When: Friday, November 7, 3:00 PM Where: Tyler 055 Abstract: This presentation introduces Boston University’s Retrieval-Augmented Generation (RAG) Framework, an open-source, low-code platform developed by the Software & Application Innovation Lab (SAIL) to make large language model (LLM)–powered applications accessible, reproducible, and secure across academic environments. The framework can integrate seamlessly with university research computing […]

When: Friday, October 24, 3:00 PM Where: Tyler 055 Abstract: For several decades, tangible and embodied interaction (TEI)—human-computer interaction that engages our bodies and physical environment—has been the topic of intense research. My recent book Weaving Fire into Form: Aspirations for Tangible and Embodied Interaction (Spring 2022, ACM Books), co-authored with Brygg Ullmer, Caroline Hummels, […]

When: Friday, October 17, 3:00 PM Where: Tyler 055 Abstract: Our current understanding of the regulation of cells is akin to solving a jigsaw puzzle. Many genomic factors governing cell development have been identified, resulting in vast data collection efforts. For example, obtaining single-cell-level spatial DNA organization or gene expression measurements at a continuous time […]

When: Friday, October 10, 3:00 PM Where: Tyler 055 Abstract: Software vulnerabilities persist as an important and costly challenge. Significant effort has been exerted toward automatic vulnerability discovery, but human intelligence generally remains required, and will remain necessary for the foreseeable future. Unfortunately, the pool of experts qualified to perform vulnerability discovery is small and […]

When: 9/26/25 3:00 PM Where: Tyler 055 Abstract: 2025 feels like the year that we started to throw caution to the winds when it came to AI deployment. AI policy priorities have shifted almost 180 degrees, global cooperation has been replaced by talk of American dominance, and the relentless march of LLMs into every nook […]

When: 9/19/25 3:00 PM Where: Tyler 055 Abstract: In this talk, we revisit online binary classification by shifting the focus from competing with the best-in-class binary loss to competing against relaxed benchmarks that capture smoothed notions of optimality. Instead of measuring regret relative to the exact minimal binary error — a standard approach that leads […]