Dr. Vasant Honavar received his Ph.D. in 1990 from the University of Wisconsin Madison, specializing in Artificial Intelligence. Honavar is currently a Professor of Information Sciences and Technology (IST), Computer Science and Engineering, Bioinformatics and Genomics, Neuroscience and Operations Research at Pennsylvania State University. He currently holds the Edward Frymoyer Endowed Chair of IST.

He is also the founding Director of the Penn State Center for Artificial Intelligence Foundations and Scientific Applications and the Center for Big Data Analytics and Discovery Informatics, Associate Director of the Institute for Computational and Data Sciences, and Co-Director of the NIH-funded Biomedical Data Sciences Ph.D. program, and Informatics lead (research) for the NIH-funded Clinical and Translational Sciences Institute.

In 2016, Honavar was appointed as the Sudha Murty Distinguished Visiting Chair of Neurocomputing and Data Science at the Indian Institute of Science. Prior to joining Pennsylvania State University, Honavar served on the faculties of Computer Science, Bioinformatics and Computational Biology, Neuroscience, and Human-Computer Interaction at Iowa State University (1990-2013) and as a program director in the Information and Intelligent Systems Division of the National Science Foundation (NSF) (2010-2013) where he led the Big Data Science and Engineering Program and contributed to multiple other programs.

Discovery of causal relationships from observational and experimental data is a central problem with applications across multiple areas of scientific endeavor. There has been considerable progress over the past decades on algorithms for eliciting causal relationships through a set of conditional independence queries from data. Much of this work assumes that the data samples are independent and identically distributed (iid).

However, data in many real- world settings violate the iid assumption because they are generated by a system of interacting objects e.g., a collaboration network, social network, or entities connected by relations stored in relational databases. Such data violate the iid assumption. Relational causal models offer a formalism for representing and reasoning about causal relationships in relational data.

This talk will motivate and introduce relational causal models, and summarize recent progress on relational causal models including: (i) characterizing the conditional independence relations that hold in a given relational causal model, (ii) sound and complete learning of the structure of a relational causal model using an independence oracle, (iii) quantifying the strength of conditional dependence and testing conditional independence among relational variables from relational data, and (iv) robust learning of relational causal models from relational data. 

Craig Nowell ’03, is VP of Global AI GTM at Databricks, where he partners with organizations to harness their data and scale advanced Generative AI solutions. Before joining Databricks, he held senior leadership roles at AWS, including leading U.S. Data and AI Specialists and building out the Global Industries Data and AI team, where he drove initiatives across AI/ML, Analytics, and Databases. 

His career began as a systems engineer at Intel, IBM, and Cisco, before joining Greenplum and later Pivotal, where he helped scale the business from its early stages through its IPO. Craig earned his degree in Computer Engineering from NC State University.

He lives in Pacific Palisades with his wife and two sons. Outside of work, he enjoys historical nonfiction, coaching youth sports, surfing, golfing, and supporting NC State athletics and Manchester United.

René Vidal is the Penn Integrates Knowledge and Rachleff University Professor of Electrical and Systems Engineering & Radiology, the Director of the Center for Innovation in Data Engineering and Science (IDEAS), and Co-Chair of Penn AI at the University of Pennsylvania.

He is also an Amazon Scholar, an Affiliated Chief Scientist at NORCE, and a former Associate Editor in Chief of TPAMI. His current research focuses on the foundations of deep learning and trustworthy AI and its applications in computer vision and biomedical data science. His lab has made seminal contributions to motion segmentation, action recognition, subspace clustering, matrix factorization, deep learning theory, interpretable AI, and biomedical image analysis.

He is an ACM Fellow, AIMBE Fellow, IEEE Fellow, IAPR Fellow and Sloan Fellow, and has received numerous awards for his work, including the IEEE Edward J. McCluskey Technical Achievement Award, D’Alembert Faculty Award, J.K. Aggarwal Prize, ONR Young Investigator Award, NSF CAREER Award as well as best paper awards in machine learning, computer vision, signal processing, controls, and medical robotics.

