In this talk, Dr. Ken Steif will present 2 public sector machine learning use cases he developed with graduate students this past spring as part of the MUSA Practicum at Penn. He is looking to recruit other data-driven students from across Penn to participate in next year's Practicum.
Ken Steif has been at the forefront of data driven public policy for more than a dozen years. He combines technical knowledge of Geographic Information Systems and applied statistics with an interest in housing policy, education, the economics of neighborhood change, transportation policy and more. Ken is the Director of the Master of Urban Spatial Analytics at the University of Pennsylvania and teaches multiple courses in the City Planning department at Penn. His work has focused on the costs and benefits of gentrification; on the Philadelphia school crisis and the connection between good schools and neighborhood economic development; and on the use of machine learning to help democratize the planning process. He is a resident of West Philadelphia where he lives with his wife Diana and their son Emil.