Machine Learning Research Intern, Lenovo Research – June - August 2022
- Manager: Grant Lloyd, Executive Director, Lenovo Research, Mentor: John Nicholson, Principal Research, Lenovo Research.
- My tasks broadly encapsulate chanlleges to deal with Low Light Imaging conditions on Lenovo ThinkPads, Tablets, and Motorola smartphones.
- During my time, I was the dataset owner for the team. My duties included understanding dataset, organization, triaging, building histograms and implementation and training model.
- I implemented and trained a redesigned Low-Light Net model that served better for the Thinkpad use case.
- Performed data structuring for better model generalization to counter distritbution shifts.
Ph.D. Summer Intern, Lawrence Berkeley National Lab, ESnet – June - August 2021
(Won the Best Paper Award at SC21 - INDIS)
- Supervisor: Dr. Inder Monga, Mentor: Dr. Ezra Kissel
- Deployed a deep learning approach to automate the dynamic auto-tuning of pacing rate in DTNs
- Developed kernel level system & traffic control operations in DTNaaS docker container.
- Integrated a supporting tool that can dynamically visualize the elastic search data for real-time parameter optimization.
Peer Mentor, Lehigh University (NSF-REU) 2020 (CNS-1757787) – May - August 2020
- Mentoring and closely guiding 15 NSF-REU Interns on their respective site projects.
- Duties included: Weekly one-on-one discussions with each intern, understanding their fallouts and helping them out with their codes and other low level academic issues.
Research Assistant, Resilience Research Group for SARS-CoV-2 – June - August 2020
- Supervisor: Dr. Brian D. Davison
- Image Gathering for face-masks in United States.
- Designing a novel face-mask detection algorithm for a survey research on SARS-CoV-2.
- Funded by Lehigh Research Grants and partially funded by (NSF-1841338)
Research Intern, Lawrence Berkeley National Lab, NERSC (link) – June - August 2019
- Supervisor: Dr. Brian Austin
- Developed scripts to fetch and analyze terabytes of data from the SLURM scheduler.
- Analyzed & estimated real-time queues in the scheduler for optimizing the policies for incoming jobs.
- Developed three real-time policies that potentially improved the allocation procedure.
- Job Cancellation
- Job Pausing
Machine Learning Intern, Persistent Systems ltd (link) – June - August 2017
- Developed a facial recognition and verification system using Google’s FaceNET research as the baseline.
- Added additional OpenCV features on top of it, which can differentiate between 3-D and 2-D images.
- Designed a purely browser-based RSA compliant module to work with FIDO keys.