Gregory Cooper Professor of Biomedical Informatics Director, Center for Causal Discovery Vice Chair, Department of Biomedical Informatics Research involves the use of probability theory, decision theory, Bayesian statistics, machine learning, and artificial intelligence to address biomedical informatics problems.

Causal discovery of biomedical knowledge from big data Problem: How to discover causal knowledge from big biomedical datasets? Approach: Improve the efficiency of existing causal discovery algorithms and develop new algorithms. Make them readily available to biomedical scientists and easy to use. Funding: NIH BD2K U54HG008540 (Cooper, Bahar, Berg) Select Data Algorithm Perform Causal Analysis Query Models View Models Compare Models Annotate Models Store Models Share Models Causal Models

Discovering tumor-specific drivers and pathways of cancer Problem: How to discover the genomic drivers of an individual tumor? Approach: Use an instance-specific Bayesian causal discovery approach and implement it to run in parallel on GPUs on the Pittsburgh Supercomputer Center. Funding: NIH BD2K U54HG008540 (Cooper, Bahar, Berg) Project lead: Xinghua Lu

Machine-learning-based clinical alerting Problem: How to detect in real time a wide variety of medical errors from data in the EMR? Approach: Use machine learning to construct a probabilistic model of usual care. If current care of a patient is highly unusual according to the model, raise an alert. Funding: NIH / NIGMS R01GM088224 (Hauskrecht, Clermont, Cooper)

Detecting and characterizing disease outbreaks using probabilistic modeling Problem: How to detect and characterize new outbreaks of infectious disease in the population? Approach: Link a probabilistic epidemiological model of outbreak disease in the population to probabilistic models of patient disease in emergency departments that are capturing patient data electronically. Apply Bayesian inference. Funding: NIH / NLM R01LM011370 (Wagner)

Predicting cancer outcomes from a combination of clinical and omic data Problem: How to accurately predict the outcomes (e.g., tumor metastasis) of a patient with cancer? Approach: Automatically construct higher-level features (e.g., cell signaling pathways) from raw omic and clinical data. Use those features and machine learning to predict clinical outcomes. Funding: PA DOH CURE grant [forthcoming] (Cooper, Bar-Joseph)