About Me

I’m a Deep Learning Software Engineer at NVIDIA, New Jersey. Previously, I was a PhD candidate at Learning Systems Laborator (LSL), Department of Electrical Engineering, New York University where I worked under the aegis of Dr. Anna Choromanska.

Research Interest

I started my journey with deep interest in medical image processing specifically high frequency ultrasound imageing. During my doctorate studies I worked at the intersection of generalization and optimization of deep neural networks for both vision and NLP applications.


Jan, 2023: Our paper titled “ERASE-Net: Efficient Segmentation Networks for Automotive Radar Signals” was accepted in ICRA, 2023.

May, 2022: I will be joining NVIDIA, New Jersey full time as Deep Learning Software Engineering.

April, 2022: I succesfully defended my dissertation thesis title “Understanding Generalization in Deep Learning: An Empirical Approach”.

Jan, 2022: Our paper titled “Low-Pass Filtering SGD for Recovering Flat Optima in the Deep Learning Optimization Landscape” was accepted to AISTATS 20222.

May, 2021: Excited to join Microsoft, Seattle as Data and Applied Scientist Intern.

Jan, 2021: Our paper “A Theoretical-Empirical Approach to Estimating Sample Complexity of DNNs” was accepted to CVPR workshop track.

Feb, 2020: Excited to join NVIDIA, New Jersey as Deep learning software spring intern

May, 2020: Excited to join NVIDIA, New Jersey as Deep learning software summer intern.