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J. Maronas, O. Hamelijnck, J. Knoblauch, T. Damoulas, Transforming Gaussian Processes with Normalizing Flows

D. Tait, T. Damoulas, Variational Autoencoding of PDE Inverse Problems, 

O. Akyildiz, T. Damoulas, M. Steel, Probabilistic Sequential Matrix Factorization, 

Y. Zhang, O. Akyildiz, T. Damoulas, S. Sabanis, Nonasymptotic estimates for Stochastic Gradient Langevin Dynamics under local conditions in nonconvex optimization, 

P. O'Hara, M.S. Ramanujan, T. Damoulas, On the Constrained Least-cost Tour Problem,

J. Knoblauch, J. Jewson, T. Damoulas, Generalized Variational Inference, Companion report by J. Knoblauch on Robust DGPs.


[47] V. Aglietti, T. Damoulas, M. Alvarez, J. Gonzalez, Multitask Causal Learning with Gaussian Processes, Thirty-fourth Conference on Neural Information Processing Systems, (NeurIPS 2020).

[46] A. Boustati, O. Akyildiz, T. Damoulas, A. Johansen, Generalised Bayesian Filtering with Sequential Monte Carlo, Thirty-fourth Conference on Neural Information Processing Systems, (NeurIPS 2020).

[45] K. Wang, O. Hamelijnck, T. Damoulas, M. Steel, Nonstationary Nonseparable Random Fields, Thirty-seventh International Conference on Machine Learning, (ICML 2020).

[44] K. Monterrubio-Gómez, L. Roininen, S. Wade, T. Damoulas, M. Girolami, Posterior Inference for Sparse Hierarchical Non-stationary Models,, Computational Statistics and Data Analysis, 106954 (148), (CSDA 2020).

[43] H. Crosby, T. Damoulas, S.A. Jarvis, Road and travel time cross-validation for urban modelling, International Journal of Geographical Information Science, 34 (1), 98-118, (IJGIS 2020).

[42] O. Hamelijnck, T. Damoulas, K. Wang, M. A. Girolami, Multi-resolution Multi-task Gaussian Processes, Thirty-third Conference on Neural Information Processing Systems, (NeurIPS 2019).

[41] V. Aglietti, E. Bonilla, T. Damoulas, S. Cripps, Structured Variational Inference in Continuous Cox Process Models, Thirty-third Conference on Neural Information Processing Systems, (NeurIPS 2019).

[40] Aglietti, V., Damoulas, T., Bonilla, E., Efficient Inference in Multi-task Cox Process Models, The 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019).

[39] Meagher, J.P., Damoulas, T., Jones, K.E. and Girolami, M., 2018. Discussion of ''The statistical analysis of acoustic phonetic data: exploring differences between spoken Romance languages'' by D. Pigoli, P.Z. Hadjipantelis, J.S. Coleman and J.A.D. Aston. To appear in the Journal of the Royal Statistical Society: Series C.

[38] J. Knoblauch, J. Jewson, T. Damoulas, Doubly Robust Bayesian Inference for Non-stationary Streaming Data with β-Divergences, Thirty-Second Annual Conference on Neural Information Processing Systems (NeurIPS 2018).

[37] A. Tsakalidis, M. Liakata, T. Damoulas, A. Cristea, Can we assess mental health through social media and smart devices? Addressing bias in methodology and evaluation, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML 2018).  

[36] J. Knoblauch, T. Damoulas, Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection, 35th International Conference on Machine Learning (ICML 2018).  

[35] H. Crosby, T. Damoulas, S.A. Jarvis, Embedding road networks and travel time into distance metrics for urban modelling, International Journal of Geographical Information Science, 1-25, (IJGIS 2018).

[34] J. Meagher, T. Damoulas, K. Jones, M. Girolami, Phylogenetic Gaussian processes for the ancestral reconstruction of bat echolocation calls. In Statistical Data Science (SDS 2017).

[33] Chuah, E., Jhumka, A., Alt, S., Damoulas, T., Gurumdimma, N., Sawley, M.-C., Barth, W. L., Minyard, T., and Browne, J. C. (2017). Enabling dependability-driven resource use and message log-analysis for cluster system diagnosis. In 2017 IEEE 24th International Conference on High Performance Computing and Communications (HiPC 2017).

