Audio Visual Enhancement: Collaborated with a senior research scientist on developing siamese neural network that detects audio-video synchronization error. The model is used to generate meaningful data embedding.

Designed and trained LSTM attention algorithms to predict battery failure time and forecast battery state-of-health with up to 92% benchmark dataset accuracy.

Investigated data-driven approaches on battery lifetime prognostics and implemented workable demos.

Implemented web crawlers using Python Scrapy and Selenium package. Scraped 37 sets of battery decay data.

Created organization Github repository for the lab and maintained 2 projects.

Developed data denoising method that improved training speed by 10%.

Acknowledged in “3D Design Using Generative Adversarial Networks and Physics-Based Validation” ASME 2019.

Collaborated with PHD students on designing a generative adversarial network. Implemented and trained the network by utilizing AWS services and Docker containers.

Alleviated mode collapse problem by modifying the discriminator structure and introducing Wasserstein loss function, improving average design aerodynamic score by 8%.

Collaborated in a team of 4 to create web app demos for 3D model showcasing and live 3D model generation.