Video Research Intern at Apple May 2023 - Aug 2023
Cupertino, CA

Teaching Assistant (TA) at Johns Hopkins University August 2022 - December 2022 (Fall'22)
Baltimore, MD

Teaching assistant for EN.520.344 (Fall 2022) – Introduction to Digital Signal Processing, Johns Hopkins University. 

Topics discussed: Review of signals and LTI systems, Z-transform, Uniform sampling, Transform analysis of LTI systems, Filter design techniques, The discrete Fourier transform, Wavelets, Compressive sampling, Time-frequency distributions

AI Research Intern at Zippin May 2022 - August 2022 (Summer'22)
San Francisco, CA

I have worked on self-supervised representation learning from videos and video-based downstream tasks, including action classification and event detection.

• We proposed an adaptive masking strategy (called AdaMAE) for MAEs that is end-to-end trainable. Our adaptive masking strategy samples visible tokens based on the semantic context using an auxiliary sampling network.

• We demonstrated that AdaMAE samples more tokens from the high spatiotemporal information regions, thereby allowing us to mask 95% of tokens, resulting in lower memory requirements and faster pre-training.

• We reported state-of-the-art results of 70.0% and 81.7% in top-1 accuracy on SSv2 and Kinetics-400 action classification datasets with a ViT-Base backbone and 800 pre-training epochs.

Teaching Assistant (TA) at Johns Hopkins University August 2021 - December 2021 (Fall'22)
Baltimore, MD

Teaching assistant for EN.520.344 (Fall 2021) – Introduction to Digital Signal Processing, Johns Hopkins University. 

Topics discussed: review of signals and LTI systems, Z-transform, Uniform sampling, transform analysis of LTI systems, filter design techniques, the discrete Fourier transform, Wavelets, compressive sampling, time-frequency distributions

Research Assistant at University of Peradeniya March 2019 - August 2020

Worked as a research assistant under the National Science Foundation Grant (Grant No: RG/2018/EA&ICT/01) “Development of a novel predictive based Smart Distribution Management System (S-DMS) to maximize the rooftop PV absorption capacity of last-mile networks” in DEEE, University of Peradeniya.