Panels

Panel A: The Future of Probabilistic Modeling in Data Mining and AI

Date and Time: November 13, 2025, 10:30-12:00 Location: Presidential Ballroom

Moderator: Dr. Farhad Pourkamali, University of Colorado Denver

Panelists:

  • Dr. Patrick Shafto, Program Manager, DARPA; Professor, Rutgers
  • Dr. Vladimir Pavlovic, Program Director, NSF; Professor, Rutgers
  • Dr. Carlotta Domeniconi, Professor, George Mason University
  • Dr. Ruqi Zhang, Assistant Professor, Purdue University
  • Dr. Amir Hossein Raffiee, Lead Applied Scientist at Salesforce

Abstract:

As data mining continues to evolve in high-stakes domains such as scientific discovery and autonomous systems, there is a growing need for models that go beyond deterministic outputs. Probabilistic modeling offers a powerful framework for representing and reasoning under uncertainty, particularly in settings characterized by non-stationary, multi-modal, or high-dimensional data. Whether applied to images, text, sensor data, or dynamic interactions, probabilistic methods such as Bayesian inference enable more robust, calibrated, and trustworthy AI systems.

This panel will explore the expanding role of probabilistic modeling across diverse areas of artificial intelligence, including reinforcement learning, agentic AI, continual learning, generative modeling, and data-efficient exploration. We will discuss methodological advances in uncertainty quantification, model calibration, and probabilistic representation learning, as well as their integration with scalable architectures such as transformers, graph networks, and diffusion models. The conversation will also address how these methods contribute to safety, interpretability, and decision-making in real-world deployments.

Attendees will hear from leading experts on how probabilistic thinking is shaping the next generation of AI. The session aims to chart a roadmap for research, development, and deployment of AI systems that are not only intelligent but also reliable and transparent.

Panel B: AI-Driven Data Mining — Reshaping the Intelligent Future

Date and Time: November 14, 2025, 11:00-12:30 Location: Presidential Ballroom

Moderators:

Panelists:

  • Dr. Phillip Regalia, Program Director, National Science Foundation (NSF)
  • Dr. Robinson Pino, Program Manager, U.S. Department of Energy (U.S. DOE)
  • Dr. Amarda Shehu, Professor of Computer Science, Associate Dean for Research, and the Vice President and Chief AI Officer, George Mason University
  • Dr. Huan Liu, Regents Professor, School of Computing & Augmented Intelligence, Arizona State University
  • Dr. Honggang Wang, Professor and Founding Chair, Graduate Computer Science and Engineering, Yeshiva University

Topic:

AI-driven data mining is the catalyst powering the next wave of transformative intelligence across industries and research. This panel brings together leading experts to explore the broad landscape of AI-driven data mining, highlighting how cutting-edge advancements, particularly agentic AI and generative AI, are accelerating this transformation.

Agentic AI systems push beyond insight generation, autonomously interpreting and acting on complex data in real time, while generative AI unlocks unprecedented creative power in pattern discovery, simulation, and problem-solving. Together, they are accelerating innovation cycles, powering resilient infrastructure, and enabling next-level scientific breakthroughs. These technologies are rewriting the rules of discovery, decision-making, and system design across critical sectors. Beyond technical advances, the discussion will confront urgent challenges in ethics, governance, and the responsible deployment of AI that acts autonomously.

Attendees will gain exclusive insights into how AI-driven data mining is not just the future, but the present force reshaping research, infrastructure, and policy, ushering in a new era of intelligent, autonomous systems.

Panelists’ Short Bios:

Dr. Phillip Regalia, Program Director, National Science Foundation (NSF)

Dr. Phil Regalia received his PhD in Electrical and Computer Engineering in 1988 from UC Santa Barbara. He was the founding Editor-in-Chief of the EURASIP J. Wireless Communications and Networking, and has served as Editor-in-Chief of the EURASIP J. Advances in Signal Processing, as well as associate editor for the IEEE Trans. Signal Processing, the IEEE Trans. Circuits and Systems, and the Int. J. Adaptive Control and Signal Processing. He was elected Fellow of the IEEE in 2000. He joined the National Science Foundation in 2012, having previously held academic appointments in both the US and France.