Large Language Models (LLMs) and Vision Language Models (VLMs) have achieved remarkable performance across a wide range of tasks. However, their growing deployment has exposed fundamental limitations in faithfulness, safety, and transparency. In this talk, I will present a unified perspective on addressing these challenges through principled model interventions and interpretable decision-making frameworks.

I first introduce Parsimonious Concept Engineering (PaCE), an approach that improves faithfulness and alignment by selectively removing undesirable internal activations, mitigating hallucinations and biased language while preserving linguistic competence.

I then present Information Pursuit (IP), an interpretable-by-design prediction framework that replaces opaque reasoning with a sequence of informative, user-interpretable queries, yielding concise explanations alongside accurate predictions. Results across text, vision, and medical tasks illustrate how these ideas advance transparency without sacrificing performance. Together, these contributions point toward a broader direction for building AI systems that are powerful, faithful, and aligned with human values.

Sergei Kalinin is the Weston Fulton Chair Professor at the University of Tennessee, Knoxville (UTK) and has a joint appointment with the Pacific Northwest National Laboratory as the chief scientist for artificial intelligence and machine learning for physical sciences. In 2022-23, he was a principal scientist at Amazon Special Projects (Moonshot Factory). Before then, he spent 20 years at Oak Ridge National Laboratory (ORNL) where he was corporate fellow and group leader at the Center for Nanophase Materials Sciences. Kalinin received his M.S. degree from Moscow State University in 1998 and Ph.D. from the University of Pennsylvania (with Dawn Bonnell) in 2002.

His research focuses on the applications of machine learning and artificial intelligence methods in materials synthesis, discovery and optimization and in automated experiment and autonomous imaging and characterization workflows in scanning transmission electron microscopy and scanning probes for applications including physics discovery, atomic fabrication, as well as mesoscopic studies of electrochemical, ferroelectric and transport phenomena via scanning probe microscopy. When at ORNL, he led several major programs integrating ML and physical sciences and instrumentation, including the Institute for Functional Imaging of Materials (IFIM 2014-19), the first program in the Department of Energy integrating ML and physical sciences, and the microscopy effort in INTERSECT program that realized first ML-controlled scanning probe and electron microscopes.

At UTK’s Department of Materials Science and Engineering, Kalinin participated in building one of the first efforts in the country on ML-driven materials exploration. At UTK, his team has now realized fully AI-controlled SPM and STEM systems and co-orchestration workflows between multiple characterization tools for scientific discovery. He has also taught multiple courses on ML for materials science and microscopy including Bayesian optimization methods.

Kalinin has co-authored more than 650 publications, with a total citation of ~55,000 and an h-index of ~117. He is a fellow of NAI, Academia Europaea, AAAS, RSC, AAIA, MRS, APS, IoP, IEEE, Foresight Institute and AVS; a recipient of the Feynmann Prize of Foresight Institute (2022), Blavatnik Award for Physical Sciences (2018), RMS Medal for Scanning Probe Microscopy (2015), Presidential Early Career Award for Scientists and Engineers (PECASE) (2009); Burton Medal of Microscopy Society of America (2010); five R&D100 Awards (2008, 2010, 2016, 2018 and 2023); and a number of other distinctions. As part of his professional services, he organized many professional conferences and workshops at MRS, APS and AVS; for 15 years organized workshop series on PFM; and serves or served on multiple editorial boards including NPJ Comp. Mat., J. Appl. Phys, and Appl. Phys Lett.

Maria Gini is a Distinguished Professor at the University of Minnesota, where her research over the last 40 years has focused on autonomous agents’ decision-making for task allocation, robot exploration of unknown environments, swarm robotics, and teamwork. She is an ACM, AAAI, and IEEE fellow. She received the IJCAI Donald E. Walker Distinguished Service award (2024), ACM/SIGAI Autonomous Agents Research Award (2022), and the Computing Research Association A Nico Habermann Award (2019). She was recently awarded the Presidential Award for Excellence in Science, Mathematics, and Engineering Mentoring (PAESMEM).