Takis Benos received his undergraduate degree in Mathematics and a PhD degree in molecular evolutionary biology. His post-graduate work included work at the EMBL-EBI, UK (advisor: Prof. Michael Ashburner) and Washington University in St. Louis (advisor: Prof. Gary Stormo). In 2002 he joined University of Pittsburgh and he is currently a tenured Full Professor and Vice Chair for Academic Affairs at the Department of Computational and Systems Biology (primary appointment), the Department of Biomedical Informatics (secondary appointment), and the Department of Computer Science (secondary appointment), University of Pittsburgh. He also holds a joint appointment at the University of Pittsburgh Hillman Cancer Center (HCC). Dr. Benos’ research interests are in the area of systems medicine. His group develops causal inference and other machine learning algorithms to perform integrative analysis of multi-scale data and use them to develop predictive models of disease phenotypes and outcomes. His work has been published in peer-reviewed journals such as Nature, Science, PNAS, PLoS Computational Biology, Genome Research, Genome Biology, and others. Dr. Benos has served as Associate Director and Director in two PhD programs of the University of Pittsburgh: the Joint CMU-Pitt PhD Program in Computational Biology (CPCB); and the Integrative Systems Biology (ISB) PhD Program from its inception in 2014 and until 2019. He serves as Academic Editor to PLoS One, Biology Direct and Frontiers in Bioengineering and Biotechnology and Genetics, while he was an elected member of the Faculty of 1000 from 2007-2020 and lifelong member of the International Society of Computational Biology (ISCB). Dr. Benos has been continuously funded through his own NIH and NSF grants since 2003 and he has established a large number of collaborations. He will deliver a CS Colloquium talk on Friday, December 10th at 2 p.m.
Location: Sennott Square room 5317 and on Zoom (password: pittcs2021, you must be signed into Zoom with your university account or chose "Sign in with SSO" to attend this meeting.)
The advancement of technologies for high-throughput collection of personal data, including environmental, lifestyle, clinical and biomedical data, has inadvertently transformed biology and medicine. Integrating and co-analyzing these different data streams has become the research bottleneck and, in all likelihood, will be a central research topic for the next decade. Machine Learning has shown promise addressing biomedical and clinical problems, especially regarding classification. Causal graphical models allow for inferring potential cause-effect relations on the whole dataset and are by nature interpretable. My group has worked in extending causal learning framework to mixed data types, incorporating prior information and learning causal graphs with latent confounders. In this talk, I will present some of our recent efforts on causal inference algorithms with applications on important biomedical and clinical questions in chronic diseases and cancer diagnosis and therapy.