Eva Yi Xie
About Me
I am a second-year PhD student in Neuroscience at the Princeton Neuroscience Institute, supported by a Centennial Fellowship. My research focuses on computational and theoretical neuroscience questions that yield insights directly testable by neural experiments, particularly on cognitive abilities such as decision-making and planning. I am advised by Carlos Brody. I collaborate with Nathaniel Daw and Ben Eysenbach. I am a visiting scientist in Stefan Mihalas’s Theory & Modeling group within the Allen Institute.
I graduated from MIT in May 2024 as a double major S.B. in Computer Science (AI + Decision Making) and Mathematics, with a concentration in Economics. During my undergrad, I worked on grid and place cell modeling with Ila Fiete, who I continue to collaborate with. Before then, I worked with Tomaso Poggio and Earl Miller on studying interhemispheric transfer of working memory. Aside, I have explored ML research in Madry Lab at MIT CSAIL, and did R&D work at Meta, Microsoft AI, and IBM Research as an intern.
Name & Pronunciation
I publish under my official name Yi Xie, but I commonly go by Eva. I often display both names together as Eva Yi Xie (pronounced: Yi-va Yi Shieh), both of which were given to me at birth.
Research
Broadly, I view cognition as a computational process grounded in the basis of neurons and synapses, and I believe we can gain insights into these processes through models of neural networks. With the very recent development of large-scale neurophysiological recordings and connectome datasets, we now have unprecedented opportunities to investigate: How do multiple brain regions interact to support cognition? To that end, my research has spanned three main directions:
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Normative: Developing biologically plausible models of interacting brain regions that perform brain-like computations, to uncover principles of neural coding and information flow that can be directly verified with neurophysiology experiments [Yi Xie, Jaedong Hwang, Carlos Brody, David Tank, Ila Fiete; ICML 2025];
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Mechanistic: Analyzing networks with biologically plausible properties, such as theoretically underexplored heavy-tailed synaptic weight distributions [Yi Xie, Stefan Mihalas, Łukasz Kuśmierz; In Review], or skip connections [Yi Xie*, Yichen Li*, Akshay Ranganmani; NeurIPS AMHN 2023], to reveal the dynamics and constraints imposed by connectivity structure alone;
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Descriptive: Creating computational tools to decode multi-region coordinated neural signals and capture the moment-to-moment computations animals perform when making decisions (e.g., When precisely do animals make up their mind?) [Ongoing].
Together, these efforts aim to advance our understanding of neural computation and may also offer useful insights for developing AI systems that reflect key properties of the brain—such as robustness and efficiency.
CV
A complete CV is available upon request.
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