Aug 2024 – Dec 2024 · 4 mos

• Served as a grader for a graduate-level Deep Learning course taught by Prof. Yan Liu, evaluating student assignments, projects, and exams. I provided detailed feedback to help students improve their understanding of Deep Learning fundamentals. This role enhanced my ability to critically assess ML solutions and reinforced my knowledge of deep learning concepts.

Sep 2023 – Jun 2025 · 1 yr 9 mos

• Worked as a Graduate Research Assistant under Prof. Daniel Seita in the Sensing, Learning, and Understanding for Robotic Manipulation (SLURM) lab at the University of Southern California. My research focused on leveraging and analyzing large-scale pre-trained models for robotic manipulation tasks. I co-led the projects GPT-Fabric: Smoothing and Folding Fabric with Pre-Trained Foundation Models (ISRR 2024) and ManipBench: Benchmarking Vision-Language Models for Low-Level Robot Manipulation (CoRL 2025), contributing extensively to their design, experimentation, and analysis.

Jul 2021 – Aug 2023 · 2 yrs 1 mo · Bengaluru, Karnataka, India · Hybrid

• Worked on document workflows as part of the Document and Interop Services team for Adobe Express. Key projects included developing a real-time monitoring system to detect upstream outages across distributed services, revamping document workflows to improve efficiency, and streamlining CI/CD pipelines through API mocking and pipeline refactoring. These projects enhanced the reliability, performance, and maintainability of Adobe Express’s document services.

Apr 2020 – Jul 2020 · 3 mos · Bengaluru, Karnataka, India · Remote

• Developed an innovative recommender system for template-based document beautification via Latent Space Interpolation and GANs. This work was patented under U.S. Patent 11,537,787, titled “Template-Based Redesign of a Document Based on Document Content”, granted on December 27, 2022. The system significantly enhanced document re-styling efficiency and impact, and I received a Pre-Placement Offer from Adobe for extraordinary performance during the internship.

Nov 2019 – Jan 2020 · 2 mos · Singapore · On-site

• Worked as a Research Assistant under Prof. Arnab Bhattacharyya at the National University of Singapore, investigating state-of-the-art streaming algorithms for the Heavy Hitters problem, conducting both theoretical analysis as well as experiments on real-world datasets such as networks and movies. I also developed the first efficient polynomial-time algorithm for causal inference, collaborating remotely after my internship, with this work published at AISTATS 2022 under the title “Efficient Interventional Distribution Learning in the PAC Framework.”