People

Jim Haxby

Principal Investigator

Jim Haxby’s research focuses on the neural systems for visual perception with an emphasis on high-level vision – perception of faces, objects, actions, and dynamic natural stimuli – and on computational approaches to modeling information embedded in distributed patterns of activity and connectivity in fMRI data. He introduced multivariate pattern analysis to investigate population codes in patterns of activity and, more recently, developed hyperalignment, a suite of computational approaches for modeling the shared information in idiosyncratic fine-grained functional topographies, and approaches for leveraging hyperalignment to investigate individual differences in fine-grained cortical functional architecture.

Ida Gobbini

Professor Ida Gobbini is a visiting professor at Dartmouth, link to her lab website is here. Her research focuses on the detection and recognition of personally familiar faces and the subsequent activation of elaborated person knowledge.

Feilong Ma

Feilong is a Postdoctoral Fellow after graduating from the lab. He is interested in how individuals differ in cortical functional architecture, and how these differences relate to differences in cognition and behavior. He also has a broad interest in computational approaches, especially multivariate pattern analysis (MVPA) and hyperalignment.

Jiahui Guo

Jiahui is a Postdoctoral Fellow. She did her PhD at Dartmouth in Brad Duchaine’s lab. Jiahui is interested in using computational methods to understand the human cognitive system, especially the face-processing system, in a wide range of populations, including patients and people with decreased abilities.

Jane Han

Jane is a graduate student. She earned her master’s degree in biomedical engineering at Sungkyunkwan University under Professor Won Mok Shim’s advice. She is interested in using computational analysis on fMRI and behavioral data to investigate how we could improve education using neural data in everyday life.

Caroline Lee

Caroline Lee transferred to our lab this year and has been working on a collaborative project with Chris Baldassano at Columbia University, developing, Hyper-HMM, an algorithm that simultaneously resolves idiosyncrasies in spatial topographies using hyperalignment and temporal idiosyncracies in event boundaries using the Hidden Markov Model.

Xiaoxuan Fan

Xiaoxuan is an undergraduate studying Cognitive Science and Computer Science at Dartmouth College. She is interested in human cognition in general and mental illnesses in particular. After graduation, she’ll be joining HaxbyLab full-time as a Project Coordinator. In her free time, Xiaoxuan likes to play video games, watch crime drama shows, and play with her bunny.