Speakers & Presentations

Risto Miikkulainen is a Professor of Computer Science at the University of Texas at Austin and a VP of AI Research at the Cognizant AI Lab. He received an M.S. in Engineering from Helsinki University of Technology (now Aalto University) in 1986, and a Ph.D. in Computer Science from UCLA in 1990.

His current research focuses on methods and applications of AI in decision-making, particularly those based on neuroevolution and generative AI, as well as neural network models of natural language processing and vision; he is an author of over 500 articles in these research areas. At the Cognizant AI Lab he is scaling up these approaches to real-world problems. Risto is an AAAI, IEEE, and INNS Fellow; his work on neuroevolution has recently been recognized with the IEEE CIS Evolutionary Computation Pioneer Award, the Gabor Award of the International Neural Network Society, and Outstanding Paper of the Decade Award of the International Society for Artificial Life.

Dr. Michael Witherell is Executive Advisor to Lawrence Berkeley National Laboratory (Berkeley Lab). He served as Berkeley Lab’s eighth Laboratory Director from March 2016 to June 2026. Prior roles included Vice Chancellor for Research for the University of California, Santa Barbara, from 2005–2014, during which time he also served as the Presidential Chair in the Physics Department. From 1999–2005, he served as Director of Fermi National Accelerator Laboratory (Fermilab), the largest particle physics laboratory in the country. From 1981 to 1999, Dr. Witherell was a faculty member in the UCSB physics department.

Michael Witherell is a leading physicist with a highly distinguished career in teaching, research and managing complex organizations. As director of the first Lab in the DOE’s National Lab system, Dr. Witherell supports the Lab’s world-renowned scientists and engineers as they search for solutions to the greatest technological challenges facing the nation, as well as for groundbreaking scientific discoveries that seed the future. As an independent researcher, Dr. Witherell has done work in particle physics with accelerators at Brookhaven National Laboratory, at the Stanford Linear Accelerator Center (SLAC), and at Cornell Laboratory for Elementary Particle Physics, in addition to Fermilab. In 1990, his work on an experiment at Fermilab studying charm quarks brought him the prestigious W. K. H. Panofsky Prize in Experimental Particle Physics, awarded annually by the American Physical Society. In 2004 he received the U. S. Secretary of Energy’s Gold Award, the highest honorary award of the Department of Energy. He is a member of the National Academy of Sciences, and a fellow both of the American Association for the Advancement of Science and of the American Physical Society.

Dr. Witherell graduated from the University of Michigan in 1968 and earned his doctorate in particle physics from the University of Wisconsin in 1973. He was a postdoctoral fellow and assistant professor at Princeton University from 1973 to 1981.

Parimal Kopardekar (PK) serves as Acting Director, NASA Airspace Operations and Safety Program. PK has been conducting research, development, technology transfer, and management activities related to airspace operations for the past 33 years.

His focus is on unmanned aircraft systems, advanced air mobility, wildland fire management, autonomy, and future airspace operations. Recently, PK served as the Senior Leader for Advanced Air Mobility Mission while also serving as the Director of NASA Aeronautics Research Institute (NARI).

In the past, he served as the NASA senior technologist for Air Transportation Systems. He invented Unmanned Aircraft Systems Traffic Management (UTM) to safely enable large-scale drone operations at lower altitudes, which is now being globally adopted. He is a recipient of many awards, including American Institute of Aeronautics and Astronautics’ Hap Arnold Award for Programmatic Excellence, NASA Government Invention of the Year, NASA Exceptional Technology Achievement Medal, NASA Outstanding Leadership Award, NASA Engineer of the Year Award, and the prestigious Samuel J. Heyman Service to America’s Promising Innovation Award. PK was named among 25 most influential people in the drone industry. He serves as the Co-Editor-in-Chief of Journal of Aerospace Operations and is a Fellow of the American Institute of Aeronautics and Astronautics as well as Fellow of the Royal Aeronautical Society. He also serves as an adjunct faculty and teaches undergraduate and graduate-level courses related to operations management, supply chain management, and innovation. He holds a Doctor of Philosophy from University of Cincinnati and a Master of Science degree from University at Buffalo in Industrial Engineering, and a Bachelor of Engineering degree from University of Bombay in Production Engineering.

Dr. Jun (Luke) Huan is a Senior Principal Scientist at AWS AI Labs. At AWS, he focuses on delivering AI-as-a-Service solutions across multiple verticals, including agentic AI, foundation model training on GPUs and AWS Trainium, AI-assisted code development, AIOps, and forecasting. Dr. Huan has published more than 200 peer-reviewed papers in leading conferences and journals, and his group has won several best paper awards at leading international conferences. Before joining AWS, he held positions at Baidu Research, the U.S. National Science Foundation, and the University of Kansas.

