Aryaman Jeendgar

Hi! I am Aryaman Jeendgar, an incoming graduate student with the School of Computation, Information and Technology at the Technische Universität München, where I am going to be working with Professor Hartwig Anzt on building high quality HPC software for randomized numerical linear algebra algorithms. I also aim to work on RandNLA theory and algorithms, work which I undertake closely with Professor Michael Mahoney's group at Berkeley.

I am currently a visiting scholar with Prof. Michael Mahoney's group at UC Berkeley.

I successfully finished my graduate thesis (in physics) at the International Computer Science Institute, UC Berkeley where I worked with Dr. Riley J. Murray on problems related to the Operator Relative Entropy Cone and made more technical contributions to the CVXPY codebase in the process! You can find my thesis here

I successfully finished my undergraduate thesis at CERN where my visit was generously funded by The Department of Physics, Princeton University. I worked with Dr. Kilian Lieret on problems concerning a data-driven pipeline for charged particle trajectory prediction and parallely, worked with Dr. Henry Schreiner on building scikit-build-core. You may find my thesis, here

During this time I also simultaneously worked as a Research Intern @ TCS Research with Prof. Mayank Baranwal and Dr. Kushal Chakrabarti on problems related to second-order neural network training dynamics, and analyzing them from an ODE-perspective!

I spent the summer of '23 as a Research Engineer Fellow @ Princeton University (under Princeton Research Computing), where I worked with Dr. Henry Schreiner, contributing to the ongoing scikit-HEP effort. I also participated in the GSoC-2023 program, where I (once again!) contributed to CVXPY!

I spent the summer of '22 writing open source code for CVXPY, under the GSoC-2022 program and interning at Intel Labs in the Cloud Systems Research group, where I worked with Dr. Sameh Gobriel on online optimization approaches to scaling out Nearest Neigbour Search queries for graph databases.

In the past, I worked with Professor Snehanshu Saha (BITS Goa) and Mr. Soma S. Dhavala (Wadhwani AI) on problems related to quantile regression in the context of neural networks and systematic methods for accelerating training for such scenarios, a pre-print of our latest work can be found HERE