Research Projects

• with Remi Munos, Mohammad Ghavamzadeh, Alessandro Lazaric, and Daniil Ryabko:
  Composing Learning for Artificial Cognitive Systems 2011 - present
  The purpose of this project is to develop a unified toolkit for intelligent control in many different problem areas. This toolkit will incorporate many of the most successful approaches to a variety of important control problems within a single framework, including bandit problems, Markov Decision Processes (MDPs), Partially Observable MDPs (POMDPs), continuous stochastic control, and multi-agent systems. In addition, the toolkit will provide methods for the automatic construction of representations and capabilities, which can then be applied to any of these problem types. Finally, the toolkit will provide a generic interface to specifying problems and analysing performance, by mapping intuitive, human-understandable goals into machine-understandable objectives, and by mapping algorithm performance and regret back into human-understandable terms.
  
• with Branislav Kveton: Multi-manifold semi-supervised learning 2010 - present
  I parallelized online harmonic solver to process 1 TB of video data in a day. I am working on the multi-manifold learning that can overcome changes in distribution. I am showing how the online learner adapts as to characters' aging over 10 years period in Married ... with Children sitcom. The research was part of Everyday Sensing and Perception (ESP) project.
  
• with Miloš Hauskrecht: Anomaly detection in clinical databases 2007 - present
  Statistical anomaly detection methods for identification of unusual outcomes and patient management decisions. I combined max--margin learning with distance learned to create and anomaly detector, which outperforms the hospital rule for Heparin Induced Thrombocytopenia detection. I later scaled the system for 5K patients with 9K features and 743 clinical decisions per day. At the recent study, from 222 alerts 50% were highly relevant.

• with Branislav Kveton: Online semi-supervised learning 2009 - 2010
  I extended graph-based semi-supervised learning to the structured case and demonstrated on handwriting recognition and object detection from video streams. Regularized harmonic function solution: The algorithm outputs a confidence of inference and uses it for learning. I came up with an online algorithm that on the real-world datasets recognizes faces at 80-90% precision with 90% recall.

• with Wendy Chapman, Roger Day and Gregory Cooper: Odd-Man-Out 2007 - 2011
  We hypothesized that clinical data in emergency department (ED) reports would increase sensitivity and specificity of case identification for patients with an acute lower respiratory syndrome (ALRS). We designed a statistic of disagreement (odd-man-out) to evalute the machine learning classifier with expert evaluation in the cases when the gold standard is not available.
• with Miloš Hauskrecht, Richard Pelikan, and Shuguang Wang
  High-throughput proteomic and genomic data and biomarker discovery 2006 - 2007
  I built a framework for the cancer prediction from high--throughput proteomic and genomic data sources. I found a way to merge heterogeneous data sources: My fusion model was able to predict pancreatic cancer from Luminex combined with SELDI with 91.2% accuracy.
• with Nuno Miguel Cavalheiro Marques and Marco Castelani:
  Evolutionary feature selection algorithms 2005
  I enhanced the existing FeaSANNT neural feature selection with spiking neuron model and yo handle inputs noised with up to 10% gaussian noise.
  

• with Juraj Pavlásek Radoslav Harman and Ján Jenča: Plastic Synapses (regularity counting) 2003 - 2005
  I was modelling basic learning function at the level of synapses. I designed a model that is able to adapt to the regular frequencies with different a rate as the time flows. I used genetic programming to find biologically plausible networks that distinguish different gamma distribution and provided explanation of the strategies evolved.