JHU Autonomy OWL

Machine Learning Edge Detection Remote Sensing

Overview:

An AI observatory, workshop and laboratory for critical infrastructure resilience.

Key Contributions:

• Leveraged machine learning techniques to adapt terrestrial applications to the unique constraints of the space domain.
• Showcased web development expertise by strategically redesigning and updating the lab website, creating a fresh visual identity, and optimizing content for improved search engine visibility.
• Earned a presentation slot at the 2022 ASCEND conference with my proposal, titled "Spacecraft Diagnostic Generation from Remote Sensing for OSAM Mission," emphasizing my contributions in remote sensing and mission diagnostics.

JHU ARCADE Lab

DaVinci Machine wavelets Brain Signals

Overview: Design Systems that pioneer human-centered solutions that are enabled by synergistic advancements across imaging, computer vision, machine learning, and interaction design that are embodied in emerging technology such as mixed reality and robots.

Key Contributions:

• Collaborated closely with professors to conduct comprehensive research on the Da Vinci surgical machine and enhance surgical precision and mitigate medical accidents caused by surgeon fatigue.
• Contributed to the development of algorithms using wavelets to enable cognitive load-aware user interfaces for mixed reality nvironments, delving into the analysis of cognitive load factors such as eye movement patterns and brain signals.