Sydney Lin is a Senior Software Engineer at Google working on Earth AI, where she builds and evaluates geospatial AI agents. Her focus is building a data flywheel: designing benchmarks, analyzing failures in agents’ reasoning capability and hill climbing. Before Earth AI, she spent nine years contributing to Google Maps and AutoML. She holds a B.S. in Applied Mathematics from the National University of Kaohsiung in Taiwan and an M.S. in Computational and Mathematical Engineering from Stanford University. She is especially interested in AI safety and how it influences the next generation of engineers, including her own two daughters.

Veena Mendiratta is an Adjunct Professor in the Machine Learning and Data Science (MLDS) program at Northwestern University, as well as an independent researcher, technology advisor, and speaker. She brings a unique perspective on the AI and Operations Research convergence, having spent over 35 years at Nokia Bell Labs and its predecessor organizations (AT&T, Lucent, and Alcatel-Lucent) focusing on the reliability, performance analysis, and optimization of large-scale systems.

In her academic role at Northwestern, she developed and currently teaches a graduate course on Explainable AI (XAI), actively bridging traditional mathematical modeling with modern machine learning applications. Her research interests span system dependability, network resiliency, and AI transparency. She has published over 65 papers in leading IEEE, ACM, and INFORMS venues, and has delivered invited tutorials at major international conferences including KDD, DSN, and ODSC.

Veena is a Senior Member of INFORMS and IEEE, a Life Member of SIAM, and has held leadership positions within INFORMS. She holds a B.Tech. in Engineering from IIT Delhi and a Ph.D. in Operations Research from Northwestern University.

Simrita Singh is an Assistant Professor of Operations Management at the Leavey School of Business at Santa Clara University. Her research examines how AI and data-driven technologies shape operational decision-making, with applications in healthcare, service, and retail operations.

Her research combines analytical modeling, econometrics, and machine learning and is motivated by real-world operational problems and data from healthcare and retail settings.

She received her Ph.D. and M.S. in Operations Management from the Kellogg School of Management at Northwestern University and her B.Tech. in Electronics and Communication Engineering from IIT Roorkee.

Harshal Raut is a Principal Engineer at Intuit on the Virtual Expert Platform, which connects customers with human experts across Intuit’s products. He works on the architecture of AI-assisted service - routing, proactive engagement, and human-in-the-loop handoff - bridging ML decisioning with production systems at scale.

Aditya Puttaparthi Tirumala is a Principal Data Scientist at Zillow, where he provides technical and strategic leadership for marketing measurement and decision science. His work focuses on building and scaling enterprise capabilities across marketing mix modeling, causal inference, experimentation, optimization, and applied AI, helping translate complex analytical methods into investment decisions for marketing and business leaders.

He has led the development and operationalization of measurement systems that enable teams to evaluate marketing effectiveness, optimize investments, and make decisions with greater confidence. His work spans not only methodological development, but also the organizational challenges of establishing trustworthy analytics, influencing cross-functional decision-making, and moving analytical capabilities from individual models into scalable production systems.

Aditya’s broader interests center on building decision intelligence systems that combine statistical rigor, AI, automation, and human judgment, and on defining how data science can operate as a strategic decision-making function within organizations.

Yu Zhang is an Associate Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Cruz. He received his Ph.D. from the University of Minnesota, followed by postdoctoral appointments at the University of California, Berkeley and the Lawrence Berkeley National Laboratory. Dr. Zhang’s research advances the resilience, efficiency, and sustainability of modern electric power systems through innovations in AI-driven optimization, machine learning, and dynamic decision-making. His work develops physics-aware learning methods, stochastic and robust optimization techniques, and cyber-physical coordination frameworks to support reliable grid operations under uncertainty.

Dr. Ed Klotz has over 30 years of experience in the mathematical optimization software industry. He is a technical expert who, over the course of his career, has worked with a wide array of customers to help them solve some of world’s most challenging mathematical optimization problems. In his role as a Senior Mathematical Optimization Specialist on the Gurobi R&D team, Dr. Klotz works closely with our customers to support them in implementing and utilizing mathematical optimization in their organizations. He also interacts heavily with the R&D team based on his experiences with the customers.

Prior to joining Gurobi, Dr. Klotz was a member of the CPLEX development team of IBM. He was involved in product development, customer training, product documentation, and numerous other tasks, with a primary focus on delivering CPLEX customer support and leveraging his experiences with customers to help inform the R&D team about customer needs and product improvements. Dr. Klotz has extensive knowledge in linear programming, integer programming, and numerical linear algebra for finite precision computing. Using this knowledge, he was able to investigate customer support issues at the source code level and identify potential improvements in CPLEX, both in terms of performance and accuracy of computation.

Before joining IBM, Dr. Klotz was a principal technical support engineer at ILOG, Inc., and a mathematical programming specialist at CPLEX Optimization, Inc.

Dr. Klotz has presented at numerous conferences, workshops, and web seminars and published numerous papers on mathematical optimization. His interests are in all aspects of mathematical programming, with a primary interest in research that can impact mathematical programming software. He obtained a BA in Math and Economics from Oberlin College and a PhD in Operations Research from Stanford University.