Join us every Friday at Boston University Faculty of Computing and Data Sciences (CDS) for cutting-edge research presentations by CDS PhD students across data science, AI, and beyond.
We are a student-run initiative within the PhD department of Boston University Faculty of Computing & Data Sciences, dedicated to fostering knowledge sharing and academic growth across our community.
Create a space where students can explore, present, and discuss the research topics they're passionate about in a supportive, collaborative environment.
Every Friday from 12:00 to 1:00 PM in CDS 1646, CDS researchers present on work that excites them—whether it's their current research, an inspiring paper they've discovered, or a hands-on workshop in their area of expertise. From artificial intelligence to biological sciences, our seminars cover the full breadth of computer and data science.
The students who run the series week to week.
Freddy is a fifth-year PhD student in the Computing and Data Science department at Boston University, and advised by the fantastic Allison McDonald. His work explores how power dynamics are shifted by technology with a focus on applying human-driven methods to complex issues. Currently, his projects are on 2nd order dynamics in digital spaces within labor unions and the motivations used by cryptographers for their research.
Lingyi Xu is a Ph.D. student in the Faculty of Computing & Data Sciences at Boston University. She seeks solutions to data missingness in multimodal learning across visual, tabular, and text data. Her work investigates how heterogeneous, incomplete data modalities can be represented and aligned to make learning more adaptable and their relationships more interpretable.
Stella is a PhD student at Boston University Computing and Data Sciences, where she works at the intersection of cognitive science and AI.
Her research centers on modeling human decision making, combining neural networks with traditional cognitive models to uncover the psychological principles behind how we choose. She is also building large-scale, high-quality datasets to drive this work forward.
by Yuke Zhang
Bilinear stochastic bandits are sequential decision-making problems where, at each round , a learner selects and observes , where is an unknown low-rank matrix. A direct reduction to a linear bandit may incur regret scaling with the ambient dimension . Existing approaches such as ESTR in Jun et al. [2019] exploit low-rank structure through subspace exploration, but require careful tuning of the exploration length. We propose Bilinear Thompson Sampling, which uses low-rank posterior samples to adaptively balance exploration and exploitation in selecting action pairs. We implement the posterior sampling procedure using a Gibbs sampler, evaluate the method empirically, and study its frequentist regret.
Ready to join our community of learners, researchers, and innovators?