Eva Yi Xie | MIT'24

About Me

I recently graduated from MIT (May 2024) with double majors in Computer Science (AI + Decision Making) and Mathematics, with a concentration in Economics. In pursuit of my passion for cognitive computational neuroscience, I just started my PhD study in Neuroscience at Princeton Neuroscience Institute in September 2024, on a Centennial Fellowship. I spent the past summer at the Theory & Modeling group within the Allen Institute to pursue research in mathematical neuroscience under the supervision of Prof. Stefan Mihalas, where I currently hold a visiting scientist position.

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

My research seeks to understand how multiple brain regions encode information and communicate with one another to coordinate the processes that allow us to interact with—and learn about—the world and ourselves. These processes include navigating our surroundings and gathering information to make everyday decisions, which together shape nearly all of our daily activities and ultimately shape who we are. Yet, we still understand remarkably little about the neural mechanisms underlying these fundamental cognitive processes and behaviors.

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:

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];
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;
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.

Before Graduate School

During my undergrad study, I conducted my research as an MIT CBMM UROP with Prof. Tomaso Poggio (collab w/ Prof. Earl Miller), and Prof. Ila Fiete (collab w/ Princeton BRAIN CoGS, including Prof. David Tank & Prof. Carlos Brody). I am continuing the collaborative ties now as a research affiliate. Previously, I conducted ML research in Madry Lab at MIT CSAIL as a SuperUROP, and did R&D work at Meta, Microsoft AI, and IBM Research as an intern.

Personally, I have been fortunate to benefit from learning opportunities and mentorships. Equity in education in my community and society at large is important to me. During my undergrad study, aside from leadership activities, I served on advisory boards/cabinet to provide insights to MIT President Sally Kornbluth, Dean Anantha Chandrakasan (School of Engineering), and MIT EECS leaderships in driving this goal.

I am happy to connect and engage in discussions related to my interests and beyond. Feel free to reach me at evayixie [at] princeton [dot] edu for any inquiry.

CV

A complete CV is available upon request.