Stanford Interdisciplinary Graduate Fellowships (SIGFs)

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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

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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

Funded research
Wu Tsai Neurosciences Institute
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.

Funded research
Wu Tsai Neurosciences Institute
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.

Funded research
Wu Tsai Neurosciences Institute
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.

Funded research
Wu Tsai Neurosciences Institute
A principled investigation into the heterogeneous coding properties of medial entorhinal cortex that support accurate spatial navigation

Navigation through an environment to a remembered location is a critical skill we use every day. How does our brain accomplish such a task? Over the last few decades, several lines of evidence have suggested that a brain region called medial entorhinal cortex (MEC) supports navigation by encoding information our location and movement within an environment.

Funded research
Wu Tsai Neurosciences Institute
Elucidating mechanisms of microglial tiling

In a process called tiling, homeostatic microglia homogenously organize in a grid-like fashion to achieve efficient surveillance of the brain. The molecular mechanisms underlying tiling are unknown. I hypothesize that microglia use cell-surface proteins to sense density of neighboring microglia, thereby contributing to constant cell-to-cell distances.