Invited talks

Bernhard Schölkopf

From ML for science to causal digital twins

Abstract: Machine learning is being deployed across the sciences, changing the very way we perform scientific inference. This not only allows us to scrutinise datasets too large for human analysis, but also expands the domain of problems amenable to mathematical modeling toward greater complexity. However, standard machine learning has weaknesses when it comes to discovering causal relationships as opposed to potentially spurious correlations. I will discuss the associated failure modes as well as some success stories (with a particular focus on astronomy), including some that use methods of causal machine learning.

Bio: Bernhard Schölkopf studies machine learning and causal inference, with applications in fields ranging from astronomy to robotics. Trained in physics and mathematics, he earned a Ph.D. in computer science in 1997 and became a Max Planck director in 2001. A professor at ETH Zurich and a Fellow of the CIFAR program Learning in Machines and Brains, he has received the ACM-AAAI Allen Newell Award, the BBVA Foundation Frontiers of Knowledge Award, and the Royal Society Milner Award. He co-founded the MLSS series of Machine Learning Summer Schools, the ELLIS Society, and helped start the Journal of Machine Learning Research, an early milestone in open access and today the field’s flagship journal.

Cynthia Rudin

TBD

Abstract:  

Bio:  

Heng Ji

Science-Inspired AI

Abstract: Unlike machines, human scientists are inherently “multilingual,” seamlessly navigating diverse modalities—from natural language and scientific figures in literature to complex scientific data such as molecular structures and cellular profiles in knowledge bases. Moreover, their reasoning process is deeply reflective and deliberate; they “think before talk”, consistently applying critical thinking to generate new hypotheses. In this talk, I will discuss how AI algorithms can be designed by drawing inspiration from the scientific discovery process itself. For example, recent advances in block chemistry involve the manual design of drugs and materials by decomposing molecules into graph substructures—i.e., functional modules—and reassembling them into new molecules with desired functions. However, the process of discovering and manufacturing functional molecules has remained highly artisanal, slow, and expensive. Most importantly, there are many instances of known commercial drugs or materials that have well-documented functional limitations that have remained unaddressed. Inspired by scientists who frequently “code-switch”, we aim to teach computers to speak two complementary languages: one that represents molecular subgraph structures indicative of specific functions, and another that describes these functions in natural language, through a function-infused and synthesis-friendly modular chemical language model (mCLM). In experiments on 430 FDA-approved drugs, we find mCLM significantly improved 5 out of 6 chemical functions critical to determining drug potentials. More importantly, mCLM can reason on multiple functions and improve the FDA-rejected drugs (“fallen angels”) over multiple iterations to greatly improve their shortcomings. Preliminary animal testing results further underscore the promise of this approach.

Bio: Heng Ji is a Professor of Computer Science at Siebel School of Computing and Data Science, and a faculty member affiliated with Electrical and Computer Engineering Department, Coordinated Science Laboratory, and Carl R. Woese Institute for Genomic Biology of University of Illinois Urbana-Champaign. She is an Amazon Scholar. She is the Founding Director of Amazon-Illinois Center on AI for Interactive Conversational Experiences (AICE), and the Founding Director of CapitalOne-Illinois Center on AI Safety and Knowledge Systems (ASKS). She received Ph.D. in Computer Science from New York University. Her research interests focus on Natural Language Processing, especially on Multimedia Multilingual Information Extraction, Knowledge-enhanced Large Language Models and Vision-Language Models, AI for Science, and Science-inspired AI. The awards she received include Outstanding Paper Award at ACL2024, two Outstanding Paper Awards at NAACL2024, "Young Scientist" by the World Laureates Association in 2023 and 2024, "Young Scientist" and a member of the Global Future Council on the Future of Computing by the World Economic Forum in 2016 and 2017, "Women Leaders of Conversational AI" (Class of 2023) by Project Voice, "AI's 10 to Watch" Award by IEEE Intelligent Systems in 2013, NSF CAREER award in 2009, PACLIC2012 Best paper runner-up, "Best of ICDM2013" paper award, "Best of SDM2013" paper award, ACL2018 Best Demo paper nomination, ACL2020 Best Demo Paper Award, NAACL2021 Best Demo Paper Award, Google Research Award in 2009 and 2014, IBM Watson Faculty Award in 2012 and 2014 and Bosch Research Award in 2014-2018. She has coordinated the NIST TAC Knowledge Base Population task 2010-2020. She served as the associate editor for IEEE/ACM Transaction on Audio, Speech, and Language Processing, and the Program Committee Co-Chair of many conferences including NAACL-HLT2018 and AACL-IJCNLP2022. She was elected as the North American Chapter of the Association for Computational Linguistics (NAACL) secretary 2020-2023.

