About
Dr. Zhiwu Huang is a Lecturer (Assistant Professor) affiliated with the Vision, Learning, and Control (VLC) group in the School of Electronics and Computer Science (ECS) at the University of Southampton.
Research
Research groups
Research interests
- Computer Vision
- Machine Learning
- Generative AI
- Continual Learning
- Affective Computing
Current research
Dr. Zhiwu Huang specializes in computer vision and machine learning for artificial general intelligence. He is committed to teaching AI/machines/robots to comprehend the world through multimodal data, enabling them to achieve animal-level, or human-level or superhuman-level general intelligence. His current focus is on making machine learning models more capable and controllable to understand the physical world, through scaling and aligning compute, data, model, and tasks for deep learning. He is interested in exploring machine learning methodologies, including generative modeling, continual learning, and affective computing.
Teaching
COMP6252: Deep Learning Technologies
COMP6237: Data Mining
AICE1006: Data Analytics
COMP6200: MSc Project
COMP3200: Part III Individual Project
ELEC1028: Personal Tutorial
IS424: Data Mining and Business Analytics, SMU Singapore
IS483: IS Application Project, SMU Singapore
Biography
Dr. Zhiwu Huang is a Lecturer (Assistant Professor) in the School of Electronics and Computer Science at the University of Southampton, where he has been serving since January 2023. Prior to joining Southampton, he held the position of Postdoctoral/Guest Researcher at ETH Zurich from September 2015 to July 2021. Following that, he served as an Assistant Professor of Computer Science at Singapore Management University from September 2021 to December 2022. Dr. Huang obtained his Ph.D. degree from the University of Chinese Academy of Sciences in 2015.
Dr Huang’s expertise lies in computer vision and machine learning for artificial general intelligence. He is dedicated to teaching AI systems, machines, and robots to understand the world through multimodal data, aiming to achieve levels of general intelligence akin to animals, humans, or even surpassing them. His current focus is on enhancing the capabilities and controllability of machine learning models to understand the physical world by scaling and aligning compute, data, models, and tasks for deep learning. He is interested in exploring machine learning methodologies, such as generative modeling, continual learning, and affective computing.
Dr. Huang also actively serves as either a reviewer or a meta-reviewer for major conferences and journals in the fields of computer vision, machine learning, and artificial intelligence. In addition, he has taken a role in organizing multiple workshops in conjunction with prestigious conferences such as CVPR, ICCV, and ECCV.