SCHEDULE
Interdependent Bilateral Trade: Information vs Approximation
by Thodoris Tsilivis
This talk will introduce the area of mechanism design, and then focus on the problem of bilateral trade. Welfare maximization in bilateral trade has been extensively studied in recent years, primarily for the private values case. This talks will focus on welfare maximization in bilateral trade with interdependent values. Designing mechanisms for interdependent settings is much more challenging because the values of the players depend on the private information of others, requiring complex belief updates and strategic inference. Based on Interdependent Bilateral Trade: Information vs Approximation (EC25).
What LLM APIs can we use for quick proof-of-concept?
by Yan (Stella) Si
Workshop on Large Language Models APIs that can be simply hooked up to and how to work with it to jump start your project.
Policy Modeling for Sex Trafficking Legislation in Massachusetts
by Gabe McDonnell-Maayan
I will present a work-in-progress project that develops a decision-support tool to guide policymaking on sex trafficking legislation in Massachusetts. Sex trafficking, the largest form of modern-day slavery, remains a serious issue across the United States. In Massachusetts, advocacy organizations are actively pushing for competing legislative approaches. In collaboration with a subject-matter expert, we constructed a simulation of the commercial sex system and calibrated it to Massachusetts using diverse data sources. Preliminary results from the model are intended to inform upcoming deliberations of the state senate judiciary committee.
I will discuss the problem of sex trafficking and the broader landscape of commercial sex work, with a focus on Massachusetts. This includes an examination of the limited data on sex work in the United States and the methods we use to generate Massachusetts-specific estimates. I will also review potential legislative approaches and the history of advocacy efforts in the state. Next, I will walk through the process of developing a simulation model of commercial sex work and sex trafficking. Finally, I will present preliminary results, highlight their implications for current policy debates, and show how the model can serve as a tool for evaluating future intervention strategies.
Spherical CNN's and DeepSurv for Psychosis Conversion and The Trick-or-Treat Index
by Phillip Angelos
Short presentation on incomplete SCNN for Psychosis Progression plus a Halloween science-related presentation.
MCP servers: why you need to know about them and how they work
by Jeff Hastings
MCP have revolutionized the way AI connects to resources. Standard API approaches required the developer to customize each connection. With MCP servers, a standardized protocol replaces these fragmented API connections with a single, universal connection method. MCP servers enable the researcher to connect to multiple data sources simultaneously, create reproducible data pipelines, get the most out of agentic AI, and build large libraries that can be quickly and easily queried/summarized.
Multi-stain learning for neuropathology evaluation
by Lingyi Xu
Definitive diagnosis of neurodegenerative diseases traditionally relies on postmortem histopathology. While whole slide imaging has modernized pathology workflows, diagnostic performance still depends heavily on stain availability. We introduce a deep learning framework for multi-stain WSI analysis that operates effectively when some stains are missing. This approach offers a promising pathway to improve diagnostic accuracy in settings with limited staining resources.
Modeling group interactions of heterogenous voters in the US Senate
by Gavin Rees
Statistical models of interacting systems on discrete spaces can be effective causal models - for example, of yes/no voting - but their discrete sample space can turn normalization into a combinatorially complex endeavour: for example, normalizing the pairwise Ising model on the N dimensional binary (hyper)cube is NP-Complete. This lack of normalization can limit their utility and prevent rigorous comparisons to other models. Pairwise interacting models also suffer from quadratic parameter growth as the dimensionality of the sample space grows, unless interactions are structured in some way: for example, homogeneous interactions between groups (a block structured model). Group-structured pairwise interacting models can be effective causal models as well, and are easily normalizable, but aren’t able to capture individual heterogeneity that we suspect exists in some systems, e.g., political systems where every representative/voter has their own ideology (that there is individual heterogeneity is part of our prior). We describe results in exactly normalizing group-interacting pairwise Ising models with heterogeneous individual (linear and local) preferences within polynomial time complexity N^k, where N is the number of individuals and k is the number of groups. We discuss generalizations of this approach to effective low rank approximations of interacting systems, as well as potential applications to social systems, namely the US Senate.
Holiday Party
End of the Fall semester - Hooray! Join us for a holiday celebration to wrap up another successful semester of student research presentations.