[32] Crosby, H., Damoulas, T., Caton, A., Jarvis, S., and Davis, P. (2017). Road distance and travel time for an improved house price Kriging predictor. Geo-spatial Information Science 21 (3), 185-194, 2018. Preliminary version appeared at the Royal Geographical Society (RGS-IBG 2017).

[31] F. Chirigati, H. Doraiswamy, T. Damoulas, J. Freire, Data polygamy: The many-many relationships among urban spatio-temporal data sets, Proceedings of the 2016 ACM SIGMOD International Conference on Management of Data, (SIGMOD 2016).

[30] A. Tsakalidis, M. Liakata, T. Damoulas, B. Jellinek, W. Guo, A. Cristea, Combining Heterogeneous User Generated Data to Sense Well-being, 26th International Conference on Computational Linguistics, (COLING 2016).

[29] H. Crosby, P. Davis, T. Damoulas, S. A. Jarvis, A Spatio-Temporal, Gaussian Process Regression, Real-Estate Price Predictor, Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, short paper, (SIGSPATIAL 2016).

[28] A Chohlas-Wood*, A Merali*, W Reed*, T Damoulas, Mining 911 Calls in New York City: Temporal Patterns, Detection, and Forecasting, AI for Cities Workshop in the 29th AAAI Conference on Artificial Intelligence, (WP @ AAAI 2015). (PDF Document)

[27] S Ermon, Y Xue, R Toth, B Dilkina, R Bernstein, T Damoulas, P Clark, S. DeGloria, A. Mude, C. Barrett, C Gomes, Learning Large-Scale Dynamic Discrete Choice Models of Spatio-Temporal Preferences with Application to Migratory Pastoralism in East Africa, 29th AAAI Conference on Artificial Intelligence, (AAAI 2015). (PDF Document)

[26] T Damoulas, J He, R Bernstein, C Gomes, A Arora, String Kernels for Complex Time-Series: Counting Targets from Sensed Movement, 22nd International Conference on Pattern Recognition (ICPR 2014). (PDF Document)

[25] H Doraiswamy, N Ferreira, T Damoulas, J Freire, C Silva, Using Topological Analysis to Support Event-Guided Exploration in Urban Data, IEEE Transactions on Visualization and Computer Graphics, 20:12, 2014. (PDF Document)

[24] D Fink, T Damoulas, NE Bruns*, FA La Sorte, WM Hochachka, CP Gomes, S Kelling, Crowdsourcing meets ecology: hemisphere-wide spatiotemporal species distribution models, AI Magazine 35 (2), 19-30, 2014. (PDF Document)

[23] BL Sullivan, JL Aycrigg, JH Barry, RE Bonney, N Bruns, CB Cooper, T Damoulas, AA Dhondt, T Dietterich, A Farnsworth, D Fink, JW Fitzpatrick, T Fredericks, J Gerbracht, C Gomes, WM Hochachka, MJ Iliff, C Lagoze, FA La Sorte, M Merrifield, W Morris, TB Phillips, M Reynolds, AD Rodewald, KV Rosenberg, NM Trautmann, A Wiggins, DW Winkler, WK Wong, CL Wood, J Yu, S Kelling, The eBird enterprise: an integrated approach to development and application of citizen science, Biological Conservation, 169, 31-40, 2014.

[22] D Fink, T Damoulas, J Dave*, Adaptive Spatio-Temporal Exploratory Models: Hemisphere-wide species distributions from massively crowdsourced eBird data, 27th AAAI Conference on Artificial Intelligence (AAAI 2013). (PDF Document)

[21] RG Pearson, SJ Phillips, MM Loranty, PSA Beck, T Damoulas, SJ Knight, Scott J Goetz, Shifts in Arctic vegetation and associated feedbacks under climate change, Nature Climate Change, 3:7, 673-677, 2013. Front Cover.

Publicity: Scientific American, Smithsonian, dailymail

[20] Y Xue, B Dilkina, T Damoulas, D Fink, C Gomes, S Kelling, Improving Your Chances: Boosting Citizen Science Discovery, 1st AAAI Conference on Human Computation and Crowdsourcing, (HCOMP 2013). (PDF Document)

[19] S Kelling, J Gerbracht, D Fink, C Lagoze, WK Wong, J Yu, T Damoulas, C Gomes, eBird: A Human/Computer Learning Network for Biodiversity Conservation and Research, (AAAI-IAAI 2012). (PDF Document) Best Deployed Application Award.

