Zhenbang Wu
About Zhenbang Wu
Zhenbang Wu is a Machine Learning Intern currently working at Data Tecnica International and The National Institutes of Health in Bethesda, Maryland. He has a background in Electronics and Computer Engineering from Zhejiang University and Computer Engineering from the University of Illinois Urbana-Champaign, where he is pursuing a PhD.
Current Work at National Institutes of Health
Zhenbang Wu is currently employed as a Machine Learning Intern at The National Institutes of Health (NIH) in Bethesda, Maryland. He has been in this position since 2023, contributing to research focused on machine learning applications for the early detection of neurodegenerative diseases. This role allows him to engage in significant research that intersects technology and healthcare.
Previous Experience in Machine Learning and Research
Prior to his current role, Zhenbang Wu held several internships that provided him with diverse experiences in machine learning and research. He worked as a Summer Intern at LEADRIVE in Shanghai, China, for two months in 2018. In 2019, he served as a Research Intern at the University of California, Los Angeles, for three months. Additionally, he completed a three-month internship as an ACOE Research Intern at IQVIA in Cambridge, Massachusetts, in 2022.
Education and Academic Background
Zhenbang Wu studied Electronics and Computer Engineering at Zhejiang University, where he earned a Bachelor of Engineering (BE) from 2016 to 2020. He furthered his education at the University of Illinois Urbana-Champaign, obtaining a Bachelor of Science (BS) in Computer Engineering during the same period. He is currently pursuing a Doctor of Philosophy (PhD) in Computer Science at the University of Illinois Urbana-Champaign, expected to complete in 2025.
Projects and Contributions in Machine Learning
At Data Tecnica International, Zhenbang Wu participated in a collaborative project that focused on innovative machine learning solutions. His work involved engaging in interdisciplinary research that combined computer science with methodologies for detecting neurodegenerative diseases. This experience has enhanced his expertise in applying machine learning techniques to real-world problems.