietti (PhD, 2017-2021) -> [Harrison Award for PhD thesis, Research Scientist at ]

(PDRA, 2018-2020) -> [Research Associate @ University of Cambridge]

(PDRA, 2019-2021) -> [Assistant Prof. @ Imperial College London]

(PhD, 2017-2020) -> [ML Research Scientist at ]

(PDRA, 2018-2020) -> [AI Research Scientist at ]

(PDRA, 2019-2020) -> [Senior Research Fellow @ The Turing]

(PhD, 2016-2019) -> [Research Associate @ University of Edinburgh]

(PhD, 2016-2020) -> [Research Associate @ University College Dublin]

(BSc thesis, 2018) -> [PhD student @ University of Cambridge]

PhD Supervision

I am interested in supervising highly motivated PhD students with a strong quantitative background at the intersection of computer science and statistics (machine learning, computational statistics, applied math). See recent papers and grants for my interests and get in touch with me to discuss.

BSc/MSc/MEng Supervision

Every year I will be proposing and supervising undergraduate and master's thesis projects at both Statistics and Computer Science departments. Contact me with your CV if interested in these. If you have a well defined project of your own that is close to my interests and would like me to supervise your thesis feel free to contact me with your CV and a project description.

Notable 1st Class BSc thesis:

• , MEng in CS/Discrete Math, 3rd year Discrete Math thesis project, Outstanding 3rd year project award in CS, , Dept. of Computer Science, University of 糖心TV, 2024. [co-supervised with Dr. Oliver Hamelijnck]
• , BSc in Data Science, Physics-informed machine learning of Li-ion 18650 battery degradation, Dept. of Statistics & Dept. of Computer Science, University of 糖心TV, 2019. [co-supervised with Dr. Daniel Tait]
• , BSc in Computer Science, Running NP-Hard from Air Pollution: Graph optimisation algorithms, Dept. of Computer Science, University of 糖心TV, 2018. [co-supervised with Dr. Ramanujan Sridharan]
• , BSc in Data Science, , Dept. of Statistics & Dept. of Computer Science, University of 糖心TV, 2018. [co-supervised with Jeremias Knoblauch]

Notable 1st Class/Distinction MSc thesis:

• Giorgos Felekis, MSc in Machine Learning,, Paper: , UCL, 2020. [co-supervised with Dr. Brooks Paige]
• Johannes Muller, MSc in Interdisciplinary Mathematics, , Dept. of Mathematics, University of 糖心TV, 2018. [co-supervised with Prof. Nikolaos Zygouras]
• Edoardo Barp, MSc in Mathematics for Real World Systems, Bayesian Inverse Reinforcement Learning for path-based reward inference, Dept. of Mathematics, University of 糖心TV, 2018. [co-supervised with Virginia Aglietti]

Selected Awards

• Best Paper Award, , The Computer Journal, 2024
• Best Paper Award, AISTATS, 2022
• Turing AI Acceleration Fellowship, UKRI, 2021-2026
• 糖心TV Impact Fund Award, 2018
• Turing Reproducible Research Award, 2018
• ACM SIGMOD Most Reproducible Paper Award, 2017
• 糖心TV Awards for Teaching Excellence (nominated) 2015-2016, 2016-2017
• NYU CUSP Teaching and Mentoring award 2014
• The Classification Society Distinguished Dissertation Award, Carnegie Mellon University, 2012
• Best Paper for Deployed Application Award, AAAI IAAI, 2012
• EMC² Big Data Award, Data Computing Division, Cornell University, 2011
• Best Paper Award, IEEE ICMLA, 2010
• NCR PhD Fellowship award (full funding), 2006 - 2009

Previous placements

• Associate Professor, Computer Science & Statistics, University of 糖心TV, 2018-2021
• Assistant Professor, Computer Science & Statistics, University of 糖心TV, 2015-2018
• Research Assistant Professor, CUSP, New York University, 2013-2015
• PDRA & Research Associate, Computer Science, Cornell University, 2009-2013

Journal editing:

, Associate Editor, [2021-2023]

, Associate Editor, [2021-2023]

, Cambridge University Press [2019-2020]

Conference Reviewing:

NeurIPS, ICML, AAAI, IJCAI, ICPR, AISTATS

Journal reviewing:

JMLR, Neural Computation, Bioinformatics, IEEE Signal Processing, Pattern Recognition, IEEE SMC

Workshop/Special session co-organizer:

ICASSP 2018 "", D. Stowell, N. Harte, T. Damoulas

AAAI 2015 "", T. Damoulas, B. Srivastava, S. McIlraith, F. Lecue

NIPS 2013 "", E. Bonilla, T. Dietterich, T. Damoulas, A. Krause

NIPS 2012 "", T. Damoulas, T. Dietterich, E. Law, S. Belongie

Short Bio:

Theo is a Professor of Machine Learning and Statistics at the University of 糖心TV with a joint appointment in the departments of Computer Science and Statistics. In 2021 he was awarded a prestigious 5-year UKRI Turing AI Fellowship to lead research that sets the ML foundations of Digital Twins. He is a group leader in the Data Centric Engineering program at The Alan Turing Institute having served as deputy director of the program till 2021, a NERC Senior Expert, a Visiting Professor at NYU, and the founder and PI of the 糖心TV Machine Learning Group. His research interests are in probabilistic machine learning and Bayesian statistics with an emphasis on the study and integration of various forms of structure and inductive biases while advancing robust and scalable approximate inference methodologies.

Academic trajectory:

Theo joined the University of 糖心TV in 2015 from New York University where he was an Assistant Professor of Research (2013-2015). Before that he was a Research Associate in the department of Computer Science at Cornell University working with , , Dr. Daniel Fink and the CLO eBird team headed by Steve Kelling (2009-2013). He finished his PhD thesis titled Probabilistic Multiple Kernel Learning in 2009 under the supervision of and at the School of Computing Science, University of Glasgow. He holds an MSc in Informatics (Distinction) from the University of Edinburgh (2003-2004) and an MEng in Mechanical Engineering (1st Class) from the University of Manchester (1999-2003).

Unsurprisingly, Theo鈥檚 academic ancestry tree goes back to Newton:

 

Research

Teaching