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Skip to content Skip to search Skip to footer Division of Computational & Data Sciences Open Menu Back Close Menu Search for: Search Close Search Program Overview Structure TracksTracks Computational Methodologies Political Science Psychological &＃038; Brain Sciences Public Health &＃038; Social Work Faculty Application &＃038; Support News FAQs – PhD Applicants Current Students Alumni Open Search Computational Methodologies Research in the social and behavioral sciences also drives the need for new computational and statistical methodology. Faculty in the Computational Methodologies track work on developing novel algorithms and techniques in the areas of machine learning, data visualization, network science, and resource allocation, among other topics. Faculty are interested in topics like automating the learning and visualization pipeline so that domain scientists can directly use modern machine learning and visual analytics tools, working collaboratively with faculty in the social science tracks to develop efficient and just methods for allocating scarce resources like spaces in homeless shelters and counseling services, effectively targeting public health interventions, and analyzing how social networks and language affect political behavior. You can learn more about the faculty on the track faculty page. Track Course Requirements Students must take Advanced Algorithms (CSE 541T) and either Introduction to Artificial Intelligence (CSE 412A) or Bayesian Methods in Machine Learning (CSE 515T). In addition, students must take two substantive classes in their area of interest (one of the other three tracks) from among the classes that would satisfy the domain depth for students in that track. William YeohTrack Chair, Computational MethodologiesAssociate Professor, Computer Science &＃038; EngineeringPhD, University of Southern California Email: [email protected] Professor Yeoh’s research focuses on artificial intelligence with an emphasis on developing optimization algorithms for agent-based systems. His primary expertise is in distributed constraint optimization, where his goal is to develop and deploy such algorithms in multi-agent systems including smart grid and smart home applications as well as cloud and edge computing applications. Application & Checklist Graduate Admissions FAQs FAQs for PhD Applicants Email Graduate Admissions Doctoral Student Handbook Explore St. Louis Washington University in St. Louis Graduate Admissions Graduate Admissions MSC 1220-122-203 One Brookings Drive St. Louis, MO 63130 314-935-5830 Email us ©2024 Washington University in St. Louis