IEEE Intelligent Systems is honored to announce the 2024 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.
“The ‘AI’s 10 to Watch’ award recognizes exceptional talent, ground-breaking innovation, and vision shaping the future of Artificial Intelligence,” remarked Bo An, the incoming Editor in Chief of IEEE Intelligent Systems. “The 2024 recipients represent the cutting edge of AI research and applications. We are proud to celebrate these rising stars whose work inspires the global AI community.”
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.
“We take pride in identifying young rising stars in AI and acknowledging and promoting their valuable contributions,” said San Murugesan, whose term as Editor in Chief of IEEE Intelligent Systems recently concluded. “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.”
This year’s recipients, whose impressive impact on the field is detailed in IEEE Intelligent Systems’s Jan/Feb 2025 Volume 40, 1 Issue, are:
Kai-Wei Chang is an Associate Professor of Computer Science at UCLA and an Amazon Scholar at Amazon AGI. He focuses on Trustworthy Natural Language Processing, emphasizing advancing reliable, responsible, and robust large language models and their multimodal variants. He is a Sloan Fellow, AAAI Senior Member, and winner of Google Research Scholar. He has garnered support from NSF, DoD, NIH, and various industry partners. He has mentored two postdocs, 12 Ph.D. students, and over 70 Master’s students, many of whom shape the field at leading institutions and companies.
Simon S. Du is an Assistant Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington. Prior to starting as faculty, he was a postdoc at the Institute for Advanced Study. He completed his Ph.D. in Machine Learning at Carnegie Mellon University. Simon’s research has been recognized by a Sloan Research Fellowship, a Samsung AI Researcher of the Year Award, a Schmidt Sciences AI2050 Early Career Fellowship, an Intel Rising Star Faculty Award, an NSF CAREER award, an Nvidia Pioneer Award, a Distinguished Dissertation Award honorable mention from CMU, among others. His notable contributions include proving the first global convergence result of gradient descent for optimizing deep neural networks, settling the sample complexity in reinforcement learning, and establishing the necessary and sufficient conditions for reinforcement learning in large state spaces.
Bo Han is an Assistant Professor in Machine Learning at Hong Kong Baptist University, and a BAIHO Visiting Scientist at RIKEN AIP. He was a Visiting Research Scholar at MBZUAI MLD, a Visiting Faculty Researcher at Microsoft Research, and a Postdoc Fellow at RIKEN AIP. He received his Ph.D. degree in Computer Science from University of Technology Sydney. He has served as Senior Area Chair of NeurIPS, and Area Chairs of NeurIPS, ICML and ICLR. He has also served as Associate Editors of IEEE TPAMI, MLJ and JAIR, and Editorial Board Members of JMLR and MLJ. He received Outstanding Paper Award at NeurIPS, Most Influential Paper at NeurIPS, and Outstanding Student Paper Award at NeurIPS Workshop.
Lingjuan Lyu is currently the Head of Privacy-preserving Machine Learning (PPML) and Vision Foundation Model (VFM) Team in Sony Research. Her recent interest lies in the intersection between foundation model and responsible AI (data synthesis, privacy and security, federated learning, IP protection). She had won a long list of awards, including AI 10 to Watch, IJCAI Early Career Spotlight, IEEE Outstanding Leadership Award, IBM Ph.D. Fellow, National Scholarships, Best/Outstanding/Spotlight paper awards from top venues like ICML, ACL, CIKM, NeurIPS, etc. She also took key roles as a chair, committee member or organizer of many well-known conferences like NeurIPS.
Shirui Pan received his Ph.D. in Computer Science from the University of Technology Sydney (UTS) and is a Professor in the School of Information and Communication Technology at Griffith University, Australia. His research focuses on data mining and machine learning, earning prestigious accolades such as the 2024 CIS IEEE TNNLS Outstanding Paper Award, the 2020 IEEE ICDM Best Student Paper Award, and the 2024 IEEE ICDM Tao Li Award. Recognized as one of the AI 2000 AAAI/IJCAI Most Influential Scholars in Australia, he is also an ARC Future Fellow and a Fellow of the Queensland Academy of Arts and Sciences (FQA).
