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…

  ABSTRACT:  Recent years have seen tremendous iterations on autonomous driving technologies, pushing the deployment of self-driving cars closer to its realization. As the experimental deployments scale, more challenging and less frequent corner cases surface to stress-test the reliability of the autonomous driving…

ABSTRACT:  Cities, humanity’s greatest inventions, offer vast opportunities for innovation in science and technology. The increasing availability of big data paints a promising future for our cities. Over the past decade, my work has focused on applying AI to address real-world city challenges. Recent collaborations with…