Stanford Interdisciplinary Graduate Fellowships (SIGFs)

Stanford Interdisciplinary Graduate Fellow shares his research on "Simulating the impact of sensorimotor deficits on reaching performance" at a poster session.

Training the next generation of interdisciplinary neuroscientists

The Stanford Interdisciplinary Graduate Fellowship (SIGF) is a competitive, university-wide program that awards three-year fellowships to outstanding doctoral students engaged in interdisciplinary research. Three independent institutes, Bio-X, Wu Tsai Neurosciences Institute, and Sarafan ChEM-H award these graduate fellowships in the biosciences. 

The Wu Tsai Neurosciences Institute partners with the Vice Provost for Graduate Education and Stanford BioX to award Stanford Interdisciplinary Graduate Fellowships (SIGFs) in the area of neuroscience. We are grateful to Bio-X and the Bio-X Leadership Council for incorporating the fellowships affiliated with the Institute into their application process.

Learn more about the 2024 application and eligibility


2024 SIGF Application

All applications submitted through the portal will be considered for fellowships associated with Stanford Bio-X, Wu Tsai Neurosciences Institute, and Sarafan ChEM-H. Students with research especially aligned with the scientific mission of the Wu Tsai Neurosciences Institute graduate fellowships should select "Wu Tsai Neurosciences Institute Fellowship" on their application.

Funded SIGF projects

Wu Tsai Neurosciences Institute
SIGF - Graduate Fellowship
Deep brain microstimulation for memory recovery

Yi Lui's project aims to use deep brain microstimulation (DBMS), which causes even less brain damage and has higher spatial resolution than DBS, for memory recovery.

Wu Tsai Neurosciences Institute
SIGF - Graduate Fellowship
Understanding why neurons die in disease

Many neurological diseases feature the death of neurons, but the mechanisms that mediate cell death in these disorders are unknown. Astrogliosis, the response of a cell-type called “astrocytes” to injury, is common to most diseases of the central nervous system (CNS), and recent studies in our lab suggest that some reactive astrocytes may release a protein that is potently toxic to neurons.

Wu Tsai Neurosciences Institute
SIGF - Graduate Fellowship
Inference via Abstraction: A framework for efficient Bayesian cognition

We propose a novel framework for efficient Bayesian cognition called Inference via Abstraction (IvA), which learns to approximate complex world models with simpler abstractions that capture main dependencies, but leverage structure in the prior distribution for efficient inference. We instantiate IvA with a combination of probabilistic graphical models and deep neural networks.

Wu Tsai Neurosciences Institute
SIGF - Graduate Fellowship
Engineering versatile deep neural networks that model cortical flexibility

In the course of everyday functioning, animals (including humans) are constantly faced with real-world environments in which they are required to shift unpredictably between multiple, sometimes unfamiliar, tasks. But how brains support this rapid adaptation of decision making schema, and how they allocate resources towards learning novel tasks is largely unknown both neuroscientifically and algorithmically.