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.
Track Chair, Computational Methodologies
Associate Professor, Computer Science & Engineering
PhD, University of Southern California
- Email: firstname.lastname@example.org
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.