Hesam Dashti


Hesam Dashti (Hesamaddin Torabi Dashti)

Instructor in Medicine,
Brigham and Women's Hospital, Harvard Medical School

Senior Computational Scientist,
The Broad Institute of MIT and Harvard

hdashti@bwh dot harvard dot edu
Google scholar


I am an Instructor in Medicine in the Division of Preventive Medicine at Brigham and Women’s Hospital (BWH) and Harvard Medical School (HMS), and I am a member of Melina Claussnitzer, Ph.D lab at the Broad Institute of MIT and Harvard and Massachusetts General Hospital.

My effort at the Broad Institute is focused on using advanced computational methods for the conversion of disease-associated genetic variants to function (V2F). These genetic V2F studies are focused on cardio-metabolic diseases, where we utilize advanced computational methods to investigate a variety of high-dimensional cellular and molecular phenotypic data, including chromatin accessibility, gene expression and high content imaging-based profiling data at the single cell level.

In collaboration with Kathryn Hall, Ph.D.,M.P.H., at BWH and HMS, we are working on pharmacogenomics and precision medicine. In this collaboration, we utilize advanced computational methods to investigate precision efficacy of drugs and dietary supplements in CVD and cancer.

In collaboration with Samia Mora, M.D. and Olga Demler, Ph.D. my efforts at BWH and HMS are in computational CVD, with a focus on using machine learning methods for predicting medical events. In addition, we identify and determine levels of disease risk biomarkers using nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS).

I am a co-founder of the Data Science Group in the Division of Preventive Medicine, BWH and I lead multiple computational and analytical collaborations between BWH and HMS and national facilities including The Osher Center for Integrative Medicine BWH, HMS, The National Center for Functional Glycomics, BIDMC and the Center for Experimental Therapeutics and Reperfusion Injury, BWH.


SARS2 logo


Simplified scores to estimate risk of hospitalization and death among patients with COVID-19 based on Sex, Age, Race, Socioeconomic, Smoking status (SARS2).

CTPIC logo


Cheminformatics Tool for Probabilistic Identification of Carbohydrates (CTPIC) analyzes the covalent structure of a chemical compound to yield a probabilistic measure for distinguishing saccharides and saccharide-derivatives from non-saccharide chemical compounds.



Guided Ideographic Spin System Model Optimization (GISSMO) enables the efficient calculation and refinement of spin system matrices (chemical shift and coupling constants) against experimental 1D-1H NMR spectra of small molecules.



Atom Label Assignment Tool using InChI String (ALATIS) operates fully within the InChI convention to provide unique and reproducible molecule and all atom identifiers.

Peak2Pic logo

Mass to Comp

Mass2Comp is a webserver for computational assignment of glycan peaks. This is an on going collaboration with Dr. Sylvain Lehoux at The National Center for Functional Glycomics

RUNER logo


Robust and Unique Nomenclature for Enhanced Reproducibility (RUNER) is a robust and standardized pipeline that enables seamless modifications of atom force field parameters for computational molecular dynamics, energy minimization, and modeling of molecular interactions.

Open positions

Through different collaborations, we have openings for talented research scientists/postdocs/programmers with expertise in one of the areas of data science, machine learning, computational epidemiology, or computational genomics.
Send me your CV and your research interest statement.


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