2024 International Conference on Intelligent Computing and Data Analytics（ICDA 2024）
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Speakers

Prof. Rajkumar Buyya, IEEE Fellow

University of Melbourne, Australia

Dr. Rajkumar Buyya is a Redmond Barry Distinguished Professor and Director of the Cloud Computing and Distributed Systems (CLOUDS) Laboratory at the University of Melbourne, Australia. He is also serving as the founding CEO of Manjrasoft, a spin-off company of the University, commercializing its innovations in Cloud Computing. He has authored over 850 publications and seven textbooks including "Mastering Cloud Computing" published by McGraw Hill, China Machine Press, and Morgan Kaufmann for Indian, Chinese and international markets respectively. Dr. Buyya is one of the highly cited authors in computer science and software engineering worldwide (h-index=167 g-index=365, and 147,200+ citations). He has been recognised as a "Web of Science Highly Cited Researcher" for seven times since 2016, "Best of the World" twice for research fields (in Computing Systems in 2019 and Software Systems in 2021/2022/2023) as well as "Lifetime Achiever" and "Superstar of Research" in "Engineering and Computer Science" discipline twice (2019 and 2021) by the Australian Research Review.

He has recently been recognized as a Fellow of the Academy of Europe. For further information on Dr.Buyya, please visit his cyberhome:www.buyya.com

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Prof. Erik Cambria ,IEEE FELLOW

Nanyang Technological University, Singapore

Erik Cambria is a Professor at Nanyang Technological University, where he also holds the appointment of Provost Chair in Computer Science and Engineering, and Founder of several AI companies, such as SenticNet (https://business.sentic.net), offering B2B sentiment analysis services, and finaXai (https://finax.ai), providing fully explainable financial insights. Prior to moving to Singapore, he worked at Microsoft Research Asia (Beijing) and HP Labs India (Bangalore), after earning his PhD through a joint program between the University of Stirling (UK) and MIT Media Lab (USA). Today, his research focuses on neurosymbolic AI for interpretable, trustworthy, and explainable affective computing in domains like social media monitoring, financial forecasting, and AI for social good. He is ranked in Clarivate's Highly Cited Researchers List of World's Top 1% Scientists, is recipient of many awards, e.g., IEEE Outstanding Early Career, 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, Associate Editor of various top-tier AI journals, e.g., Information Fusion and IEEE Transactions on Affective Computing, and is involved in several international conferences as keynote speaker, program chair and committee member.

Title: 7 Pillars for the Future of AI

Abstract:In recent years, AI research has showcased tremendous potential to impact positively humanity and society. Although AI frequently outperforms humans in tasks related to classification and pattern recognition, it continues to face challenges when dealing with complex tasks such as intuitive decision-making, sense disambiguation, sarcasm detection, and narrative understanding, as these require advanced kinds of reasoning, e.g., commonsense reasoning and causal reasoning, which have not been emulated satisfactorily yet. The Seven Pillars for the future of AI (https://sentic.net/7-pillars-for-the-future-of-ai.pdf) address these shortcomings and pave the way for more efficient, scalable, safe and trustworthy AI systems.



Prof. Xiangjie Kong, IEEE Senior Member

Zhejiang University of Technology, China

Dr. Xiangjie Kong is currently a Full Professor and Associate Academic Dean with the College of Computer Science & Technology, Zhejiang University of Technology (ZJUT), Hangzhou, China. Previously, he was an Associate Professor with the School of Software, Dalian University of Technology (DUT), Dalian, China, where he was the Head of the Department of Cyber Engineering. He is the Founding Director of City Science of Social Computing Lab (The CSSC Lab) (http://cssclab.cn/). He is/was on the Editorial Boards of 6 International journals. He has served as the General Co-Chair, Workshop Chair, Publicity Chair or Program Committee Member of over 30 conferences. Dr. Kong has authored/co-authored over 200 scientific papers in international journals and conferences including IEEE TKDE, IEEE TMC, ACM TKDD, IEEE TNSE, IEEE TII, IEEE TITS, IEEE NETW, IEEE COMMUN MAG, IEEE TVT, IEEE IOTJ, IEEE TSMC, IEEE TETC, IEEE TASE, IEEE TCSS, WWWJ, etc.. 5 of his papers is selected as ESI- Hot Paper (Top 1‰), and 18 papers are ESI-Highly Cited Papers (Top 1%).  His research has been reported by Nature Index and other medias. He has been invited as Reviewers for numerous prestigious journals including IEEE TKDE, IEEE TMC, IEEE TNNLS, IEEE TNSE, IEEE TII, IEEE IOTJ, IEEE COMMUN MAG, IEEE NETW, IEEE TITS, TCJ, JASIST, etc.. Dr. Kong has authored/co-authored three books (in Chinese). He has contributed to the development of 14 copyrighted software systems and 20 filed patents. He has an h-index of 51 and i10-index of 120, and a total of more than 8400 citations to his work according to Google Scholar. He is named in the2019-2023 world’s top 2% of Scientists List published by Stanford University. Dr. Kong received IEEE Vehicular Technology Society 2020 Best Land Transportation Paper Award, and The Natural Science Fund of Zhejiang Province for Distinguished Young Scholars. He has been invited as Keynote Speaker at 5 international conferences, and delivered a number of Invited Talks at international conferences and many universities worldwide.  His research interests include big data, network science, and computational social science. He is a Distinguished Member of CCF, a Senior Member of IEEE, a Full Member of Sigma Xi, and a Member of ACM.

