When: Wednesday, February 11, 10:00 AM Where: Tyler 053 Abstract Neuroimaging data present fundamental statistical challenges: they are high-dimensional and exhibit complex structures. In this talk, I present Bayesian methods developed for functional data and matrix-valued data motivated by neuroimaging applications, emphasizing interpretability, scalability, and uncertainty quantification. I first introduce Bayesian methods developed for two […]
Continue reading "[Talk] Xiaomeng Ju: Bayesian Modeling for Functional and Matrix Data with Applications to Neuroimaging Analysis"Category: Seminars
[Talk] Jinghao Sun: Panel Data Meets Unmeasured Confounding: A Nonlinear Difference-in-Differences Framework
When: Friday, February 13, 3:00 PM Where: Tyler 055 Abstract Difference-in-differences (DiD) is a foundational tool for causal inference in panel data, widely used in policy, economics, and health research. Its appeal lies in its intuitive design and robustness to time-invariant unmeasured confounding. However, standard DiD relies on strong assumptions—particularly parallel trends—that are often violated […]
Continue reading "[Talk] Jinghao Sun: Panel Data Meets Unmeasured Confounding: A Nonlinear Difference-in-Differences Framework"[Talk] Tian Wang: Modeling of Complex Data
When: Friday, February 6, 3:00 PM Where: Tyler 055 Abstract In this talk, we will first discuss the proposed α-separability for functional data. Functional data consist of random samples observed over a continuum, such as curves over a time range. These data often exhibit two kinds of variation: amplitude variation in the vertical direction and […]
Continue reading "[Talk] Tian Wang: Modeling of Complex Data"[Talk] Jiajun Tang: Network Goodness-of-Fit for the Block-Model Family
When: Wednesday, February 4, 10:00 AM Where: Tyler 053 Abstract The block-model family has four popular network models (SBM, DCBM, MMSBM, and DCMM). A fundamental problem is how well each of these models fits with real networks. We propose GoF-MSCORE as a new Goodness-of-Fit (GoF) metric for DCMM (the broadest one among the four), with […]
Continue reading "[Talk] Jiajun Tang: Network Goodness-of-Fit for the Block-Model Family"[Talk] Harini Suresh: From universal models to local agency: opportunities for more community-controlled AI
When: Friday, January 30, 3:00 PM Where: Tyler 055 Abstract As AI systems are increasingly introduced into our everyday lives and high-stakes domains, it’s critical that decisions around their use, design, and governance center the specific contexts and communities they affect. My research explores participatory approaches that support community control, domain specificity, and local agency […]
Continue reading "[Talk] Harini Suresh: From universal models to local agency: opportunities for more community-controlled AI"[Talk] Krishna Venkatasubramanian: Designing for accessibility for adults with intellectual and developmental disabilities around traumatic contexts
When: Friday, December 5, 3:00 PM Where: Tyler 055 Abstract There are over 7 million people with intellectual and developmental disabilities (I/DD) in the US. I/DD (e.g., Down syndrome, Williams syndrome, Autism, Smith-Magenis syndrome, etc.) are a set of disabilities that negatively affect the trajectory of an individual’s intellectual, emotional, and/or physical development. The abuse […]
Continue reading "[Talk] Krishna Venkatasubramanian: Designing for accessibility for adults with intellectual and developmental disabilities around traumatic contexts"[Talk] Elizabeth Bersson: Feature aware covariance estimation, with application to mixtures of chemical exposures
When: Friday, November 14, 3:00 PM Where: Tyler 055 Abstract The motivation of this research is to improve inferences on the covariation in environmental exposures, motivated by data from a study of Toddlers Exposure to SVOCs in Indoor Environments (TESIE). The challenge is that the sample size is limited, so empirical covariance provides a poor […]
Continue reading "[Talk] Elizabeth Bersson: Feature aware covariance estimation, with application to mixtures of chemical exposures"[Talk] Will Tomlinson: Providing Reproducible and Equitable AI Access in Academia
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 […]
Continue reading "[Talk] Will Tomlinson: Providing Reproducible and Equitable AI Access in Academia"[Talk] Student seminar talks October 31, 2025
When: Friday, October 31, 3:00 PM Where: Tyler 055 Arup Mazumder: Structured Noise in AMSR-E SST Fields and Its Impact on Their Deconvolution Abstract: AMSR-E sea surface temperature (SST) fields are significantly oversampled, with a footprint of approximately 45×65 km but gridded at 10×10 km resolution. This oversampling suggests that, with the aid of additional […]
Continue reading "[Talk] Student seminar talks October 31, 2025"[Talk] Orit Shaer: Tangible, Embodied, and AI-Augmented Interaction for Work and Learning
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, […]
Continue reading "[Talk] Orit Shaer: Tangible, Embodied, and AI-Augmented Interaction for Work and Learning"