Stanford University
Showing 11-20 of 616 Results
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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. Her work addresses a fundamental challenge in contemporary medicine: prevailing cohort-based diagnostic approaches are not always equipped to capture the biological mechanisms relevant to individual patients, particularly in long-pending, complex “outlier” cases.
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 not sufficient for the level of biological and clinical complexity being attempted, particularly for individual patients who lack relevant statistical cohorts. Her conclusion echoes the perspective of the mathematician Mikhail Gromov: “This area does not yet exist. It will have to be invented.” This gap has important clinical consequences: individual biological differences may be treated as “noise” or as "outliers" rather than as clinically meaningful information. As a result, many patients with complex or multi-system conditions remain undiagnosed or incorrectly diagnosed, sometimes for decades, delaying effective treatment and, in some cases, allowing disease processes to worsen.
Mathematical Medicine addresses these limitations by developing an intermediate translational layer between cohort-based statistical models and individualized multi-omic diagnosis and clinical decision-making. The approach reflects the data-first philosophy articulated by the late mathematician Jim Simons: “We don’t start with models. We start with data. We don’t have any preconceived notions.” By developing new mathematical frameworks for interpreting an individual’s genomic and multi-omic data, Mathematical Medicine seeks to seeks to let the data speak for itself while enabling quantitative, individualized diagnosis and clinical decision-making.
Sharada’s research has led to the diagnosis and identification of appropriate treatment pathways for patients with previously undiagnosed, complex conditions. These rare and atypical cases also reveal biological relationships not apparent in population-level analyses, leading to insights that can inform broader research, clinical applications, and drug development.
Further information on this field, including opportunities for early philanthropic partnerships, is available at: https://mathmed-2026.web.app
