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, neuropsychiatric heterogeneity, and computational neuroscience. A primary theme of my research is building tight, collaborative feedback loops with experimental scientists to turn complex, noisy, or multi-modal biological datasets into actionable mathematical and biological insights.

Primary Research Areas

Computational Cryo-EM & Structural Biology

Developing fast alignment algorithms, ab-initio 3D reconstruction methodologies, Hessian-based log-likelihood sensitivity analysis, and reconstruction tools for single-particle conventional TEM and 4D-STEM/ptychography.

Genomics & Heterogeneity Analysis

Formulating covariate-corrected low-rank biclustering and loop-counting strategies to resolve genetic subtypes in Bipolar Disorder and Alzheimer's Disease across large consortia datasets (PGC, Longevity Consortium, PAN).

Spiking Neuronal Networks

Constructing multi-scale computational models of pulse-coupled integrate-and-fire networks to analyze emergent spatiotemporal dynamics, transient synchrony, visual cortex processing, and insect olfaction.

Biometric Time-Series & RNA Dynamics

Fitting stochastic differential equations (SDEs) to irregularly sampled longitudinal biometric data, alongside modeling latent dynamical mechanisms governing cellular processes such as RNA editing in cephalopods.

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 complete publication list and CV are available upon request 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.

Teaching & Academic Leadership

Director of Graduate Studies (M.S. Program): Courant Institute of Mathematical Sciences (2024–2025).
Graduate & Undergraduate Instruction: Regular instructor for graduate-level numerical methods, computational biology, and applied mathematics courses at Courant.
Educational Outreach: Director and lead instructor for the Summer Mathematics Program for Twice-Exceptional High School Students (NYC DOE / Hunter College High School).
Student Mentorship: Primary advisor to Ph.D., Master's, and undergraduate students pursuing research at the intersection of mathematics and life sciences.

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