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Dr Theo Damoulas

I am an Associate Professor in Data Science with a joint appointment in Computer Science and Statistics. I am also the Deputy Director for the Data-Centric Engineering programme at The Alan Turing Institute. My research interests are in probabilistic machine learning and Bayesian statistics with applications to spatio-temporal problems in urban science and computational sustainability. I am a member of the Warwick Machine Learning Group.

I am a Turing Fellow of the Alan Turing Institute and affiliated with NYU as a Visiting Exchange Professor at the Center for Urban Science and Progress (CUSP).

Projects

DCE Covid 19 response: Project Odysseus [04/2020 - Present]

London Air Quality project, Turing - Lloyds Register Foundation programme on Data Centric Engineering. [10/2017 - Present]

Probabilistic Deep Learning for EV battery life-time prediction, Innovate UK, HVM Catapult & LRF. [10/2018 - Present]

Robust, Scalable Sequential Monte Carlo with Application to Urban Air Quality, EPSRC, Co-I together with Adam Johansen (PI), [Start: 1/4/2020] *Open position: Hiring soon a 3-year PDRA at Turing in Statistical ML/Bayesian non-parametrics - contact me if interested*

Publications

(in chronological order)

Teaching

CS342 Machine Learning (2016-2019)

ST343 Topics in Data Science [Topic: Reinforcement Learning] (2017-2019)

Research Group

Post-doctoral Research Associates/Research Fellows

Dr. Omer Deniz Akyildiz

Dr. Daniel Tait

Dr. Kangrui Wang

Dr. Neil Dhir

PhD Students

Virginia Aglietti (2017-2020)

Jeremias Knoblauch (2017-2020)

Ayman Boustati (2017-2020)

Maud Lemercier (2019-2022)

Oliver Hamelijnck (2019-2022)

Research Assistants

Patrick O'Hara

James Walsh

Group Alumni

Karla Monterrubio Gomez (PhD, 2016-2019) [PDRA with Dr. Catalina Valejos]

Joe Meagher (PhD, 2016-2020) [PDRA with Prof. Nial Friel]

Yannis Zachos (BSc thesis, 2018) [Now PhD student at Cambridge with Prof. Mark A. Girolami]

Office Hours

- (@Turing)

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). Get in touch with me and apply directly via OxWaSP and/or WISC Doctoral Training Centers and/or The Alan Turing Institute. Some areas of interest are i) probabilistic spatio-temporal inference and algorithms, ii) learning and Bayesian inference in dynamic systems and changing (non-stationary) environments iii) scalable approximate inference and learning algorithms.

BSc/MSc/MEng Supervision

[***Update***: In 2019 I am moving to Turing and will only be able to offer supervision on few thesis related to my research projects]. 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:

  • James Walsh, BSc in Data Science, Physics-informed machine learning of Li-ion 18650 battery degradation, Dept. of Statistics & Dept. of Computer Science, University of Warwick, 2019. [co-supervised with Dr. Daniel Tait]
  • Patrick O'Hara, BSc in Computer Science, Running NP-Hard from Air Pollution: Graph optimisation algorithms, Dept. of Computer Science, University of Warwick, 2018. [co-supervised with Dr. Ramanujan Sridharan]
  • Yannis Zachos, BSc in Data Science, Bayesian Online Changepoint Detection: Spatio-temporal point processes, Dept. of Statistics & Dept. of Computer Science, University of Warwick, 2018. [co-supervised with Jeremias Knoblauch]

Notable 1st Class MSc thesis:

  • Johannes Muller, MSc in Interdisciplinary Mathematics, Parameter estimation and consistency for discrete determinantal point processes, Dept. of Mathematics, University of Warwick, 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 Warwick, 2018. [co-supervised with Virginia Aglietti]

Selected Awards

  • Warwick Impact Fund Award, 2018
  • Turing Reproducible Research Award, 2018
  • ACM SIGMOD Most Reproducible Paper Award, 2017
  • Warwick 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

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

Journal editing:

Data-Centric Engineering, Cambridge University Press

Conference Reviewing:

NeurIPS, ICML, AAAI, IJCAI

Journal reviewing:

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

Workshop/Special session co-organizer:

ICASSP 2018 "Wildlife Bioacoustics and Adaptive Signal Processing", D. Stowell, N. Harte, T. Damoulas

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

NIPS 2013 "Machine Learning for Sustainability", E. Bonilla, T. Dietterich, T. Damoulas, A. Krause

NIPS 2012 "Human Computation for Science and Computational Sustainability", T. Damoulas, T. Dietterich, E. Law, S. Belongie

Academic Bio:

Theo joined the University of Warwick 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 Prof. Carla P. Gomes, Prof. Bart Selman, 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 Prof. Mark A. Girolami at the School of Computing Science, University of Glasgow. He was also co-supervised by Prof. Cornelis Just "Keith" van Rijsbergen. He holds an MSc in Informatics (Distinction) from the University of Edinburgh (2003-2004) and an MEng in Engineering (1st Class) from the University of Manchester (1999-2003).