[18] S Kelling, J Gerbracht, D Fink, C Lagoze, WK Wong, J Yu, T Damoulas, C Gomes, A human/computer learning network to improve biodiversity conservation and research, AI magazine, 34 (1), 2012. Front Cover. (PDF Document)

[17] R LeBras, T Damoulas, JM Gregoire, A Sabharwal, C Gomes, RB Van Dover, Constraint reasoning and kernel clustering for pattern decomposition with scaling, Principles and Practice of Constraint Programming, (CP 2011). (PDF Document)

[16] B Dilkina, T Damoulas, C Gomes, D Fink, AL2: learning for active learning, Machine Learning for Sustainability workshop in the 25th Conference of Neural Information Processing Systems (WP @ NIPS 2011). (PDF Document)

[15] T Polajnar, T Damoulas, M Girolami, Protein interaction sentence detection using multiple semantic kernels, Journal of Biomedical Semantics 2, 1, 2011.

[14] S Kelling, R Cook, T Damoulas, D Fink, J Freire, W Hochachka, W Michener, K Rosenberg, C Silva, Estimating Species Distributions, Across Space Through Time and with Features of the Environment, Data Intensive Research, Editors: M. Atkinson and P. Brezany, 2011.

[13] T Damoulas, S Henry*, A Farnsworth, M Lanzone, C Gomes, Bayesian classification of flight calls with a novel dynamic time warping kernel, 9th International Conference on Machine Learning and Applications (ICMLA 2010). Best Paper Award (PDF Document)

[12] R Le Bras, T Damoulas, J Gregoire, A Sabharwal, C Gomes, RB van Dover, Computational Thinking for Material Discovery: Bridging Constraint Reasoning and Learning, 2nd International Workshop on Constraint Reasoning and Optimization for Computational Sustainability, (CROCS 2010).

[11] I Psorakis*, T Damoulas, MA Girolami, Multiclass relevance vector machines: sparsity and accuracy, IEEE Transactions on Neural Networks, 21 (10), 1588-1598, 2010. (PDF Document)

[10] T Damoulas, Probabilistic multiple kernel learning, PhD thesis, Classification Society Distinguished Dissertation Award, 2009. Codes (ZIP or other archive)

[9] T Damoulas, MA Girolami, Combining feature spaces for classification, Pattern Recognition 42 (11), 2671-2683, 2009. (PDF Document)

[8] T Damoulas, MA Girolami, Probabilistic multi-class multi-kernel learning: on protein fold recognition and remote homology detection, Bioinformatics 24 (10), 1264-1270, 2009. (PDF Document) 

[7] T Damoulas, MA Girolami, Pattern recognition with a Bayesian kernel combination machine, Pattern Recognition Letters 30 (1), 46-54, 2009.

[6] T. Damoulas, M. A. Girolami and S. Rogers, Preliminary Analysis of Multiple Kernel Learning: Flat Maxima, Diversity and Fisher Information, Understanding Multiple Kernel Learning Methods workshop, (WP @ NIPS 2009). (PDF Document)

[5] C. He, T. Damoulas and M. A. Girolami, Self - Service Terminals, US Patent 7,942,315, 2009.

[4] T Damoulas, Y Ying, MA Girolami, C Campbell, Inferring sparse kernel combinations and relevance vectors: an application to subcellular localization of proteins, 7th International Conference on Machine Learning and Applications, 2008. (ICMLA 2008). (PDF Document)

[3] T Damoulas, MA Girolami, Combining Information with a Bayesian Multi-class Multi-kernel Pattern Recognition Machine, Machine Interpretation of Patterns: Image Analysis, Data Mining and Bioinformatics. Editors: R. K. De, D. P. Mandal and A. Ghosh, Indian Statistical Institute, World Scientific Review, 2008.

[2] T Damoulas, I Cos-Aguilera, G Hayes, Valency as a mechanism for agent adaptation, Proceedings of Towards Autonomous Robotic Systems (TAROS 2005).

[1] T Damoulas, I Cos-Aguilera, GM Hayes, T Taylor, Valency for adaptive homeostatic agents: Relating evolution and learning, Proc. Adv. Artif. Life: 8th Eur. Conf., Berlin, Germany, 2005, vol. 3636, LNAI, pp. 936–945 (ECAL 2005). (PDF Document)

* = Supervised Master's student at the time of submission.