ARISE Webinar Series

As demand for data science and AI skills continue to grow, understanding where these learning opportunities exist is more important than ever. Join us to explore the Kansas Data Science Education Atlas, an innovative project designed to map and analyze data science and AI education opportunities across Kansas. Through cutting-edge data integration and visualization, the project uncovers striking geographic disparities in access, revealing concentrations of programs in urban centers and significant gaps across many rural communities. In addition, you will also learn about resources available from the Kansas Data Science Consortium, which is building partnerships, nurturing talent, and expanding research capacity statewide.

Safia Malallah is Program Coordinator for the NSF-funded Kansas Data Science Consortium at Kansas State University, where she works to expand data science education and student engagement across Kansas. With more than 10 years of experience in software development and systems analysis and 17 years in graphic design, she has worked across government, engineering, and private sectors. Her research focuses on early childhood computing education, computational thinking, and broadening participation in computing, particularly for women and girls in technology. Safia has been recognized for excellence in both research and professional service, including nominations for Employee of the Year and Graduate Student of the Month. Safia earned a Ph.D. in Computer Science from Kansas State University, a Graduate Certificate in Technology and Early Childhood from Tufts University, and an M.S. in Computer Science from Montana State University. She also holds a B.S. in Information Science from Kuwait University.

William Duncan is Program Director for the Data Science major at the University of Kansas, Assistant Research Professor of Data Science in the College of Liberal Arts and Sciences, and Co-Director of the Kansas Data Science Consortium. In these roles, he coordinates data science projects with community partners, mentors students and research assistants, and helps advance data science talent development and research across Kansas. He is a faculty member with the Institute for Policy and Social Research at KU, where his research focuses on trade, labor, and education economics. Will earned a Ph.D. in Economics from the University of Kansas, an M.S. in Global Governance and Diplomacy from the University of Oxford, and a B.S. in International Relations and Mathematics from Baker University.

Lior Shamir is a data scientist and researcher whose work combines machine learning, computational statistics, and data science to transform complex data into scientific discoveries. With several years of industry experience spanning roles from software developer to Chief Technology Officer, Lior brings expertise in both research and technology leadership to K-State. His interdisciplinary research has led to discoveries across fields including astronomy, biology, medicine, psychology, and the humanities, and he collaborates with major initiatives such as the Large Synoptic Survey Telescope (LSST), the Astrophysics Source Code Library (ASCL), and the Midwest Big Data Hub. He is the author of nearly 150 peer-reviewed publications and is committed to expanding access to STEM education. Lior earned a Ph.D. in Computational Science and Engineering from Michigan Technological University, an M.Sc. in Computer Science from Open University, and a B.A. in Computer Science from Tel Aviv University.

Abstract: Ammonia plays a central role in emerging polygeneration systems, serving as both an essential chemical feedstock and a promising hydrogen carrier and energy storage medium. Integrating ammonia production with hydrogen generation, energy conversion, storage, and distribution offers opportunities to build more efficient, flexible, and resilient chemical-energy infrastructure. However, the multiscale complexity and strong interactions among interconnected subsystems challenge conventional modeling, optimization, and control strategies. Addressing these challenges requires digital intelligence architectures that integrate physics-based modeling, artificial intelligence, and optimization to enable real-time, resilient control and decision-making. This seminar presents recent advances in developing such architectures, using ammonia-centered systems as a representative application. At the reactor scale, high-fidelity multiphysics modeling is combined with deep learning-based reduced-order models to enable computationally efficient model predictive control of ammonia synthesis. Learning-based state estimation further supports feedback control by reconstructing hard-to-measure process variables. At the process scale, nonlinear predictive control coordinates hydrogen-ammonia production under variable renewable energy availability while optimizing supplemental energy use. At the network scale, moving-horizon dynamic optimization coordinates production, storage, and distribution in integrated chemical-energy communities while evaluating resilience to supply disruptions and fluctuating demands. Building on these advances, the seminar explores opportunities to integrate adaptive digital twins, physics-informed learning, uncertainty-aware predictive control, and network-level optimization into scalable digital intelligence architectures. These developments lay the foundation for autonomous polygeneration systems that can coordinate decisions across multiple scales while maintaining efficient, flexible, and resilient operation under changing conditions.

About the speaker: Davood B. Pourkargar is an Associate Professor in the Tim Taylor Department of Chemical Engineering at Kansas State University and a faculty researcher at the Johnson Cancer Research Center. His research interests include computational multiscale modeling and digital twins, applied artificial intelligence, optimization-based control of complex process networks, cybersecurity and resilience, advanced manufacturing, and smart materials synthesis. Before joining Kansas State University, Dr. Pourkargar served as a Senior Engineer at ExxonMobil Research and Engineering (2019-2020), a Postdoctoral Associate at the University of Minnesota (2016-2019), and a Postdoctoral Researcher at the Catalysis Center for Energy Innovation, a DOE Energy Frontier Research Center at the University of Delaware (2015-2016). He earned his Ph.D. from Pennsylvania State University in 2015 and his M.S. and B.S. from Sharif University of Technology in 2010 and 2008, respectively. Dr. Pourkargar has authored more than 60 peer-reviewed journal and conference papers and delivered over 100 research presentations at international conferences, research institutions, and universities. His major awards and recognitions include the NSF CAREER Award, Carl R. Ice College of Engineering Outstanding Assistant Professor Award, NSF EPSCoR Research Fellowship, Kansas EPSCoR First Award, AFOSR Faculty Fellowship, Big XII Faculty Fellowship, and the O. Hugo Schuck Best Paper Award from the American Automatic Control Council.