ABSTRACT: Modern science increasingly relies on complex workflows that span AI, simulation, data analysis, cloud, edge, and HPC systems. Yet creating and managing these workflows remains difficult and time-consuming. This talk introduces the Pegasus Workflow Management System, which automates large-scale scientific workflows…

ABSTRACT: It has long been a research topic in the social sciences to examine whether technology bridges or reinforces existing social gaps. With the emergence of generative AI based on LLMs, there is growing optimism that people with lower skill levels may benefit the most from these technologies. However, the ways in which…

ABSTRACT: Applications of generative modeling and deep learning in physics-based systems have traditionally focused on building emulators - computationally inexpensive approximations of input-to-output maps. However, the remarkable flexibility of data-driven architectures opens opportunities to broaden their scope to include…

ABSTRACT: Large Language Models are increasingly deployed in applications that require reasoning over long and complex context, such as extended documents, multi-turn interactions, retrieved evidence, and multimodal inputs. While these capabilities make LLMs more powerful, they also introduce new and underexplored safety risks.…

ABSTRACT: This talk presents two recent efforts where machine learning addresses fundamental challenges in wearable robot modeling and control. The first contribution addresses the robot side: a hybrid Neural ODE framework for modeling artificial muscle dynamics that embeds physical structure into a learned model, enabling…

ABSTRACT: I will introduce In-Context Operator Networks (ICON), a framework in which a single neural network learns solution operators for differential equations directly from a few prompted input-output examples at inference time, without any weight updates. ICON acts as a few-shot learner across forward and inverse problems for…

ABSTRACT: Open-world mobility describes dynamic real-world environments in which vehicles, robots, infrastructure, and people interact under partial observability, distribution shift, long-tail events, and changing operational conditions. These settings require AI systems that can jointly perform perception, semantic reasoning,…

ABSTRACT: Graph neural networks (GNNs) have emerged as a powerful framework for learning from graph-structured data, yet their theoretical understanding—particularly regarding the behavior of different architectural choices across various graph-based tasks—remains limited. In parallel, random geometric graphs (RGGs) provide a…

ABSTRACT: Deep learning models, despite their power, lag behind the biological brain in interpretability, energy efficiency, and physical plausibility. This presentation explores the mathematical design principles of biological neural circuits -- building upon the classic concept that neural activity is fundamentally driven by…

ABSTRACT: As the Deputy Director for Innovation and Emerging Technologies at the California Governor’s Office of Business and Economic Development (GO-Biz), Tre Bradley is the primary advisor to California state leaders on technology trends and opportunities, focusing on commercialization, technology transfer, and talent pipeline…