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 SIGFs affiliated with the Institute into this year’s round of their long-standing and successful program.
Funded SIGF projects
Phospholipid dysregulation is implicated in the pathogenesis of lysosomal storage disorders (LSDs). We found that glycerophosphodiesters (GPDs) accumulate in lysosomes derived from Batten disease models, a life-limiting LSD whose pathological mechanism remains elusive. GPDs are the degradation products of glycerophospholipid catabolism by phospholipases.
Optimizing computational modeling of traumatic brain injury with machine learning: biomechanics and beyond
Traumatic brain injury (TBI) has become a global health hazard. If undetected, the brain damage of TBI can accumulate, calling for better TBI modeling and warning systems. TBI modeling involves three stages: head impact kinematics, brain deformation, and injuries.
Leveraging screenomics to identify mental illness: Detecting bipolar disorder through computational analysis of smartphone screen data
Mental illnesses like bipolar disorder affect millions of people around the world, but early symptoms are often difficult to detect. Working across the disciplines of clinical psychology, communication, and computer science, my research will develop a novel computational tool to identify signals of mania and depression in real-time.
Many neurological injuries and diseases such as brainstem stroke and Amyotrophic Lateral Sclerosis (ALS) result in severe speech impairment, drastically reducing quality of life. Recent progress in brain-computer interfaces (BCI) has allowed these individuals to communicate, but performance is still far lower than typical spoken conversation speeds.
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.
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.
Nanoscale to circuit-level computational and experimental studies of the biophysical mechanism of ultrasound-mediated mechanical neurostimulation
Although ultrasonic neurostimulation has the potential to outperform traditional treatments for many debilitating neurological disorders, it remains unclear how ultrasound affects nervous system activity on the molecular level.
Absence epilepsy is a form of pediatric epilepsy which causes seizures with brief lapses in awareness. Electron microscopy results in a murine model of absence epilepsy support the hypothesis that maladaptive myelination plays a role in disease progression.