• Randomized numerical linear algebra — Subspace embeddings, least-squares, low-rank approximation, randomized preconditioners
• Mixed / low-precision GPU computing — FP16/BF16 kernels, stochastic and dithered rounding, deterministic rounding rules
• Numerical linear algebra — sparse direct/iterative solvers, Krylov methods
• Convex optimization and modeling systems — CVXPY, interior point methods, deep learning optimizers

• Randomized Sketching is Robust to Low-Precision Rounding on GPUs Aryaman Jeendgar, Clément Flint, Hartwig Anzt arXiv:2606.20195, 2026 arXiv
• CVXPY 1.9: Recent Advances in Optimization Modeling Software William Zhang, Parth Nobel, Aryaman Jeendgar, Riley Murray, Philipp Schiele, Steven Diamond arXiv:2606.14891, 2026 arXiv
• Preconditioning via Randomized Range Deflation (RandRAND) Oleg Balabanov, Caleb Ju, Kaiwen He, Aryaman Jeendgar, Michael W. Mahoney arXiv:2509.19747, 2025 arXiv
• LogGENE: A smooth alternative to check loss for Deep Healthcare Inference Tasks Aryaman Jeendgar, Aditya Pola, Soma S. Dhavala, Snehanshu Saha arXiv:2206.09333v3, 2022 arXiv

• 2026-06 New paper: Randomized Sketching is Robust to Low-Precision Rounding on GPUs — arXiv:2606.20195
• 2026-06 New paper co-authored with the CVXPY team: CVXPY 1.9: Recent Advances in Optimization Modeling Software — arXiv:2606.14891
• 2025-10 Finished my wonderful time in Berkeley now moving to Germany to become a graduate student at the TUM in Prof. Hartwig Anzt's group
• 2025-09 New paper: Preconditioning via Randomized Range Deflation (RandRAND) — arXiv:2509.19747
• 2024-08 Wrapped up my thesis in Geneva, moving to the Bay! Starting as a researcher with Prof. Michael W. Mahoney's group at ICSI, Berkeley working on RandNLA!
• 2023-06 Started my second thesis at CERN / Princeton with Dr. Kilian Lieret working on charged particle tracking!

Before joining TUM, I spent a wonderful year in Berkeley doing research with Prof. Michael W. Mahoney's group, I worked closely with Oleg Balabanov on randomized preconditioners.

Before TUM and Berkeley, I spent 4 wonderful years at BITS-Pilani at the beautiful Hyderabad campus double majoring in Physics (masters) and Electrical Engineering (bachelor). During this time, I did multiple internships in industry and in academia — one at TCS Research where I was supervised by Prof. Mayank Baranwal (also adjunct faculty at IITB) where I worked on approximate second-order optimizers and one at Intel Labs (supervised by Sameh Gobriel) where I worked on scaling out the VDMS database and worked on a cool online optimization problem.

At BITS, I completed my first thesis (in physics) at the International Computer Science Institute, UC Berkeley (remotely), where I worked with Dr. Riley J. Murray on the Operator Relative Entropy Cone and made (further) technical contributions to the CVXPY codebase in the process. You can find my thesis here.

I completed my second thesis at CERN with funding from Princeton University's Department of Physics. I worked with Dr. Kilian Lieret on data-driven pipelines for charged-particle trajectory prediction, and concurrently with Dr. Henry Schreiner on scikit-build-core. My corresponding thesis is here.

I was also fortunate enough to do several REU's which allowed me to contribute to cutting edge open source software. I spent the summer of 2023 as a Research Engineer Fellow at Princeton University (under Princeton Research Computing), contributing to scikit-HEP with Dr. Henry Schreiner, and participated in GSoC-2023 with CVXPY. I spent the summer of 2022 writing open-source code for CVXPY under GSoC-2022

Far, far earlier, I worked with Prof. Snehanshu Saha (BITS Goa) and Soma S. Dhavala (Wadhwani AI) on quantile-regression methods for neural networks, with a pre-print here.

I read on my reading list and write (occasionally!) on my blog. The most up-to-date version of my CV is here.