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Professor Erik Cambria is the Founder of SenticNet, a Singapore-based company offering B2B sentiment analysis services, and a Professor at Nanyang Technological University, where he also holds the appointment of Provost Chair in Computer Science and Engineering. Prior to joining NTU, he worked at Microsoft Research Asia (Beijing) and HP Labs India (Bangalore) and earned his PhD through a joint program between the University of Stirling and MIT Media Lab. His research focuses on neurosymbolic AI for explainable natural language processing in domains like sentiment analysis, dialogue systems, and financial forecasting. He is the recipient of several awards, e.g., the IEEE Outstanding Career Award, was listed among the AI's 10 to Watch, and was featured in Forbes as one of the 5 People Building Our AI Future.

He is an IEEE Fellow, and Associate Editor of many top-tier AI journals, e.g., Information Fusion and IEEE Transactions on Affective Computing, and is involved in various international conferences as a keynote speaker, program chair, and senior program committee member. His research interest is neuro symbolic AI sentic computing, sentiment analysis, commonsense reasoning, and natural language understanding. He won the Recipient of several awards, e.g., the IEEE Outstanding Career Award, was listed among the AI's 10 to Watch, and was featured in Forbes as one of the 5 People Building Our AI Future.

Prof. Eric Cambria 

Nanyang Technological University,Singapore


Assoc. Prof. Heng Luo

Central South University, China


He received PhD in Materials Physics and Chemistry from Central South University. His appointment at Central South University is associate professor and postgraduate tutor. He leads the industry of computer engineering. Selected from the World Top 2% Scientists list. An IEEE member. Deputy Director of Hunan Engineering Research Center of New Generation Mobile Communication RF Inductive Components, Expert Member of Hunan Sensor Industry Promotion Association. He presided over the National Natural Science Foundation of China, the Equipment Pre-research Field Fund, the 173 Program sub-project, the Hunan Provincial Natural Science Foundation, and several horizontal projects, and participated in the "13th Five-Year Plan" National Key R&D Program, the "12th Five-Year Plan" National Defense 973 Program, and the major pre-research projects of the final assembly as a core scientific research member. The research papers have been published in Advanced Materials, Photonics Research, ACS Applied Materials & Interfaces, Composites Part A, Applied Physics Letters, Journal of the European Ceramic Society, Journal of Applied More than 50 international authoritative SCI journals such as Physics, Carbon, Ceramics International, etc., and 16 authorized invention patents (including 2 national defense invention patents). He won the second prize in Hunan Provincial Technological Invention Award. He undertook the undergraduate courses "Fundamentals of Microwave Technology" and "Sensor Technology and Application", and the postgraduate course "Physics in Functional Materials", and won the Teaching Quality Excellence Award of Central South University.

Prof. Jiliang Zhang

Northeastern University, China


Professor Zhang is a doctoral supervisor in the College of Information Science and Engineering. He received a Ph.D. degree in the School of Electronics and Information Engineering from Harbin Institute of Technology.

He leads the project "AceLSAA: Optimal Design of Admixtures for Concrete Embedded Large Scale Antenna Array", which is the EU Marie Curie Scholars Talent Program. He is an IEEE senior member.

Based on the above work, "Fundamental wireless performance of a building", the first review paper on the wireless friendliness of buildings published by the first author, was published by IEEE Wireless Commun. Mag. (impact Factor 12.777) was selected as the most popular article from April to May in 2022 and ranked first in May and June 2022. The industry's first paper on the wireless-friendly evaluation of built-in antenna building materials, "Wireless performance evaluation of building materials integrated with antenna arrays", published by communication authors, has been IEEE Commun. Lett. (impact factor 3.553) was selected as the most popular article in May and June 2022. He has been selected as a national youth talent project and has presided over several scientific research projects, such as the sub-projects of the national key R & D plan and the youth fund of the National Natural Science Foundation of China. He serves on the editorial board of CCF-C periodicals Wireless Communications and Mobile Computing.


Prof. Xinmin Zhang

Zhejiang University, China


Xinmin Zhang is a Professor with the State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, China. He received the Ph.D. degree in System Science from Kyoto University, Japan, in 2019. From April 2019 to December 2019, he was a Postdoctoral Research Fellow in the Department of Systems Science, Kyoto University, Japan. From 2020 to 2023, he was an Associate Professor at the College of Control Science and Engineering, Zhejiang University, China. He has published more than 60 technical papers in peer-refereed journals and prestigious conference proceedings, including IEEE Transactions on Neural Networks and Learning Systems、IEEE Transactions on Artificial Intelligence、IEEE Transactions on Industrial Informatics、Engineering Applications of Artificial Intelligence、Science China Information Sciences. He has received several research projects, including Scientific and Technological Innovation 2030 - "New Generation Artificial Intelligence", National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province, and University-enterprise cooperation projects. He has been invited as a speaker and TPC member for several international conferences. His research interests include Industrial Artificial Intelligence, Industrial Big Data, Fault Diagnosis, Virtual Sensing Technology/Soft-sensor, Machine Learning, Reinforcement Learning, and Deep Learning with applications to industrial processes.

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