Luc De Raedt

Neurosymbolic AI : combining Data and Knowledge

Abstract: The focus in AI today is very much on using just data for learning, but one should not learn what one already knows. The challenge therefore is to use the avaible knowledge to guide and constrain the learning, and to reason with the resulting models in a trustworthy manner. This requires the integration of symbolic AI with machine learning, which is the focus of neurosymbolic AI, often touted as the next wave in AI.

I will argue that Neurosymbolic AI = Logic + Probability + Neural Networks. This will allow me to specify a high-level recipe for incorporating background knowledge into any neural network approach. The recipe starts from neural networks, interprets them at the symbol level by viewing them as “neural predicates” (or relations) and adding logical knowledge layers on top of them. The glue is provided by a probabilistic interpretation of the logic. Probability is interpreted broadly, it provides the quantitative differentiable component necessary to connect the symbolic and subsymbolic levels.

Finally, I will show how the recipe and its ingredients can be used to develop the foundations of neurosymbolic AI.

Bio: Prof. Dr. Luc De Raedt is Director of Leuven.AI, the KU Leuven Institute for AI, full professor of Computer Science at KU Leuven, and guest professor at Örebro University (Sweden) at the Center for Applied Autonomous Sensor Systems in the Wallenberg AI, Autonomous Systems and Software Program. He is working on the integration of machine learning and machine reasoning techniques, also known under the term neurosymbolic AI. He has chaired the main European and International Machine Learning and Artificial Intelligence conferences (IJCAI, ECAI, ICML and ECMLPKDD) and is a fellow of EurAI, AAAI and ELLIS, and member of Royal Flemish Academy of Belgium. He received ERC Advanced Grants in 2015 and 2023.

Aditya Grover

TBD

Abstract:  

Bio:  

Yoshua Bengio

TBD

Abstract:  

Bio:  

Rina Dechter

Graphical Models Meet Heuristic Search: A Personal Journey into Automated Reasoning

Abstract: A natural intuition in AI is that smart agents should tackle hard problems by building on solutions to easier ones. This idea has inspired what's known as the tractable islands paradigm: focus on parts of a problem that are computationally manageable and use them as stepping stones toward solving the whole.

In this talk, I’ll focus on probabilistic reasoning with graphical models and give an overview of algorithms that follow this approach. I’ll introduce the Bucket Elimination, Mini-Bucket Elimination, and AND/OR search frameworks, and explain how they navigate the tradeoff between time and memory. I’ll then show how heuristics grounded in tractable islands can guide both heuristic search and Monte Carlo sampling, leading to anytime algorithms —solvers that provide increasingly accurate approximations over time, with guaranteed bounds, and converge to exact solutions if given enough time.

Bio: Rina Dechter is a Distinguished Professor of Computer Science at the University of California, Irvine. She holds a Ph.D. in computer science from UCLA (1985), an M.S. in applied mathematics from the Weizmann Institute (1975), and a B.S. in mathematics and statistics from the Hebrew University in Jerusalem (1973).

Dechter’s research centers on computational aspects of automated reasoning and knowledge representation including search, constraint processing and probabilistic reasoning. She is the author of Constraint Processing published by Morgan Kaufmann (2003), and of Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms published by Morgan and Claypool Publishers (2013, second ed. 2019). She co-edited (with Hector Geffner and Joe Halpern) the ACM book Probabilistic and Causal Inference: The Works of Judea Pearl (2022). She has authored and co-authored close to 200 research papers. Dechter was awarded the Presidential Young investigator award in 1991, is a Fellow of AAAI (1994) and of ACM (2013) and was a Radcliffe Fellow during 2005–2006. She received the Association of Constraint Programming (ACP) Research Excellence Award (2007). She is a Fellow of the American Association of the Advancement of Science (AAAS, 2022), has been elected as a member of the American Academy of Arts and Sciences in 2025, and is the winner of the IJCAI Research Excellence Award in 2025. She served as a co‐Editor‐in‐Chief of Artificial Intelligence from 2011 to 2018. She also served on the editorial boards of several AI journals (AIJ, JAIR, JMLR) and served as a program chair or co-chair of several AI conferences (CP-2000, AAAI-2002, UAI-2006). She was the conference chair of IJCAI‐2022.