Dr. Robinson Pino, Program Manager, U.S. Department of Energy (U.S. DOE)

Dr. Robinson Pino is a Program Manager for the Advanced Scientific Computing Research (ASCR) program office in the U.S. Department of Energy’s (DOE) Office of Science and prior Senior Advisor to the CHIPS Program Office in the U.S. Department of Commerce (DOC), National Institute of Standards and Technology (NIST). In his portfolio, Dr. Pino focuses on revolutionary basic research and development efforts for high performance computing (HPC), artificial intelligence, edge computing, neuromorphic computing, photonics, microelectronics, advanced wireless, and applications that will enable our continued global leadership. Dr. Pino has expertise in technology research, development, prototyping, program management, government, industry, and academia.

Dr. Amarda Shehu, Professor of Computer Science, Associate Dean for Research, and the Vice President and Chief AI Officer at George Mason University

Dr. Amarda Shehu is Professor of Computer Science, Associate Dean for Research, and the Vice President and Chief AI Officer at George Mason University. She leads the university’s AI strategy across research, education, workforce development, and partnerships. She has led the Institute for Digital Innovation and has launched multiple transdisciplinary centers. She is also the architect of Mason’s new M.S. in Artificial Intelligence degree program and chairs the university’s AI-In-Government Council, advancing AI collaboration across academia, industry, and public agencies. An active AI researcher, Dr. Shehu has published over 200 papers with students and collaborators. She is a fellow of several societies and has received several recognitions and awards for her research, education, mentorship, and service. Her research lab has sustained a long thread of inquiry at the intersection of AI and molecular biology, pioneering probabilistic, machine learning, and generative methods to advance understanding and discovery in protein science, genomics, and molecular design.

Dr. Huan Liu, Regents Professor, School of Computing & Augmented Intelligence, Arizona State University

Dr. Huan Liu is a Regents Professor and Ira A. Fulton Professor of Computer Science and Engineering at Arizona State University. At Arizona State University, he was recognized for excellence in teaching and research in Computer Science and Engineering and received the 2014 President’s Award for Innovation. He is the recipient of the ACM SIGKDD 2022 Innovation Award. He is a co-author of a text, Social Media Mining: An Introduction, Cambridge University Press. He is a founding organizer of the International Conference Series on Social Computing, Behavioral-Cultural Modeling, and Prediction, Editor in Chief of ACM TIST, and Field Chief Editor of Frontiers in Big Data and its Specialty Chief Editor of Data Mining and Management. He is a Fellow of ACM, AAAI, AAAS, and IEEE.

Dr. Honggang Wang, Professor and Founding Chair, Graduate Computer Science and Engineering, Yeshiva University

Dr. Honggang Wang is the founding chair and a professor in the Department of Graduate Computer Science and Engineering at Yeshiva University's Katz School of Science and Health in New York City. He is an affiliate faculty member at the Albert Einstein College of Medicine. He is an alumnus of the NAE Frontiers of Engineering program. Dr. Wang has produced high-quality publications in prestigious journals and conferences across several fields, including Artificial Intelligence (AI), the Internet of Things, Multimedia Communications and Processing, Mobile Networks, Cybersecurity, and Smart and Connected Health. Throughout his career, he has received multiple IEEE best paper awards. He is an IEEE Distinguished Lecturer and a Fellow of IEEE. He served as the Editor-in-Chief of the IEEE Internet of Things Journal from 2020 to 2022 and was the past Chair of the IEEE Multimedia Communications Technical Committee from 2018 to 2020, as well as the Chair of the IEEE eHealth Technical Committee from 2020 to 2021. He has been elected as the Editor-in-Chief of IEEE Transactions on Multimedia, starting in January 2026.