Aditya Dendukuri
Aditya Dendukuri

Aditya Dendukuri

PhD Candidate in Computer Science
University of California, Santa Barbara

I work on numerical methods and scientific computing. My dissertation focuses on forward simulation and inverse problems for discrete stochastic systems. I am advised by Linda Petzold and am currently a graduate student researcher at Lawrence Berkeley National Laboratory.

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Research

Forward problems

Flux-preserving adaptive finite state projection

An adaptive method for simulating multiscale stochastic reaction networks. Accepted for publication in SIAM Journal on Multiscale Modeling & Simulation. Preprint

Multigrid finite state projection

A multilevel method for spatially inhomogeneous reaction networks. Presented at the 2026 Copper Mountain Conference on Multigrid Methods. Paper in preparation for a SISC special issue.

Inverse problems

Sparse identification of stochastic reaction networks

Sparse optimization with adjoint Krylov methods for learning structured Markov generators. Preprint coming soon; manuscript in preparation for SISC.

Structured Koopman generator learning

Reaction-rate recovery from ensemble snapshots using structured observable generators. Preprint coming soon; manuscript in preparation for SIADS.

LBNL

Exact linear subnetwork elimination

My current internship project develops subnetwork-elimination methods for accelerating simulations of discrete Markov processes.

I am also interested in sparse numerical linear algebra, matrix functions, reduced-precision solvers, and adaptive numerical methods.

Selected publications

  1. Flux-Preserving Adaptive Finite State Projection for Multiscale Stochastic Reaction Networks. Aditya Dendukuri, Shivkumar Chandrasekaran, and Linda Petzold. SIAM Journal on Multiscale Modeling & Simulation, accepted and forthcoming, 2026. arXiv
  2. Preconfigured neuronal firing sequences in human brain organoids. Tjitse van der Molen, Alex Spaeth, Mattia Chini, et al. Nature Neuroscience 29, 123–135, 2026. Journal
  3. Unified Framework for Real-Time Fluid Simulation in Virtual Rotator Cuff Arthroscopic Skill Trainer. Aditya Dendukuri, Mustafa Tunc, Doga Demirel, Sinan Kockara, and Tansel Halic. IEEE BIBE, 2023. Best Paper Award. Paper
  4. Machine Learning to Identify Variables in Thermodynamically Small Systems. David M. Ford, Aditya Dendukuri, Gulce Kalyoncu, Khoa Luu, and Matthew J. Patitz. Computers & Chemical Engineering, 2020.
  5. A hierarchical task analysis of shoulder arthroscopy for a virtual arthroscopic training platform. Doga Demirel, Alexander Yu, Seth Cooper-Baer, Aditya Dendukuri, et al. International Journal of Medical Robotics and Computer Assisted Surgery, 2017.

Software and teaching