IEEE Intelligent Systems is proud to announce the 2022 recipients of its prestigious “AI’s 10 to Watch” award, which recognizes 10 rising stars in the field of Artificial Intelligence (AI) from among dozens of nominees.
“We take pride in identifying young rising stars in AI and acknowledging and promoting their valuable contributions,” said San Murugesan, IEEE Intelligent System’s Editor in Chief in congratulating the 2022 AI’s 10 to Watch honorees. “In today’s rapidly evolving landscape of AI, which also offers better development tools and an enabling environment, young professionals and researchers are fortunate to be part of a golden era of AI. They have a unique opportunity to make a meaningful impact by directing their efforts toward areas that matter and have benefits for all of humanity. They should leverage their skills and expertise for the greater good and shape the future of AI for the better,” said San Murugesan, Editor in Chief (Interim) of IEEE Intelligent Systems
The magazine’s popular biennial award, established by the magazine in 2006 in honor of AI’s 50-year anniversary, celebrates young professionals for their early career accomplishments in a field that is also still rather new.
AI’s Top Ten to Watch Recipients
Recipients, whose accomplishments are detailed in IEEE Intelligent Systems’s March/April 2023 issue, are:
Bo Li 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 has been deployed in industrial applications. Li is an assistant professor with the University of Illinois at Urbana-Champaign (USA).
Tongliang Liu 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. Liu is a senior lecturer at the School of Computer Science, University of Sydney (Australia), and a visiting associate professor at the Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence (UAE).
Liqiang Nie 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. Nie is the dean of and a professor with the School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen (China).
Soujanya Poria conducts 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. Poria is an assistant professor at Singapore University of Technology and Design (Singapore).
Deqing Sun 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. Sun is a staff research scientist at Google (USA).
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Yizhou Sun 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. Sun is currently an associate professor at the University of California, Los Angeles (USA).
Jiliang Tang 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. Tang is a University Foundation Professor at Michigan State University (USA).
Zhangyang “Atlas” Wang 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 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. Yin is an associate professor and ARC Future Fellow at the University of Queensland (Australia).
Liang Zheng 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. Zheng is a senior lecturer at the Australian National University (Australia).
From the Editor-in-Chief, San Murugesan
Given the rapidly developing and broad-reaching new age of AI, the criteria for career length for the AI’s 10 to Watch nominees was extended to ten years post-PhD for this award cycle. With additional time for nominees to make significant contributions to the field of AI, Murugesan noted that nominees “have a unique opportunity to make a meaningful impact by directing their efforts toward areas that matter and have benefits for all of humanity.” And, reflecting on their future potential, he noted that they are well positioned to “leverage their skills and expertise for the greater good and shape the future of AI for the better.”
To evaluate and select the nominees, IEEE Intelligent Systems assembled a diverse committee consisting of prominent AI leaders from a variety of AI subfields and different geographic regions of the world.
We congratulate this year’s AI’s 10 to Watch recipients and look forward to witnessing their rise as future leaders in the field of AI!
Previous AI Top Ten Awardees
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- 2020 Awardee Details: Tathagata Chakraborti, John Dickerson, Fei Fang, Song Han, Kuldeep Meel, Nisarg Shah, William Wang, Martha White, Diyi Yang, Hanwang Zhang
- 2018 Awardee Details: Bo An, Erik Cambria, Yoav Goldberg, Akshat Kumar, Wei Liu, Cynthia Matuszek, Sinno J. Pan, B. Aditya Prakash, Maria Vanina Martinez, and Yang Yu
- 2016 Awardee Details: Haris Aziz, Elias Bareinboim, Yejin Choi, Daniel Hsu, Shivaram Kalyanakrishnan, Reshef Meir, Suchi Saria, Gerardo I. Simari, Lirong Xia, William Yeoh
- 2013 Awardee Details: Nora Ayanian, Finale Doshi-Velez, Heng Ji, Brad Knox, Honglak Lee, Nina Narodytska, Ariel Procaccia, Stefanie Tellex, Jun Zhu, and Aviv Zohar
- 2011 Awardee Details: Yiling Chen, Vincent Conitzer, Matthieu d’Aquin, Kristen Grauman, Tom Heath, Jure Leskovec, Daniel B. Neill, Andre Platzer, Talal Rahwan, and Liwei Wang
- 2008 Awardee Details: Philipp Cimiano, Dmitri Dolgov, Anat Levin, Peter Mika, Brian Milch, Louis-Philippe Morency, Boris Motik, Jennifer Neville, Erik Sudderth, and Luis von Ahn
- 2006 Awardee Details: Eyal Amir, Regina Barzilay, Jennifer Golbeck, Tom Griffiths, Steve Gustafson, Carsten Lutz, Pragnesh Jay Modi, Marta Sabou, and Richard A. Watson
