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PhD, Computer Science

• Advised by Dr Ali Butt, a professor at Virginia Tech, leading Distributed Systems and Storage Lab at Virginia Tech

Masters, Computer Science

• Advised by Dr Ali Butt, a professor at Virginia Tech, leading Distributed Systems and Storage Lab at Virginia Tech

Bachelor of Computer Software Engineering

Research Assistant at Distributed System and Storage Lab at Virginia Tech

• \(\small \textbf{Metis:}\) The project introduces a data access pattern for Deep Learning workloads to improve cachability. With a small cache, it improves the hit ratio by up to 4.5× compared to the LRU policy.
• \(\small \textbf{User I/O profiling:}\) Led a collaborative research with Analytics \& AI Methods at Scale Group at ORNL on analytically recognizing user/job behavior in HPC to improve I/O efficiency. The analysis revealed that a users' job I/O pattern can be predicted over 10 days with >90% accuracy based on app-level I/O stats and app patterns.
• \(\small \textbf{LLMStore:}\) A collaborative project with ORNL and LBNL to minimize offloading of parameters, optimizer state, and activations in HPC via caching and deduplication. Initial analysis showed >300 GB tensor-offloading traffic for LLaMA fine-tuning.

Student Assistant at NERSC, Lawrence Berkeley National Laboratory (LBNL), internship

• I investigated the causes of I/O hotspots in HPC applications and analyzed common performance issues. Specifically, I examined Drishti, an HPC I/O recommendation tool, and found it generates many false positive warnings, and derived insights to improve the accuracy of the the Drishti I/O recommendation tool.

Student Assistant at Lawrence Berkeley National Laboratory (LBNL), internship

• I continued my research on characterizing the sources of I/O performance variation in HPC, and striving to alleviate the I/O performance variability. I found out a significant I/O variability for various HPC applications like E3SM (For earth modeling) and LAMMP (For molecular simulations). Later continued my research for Large AI models and language models which I'm working on in the LLMStore project

Instructor

• Taught CS3214, Computer Systems, at Virginia Tech in Fall 2022 and Spring 2023

Internship at Oak Ridge National Laboratory, Analytics & AI Methods at Scale Group.

• I studied and characterized the application I/O pattern using clustering techniques, and then extracted features from their submitter like the job runs of the same application over a time-window for that user, the scale of the job submissions the submitter tends to submit. This work resulted in my ICDCN'25 paper, where I was able to predict the I/O pattern of the next job given the features from the past submissions of the same user with an accuracy of nearly 90% for HPC jobs.

• Was awarded travel support by USENIX for USENIX FAST'24 conference in Santa Clara, CA, US
• Had a poster at IPDPS24 PhD forum about our I/O interference characterization project with Berkeley Lab (LBNL). I was also awarded travel support by TCPP for IPDPS24.

• Being appointed as the student volunteer for SC23 hosted in Denver, CO, US

• Being appointed as the student volunteer for SC22 hosted in Dallas, TX, US