Yifeng Bu
Development
CA, United States
Skills
Machine Learning (ML)
About
Yifeng Bu's skills align with IT R&D Professionals (Information and Communication Technology). Yifeng Bu appears to be a low-to-mid level candidate, with 5 years of experience.
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Work Experience
Lecturer
Qualcomm Institute Artificial Intelligence Program at UC San Diego
July 2022 - February 2024
- Delivered lectures on essential machine learning topics to more than 80 students from 9 universities in South Korea. Led project-based sessions on EDA, feature engineering, machine learning, and model evaluation techniques in KNIME and Python environments. Facilitated the development of students' skills in presenting research findings and writing scientific reports. Research
Graduate Student Researcher
Radiology Imaging Laboratory at UC San Diego
August 2021 - April 2023
- Developed a noninvasive brain-computer-interface using Magnetoencephalogram (MEG) to classify Rock-Paper- Scissor gestures using deep learning methods. Designed the MEG-RPSnet deep learning architecture and achieved 85.56% classification accuracy in 12 subjects. Operated MEG and Magnetic Resonance Imaging (MRI) for subject data collection and applied signal processing pipelines to eliminate external noises, thereby enhancing classification accuracy.
Electrical Engineer
InflammaSense Inc
May 2023 - Present
- Assist in a fast-paced startup to rapidly adapt to evolving requirements. Foster cross-functional collaboration and contribute to proposals for fundraising. Design a wearable hardware system that is capable of monitoring Electrocardiography (ECG), Photoplethysmography (PPG), Respiration, Temperature, and Cervical Electroneurography (ENG), equipped with novel signal processing and machine learning capabilities for real-time inflammation risk prediction. Propose integration plans and conduct quality tests on hardware and software components to ensure a reliable and robust device performance in clinical environments.
Graduate Student Researcher
Lerman Lab at UC San Diego
July 2019 - Present
- Designed signal processing, machine learning, and statistical pipelines, reducing the average detection time for potential systemic inflammation risk by 40% using non-invasive physiological and neurological measurements. Developed approaches for evaluating immune response severity during inflammation from patients' resting state data and autonomic stress challenges. Manufactured a bedside vital sign monitoring system and designed a graphic user interface (GUI) that actively collects physiological signals and computes real-time heart rate variability changes. Managed and led multiple projects, offered technical support, mentored students, and crafted research proposals.
Education
University of California
Doctor of Philosophy in Electrical and Computer Engineering
Master of Science
