When: Friday, April 10, 3:00 PMWhere: Tyler 055 AbstractEnvironmental epidemiologists are increasingly interested in establishing causality between exposures and health outcomes. A popular model for causal inference is the Rubin Causal Model (RCM). An important assumption under RCM is no interference, that is, the potential outcomes of one unit in the study are not affected […]

When: Friday, April 3, 12-2 pm Where: LIB 166 Join us for an engaging Data Science event co-hosted with the Women in Data Science club. Our featured speaker is Alena Korshunova (MBA), a Principal Business Intelligence Analyst in Innovation, Analytics & AI at FM Global. She will share insights into her career path and experience […]

When: Friday, April 3, 3:00 PMWhere: Tyler 055 AbstractEvery generation of developer infrastructure faces the same core tension: how do you give increasingly powerful systems the ability to act autonomously while maintaining the safety and governance guarantees that organizations require? For decades, this played out in distributed systems — microservices, data pipelines, and platform engineering […]

When: Tuesday, March 31, 11:00 AMWhere: Bliss Hall 190 AbstractDecision tree optimization is fundamental to interpretable machine learning. The most popular approach is to greedily search for the best feature at every decision point, which is fast but provably suboptimal. Recent approaches find the global optimum using branch and bound with dynamic programming, showing substantial […]

When: Friday, March 27, 3:00 PMWhere: Tyler 055 AbstractIn many empirical settings, individuals are interconnected, and an individual’s outcome may depend on the treatment of others, leading to interference. When interference is heterogeneous, treating individuals with specific characteristics can influence the population average outcome differently, either through their direct response or their impact on others. […]

When: Friday, March 13, 3:00 PM Where: Tyler 055 Abstract AI models now perform high-stakes tasks traditionally reserved for skilled professionals, often surpassing human expert performance. Despite these advances, the “black box” (i.e., uninterpretable) nature of many machine learning algorithms poses significant challenges. Opaque models resist troubleshooting, cannot justify their decisions, and lack accountability—limitations that […]

When: Friday, February 27, 3:00 PM Where: Changed to Zoom Abstract Computer science, like many STEM disciplines, faces persistent challenges in recruiting and retaining women and individuals from racially and ethnically minoritized backgrounds. This study examines whether targeted interventions can produce sustained improvements in academic performance and sense of belonging among these underrepresented groups. By […]