ABSTRACT: Modern intelligent embodied agents, such as service robots and autonomous vehicles, interact frequently with humans in dynamic, uncertain environments. They may also collaborate as a team through effective communication and coordination to enhance task success, safety, and efficiency. These bring a few significant…

ABSTRACT: Integrating artificial intelligence (AI) and machine learning (ML) into computational modeling frameworks is revolutionizing pharmacokinetic evaluation, offering transformative insights for both small-molecule drugs and the complex field of nanomedicine. This presentation highlights the pivotal role of AI-assisted…

ABSTRACT: Sensing is a universal task in science and engineering. Downstream tasks from sensing include learning dynamical models, inferring full state estimates of a system (system identification), control decisions, and forecasting. These tasks are exceptionally challenging to achieve with limited sensors, noisy…

ABSTRACT:  Large language models (LLMs) like Claude Sonnet 3.7, GPT-4, Llama 3.1, and Gemini 2.5 have shown promise in AI-assisted coding, transforming natural language descriptions into code. While LLMs perform well on simple benchmarks like LeetCode, MBPP, and HumanEval, their success rate remains low in real-world…

ABSTRACT:  Anomaly Detection (AD) focuses on identifying anomalies by learning exclusively from normal samples, as collecting anomalous data is often costly and limited due to its long-tailed distribution. Given its practical significance, AD has been widely deployed in applications such as industrial defect inspection and…

ABSTRACT:  Embodied AI systems are cyber-physical systems that employ advanced machine learning techniques to perceive, analyze, and interact with their environment. As these systems become more prevalent in fields such as self-driving cars and robotics, ensuring their safety and robustness has become a critical and yet…

ABSTRACT:  Sequential decision-making in dynamic, uncertain, and adversarial environments is a fundamental challenge in many applications such as online resource allocation, planning, and scheduling. While modern AI and ML algorithms offer transformative potential, their lack of reliability guarantees and possible…

ABSTRACT:  The Dept of Energy (DOE) complex comprises of many science facilities that could be classified as data producing (eg. the Advanced Photon Source at Argonne National Laboratory) and consuming (eg. the Leadership Class Computing Facilities at the Oak Ridge National Laboratory) facilities. Modern science campaigns…

ABSTRACT:  This presentation focuses on extending the success of large language models (LLMs) to sequential decision making. Existing efforts either (i) re-train or finetune LLMs for decision making, or (ii) design prompts for pretrained LLMs. The former approach suffers from the computational burden of gradient updates, and…

ABSTRACT:  Rapid and accurate triage of acute ischemic stroke (AIS) is essential for early revascularization and improved patient outcomes. Response to acute reperfusion therapies varies significantly based on patient-specific cerebrovascular anatomy that governs cerebral blood flow. We present an end-to-end machine learning…