AI’s 10 to Watch, 2022

Abstract

IEEE Intelligent Systems is promoting young and aspiring artificial intelligence (AI) scientists and recognizing the rising stars as “AI‘s 10 Watch.” This biennial 2022 edition is slightly different from the previous editions: We solicited submissions from individuals who had obtained their Ph.D. up to 10 years prior (as opposed to 5 years in all of the previous editions). This led to more applications of the highest quality. The selection committee finally had to select 10 outstanding contributors from a pool of 30+ highly competitive and strong nominations, which made the selection decisions rather difficult. After a careful and detailed selection process through many rounds of discussions via e-mails and live meetings, the committee voted unanimously on a short list of 10 top candidates who have all demonstrated outstanding achievements in different areas of AI. The selection was based solely on scientific quality, reputation, impact, and expert endorsements accumulated since their Ph.D. It is our honor and privilege to announce the following 2022 class of “AI’s 10 to Watch.”• Bo Li. She is working on trustworthy machine learning (ML) at the intersection of ML, security and privacy, and game theory. She was able to integrate domain knowledge and logical reasoning abilities into data-driven statistical ML models to improve learning robustness with guarantees, and she has designed scalable privacy-preserving data-publishing frameworks for high-dimensional data. Her work has provided rigorous guarantees for the trustworthiness of learning systems and been deployed in industrial applications. She is an assistant professor with the University of Illinois at Urbana-Champaign.• Tongliang Liu. He is working in the fields of trustworthy ML. His work in theories and algorithms of ML with noisy labels has led to significant contributions and influence in the fields of ML, computer vision, natural language processing (NLP), and data mining, as large-scale datasets in those fields are prone to suffering severe label errors. He is a senior lecturer at the School of Computer Science, University of Sydney, and a visiting associate professor at the Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence.• Liqiang Nie. He is the dean of and a professor with the School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen). He works on multimedia content analysis and search, with a particular emphasis on data-driven multimodal learning and knowledge-guided multimodal reasoning. He pioneered the explicit modeling of consistent, complementary, and partial alignment relationships among modalities.• Soujanya Poria. He is an assistant professor at Singapore University of Technology and Design (SUTD). His seminal research on fusing information from textual, audio, and visual modalities for diverse behavioral and affective tasks significantly improved systems reliant on multimodal data, paving the way to various novel research avenues. His latest works are on information extraction, vision–language reasoning, and understanding human conversations in terms of common sense-based, context-grounded causal explanations.• Deqing Sun. He is a staff research scientist at Google. He has made significant contributions to computer vision, in particular in motion estimation. His work on optical flow (“Classic+NL” and “PWC-Net”) has been very influential and has been powering commercial applications such as Super SloMo in NVIDIA’s RTX platform, Face Unblur, and Fusion Zoom on Google’s Pixel phone.• Yizhou Sun. She is a pioneer in heterogeneous information network (HIN) mining, with a recent focus on deep graph learning, neural symbolic reasoning, and providing neural solutions to multiagent dynamical systems. Her work has a wide spectrum of applications, ranging from e-commerce, health care, and material science to hardware design. She is currently an associate professor at the University of California, Los Angeles (UCLA).• Jiliang Tang. He is a University Foundation Professor at Michigan State University. He works on graph ML and trustworthy AI and their applications in education and biology. His contributions to these fields include highly cited algorithms, well-received systems, and popular books.• Zhangyang “Atlas” Wang. He works on efficient and reliable ML. Recently, his core research theme is to leverage, understand, and expand the role of sparsity, from classical optimization to modern neural networks (NNs), whose impacts span the efficient training/inference of large-foundation models, robustness and trustworthiness, generative AI, graph learning, and more.• Hongzhi Yin. He has worked on trustworthy data intelligence to turn data into privacy-preserving, robust, explainable, and fair intelligent services in various industries and scenarios. He is also a leading expert researching and developing next-generation intelligent systems and algorithms for lightweight on-device predictive analytics as well as recommendation and decentralized ML on massive and heterogeneous data. He is an associate professor and ARC Future Fellow at the University of Queensland.• Liang Zheng. He is a senior lecturer at the Australian National University and works on data-centric computer vision, where he seeks to improve the quality of training and validation data, predict test data difficulty without labels, and more. These efforts provide a complementary perspective to model-centric developments. He has also made significant contributions to object re-identification and the broader smart city initiative through the introduction of widely used benchmarks and baseline methods.