Manuscripts that are not yet linked here are available upon request.

Google Scholar citations & analysis

*Name in publications below indicates a Master’s student working under my supervision.

T. Damoulas, J. He, R. Bernstein, C. Gomes, A. Arora.

String kernels for complex time-series: Counting targets from sensed movement

International Conference on Pattern Recognition (ICPR), 2014.

D. Fink, T. Damoulas, N. Bruns, F. La Sorte, W. Hochachka, C. Gomes.

Crowdsourcing meets ecology: Hemisphere-wide Spatiotemporal Species Distribution Models

Artificial Intelligence Magazine, 2013.

Y. Xue, B. Dilkina, T. Damoulas, D. Fink, C. Gomes, S. Kelling

Improving Your Chances: Boosting Citizen Science Discovery

First AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2013.

  1. S.Kelling, C. Lagoze, W. K. Wong, J. Yu, T. Damoulas, J. Gerbracht, D. Fink, C. Gomes

eBird: A Human/Computer Learning Network to Improve Biodiversity Conservation

and Research,

Front Cover

Artificial Intelligence Magazine, 34 (1), 2013.

D. Fink, T. Damoulas, *J. Dave

Adaptive Spatio-Temporal Exploratory Models,

Twenty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2013.

R. G. Pearson, S. J. Phillips, M. M. Loranty, P. S. A. Beck, T. Damoulas, S. J. Knight, S. J. Goetz

Shifts in Arctic vegetation and associated feedbacks under climate change.

Front Cover [Impact Factor: 14.472]

Nature Climate Change, 2013.

Publicity: [Nature, Scientific American, Smithsonian, dailymail,

Woods Hole Research Center, HPCwire]

  1. S.Kelling, J. Gerbracht, D. Fink, C. Lagoze, W-K. Wong, J. Yu, T. Damoulas, C. Gomes

IAAI Deployed Application Award

eBird: A Human-Computer Learning Network for Biodiversity Conservation and Research

Twenty-Fourth Annual Conference on Innovative Applications of Artificial Intelligence, IAAI 2012.

T. Damoulas, *N. Bruns, A. Farnsworth

Machine Learning Techniques for Automated Flight Call Detection.

Symposium on Bioacoustic Monitoring, 5th North American Ornithological Conference NAOC-V 2012.

  1. D.Fink, W. Hochachka, T. Damoulas, *J. Dave, S. Kelling

Exploratory analysis and inference  with broad-scale citizen science data.

Ecological Society of America, ESA 2012.

  1. R.G. Pearson, S. J. Phillips, P. S. A. Beck, M. M. Loranty, T. Damoulas, S. J. Goetz

Arctic greening under future climate change predicted using Machine Learning.

American Geophysical Union, December 2011.

S. J. Phillips, R. G. Pearson, P. S. A. Beck, M. M. Loranty, S. J. Goetz and T. Damoulas

Estimating vegetation expansion in the Arctic under climate change using Machine Learning.

Society of Conservation Biology, International Congress for Conservation Biology, ICCB 2011.

B. Dilkina, T. Damoulas, C. Gomes, D. Fink

AL2: Learning for Active Learning

Workshop “Machine Learning for Sustainability”, NIPS 2011.

R. LeBras, T. Damoulas, J. M. Gregoire, A. Sabharwal, C. Gomes, R. B. van Dover

Constraint Reasoning and Kernel Clustering for Pattern Decomposition with Scaling.

17th International Conference on Principles and Practise of Constraint Programming, CP 2011.

  1. 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 [In Print].


T. Polajnar, T. Damoulas, Mark A. Girolami

Protein Interaction Sentence Detection using Multiple Semantic Kernels.

Journal of Biomedical Semantics, 2 (1), BMC, 2011.

T. Damoulas, *S. Henry, A. Farnsworth, M. Lanzone, C. Gomes

Best Paper Award

Bayesian Classification of Flight Calls with a novel Dynamic Time Warping Kernel.

