School of Medicine
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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 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/ -
Mausam Kalita
Physical Science Research Professional 2, Rad/Molecular Imaging Program at Stanford
Current Role at StanfordSenior Research Scientist: a) cold chemical synthesis— Synthesis of the 12C and 19F- HPLC standards and precursors for 11C- and 18F- labeling
b) Radiosynthesis— Introduction of 11C or 18F radioisotopes into small molecules to develop novel PET tracers, that can track activated myeloid cells in neurodegenerative disease, c) radiometal labeling— 64Cu and 89Zr labeling of monoclonal antibodies that target immune receptors, d) clinical translation— To follow FDA guidelines for translating preclinically validated tracers into humans in the cyclotron and radiochemistry facility (CRF) of the Stanford University -
Neil Kamdar
Assistant Director of Analytics, Center for Population Health Sciences
Biostatistician 3, Center for Population Health SciencesBioI am a health services researcher and applied methodologist focused on clinical and policy applications in disabilities research, women's health, general surgery, and mental health analyses. My focus has been on leveraging Medicare, private payer claims (Health Care Cost Institute (HCCI), MarketScan, OptumInsight, etc.) and Medicaid to understand cost, utilization, and outcomes. This work has been foundational in the development of large-scale studies on vulnerable populations that have typically been under-served or insufficiently studies in the health services research domain.
I serve as the Assistant Director of Analytics at the Center for Population Health Sciences at Stanford, focused on the development of the American Family Cohort (AFC) data, a primary care registry that provides substantial insights into clinical outcomes, utilization, and a particular focus on COVID-19 and Long COVID outcome analyses. Ancillary work would involve the development of research collaborations throughout Stanford Medicine with the interest in creating scholarship across the many domains of the Center for Population Health Sciences.
In addition to this role at Stanford, I also maintain an appointment at the University of Michigan, Institute for Healthcare Policy and Innovation, where I serve as an analytic lead in the development of administrative claims and electronic medical records analyses leading to publications in general and subject-specific journals.
I have been successful in being funded as a co-investigator with several federal and foundation agencies, including the National Institutes of Health (NIH), Department of Defense (DOD), Agency for Healthcare Research and Quality (AHRQ), Neilsen Foundation focused on traumatic spinal cord injury, among many others. I have also provided foundational analyses in the development of Clinical Quality Initiatives (CQIs), which are state-wide initiatives aimed at improving the health and efficiency of hospitals and institutions, with a focus on maternal and fetal medicine in the State of Michigan. -
Jennifer Kang
Academic Prog Prof 2, Pediatrics - Infectious Diseases
Current Role at StanfordProgram Manager, Global Child Health Program