Xiaojuan Qi is an Assistant Professor at the University of Hong Kong. She earned her PhD in Computer Science from the Chinese University of Hong Kong and completed her postdoctoral research at the University of Oxford. Her research focuses on understanding and simulating the visual world, with an emphasis on fundamental tasks such as 3D reconstruction, understanding, and synthesis. She has published over 100 papers in top-tier venues, many of which have been highly cited and recognized as oral or spotlight presentations at leading conferences. She has received many honors and awards, notably a Best Paper Honorable Mention at SIGGRAPH Asia. She actively serves the research community, frequently taking on roles such as Area Chair for CVPR, ICCV, NeurIPS, and ICML.
Sarath Sreedharan is an Assistant Professor at Colorado State University. His core research interests include designing human-aware decision-making systems to generate behaviors that align with human expectations. He completed his Ph.D. at Arizona State University, where his doctoral dissertation received one of the 2022 Dean’s Dissertation Awards for Ira A. Fulton Schools of Engineering and was an Honorable mention for the ICAPS-23 Outstanding Dissertation Award. He is the lead author of a Morgan Claypool monograph on explainable human-AI interactions and has given tutorials and invited talks on the topic at various venues. He was selected as a DARPA Riser Scholar for 2022 and was a Highlighted New Faculty at AAAI-23.
Xinchao Wang is a Presidential Young Professor at the National University of Singapore (NUS). His research focuses on efficient and trustworthy machine learning, with applications in computer vision, natural language processing, and multimedia. He has served as an associate editor for the IEEE Transactions on Image Processing and the IEEE Transactions on Circuits and Systems for Video Technology, and as an area chair for major AI conferences. His publications have received the Best Paper Award at VCIP 2022, a Best Paper nomination at NeurIPS 2022, and the Young Author Best Paper Award from the IEEE Signal Processing Society in 2023. His PhD students have been awarded fellowships from Google, Snapchat, and ByteDance. He is a recipient of the NUS Young Researcher Award 2024.
Jiajun Wu is an Assistant Professor of Computer Science and, by courtesy, of Psychology at Stanford University, working on computer vision, machine learning, and computational cognitive science. Before joining Stanford, he was a Visiting Faculty Researcher at Google Research. He received his PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology. Wu’s research has been recognized through the Young Investigator Programs (YIP) by ONR and by AFOSR, the NSF CAREER award, the Okawa research grant, paper awards and finalists at ICCV, CVPR, SIGGRAPH Asia, CoRL, and IROS, dissertation awards from ACM, AAAI, and MIT, the 2020 Samsung AI Researcher of the Year, and faculty research awards from J.P. Morgan, Samsung, Amazon, and Meta.
Junchi Yan is currently a Professor with Shanghai Jiao Tong University. He has a wide span of research interests in machine learning with the intersection to combinatorial optimization (e.g. learning for mixed integer programming, satisfiability), quantum computing and more broadly speaking, AI4Science. His recent work also covers the paradigm of end-to-end autonomous driving via reinforcement learning, in a joint pipeline of perception and planning. He is a Fellow of IAPR and IET.
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 their increasing impact on the field as they continue on a path to become future leaders in the field of AI.
Previous Awardees
The complete information on previous nominees can be found in the article links below:
View 2022 Awardee Details
Bo Li, Tongliang Liu, Liqiang Nie, Soujanya Poria, Deqing Sun, Yizhou Sun, Jiliang Tang, Zhangyang “Atlas” Wang, Hongzhi Yin, Liang Zheng
View 2020 Awardee Details
Tathagata Chakraborti, John Dickerson, Fei Fang, Song Han, Kuldeep Meel, Nisarg Shah, William Wang, Martha White, Diyi Yang, Hanwang Zhang
View 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
View 2016 Awardee Details
Haris Aziz, Elias Bareinboim, Yejin Choi, Daniel Hsu, Shivaram Kalyanakrishnan, Reshef Meir, Suchi Saria, Gerardo I. Simari, Lirong Xia, William Yeoh
View 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
View 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
View 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
View 2006 Awardee Details
Eyal Amir, Regina Barzilay, Jennifer Golbeck, Tom Griffiths, Steve Gustafson, Carsten Lutz, Pragnesh Jay Modi, Marta Sabou, and Richard A. Watson
