research

Research Interests

Statistical Machine Learning, Bayesian Inference, Pattern Recognition, Information Theory, Statistics.

My PhD research focused in multi-feature multinomial classification and kernel combination for information fusion (data integration). My PhD thesis is entitled Probabilistic Multiple Kernel Learning and has won the “Classification Society Distinguished Dissertation Award” in 2012.

My recent research is on Big Data problems from the field of sustainability. These include spatiotemporal inference for the eBird crowdsourcing domain (with CLO) and for landcover prediction under climate change scenarios in the Arctic, dynamic time warping kernels for bioacoustics, human-computer learning networks and human sensing.

Research Methodology

Kernel Methods, Bayesian Inference, Multiple Kernel Learning, MCMC, Variational Methods, Approximate Inference, Graphical Models, mRVMs, Reinforcement Learning.

Research Applications

Spatiotemporal Inference, Ecology, Computational Sustainability, Crowdsourcing, Engineering, Bioinformatics.

Workshops

Organizing a NIPS 2012 workshop on Human Computation for Science and Computational Sustainability with Thomas G. Dietterich, Edith Law and Serge J. Belongie.

Program Committee Member

IJCAI 2013 (Senior PC), AAAI 2013, ICPR 2012, AAAI 2012, AAAI 2011, ICPR 2010, ICANN 2010, MLSB 2010, PRIB 2010, PRIB 2009

Reviewer for the following Journals

IEEE Transactions on Computers, Bioinformatics, BMC Bioinformatics, Pattern Recognition,
IEEE Transactions on Systems, Man and Cybernetics, IEEE Signal Processing Letters, IEEE Transactions on Neural Networks, Neurocomputing, Pattern Recognition Letters, Artificial Intelligence.

Invited Talks

University of Rochester, Department Seminar Series, 2013
Probabilistic Inference with Multiple Sensors and Crowdsourcing

Iowa State University, Big Data Analytics Seminar, 2013
Probabilistic Inference with Multiple Sensors and Crowdsourcing

Carnegie Mellon University, Classification Society Distinguished Dissertation Award, 2012
Probabilistic Multiple Kernel Learning

Ohio State University, Invited Talk, 2012
Probabilistic Machine Learning in Biology and Computational Sustainability

Cornell University, AI Seminar, 2010
Probabilistic Multiple Kernel Learning

University of Glasgow, Cakes Talk, 2009.
Computational Sustainability
 University of Edinburgh, School of Informatics, Institute of Perception, Action, Behaviour (IPAB), 2008.
Probabilistic Multiple Kernel Learning

University of Cambridge, Cavendish Laboratory, Inference Group, 2008.
Probabilistic Multiple Kernel Learning