Toby Walsh

TBD

Abstract:  

Bio:  

Harry Shum

Exploring the Low Altitude Airspace: From Natural Resource to Economic Engine

Abstract: Harry Shum is the Council Chairman of Hong Kong University of Science and Technology. He was previously Executive Vice President of Microsoft Corporation, responsible for AI and Research. He received his PhD in Robotics from School of Computer Science, Carnegie Mellon University. He is a Fellow of IEEE and ACM, and an international member of National Academy of Engineering and Royal Academy of Engineering.

Bio: The low altitude airspace, generally defined as the region below 1000 meters above ground level, remains a frontier ripe for exploration and economic exploitation. With advancing technology, this domain is poised to become a crucible for diverse economic activities, transmuting a mere natural resource into a potent economic asset. This presentation offers a comprehensive overview of the burgeoning low altitude economy (LAE), bolstered by first-hand insights into the infrastructure developments enabling LAE's realization. Specifically, I will delve into the research and development towards constructing a smart integrated infrastructure for the LAE. At the core of this infrastructure lies the Smart Integrated Low Altitude System (SILAS), an operating system designed to address the multifaceted needs of operations, regulations, and end-users.



Similar to conventional operating systems such as Windows, SILAS orchestrates resource management, activity coordination, and user administration within the low altitude airspace. This comprehensive management spans from the registration and operation of drones to the establishment of landing posts and the seamless orchestration of communication channels, ensuring all airborne activities are scheduled efficiently in both space and time. SILAS is engineered to perform real-time spatiotemporal flow computing for numerous flying objects, a critical capability to ensure safety within the low altitude airspace. This advanced system must adeptly manage the intricate and high-frequency flying activities, from observation to proactive guidance, overcoming numerous technological hurdles. Designed to handle one million daily flights in a major city, with a peak online presence of one hundred thousand, SILAS sets a new benchmark for airspace management. In comparison, contemporary metropolitan airports currently manage only a few thousand commercial flights daily. The volume and complexity of future flights in the low altitude airspace surpass the capabilities of traditional airspace management systems employed in commercial airports, underscoring the necessity of SILAS.

Yew Soon Ong

Physically Grounded AI for Scientific Discovery: From Prediction to Generative Design

Abstract: This talk presents the role of AI in science and engineering, from learning for prediction to optimization for precision, and onward to generative models that potentially reshape solution spaces. It highlights the possible shift from purely data-driven abstraction to physically grounded intelligence, where AI systems are increasingly aligned with the laws of nature. Advances in generalizable physics-informed neural networks, guided diffusion model generation, and prompt evolution are empowering AI to simulate, predict, plan, and design within real-world constraints. As language models become engines of scientific exploration, navigating complex design spaces and abstract knowledge landscapes, they converge with physics-based signals and evolutionary principles such as multifactorial optimization to achieve high-fidelity modeling and creative physical design. The talk ends with a vision of AI not merely as a tool but as a co-explorer, fusing data, theory, and imagination to uncover latent structures, accelerate discovery, and deliver real-world impact.

Bio: Fellow of IEEE and the National Academy of Engineering, Singapore, Professor Yew-Soon Ong received his Ph.D. in Artificial Intelligence for Complex Design from the University of Southampton, UK., in 2003. He is currently a President’s Chair Professor in Computer Science at Nanyang Technological University (NTU), Singapore, and the Chief Artificial Intelligence Scientist at the Agency for Science, Technology and Research (A*STAR), Singapore. Professor Ong previously served as Chair of the School of Computer Science and Engineering at NTU. His research interests span artificial intelligence, statistical machine learning, and optimization. He has held key leadership roles in international AI conferences, including serving as General Co-Chair of the 2024 IEEE Conference on Artificial Intelligence, and has delivered invited keynote speeches and participated in high-level panels at AI events He is the founding Editor-in-Chief of the IEEE Transactions on Emerging Topics in Computational Intelligence, and serves as Senior Associate Editor or Associate Editor for IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Evolutionary Computation, and IEEE Transactions on Artificial Intelligence. Additionally, he contributes as an Area Chair for several top-tier AI conferences. Professor Ong has received five IEEE Outstanding Paper Awards and was named a Thomson Reuters Highly Cited Researcher and one of the World’s Most Influential Scientific Minds in 2016. He chairs the 2024 and 2025 IEEE Computational Intelligence Society Fellow Evaluation Committee.

Shing-Tung Yau

TBD

Abstract:  

Bio:  

Wen Gao

TBD

Abstract:  

Bio: