Biography

Kyungwhoon (Paul) Cheun, is Corporate President & Chief Technology Officer (CTO) of Device eXperience (DX) Division and Head of Samsung Research at Samsung Electronics. As CTO, he is driving the company’s technological innovations and strategy to align them with Samsung’s corporate goals. He also heads up Samsung Research, the advanced Research & Development hub of the DX Division at Samsung Electronics.

Samsung Research leads the development of future technologies and identifies new growth opportunities for the company through efforts spanning 15 R&D and AI centers located globally. Samsung Research's main focus is Artificial Intelligence, Next Generation Communications, Robotics, Security & Privacy, etc. He leads Samsung Research in constantly driving innovation across AI technologies and developing new research-powered products to provide better and more meaningful experiences to our valued customers.

Biography

Joseph Gonzalez is a Professor of Computer Science at UC Berkeley, co-director of the RISELab and Sky Computing Lab, and a core member of the Berkeley AI Research (BAIR) group. His research focuses on large language models, agentic systems, and scalable machine learning infrastructure. He is known for his contributions to projects such as Chatbot Arena, Gorilla, MemGPT, and vLLM, which have significantly advanced the field of open LLM research. Joseph is also the co-founder and Head of AI at RunLLM.com. His broader work spans data systems, robotics, computer vision, graph analytics, and autonomous systems. Prior to Berkeley, he co-founded Turi Inc. (formerly GraphLab) based on his Ph.D. work. His research has been recognized with the Okawa Research Grant, NSF CAREER Award, VLDB Test-of-Time Award, and NSF Expeditions in Computing Award.

Advancing Agentic Intelligence

Large language models (LLMs) are evolving from passive completion engines into persistent agents capable of executing complex tasks over extended interactions. In this talk, I will present recent efforts from my lab to enhance the agentic capabilities of LLM-based systems. I will introduce sleep-time compute, a paradigm that allows agents to reason, learn, and plan between interactions—unlocking new modes of intelligence beyond reactive prompt completion. Next, I will share work on enabling agents to mirror human behavior and reason about human preferences, paving the way toward more socially aligned and intuitive AI. Finally, I will preview early efforts on advisor agents—meta-agents designed to guide and critique other agents as they work—laying the foundation for self-improving AI systems. Together, these capabilities move us closer to a future where LLMs operate as adaptive, collaborative, and trustworthy digital agents.

Biography

Pascal Hitzler is University Distinguished Professor and endowed Lloyd T. Smith Creativity in Engineering Chair at the Department of Computer Science at Kansas State University, Director of the Center for Artificial Intelligence and Data Science (CAIDS), and Director (Research) of the Institute for Digital Agriculture and Advanced Analytics (ID3A). He obtained his PhD in Mathematics from the University of Ireland, University College Cork, in 2001. His research record lists over 400 publications in diverse areas such as neurosymbolic artificial intelligence, semantic web, knowledge graphs, knowledge representation and reasoning, denotational semantics, and set-theoretic topology. He was founding Editor-in-chief of the Semantic Web journal, the leading journal in the field, and is founding Editor-in-chief of the new Neurosymbolic Artificial Intelligence journal, and of the IOS Press book series Studies on the Semantic Web. He is co-author of the W3C Recommendation OWL 2 Primer, and of the book Foundations of Semantic Web Technologies by CRC Press, 2010, which was named as one out of seven Outstanding Academic Titles 2010 in Information and Computer Science by the American Library Association's Choice Magazine, and has translations into German and Chinese. He is a founding steering committee member of the Neural-Symbolic Learning and Reasoning Association and the Association for Ontology Design and Patterns, and he frequently acts as conference chair in various functions. For more information about him, see http://www.pascal-hitzler.de.

Accelerating Knowledge Graph Based Data Management with Large Language Models

Semantic Web Technologies, including Knowledge Graphs and Ontologies, are not only powerful tools for data integration and management, but are also increasingly used as fact databases to improve Large Language Models. However, producing high-quality ontologies and knowledge graphs is an expensive process that has proven to be hard to automate. But the recent rise of Large Language Models with increasing capabilities such as solid coding assistance carries the promise that it may help to significantly accelerate knowledge graph and ontology engineering through at least semi-automatic processes. We will discuss the current opportunities arising from this development, and elucidate the state of the art and some promising directions.

Biography

Subbarao Kambhampati is a professor of computer science at Arizona State University. Kambhampati studies fundamental problems in planning and decision making, motivated in particular by the challenges of human-aware AI systems. He is a fellow of Association for the Advancement of Artificial Intelligence, American Association for the Advancement of Science,  and Association for Computing machinery, and a recent recipient of the AAAI Patrick H. Winston Outstanding Educator award. He served as the president of the Association for the Advancement of Artificial Intelligence, a trustee of the International Joint Conference on Artificial Intelligence,  the chair of AAAS Section T (Information, Communication and Computation), and a founding board member of Partnership on AI. Kambhampati’s research as well as his views on the progress and societal impacts of AI have been featured in multiple national and international media outlets. He can be followed on Twitter @rao2z.

(How) Do LLMs Reason?

Large Language Models, auto-regressively trained on the digital footprints of humanity, have shown impressive abilities in generating coherent text completions for a vast variety of prompts. While they excelled from the beginning in producing completions in appropriate style, factuality and reasoning/planning abilities remained their Achilles heel (premature claims notwithstanding). More recently a breed of approaches dubbed “reasoning models” (LRMs). These approaches leverage  two broad and largely independent ideas: (i) test-time inference – which involves getting the base LLMs do more work than simply providing the most likely completion, including using them in generate and test approaches such as LLM-Modulo (that pair LLM generation with a bank of verifiers) and (ii) post-training methods–which  go beyond simple auto-regressive training on web corpora by collecting, filtering and training on derivational traces (that are often anthropomorphically referred to as “chains of thought” and “reasoning traces”), and modifying the base LLM with it using supervised finetuning or reinforcement learning methods. Their success on benchmarks notwithstanding, there are significant questions and misunderstandings about these methods–including whether they can provide correctness guarantees, whether they do adaptive computation, whether the intermediate tokens they generate can be viewed as reasoning traces in any meaningful sense, and whether they are costly Rube Goldberg reasoning machines that incrementally compile verifier signal into the generator or truly the start of a golden era of general purpose System 1+2 AI systems.  Drawing from our  ongoing work in planning, I will  present a broad perspective on these approaches and their promise and limitations.

Biography

Ben Zhao is Neubauer Professor of Computer Science at University of Chicago. He completed his Ph.D. at U.C. Berkeley (2004), and B.S. from Yale (1997). He is a Fellow of the ACM, TED/AI speaker, and a recipient of the NSF CAREER award, MIT Technology Review's TR-35 Award (Young Innovators Under 35), TIME100/AI (100 Influential people in AI), USENIX Internet Defense Prize, Concept Art Association's Community Impact Award, ComputerWorld Magazine's Top 40 Technology Innovators award, IEEE ITC Early Career Award, and Faculty awards from Google, Amazon, and Facebook. His work has been covered by many media outlets including New York Times, CNN, NBC, ABC, BBC, MIT Tech Review, Wall Street Journal, Forbes, and New Scientist. His current research focuses on mitigating the harms of misused AI. He has published over 190 articles in areas of security and privacy, machine learning, networking, and HCI. He served as TPC (co-)chair for the World Wide Web conference (WWW 2016) and ACM Internet Measurement Conference (IMC 2018), and on the Steering Committee of IEEE Conference on Secure and Trustworthy Machine Learning (SaTML).

Mitigating Harms of Generative AI using Adversarial Tools

Generative AI models promise tremendous benefits in the name of novel capabilities and innovation. Yet it also brings with it numerous harms often ignored by developers and proponents. Beyond issues of training on creative work product without consent, there are numerous issues of misuse and abuse, many of which produce concrete and tangible harms on some of the most vulnerable and creative segments of our population.  In this talk, I will discuss some of the technical solutions we have developed in our lab to combat and mitigate these harms, most notably Glaze and Nightshade, adversarial ML tools that disrupt model training and fine tuning. Since their releases in March 2023 and January 2024, Glaze and Nightshade have been downloaded more than 10 million times by artists world wide. Beyond the technical details of adversarial disruption of diffusion models, I will also discuss the evolution of our lab from an initial focus on AI security, to a mission focused on protecting and advocating for human creatives across industries, and our progress in recent years in the media, in legislature bodies, and in the courtroom. 

Biography

Stefano Ermon is an Associate Professor of Computer Science in the CS Department at Stanford University where he is affiliated with the Artificial Intelligence Laboratory. His research is centered on techniques for probabilistic modeling of data and generative models. He has won several awards, including multiple Best Paper Awards, a NSF Career Award, ONR and AFOSR Young Investigator Awards, Microsoft Research Fellowship, Sloan Fellowship, and the IJCAI Computers and Thought Award. Stefano earned his Ph.D. in Computer Science at Cornell University in 2015.

Diffusion Language Models

Diffusion models are at the core of many state-of-the-art generative AI systems for content such as images, videos, and audio. Most generative models for language, however, are instead based on autoregressive modeling (i.e. next token prediction). In this talk, we review recent advances in diffusion-based language modeling frameworks that achieve highly competitive performance with autoregressive models. These recent results challenge the longstanding dominance of autoregressive modeling and could pave the way for an alternative class of more efficient language models built from radically different principles.

Biography

Daehyun Kim is an executive vice president at Samsung research, leading the Global AI Center. Before joining Samsung, he has been at Google and Intel working on processor architecture and workload optimization for 15 years since he received the Ph.D. from Cornell University.

His research interests include on-device AI, machine learning accelerators, mobile SoC architecture, high performance computing, intelligent memory systems, and future applications. Main approach is workload-driven architecture design. He has done path-finding research for Samsung’s on-device AI consumer devices, Google’s Android & ChromeOS products, and Intel’s Xeon Phi coprocessors based on workload analysis and optimization of AI services, mobile applications, HPC scientific computing, medical imaging, and physical simulation.