May 21 - Women in AI Meetup

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,