Sharada Kalanidhi
Director of Data Science, Biochemistry - Genome Center
Bio
Sharada Kalanidhi is Director of Data Science at Stanford Genome Technology Center, SGTC (Dept of Biochemistry), Stanford University School of Medicine. Prior to this role, she worked in industry for 20+ years in roles involving quantitative strategy, data science and statistics. Her experiences shape her multi-disciplinary outlook and approach.
A decade of her experience was in the bond markets, where she developed trading and portfolio strategies on interest rate derivatives and mortgage portfolios. When a family member developed symptoms of unexplained fatigue, she became drawn to biostatistical problems. She assisted researchers at SGTC with data science and statistical analysis on ME/CFS patients, and finally joined them full-time. Her recent research has involved multivariate and machine learning analysis of the genomics, proteomics and metabolomics underlying ME/CFS, and post-viral fatigue (such as long Covid.) Her research interests are biological and chemical intersections with (pure) mathematics. She is an inventor on several granted US patents.
Current Role at Stanford
Paraphrasing 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/
Patents
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"United States Patent 8996510 Identifying digital content using bioresponse data", Mar 31, 2015
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"United States Patent 8719278 Method and system of scoring documents based on attributes obtained from a digital document by eye-tracking data analysis", May 6, 2014
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"United States Patent 8509826 Biosensor measurements included in the association of context data with a text message", Aug 13, 2013
All Publications
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Immunoglobulin G complexes from post-infectious ME/CFS, including post-COVID ME/CFS disrupt cellular energetics and alter inflammatory marker secretion.
Brain, behavior, & immunity - health
2026; 52: 101187
Abstract
Autoimmunity is a key clinical feature in both post-infectious Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and Post-Acute Sequelae of COVID (PASC). Passive transfer of immunoglobulins from patients' sera into mice induces some clinical features of PASC. However, the physiological effects of immunoglobulins on cellular alterations remain elusive. In this study, we tested the potential effects of immunoglobulins from ME/CFS patients on endothelial cell dysfunction.We have isolated immunoglobulins from 106 individuals, including ME/CFS (n = 39), PCS-CFS (n = 15), MS (n = 20) patients, and healthy controls (n = 41). Protein composition of the isolated immune complexes was studied using mass spectrometry. The effect of isolated immune complexes on mitochondria was evaluated using confocal microscopy and a Seahorse XFe96 Extracellular Flux Analyzer, and the impact on inflammatory cytokine secretion was studied using a multiplex bead-based assay.Here, we demonstrate that IgG isolated from post-infectious ME/CFS patients selectively induces mitochondrial fragmentation in human endothelial cells and alters cellular energetics. This effect is lost upon cleavage of IgG into its Fab and Fc fragments. The digested Fab fragment from ME/CFS alone was able to alter the cellular energetics, resembling the effect of intact IgG. IgG from post-infectious ME/CFS, including post-COVID ME/CFS patients, induced distinct but separate cytokine secretion profiles in healthy PBMCs. Proteomics analysis of IgG-bound immune complexes revealed significant changes in immune complexes from ME/CFS patients, affecting extracellular matrix organization, whereas those from post-COVID ME/CFS patients pointed to alterations in hemostasis and blood clot regulation.We demonstrate that IgGs from ME/CFS patients carry a chronic protective stress response that promotes mitochondrial adaptation via fragmentation, without altering mitochondrial ATP generation capacity in endothelial cells. Together, these results highlight a potential pathogenic role of IgG in post-infectious ME/CFS and point to novel therapeutic strategies targeting antibody-mediated metabolic dysregulation.
View details for DOI 10.1016/j.bbih.2026.101187
View details for PubMedID 41704659
View details for PubMedCentralID PMC12907502
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Increased circulating fibronectin, depletion of natural IgM and heightened EBV, HSV-1 reactivation in ME/CFS and long COVID.
medRxiv : the preprint server for health sciences
2023
Abstract
Myalgic Encephalomyelitis/ Chronic Fatigue syndrome (ME/CFS) is a complex, debilitating, long-term illness without a diagnostic biomarker. ME/CFS patients share overlapping symptoms with long COVID patients, an observation which has strengthened the infectious origin hypothesis of ME/CFS. However, the exact sequence of events leading to disease development is largely unknown for both clinical conditions. Here we show antibody response to herpesvirus dUTPases, particularly to that of Epstein-Barr virus (EBV) and HSV-1, increased circulating fibronectin (FN1) levels in serum and depletion of natural IgM against fibronectin ((n)IgM-FN1) are common factors for both severe ME/CFS and long COVID. We provide evidence for herpesvirus dUTPases-mediated alterations in host cell cytoskeleton, mitochondrial dysfunction and OXPHOS. Our data show altered active immune complexes, immunoglobulin-mediated mitochondrial fragmentation as well as adaptive IgM production in ME/CFS patients. Our findings provide mechanistic insight into both ME/CFS and long COVID development. Finding of increased circulating FN1 and depletion of (n)IgM-FN1 as a biomarker for the severity of both ME/CFS and long COVID has an immediate implication in diagnostics and development of treatment modalities.
View details for DOI 10.1101/2023.06.23.23291827
View details for PubMedID 37425897
View details for PubMedCentralID PMC10327231
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Off label use of Aripiprazole shows promise as a treatment for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS): a retrospective study of 101 patients treated with a low dose of Aripiprazole.
Journal of translational medicine
2021; 19 (1): 50
View details for DOI 10.1186/s12967-021-02721-9
View details for PubMedID 33536023