Artificial Intelligence Collaboratory

The goal of this collaboratory is to help the biomedical research community identify collaborators with expertise in artificial intelligence, machine learning, natural language processing, and other related areas. Below is a list of faculty who are seeking new collaborations.

• Mary Regina Boland, Ph.D. – artificial intelligence, data mining, machine learning, feature selection
• Yong Chen, Ph.D. – machine learning models for dynamic risk prediction, data privacy, distributed algorithms, semi-automated rapid systematic review and meta-analysis
• Tessa S. Cook, M.D., Ph.D. – artificial intelligence, machine learning, natural language processing (particularly as applied to radiology reports and the electronic medical record)
• John H. Holmes, Ph.D. – machine learning for knowledge discovery, classification and prediction, complex systems, dimensionality reduction, network models, agent-based modeling and simulation, knowledge representation for intelligent systems
• Danielle Mowery, Ph.D. – clinical natural language processing, patient phenotyping, machine learning, risk prediction
• Dokyoon Kim, Ph.D. – interpretable deep learning, graph deep learning, machine learning on graphs, feature selection
• Despina Kontos, Ph.D. – machine learning, pattern recognition, medical image analysis, radiomics, radiogenomics, imaging biomarkers, integrated diagnostics
• Jason H. Moore, Ph.D. – artificial intelligence, automated machine learning, feature engineering, feature selection, genetic programming
• Marylyn Ritchie, Ph.D. – supervised machine learning, unsupervised phenotype clustering, feature selection, evolutionary algorithms
• Li Shen, Ph.D. – machine learning, biomedical informatics, medical image computing, network science, visual analytics, shape analysis, big data science in biomedicine
• Ryan J. Urbanowicz, Ph.D. – artificial intelligence, interpretable machine learning, feature selection, data mining pipelines, rule-based evolutionary algorithms