Hear talks from experts on the latest topics in AI, ML, and computer vision on May 21.

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.

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:

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.

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.

Vision–language models (VLMs) have achieved remarkable performance by aligning images and text in a shared Euclidean space. However, Euclidean embeddings struggle to capture hierarchical structures inherent in multimodal data, such as conceptual taxonomies or fine-grained categories. We propose HVL (Hyperbolic Vision–Language), a geometry-grounded framework that maps images and text into a Poincaré manifold to induce hierarchy-aware representations. HVL leverages the exponential capacity of hyperbolic space to preserve semantic distances across multiple levels of abstraction, while an adaptive, entropy-driven entailment loss enforces hierarchical ordering between modalities. Extensive experiments on zero-shot classification and image–text retrieval benchmarks demonstrate that HVL consistently outperforms Euclidean baselines (e.g., CLIP) and Lorentz-based hyperbolic models (e.g., MERU), particularly in scenarios requiring fine-grained hierarchical understanding. These results highlight the importance of respecting geometric structure and establish hyperbolic embeddings as a principled foundation for hierarchical multimodal representation learning.