Aaditya V. Rangan | Courant Institute of Mathematical Sciences

Biography & Research Overview

I am a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. My research lies at the intersection of computational applied mathematics, numerical analysis, data science, and theoretical biology.

My work focuses on developing efficient computational and statistical tools to tackle high-dimensional problems in structural biology (Cryo-EM), computational genomics and computational neuroscience. The projects I'm most interested in are those that have direct applications to experimental data.

Primary Research Areas

Computational Tools for Cryo-EM

The computational pipeline for cryo-electron microscopy (cryo-EM) requires processing noisy 2-dimensional images to infer the underlying 3D structure of the molecules (e.g., proteins) corresponding to these images. Principles from scientific computing can be used to improve many of the steps in this pipeline, such as rotational and translational image alignment and volume reconstruction, allowing for efficient single-particle reconstruction. These computational methods can be extended to produce sensitivity maps and aid in the reconstruction of heterogeneous samples, as well as to other imaging modalities (e.g., 4D-STEM/ptychography).

Heterogeneity Analysis of Genomic Data

Genome-wide association studies (GWAS) typically pool genotyped data across many cohorts, building high-powered data-sets which can reveal weak genetic signals (e.g., those contributing to pleiotropic diseases). Often, these complex diseases are genetically-heterogeneous, and specialized techniques are required to identify hidden genetic sub-populations that can be masked by traditional population-wide meta-analyses. Computational methods for heterogeneity analysis can be used to find genetic-subtypes and uncover drivers of disease etiology in complex diseases such as Bipolar Disorder and Alzheimer's Disease.

Latent Dynamical Models

Analyzing longitudinal data often involves using noisy or irregularly sampled measurements to infer the parameters governing an underlying 'hidden' dynamical process. In certain cases it can be possible to fit the dynamics of the data with stochastic differential equations (SDEs) of various complexity. This framework can be used to analyze longitudinal biometric data collected in dolphins and primates. A related approach (applied to a different underlying dynamical process) can be used to analyze certain mechanisms of RNA editing in cells.

Spiking Neuronal Network Models of Cortical Activity

Many brain regions exhibit complicated dynamics that cannot be easily captured using standard mean-field models. One approach to studying these systems involves building and benchmarking spiking neuronal networks which can capture the discrete, pulse-coupled interactions that drive phenomena such as cascades of action-potentials and transient synchrony. These spiking neuronal network models can help illuminate the dynamical mechanisms at work in brain regions such as the mammalian visual cortex and insect antennal lobe.

Research Funding & Grants

My research has been supported in part by the following grants and awards:

Longevity Consortium Award

NIH Grant U19 AG023122 | (2018–2023)

PIs: Aaditya V. Rangan & Nicholas Schork (TGen) | Role: Co-Investigator
Global Seed Grants for Collaborative Research: Determining Treatment Algorithms for Patient Subgroups in Stroke Rehabilitation

NSF-NIH Seed Grant 24-74501-X0096-R9834 | (2016–2017)

PIs: Aaditya V. Rangan & Preeti Raghavan (NYU Langone) | Role: Site PI and Co-Investigator
Collaborative Research: Investigation of Odor-Triggered Neuronal Dynamics and Experience

NSF Grant F7163 | (2012–2015)

PIs: Aaditya V. Rangan & Hong Lei (University of Arizona) | Role: Principal Investigator
Computational Methods for Studying Heterogeneous Pulse-Coupled Network

NSF Grant F6532 | (2009–2012)

PI: Aaditya V. Rangan | Role: Principal Investigator
Characterization of Information and Functions by Spatiotemporal Chains of Events in Primary Visual Cortex

Swartz Foundation Fellowship | (2006–2008)

PI: Aaditya V. Rangan | Role: Principal Investigator

Selected Publications

Estimating the tails of the spectrum of the Hessian of the log-likelihood for ab-initio single-particle reconstruction in electron cryomicroscopy

A.V. Rangan, W.S. Tang, P. Cossio, K. Zhang, N. Grigorieff

Inverse Problems (Submitted / arXiv:2411.13263)

[arXiv]
Genomics yields biological and phenotypic insights into bipolar disorder

K.S. O'Connell, M. Koromina, T. van der Veen, ..., A.V. Rangan, et al.

