My research focuses on machine learning applications for biomedical and mental health, with a particular emphasis on cardiovascular signal processing. I apply both deep learning and classical signal processing techniques, combined with statistical analysis, to advance data-driven healthcare solutions. Additionally, I explore alternative machine learning paradigms that enhance performance while improving the security and privacy of sensitive biomedical data.
[2024.6] [Article] Seven ECE Graduates Receive NSF Research Fellowship [link]
[2024.5] [Conference] Honored by the Vice Chancellor for Academic Affairs, Sharon Jones for his fellowship at the University of Washington Bothell Student Academic Showcase [link]
[2024.5] [Conference] Incarceration to Innovation - News from School of Science, Technology, Engineering & Mathematics at the University of Washington Bothell [link]
[2024.4] [Fellowship] Awarded the National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP)
[2022.4] [Presentation] Independent Measurement Platform for Federated Learning Models on Android Devices - Gabriel E. Gallardo Symposium at University of Washington [poster]
[2022.3] [Article] Giving a voice to the community through excellence in machine learning research - The Daily at University of Washington [link]
[2022.1] [Article] From Prison to Purpose - News at University of Washington, Bothell [link]
[2021.12] [Award] Awarded the Mary Gates Research Scholarship - University of Washington
[2021.11] [Conference] Caring Without Sharing: A Federated Learning Crowdsensing Framework for Diversifying Representation of Cities - International Workshop on Smart Society Technologies/EAI Mobiquitous in Japan (Online) [link] [video]
[2021.10] [Conference] Android On-device Federated Learning of PyTorch Models with Flower - SACNAS National Diversity in STEM [pdf]