I am a graduate student studying Robotics in the Laboratory of Computational Sensing and Robotics at Johns Hopkins University, passionate about applications of AI for Social Good; in particular, designing technologies that improve accessibility. My current research focuses on designing cognitive workload aware dynamic interfaces to improve human-AI interactions.
My prior experiences involve research in point-of-care diagnostic device design and industry experience in technologies enabling accelerated R&D at global medical device and biotechnology companies.
I am seeking full-time research opportunities on human-computer interaction or AI teams focused on improving accessibility of AI applications. Please contact me if you have opportunities within this space at firstname.lastname@example.org.
Johns Hopkins University
Whiting School of Engineering, Laboratory for Computational Sensing and Robotics
M.S.E. in Robotics
Master’s Thesis: Cognitive Load Aware User Interfaces for Mixed Reality Environments
University of Pennsylvania
The Wharton School
B.S. in Economics, Concentration in Operations and Information Management
School of Engineering and Applied Sciences
B.S.E. in Bioengineering
Senior Desisgn: Automated Pancreatic Cancer Diagnostic
Our deep learning course final project is featured in the Hub!
My deep learning course final project looking at fall detection and localization from RGB video feed wins the Intuitive Surgical Best Project Award! Great way to end an amazing semester, had an amazing time working with an amazing team! Github repo coming soon.
My thesis work was accepted as a poster presentation at the 2020 Women in Machine Learning (WiML) workshop, co-located with NeurIPS.
Our paper, Feasibility of Image-based Augmented Reality Guidance of Total Shoulder Arthroplasty Using Microsoft HoloLens 1 wins Outstanding Paper Award at the MICCAI 2020 Joint Workshop on Augmented Environments for Computer Assisted Interventions
Our COVID-19 county-level summaries dataset wins the Kaggle COVID-19 Dataset Award
Our machine readable COVID-19 county-level summaries dataset aggregating data for 300+ variables is now available on Github. We hope that this dataset can support COVID-19 research.