Nature, 639:968–975 (2025)

[Publisher]
CryoLike: A python package for Cryo-EM image-to-structure likelihood calculations

W.S. Tang, J. Soules, A.V. Rangan, P. Cossio

Acta Crystallographica Section D, 81(12): 660–667 (2025)

[Publisher]
Heterogeneity analysis provides evidence for a genetically homogeneous subtype of bipolar-disorder

C. McGrouther, A.V. Rangan, A. Di Florio, J.A. Elman, N.J. Schork, J. Kelsoe, et al.

PLoS ONE, 20(1): e0314288 (2025)

[Open Access]
Robust ab initio solution of the cryo-EM reconstruction problem at low resolution with small data sets

A.V. Rangan, L. Greengard

Journal of Structural Biology, 215(3): 107994 (2023)

[Publisher]
A time-series analysis of blood-based biomarkers within a 25-year longitudinal dolphin cohort

A.V. Rangan, C. McGrouther, N. Bhadra, S. Venn-Watson, E.D. Jensen, N.S. Schork

PLoS Computational Biology, 19(2): e1010890 (2023)

[Open Access]
A loop-counting method for covariate-corrected low-rank biclustering of gene-expression and genome-wide association study data

A.V. Rangan, C.C. McGrouther, J. Kelsoe, N. Schork, et al.

PLoS Computational Biology, 14(5): e1006105 (2018)

[Open Access]

A CV and full publication list (last updated 2026/10/18) is available here or via Google Scholar.

Open-Source Software & Code Repositories

dir_cryoem

Comprehensive toolkit for Cryo-EM image processing, fast rotational/translational alignment, and 3D volumetric reconstruction (MATLAB and Python implementations).

CryoLike

Python package for fast Cryo-EM image-to-structure likelihood calculations (co-developed with Flatiron Institute CCM).

lakcluster_c

High-performance C implementation of covariate-corrected loop-counting algorithms for large-scale genomic heterogeneity analysis.

dir_PAD

Stochastic differential equation (SDE) solver pipelines for fitting irregularly spaced longitudinal biometric and biomarker datasets.

Current Graduate Students

Zhongyi Wang — Ph.D. Student

Alumni & Former Mentees

Prof. Mainak Patel — Ph.D. Thesis (2007–2009), now Associate Professor at College of William & Mary
Prof. Yi Sun — Postdoctoral Fellow (2007–2008), now Professor at University of South Carolina
Prof. Douglas Zhou — Postdoctoral Fellow (2008–2009), now Distinguished Professor at Shanghai Jiao Tong University
Prof. Jiwei Zhang — Postdoctoral Fellow (2012–2013), now Professor at Wuhan University
Arjang Talattof — M.S. Student (2008–2009), now Staff Scientist at Meta
Brenda Jiminez — M.S. Student (2009–2010), Alan Turing Institute
Dr. Rachid Ounit — M.S. Student (2010–2012), Software Manager at Lewis-Sigler Institute for Integrative Genomics, Princeton University
Zhuo Xi — M.S. Student (2011–2012), Instructor at NYU Abu Dhabi
Andrew Ronan — M.S. Student (2012–2013), Quantitative Researcher at Delta Global Management
Prof. Sijing Shao — M.S. Student (2013–2014), Assistant Professor at Florida International University
Quentin Chediak — M.S. Student (2016–2017), Ph.D. Student at University of Iowa
Haosheng Zhao — M.S. Student (2021–2022), Ph.D. Student at UC Santa Barbara
Shreya Thirumalai — M.S. Student (2022–2023), Quantitative Researcher at Equity Multiple
Shawn Kern — M.S Student (co-advised 2024-2025), Director for Safety and Mission Assurance, NASA

Contact Information

Mailing Address:
Courant Institute of Mathematical Sciences
New York University
251 Mercer Street, Desk 1012
New York, NY 10012

Email: rangan@cims.nyu.edu
Phone: (212) 998-3303