Details
Hear talks from experts on the latest topics in AI, ML, and computer vision on May 21.
Date, Time and Location
May 21, 2026
9 - 11 AM pacific
Online. Register for the Zoom!
Beyond Models: LLM-Guided Reinforcement Learning for Real-World Wireless Systems
Reinforcement learning agents often perform well in simulation but break down when deployed in real, non-stationary, constraint-driven environments such as wireless systems. This work explores using large language models not as annotators or reward hacks, but as a reasoning layer that guides RL decision-making with domain logic, scenario interpretation, and adaptive constraints.
We present an architecture where the LLM provides structured, high-level advisory signals while the RL policy remains the final action authority to avoid hallucination-driven failures. Early experiments show that this hybrid setup improves robustness under distribution shifts and complex constraint scenarios where standard RL collapses. The goal is not to replace RL with LLMs, but to combine learning and reasoning into a more deployable control-intelligence framework.
About the Speaker
Fatemeh Lotfi is a Ph.D. researcher focused on integrating large language models and reinforcement learning for adaptive wireless control systems. Her work targets the limitations of classical RL under real-world uncertainty by introducing reasoning-driven guidance mechanisms using LLMs. She has contributed to multiple AI-for-infrastructure projects, including advanced O-RAN automation.
Responsible and Ethical AI in Healthcare: Building Trustworthy and Inclusive Intelligent Systems
In this session, I will discuss how Responsible AI principles: including fairness, transparency, accountability, and reliability can be practically embedded into healthcare AI systems. Key discussion points will include:
- Addressing bias and equity challenges in healthcare datasets and model training.
- Building explainable and interpretable AI to strengthen clinician trust and adoption.
- Ensuring ethical deployment of generative AI models within regulated healthcare environments.
- Establishing governance frameworks for data privacy, model monitoring, and regulatory compliance.
About the Speaker
Jahnavi Kachhia is the Global Product Owner, AI & ML at Abbott, leading large-scale AI initiatives for the FreeStyle Libre platform to enhance clinical decision-making and patient outcomes. Previously at Meta’s Reality Labs, she advanced AR/VR innovation and LLM-based intelligent systems. An active contributor to the AI research community, she serves on the IJCAI 2025 Program Committee and reviews for AAAI, IJCNN, and IEEE conferences.
AI Applications in Drug Repurposing
Drug repurposing is increasingly important because it offers a faster, lower-cost path to therapeutic discovery compared to de novo drug development, especially in oncology where many cancers still lack effective targeted options. In under-studied cancers such as endometrial cancer, the challenge is often a lack of large, high-quality clinical or response datasets, making purely data-dependent approaches difficult to scale reliably. This motivates combining data-independent strategies (e.g., pathway- and mechanism-driven modeling) with data-dependent learning when interaction evidence is available. A practical and scalable direction is drug–target interaction (DTI) prediction, where AI models can leverage molecular and protein representations to prioritize mechanistically plausible drug candidates for repurposing.
About the Speaker
Madhurima Mondal's academic journey has been shaped by strong foundations in mathematical and scientific problem-solving, including multiple national-level achievements such as Regional Mathematics Olympiad (RMO), NTSE, and the KVPY fellowship. She completed my B.Tech and M.Tech in Electronics & Electrical Communication Engineering from IIT Kharagpur, and I am currently a PhD candidate in Electrical & Computer Engineering at Texas A&M University,
Mapping to Belonging: How Ethically Governed AI Can Make Real Places More Accessible, Legible, and Human
Can AI help people belong in the places where they live, work, travel, and get together?
This talk explores that question through real-world work at the intersection of accessibility, computer vision mapping, civic data, and ethically governed AI. I will show how AI can support the collection and interpretation of pedestrian accessibility data, reduce the burden of documenting barriers, and help transform lived experience into structured information that can be used across routing tools, planning systems, and public decision-making. I will also argue that public-interest AI only works when it is governed well. In accessibility work, the risks are clear: over-averaging, hidden bias, false completeness, and systems that optimize for efficiency while overlooking the people most affected by missing or poor-quality data. Ethically governed AI must therefore be designed to preserve local context, support transparency, include community participation, and make room for experiences that conventional systems often ignore.
About the Speaker
Anat Caspi is Director of the Taskar Center for Accessible Technology at the University of Washington, where she leads research and public-interest technology efforts focused on accessibility, mobility, and inclusive transportation data.