9th International Conference on Machine Learning and Applications, Washington, IEEE ICMLA 2010.

[Extended Technical Report] [Matlab Codes] [Data]

R. Le Bras, T. Damoulas, J. Gregoire, A. Sabharwal, Carla Gomes, R. Bruce van Dover

Computational Thinking for Material Discovery: Bridging Constraint Reasoning and Learning.

2nd International Workshop on Constraint Reasoning and Optimization for Computational Sustainability, CROCS’10, Bologna, Italy, 2010.

*I. Psorakis, T. Damoulas, M. A. Girolami

Multiclass Relevance Vector Machines: Sparsity and Accuracy

IEEE Transactions on Neural Networks, 2010.

[Matlab Codes]

T. Damoulas and M. A. 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 Press, 2010.

T. Damoulas, M. A. Girolami and S. Rogers

Preliminary Analysis of Multiple Kernel Learning: Flat Maxima, Diversity and Fisher Information.

Workshop “Understanding Multiple Kernel Learning Methods”, Whistler, NIPS 2009.


C. He, T. Damoulas and M. A. Girolami {US Patent}

Self - Service Terminals.

US Patent 7,942,315

T. Damoulas

Classification Society Distinguished Dissertation Award

Probabilistic Multiple Kernel Learning. PhD Thesis, University of Glasgow, 2009.

Advisor: Prof. Mark. A. Girolami

Committee: Prof. Guido Sanguinetti (External), Prof. Paul Siebert (Internal), Prof. Joemon Jose (Convenor).

[Website][Note: Thesis is not available due to Embargo/Commercial Interests]

T. Damoulas and M. A. Girolami

Combining Feature Spaces for Classification.

Pattern Recognition, 42 (11), 2671-2683, 2009.

T. Damoulas and M. A. Girolami

Pattern Recognition with a Bayesian Kernel Combination Machine.

Pattern Recognition Letters, 30 (1), 46-54, 2009.

Y. Ying, C. Campbell, T. Damoulas and M. A. Girolami

Class Prediction from Disparate Biological Data Sources using a Simple Multi-class Multi-kernel Algorithm.

4th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB, 2009.

T. Damoulas and M. A. Girolami

Probabilistic multi-class multi-kernel learning: On protein fold recognition and remote homology detection.

[5-year Impact Factor: 6.911]

Bioinformatics, 24 (10), 1264-1270, 2008.

T. Damoulas, Y. Ying, C. Campbell, M. A. Girolami

Inferring Sparse Kernel Combinations and Relevance Vectors: An application to subcellular localization of proteins.

7th International Conference on Machine Learning and Applications, San Diego, IEEE ICMLA 2008.

T. Damoulas

Evolving a Sense of Valency. MSc Thesis (Distinction), University of Edinburgh, 2005.

Advisor: Dr. Gillian Hayes.


T. Damoulas, I. Cos Aguilera, G. Hayes, T. Taylor

Valency for Adaptive Homeostatic Agents: Relating Evolution and Learning.

In Proceedings of the 8th European Conference on Artificial Life, ECAL 2005.

T. Damoulas, I. Cos Aguilera, G. Hayes

Valency as a mechanism for agent adaptation.

Towards Autonomous Robotic Systems, TAROS 2005.

Commercial Reports

Inferring Sparse Kernel Combinations and Relevance Vectors.

NCR Internal Report, Rev. B. No. 010, April 2009.

Feature Selection of Diverse Signals for Hierarchical Bayesian Kernel Machine.

NCR Internal Report, Rev. B. No. 008, June 2008.

Learning Curve Investigation for Multinomial Probit Classifier.

NCR Internal Report, Rev. B. No. 007, April 2008.

Bayesian Covariate Ranking via Markov chain Monte Carlo on generalized linear regression models.

NCR Internal Report, Rev. B. No. 002, December 2006.