New ECE Professor Harry Chao’s Approach to Machine Learning Meets Reality Where It Is
by A.J. Kleber
Outside the tidy binaries of digital computation, the world is a messy place. Few things in life are clearly observable and predictable, whether you’re looking at individual human behavior, traffic, organisms, or the weather. This is a truth that brand-new BU ECE Assistant Professor Wei-Lun (Harry) Chao not only recognizes, but has centered in his research: real-world applications of AI/ML, especially those with significant safety implications, have to take that inherent messiness and imperfection into account. Large “high-quality” datasets have unavoidable limitations; they are laborious and costly to collect and annotate due to the nature of the data they collect. In order to develop generalizable AI models, robust machine learning technologies must be developed to compensate for these limitations.
Professor Chao’s research to date has engaged deeply with this challenge, tackling it from different angles. To address the unique challenges of data in privacy- and security-critical scenarios, he has developed algorithmic and strategic improvements to federated learning, a training approach which utilizes decentralized data sources while protecting raw data. He co-led the first systematic study of 3D perception generalizability in AV systems across diverse environments, and co-developed the first visual foundation model dedicated to biodiversity, in pursuit of enhancing biological research by making the overwhelmingly varied traits of organisms computable from images.
Chao also worked to shore up under-resourced and imbalanced training data for deep learning models via algorithms which exploit multiple views of data, among other adaptive approaches. Each project has been grounded in the understanding that technology will ultimately be more accurate and effective if it can embrace the imperfect, dynamic nature of the data that constitutes “real life.”
In recognition of the obvious potential and promise of his work, Professor Chao has been awarded a 2025-2028 Peter J. Levine Career Development Professorship, given to talented early-career ECE faculty who have been identified as emerging leaders in their fields. Prior to joining BU ECE, Chao was appointed a College of Engineering Innovation Scholar and associate professor at Ohio State University. In his six and a half years at OSU, he was honored with a variety of awards recognizing his innovation, early career excellence, and achievements in interdisciplinary research. He and his co-authors received the 2024 Best Student Paper Award at the IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR) for their work on BioCLIP and the TreeOfLife-10M biodiversity dataset, a prime example of his affinity for convergent research. In his own words, “interdisciplinary research thrives on collaboration. Each researcher’s contribution is indispensable.”
Professor Chao received his Ph.D. from the University of Southern California in 2018.