CV
Education
Oct 2025 – present
Technische Universität München
Ph.D. in Computer Science
- Advisor: Prof. Hartwig Anzt
- Research on randomized numerical linear algebra (RandNLA) and efficient GPU implementations.
Aug 2019 – May 2024
BITS Pilani, India
M.Sc. Physics & B.E. (Hons.) Electrical & Communications Engineering
Research Experience
Aug 2024 – Aug 2025
Machine Learning Research Engineer, International Computer Science Institute, UC Berkeley
Berkeley, California
- Host: Prof. Michael Mahoney
- Researching preconditioners for solving large-scale linear systems leveraging randomized sketching.
- Building the RandBLAS and RandLAPACK libraries as part of the BALLISTIC project.
- Working on a highly-parallelized, differentiable path integration library,
padaquad, and exploring problems in neural network mode-connectivity.
Jan 2024 – Jun 2024
Research Fellow via Princeton University, CERN
Geneva, Switzerland
- Host: Dr. Peter Elmer; Mentors: Dr. Kilian Lieret, Dr. Gage DeZoort, Dr. Henry Schreiner
- Worked on extending the GNN-tracking pipeline for charged-particle-tracking in various ways.
- Explored the introduction of a noise-classifier module and the use of uncertainty quantification techniques.
Sept 2023 – Dec 2023
Research Intern, TCS Research
Mumbai, Maharashtra
- Host: Prof. Mayank Baranwal
- Worked on developing new second-order optimizers for deep learning using tools from control and dynamical systems theory.
- Designed and implemented experiments and assisted in theoretical analyses.
Aug 2023 – Dec 2023
Master's Thesis, International Computer Science Institute, UC Berkeley
Remote
- Host: Dr. Riley J. Murray
- Worked on a variety of problems centered around the Operator Relative Entropy Cone and its semidefinite approximation.
- Introduced dual variables for the implementation of the N-dimensional power cone
PowConeND. - Implemented an atom for the Quantum Relative Entropy (QREP) and various other quantum information modelling functionality.
June 2023 – Sep 2023
Summer Research Software Engineer Fellow, Princeton Research Computing
Hybrid, Princeton, NJ
- Mentor: Dr. Henry Schreiner
- Worked on tools part of the ongoing scikit-HEP project.
- Implemented serialization spec within HDF5 for the UHI-interface and built a textual powered TUI for copier and cookiecutter projects.
June 2022 – Sep 2022
Graduate Technical Intern, Intel Labs
Cloud Systems Research Lab · Bangalore, Karnataka
- Manager: Nilesh Jain, collaboration with Dr. Sameh Gobriel
- Wrote a shard mode of operation for VDMS that linearly scaled out Add queries.
- Researched online clustering algorithms for optimization of Approximate Nearest Neighbor queries.
Aug 2021 – Feb 2022
Undergraduate Researcher, BITS Pilani
Goa Campus, Dept. of CS
- With Prof. Snehanshu Saha and Dr. Soma S. Dhavala
- Developed a novel Quantile Regression based framework around the proposed loss function in the Deep Learning setting.
Publications
- Randomized Sketching is Robust to Low-Precision Rounding on GPUs arXiv:2606.20195, 2026 arXiv
- CVXPY 1.9: Recent Advances in Optimization Modeling Software arXiv:2606.14891, 2026 arXiv
- Preconditioning via Randomized Range Deflation (RandRAND) arXiv:2509.19747, 2025 arXiv
- LogGENE: A smooth alternative to check loss for Deep Healthcare Inference Tasks arXiv:2206.09333v3, 2022 arXiv
Workshops & Talks
2026-08
Fused GPU Kernels for Two-Sided Randomized Sketching
GAMM Workshop on Applied & Numerical Linear Algebra, 2026 · Magdeburg · Contribution Link
- Authors: Aryaman Jeendgar, Clément Flint, Kanaya Ozora, Hartwig Anzt
2026-06
Petrov-Galerkin Approximations in (Multi)-Linear Algebra from the Lens of Optimization
2026 SIAM Conference on Optimization (OP26) · Edinburgh · Session MS97
- Authors: Nicolas Venkovic, Aryaman Jeendgar, Hartwig Anzt
2026-03
Parallel Locally Optimal Iterative Methods
2026 SIAM Conference on Parallel Processing for Scientific Computing · Berlin · Session CP10
- Authors: Nicolas Venkovic, Amir Bouslama, Aryaman Jeendgar, Hartwig Anzt
Teaching
Summer 2026
CITHN4021 Convex Optimization
Master's level · TUM
- Designed (from scratch) and was the lead instructor for a master's-level convex optimization course at TUM. Covering convex analysis, first and second order methods, deep-learning optimizers and advanced conic modelling via CVXPY
- Course material (slides, exercises, projects, final exam --- all with solutions) will be uploaded here soon.
Open Source
2022 / 2023
Google Summer of Code — CVXPY
- 2022: Implemented the Von Neumann entropy atom and an approximate scalar relative entropy cone via Gauss–Legendre quadrature-based semidefinite approximations.
- 2023: Added stationarity-of-Lagrangian verification primitives for differentiable CVXPY problems (KKT-style debugging helpers).
2022 – 2023
scikit-HEP & scikit-build-core
- General build-system and packaging improvements across the scikit-HEP ecosystem, including UHI-interface serialization in HDF5.
- Built a TUI: CopyCuTTer for the copier and cookiecutter projects
Skills
- Tools and Languages: Python, NumPy, CVXPY, scikit-learn, PyTorch, C++, CUDA, Git, Emacs, LaTeX
- HPC / GPU: CUDA, cuBLAS, cuSPARSE, mixed-precision arithmetic
- Numerical: Randomized NLA (CountSketch, OSNAP, SparseStack), iterative solvers, SDP / SOCP / LP solvers
- Optimization modeling: CVXPY, disciplined convex programming
- Tools: Git, CMake, scikit-build-core, Docker, GitHub Actions