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Prof. Ou Zhonghong

Beijing University of Posts and Telecommunications, China

Ou Zhonghong, professor of Computer School (National Demonstration Software School), doctoral supervisor, deputy director of Personnel Office, director of Talent Office, 1551 talents, young scientist of National Key Research and development Program, Beijing Famous young teacher. He used to be the vice president of the School of Computer Science (National Demonstration Software School), and now he is the director of the Computer Class Special Committee of the Virtual Simulation Experiment Teaching Innovation Alliance, the deputy director of the Education Innovation and Production-Education Integration Special Committee of the National Institute of Computer Basic Education of Colleges and Universities, and the deputy leader of the TC11 VR/AR sub-working group of China Communication Standardization Association. Standing member of the Intelligent Service Committee of the Chinese Society for Artificial Intelligence, member of the CCF Big Data/Computer Vision/Education Committee, young expert of the Internet Society of China, senior member of CCF. He has undertaken a number of national key research and development projects and National Natural Science Foundation projects, and has published more than 80 high-level papers in IEEE TMC, TMM, TCC, ACM SenSys, etc. His research results have been reported by BBC News, ACM TechNews, The Register and other internationally renowned media. His research interests include small sample learning, cross-domain adaptive, small target detection, etc.



Prof. Zhexue Huang

Shenzhen University, China

Dr. Joshua Zhexue Huang is a distinguished professor at College of Computer Science and Software Engineering and the founding director of Big Data Institute of Shenzhen University. He is also the deputy director of the National Engineering Laboratory for Big Data System Computing Technology. Prof. Huang is known for his contributions to the development of a series of k-means type clustering algorithms in data mining, such as k-modes, fuzzy k-modes, k-prototypes and w-k-means, which are widely cited and used, and some of which have been included in commercial software. He has extensive industry expertise in business intelligence, data mining and big data analysis. He has been involved in numerous consulting projects in Australia, Hong Kong, Taiwan and mainland China. Dr Huang received his PhD degree from the Royal Institute of Technology in Sweden. He has published over 250 research papers in conferences and journals with over 10000 citations. In 2006, he received the first PAKDD Most Influential Paper Award. He has served as conference and program chairs of several national and international conferences in the areas of data mining and big data. He is recognized as a scientist of Career Scientific Impact in Stanford University World’s top 2% scientists list.

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Prof. MESSAOUD ABBAS 

University of El Oued, Algeria

Professor MESSAOUD ABBAS received his degree in Systems Engineering in Computer Science from ESI, ex.INI, Algiers, Algeria, in October 1996. At the University of Science and Technology (USTHB) in Algiers, Algeria, he received his Master's degree in Computer Science in July 2007, majoring in programming and systems. In November 2018, he received his PhD in Computer Science, specializing in programming and systems, through a collaboration between USTHB(Technical University of Algiers, Algeria), the National School of Computer Science (ENSIIE) in Paris, France, and the Technical University of Valence (IUT) in France. Throughout his career, he has played a vital role in developing and managing academic computer science programs. In 2009, he headed the Department of Computer Science at the Technical Faculty of El-Oued University and joined the Scientific Committee of the Faculty of Precise Sciences in 2014. Since 2019, he has been active in the doctoral training Committee and doctoral guidance. In June 2022, he became Team Leader of the Artificial Intelligence and Its Applications Laboratory (LIAP). He has published extensively on formal methods in top journals and conferences, and more recently on artificial intelligence.

Title: The Power of Machine Learning/Deep Learning for Early and Precise Diagnosis of Severe Diseases.

Abstract: The advent of Machine Learning (ML) and Deep Learning (DL) technologies has revolutionized the medical field, particularly in diagnosing severe diseases. These advanced analytical tools harness vast datasets to identify patterns and anomalies that might elude human experts, enabling earlier and more accurate detection of conditions. This capability is critical for diseases where early intervention can dramatically alter outcomes. ML and DL applications range from analyzing medical images and audio to predicting disease progression and treatment response, showcasing a promising future where technology empowers healthcare professionals to make more informed decisions, ultimately improving patient care and survival rates.




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