Stanford University
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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/ -
Kirill Olegovich Kalinin
Staff, Finance
Casual - Non-Exempt, Hoover InstitutionBioI am a political methodologist and comparativist with expertise in statistical analysis, machine learning, artificial intelligence, election forensics, survey methodology, and statistical software development. Since 2017, I have worked at Stanford University's Hoover Institution in various positions, including National Fellow, Researcher, and Data Analyst. Prior to joining Stanford, I studied and worked at the University of Michigan, where I earned my PhD in political science in 2017. For more information, please visit my website at www.kirillkalinin.com.
