Dr. Roberts is an Assistant Professor of Physics at CU Denver, where she leads a research group focused on dark matter detection. She builds accessible analysis tools for the Cryogenic Dark Matter Search and develops software that solves common data access problems across experimental physics. Her group relies on these tools and other open-source software projects to develop a better understanding of some of the world’s most sensitive radiation detectors.
Dr. Langou is a Professor in the Department of Mathematical and Statistical Sciences at CU Denver. His research expertise is Numerical Linear Algebra and High Performance Computing. He is a contributor of the LAPACK open-source software.
Dr. Fritz is the Geospatial Data Scientist at Auraria Library, which serves CU Denver, MSU Denver, and Denver Community College. Dr. Fritz obtained her Ph.D. in Geological Sciences from the University of Colorado Boulder and a B.A. in Biochemistry from the University of California San Diego. Dr. Fritz’s main role on the Auraria Campus is supporting faculty and student researchers in geospatial methods and data in their projects. She is an integral part of the library’s Data to Policy Project and runs the Maptime Mile High and PyFAST (Python for Facility for Advanced Spatial Technology) groups. She also lectures for the Department of Geography and Environmental Sciences at CU Denver.
Dr. Jafarian is an assistant professor in the Department of Computer Science and Engineering and director of Active Cyber and Infrastructure Defense (ACID) lab at University of Colorado Denver. He received his Ph.D. from University of North Carolina Charlotte in 2017. His research interest lies in a spectrum of topics in security and privacy, including but not limited to active cyber defense (Moving Target Defense, Deception), security analytics and automation, social networks security and analytics, big data analytics for security and privacy, and securing critical infrastructures including cyber-physical systems.
Yu Du’s research interest is in large scale data-driven mathematical optimization. The majority of her research focuses on developing nonlinear optimization algorithms for solving large-scale problems with applications in machine learning and data mining, specifically statistical convex and non-convex optimization problems.
Du has also been working on modeling and solving combinatorial optimization problems. Much of her efforts are concerned with new ways of modeling quadratic binary problems and with designing and testing new algorithms for solving these problems.