Accepting applications from PhD students.
Email: Zhiwu.Huang@soton.ac.uk
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 天发娱乐棋牌_天发娱乐APP-官网|下载.
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 Riemannian computing, generative modeling, continual learning, and affective computing.
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
Dr. Zhiwu Huang is a Lecturer (Assistant Professor) in the School of Electronics and Computer Science at the University of 天发娱乐棋牌_天发娱乐APP-官网|下载, where he has been serving since January 2023. Prior to joining 天发娱乐棋牌_天发娱乐APP-官网|下载, 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 Riemannian computing, generative modeling, continual learning, and affective computing.
Dr. Huang is an accomplished researcher with numerous publications in top-tier conferences focused on computer vision, machine learning, and artificial intelligence. He is a member of IEEE and serves as an Associate Editor for IET Computer Vision. He is also actively involved in the academic community, contributing as a reviewer and meta-reviewer for major conferences and journals in these fields. He has played a key role in organizing multiple workshops in collaboration with prestigious conferences, including CVPR, ICCV, and ECCV.