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 third-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.
Clark is a second-year PhD student at Boston University's Faculty of Computing and Data Sciences. He is broadly interested in understanding biological systems and spatiotemporal processes with statistical modeling. Previously he worked at the Mayo Clinic at Jacksonville, FL, and before that earned a Master of Science in Bioengineering from Stanford University and a Bachelor of Science from Boston University in Biomedical Engineering.
by Yan (Stella) Si
Understanding how people make decisions is both an important scientific question in its own right and increasingly important in designing and aligning intelligent systems. In this talk, I will present work around these two questions: what can we learn about human decision making and preferences, and how can it help us build AI systems that are better aligned with people?
I will first present preliminary findings on context effects in multiattribute choice (MAC). Rational models of choice assume that preference between two options remains unchanged by the introduction of an alternative. Context effects break these assumptions and show that preferences shift with the added context of a third option. These effects are fragile in the literature, though, frequently disappearing or reversing in experiments. We examine whether the geometry of choice problems explains this variability. In purchasing decisions, things like price tier, relative value advantage of one option over the other, and price difference are geometric parameters that impact outcomes. We capture a large-scale map of multiattribute choice behavior by systematically varying the geometric parameters proposed and explore how they modulate context effects. We further evaluate competing MAC theories and work toward a more unified account of when and why these effects emerge.
I will then present another line of work on using knowledge of human preference to build better AI systems. We find that learning preferences for LLM response styles and routing across model providers can better align system behavior with user preferences and increase utility. And finally, I will show a quick demo of a conversational AI system that I am building to explore how AI can augment human decision making.
Ready to join our community of learners, researchers, and innovators?