School of Medicine
Showing 1,761-1,780 of 4,362 Results
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Siddhartha Joshi, PhD
Senior Research Scientist, Neurosurgery
BioI am a neuroscientist with over 20 years of experience in empirical, hypothesis-driven research. My knowledge and expertise cover a wide range of topics and methods within systems neuroscience including sensory perception, neurophysiology and neuroanatomy, eye-movements and pupillometry. My research is focused on how the brain represents and uses sensory information to drive goal-directed behaviors and in exploring how intrinsic neuromodulatory systems influence the neural circuits that drive such behaviors. At Stanford, I am looking to channel my experience towards studying human neural signals that underlie computations governing pain and attention.
My work thus far [1-4] supports the idea that there is a need for simultaneous measurements of behavior, brain state and large-scale cortical activity to understand how the brain’s circuits: (i) are modulated by ascending sympathetic activation and (ii) provide top-down control of descending sympathetic control. These are technically challenging experiments [3,4] that have thus far largely been explored in animal models. My current goal is to leverage opportunities to directly measure human brain activity via electrodes implanted for monitoring epilepsy. Towards this end, I will use state-of-the-art neurophysiological, behavioral, pupillometric techniques combined with quantitative analyses.
Representative publications:
1. Joshi S, Gold JI (2020) Pupil Size as a Window on Neural Substrates of Cognition. Trends in Cognitive Sciences 24(6), 466-480. PMCID: PMC7271902.
2. Joshi S (2024). Control of Pupil Responses. Encyclopedia of the Human Brain (Elsevier), Second Edition, Vol.1, 374-387.
3. Joshi S, Li, Y, Kalwani R, Gold JI (2016). Relationships between pupil diameter and neuronal activity in the locus coeruleus, colliculi and cingulate cortex. Neuron 89:221-234. PMCID: PMC4707070.
4. Joshi S, Gold JI (2022) Context-Dependent Relationships between Locus Coeruleus Firing Patterns and Coordinated Neural Activity in the Anterior Cingulate Cortex. eLife 11:e63490. PMCID: PMC8765756. -
Neda Kaboodvand
Basic Life Research Scientist, Neurosurgery
Current Role at StanfordNeda Kaboodvand, PhD, is an Applied Scientist at Stanford University School of Medicine. Her work focuses on modeling human behavior and leveraging multimodal data to improve clinical decision-making and system performance. She designs and leads experimental and observational studies and develops machine learning and computational models to evaluate interventions, predict outcomes, and enable adaptive, personalized systems. Her research integrates high-dimensional behavioral and physiological data to generate actionable insights and optimize real-world systems.
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Alexander D. Kaiser
Instructor, Cardiothoracic Surgery
BioAlexander Kaiser, PhD, is an applied mathematician and computational scientist who researches modeling and simulation of heart valves, focused on congenital heart valve disease and its surgical treatment. His recent research explores simulation-guided design of aortic valve repair of complex congenital heart defects. He has developed novel, nearly first-principles modeling methods for heart valves called elasticity-based design. These methods produce robust and realistic flows in fluid-structure interaction simulations. Dr. Kaiser is an Instructor in Cardiothoracic Surgery at Stanford University working with Michael Ma and Alison Marsden. He completed his PhD in Mathematics with Charles Peskin at the Courant Institute of Mathematical Sciences at New York University, where he was awarded the Kurt O. Friedrichs Prize for Outstanding Dissertation in Mathematics.
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Sharada Kalanidhi
Director of Data Science, Biochemistry - Genome Center
Current Role at StanfordParaphrasing the mathematician Alexander Grothendieck: the essential thing is to pose problems in the right framework.
Sharada is developing a new field, Mathematical Medicine, which applies pure mathematical frameworks to genomic and multi-omic data for quantitative, personalized diagnosis. This approach explores alternatives to prevailing cohort-based statistical paradigms, particularly in complex clinical cases that have resisted standard methods.
After more than a decade of research and close collaboration with biochemists at the Stanford Genome Technology Center (Dept. of Biochemistry), Sharada concluded that the mathematics currently used for multi-omic diagnosis is inadequate for the level of biological and clinical complexity being attempted. Her conclusion echoes the perspective of the mathematician Mikhail Gromov: “This area does not yet exist. It will have to be invented.” Mathematical Medicine represents one possible construction of such an area. This approach is aligned in spirit with the philosophy of the late mathematician Jim Simons: "We don't start with models. We start with data. We don't have any preconceived notions." Mathematical Medicine lets the data speak for itself.
This field is focused on the development of an intermediate translation layer between cohort-based statistical models and individualized multi-omic diagnosis and clinical decision-making. Without this mathematical layer, the clinical adoption of multi-omic data- particularly for complex cases- has been limited. As a result, many complex, multi-system conditions remain undiagnosed or misdiagnosed for long periods, delaying effective treatment and, in some cases, allowing disease processes to worsen. Additionally, what is learned from rare and extreme cases proves highly informative for the rest of the population.
Further information on this field, including opportunities for early philanthropic partnerships, is available at: https://mathmed-2026.web.app/
