Prof. Haibin Zhu, Nipissing University, Canada (IEEE Fellow)

Biography: Dr. Haibin Zhu is a Full Professor and the Coordinator of the Computer Science Program, the Founding Director of the Collaborative Systems Laboratory, a member of Arts and Science Executive Committee, Nipissing University, Canada. He is an affiliate professor of Concordia Univ. and an adjunct professor of Laurentian Univ., Canada. He received his PhD degree in computer science from the National Univ. of Defense Tech. (NUDT), China. He was the chair of the Department of Computer Science and Mathematics, Nipissing University, Canada (2019-2021), a visiting professor and special lecturer in the College of Computing Sciences, New Jersey Institute of Technology, USA (1999-2002) and a lecturer, an associate professor and a full professor at NUDT (1988-2000). He has accomplished (published or in press) over 300+ research works including 50+ IEEE Transactions articles, six books, five book chapters, four journal issues, and four conference proceedings. He is a Fellow of IEEE and I2CICC (International Institute of Cognitive Informatics and Cognitive Computing), a Senior Member of ACM, a Full Member of Sigma Xi, and a Life Member of CAST-USA (Chinese Association of Science and Technology, USA).
He is serving as Vice President, Systems Science and Engineering (SSE) (2023-), a member-at-large of the Board of Governors (2022-), and a co-chair (2006-) of the technical committee of Distributed Intelligent Systems of IEEE Systems, Man and Cybernetics (SMC) Society (SMCS), SMCS Primary Representative, IEEE Systems Council, Editor-in-Chief of IEEE SMC Magazine (2022), Associate Editor (AE) of IEEE Systems Journal (2024-), IEEE Transactions on SMC: Systems (2018-), IEEE Transactions on Computational Social Systems(2018-), Frontiers of Computer Science (2021-), and IEEE Canada Review (2017-). He was AE of IEEE SMC Magazine (2015-2021), Associate Vice President (AVP), SSE (2021), IEEE SMCS, a Conference (Co-)Chair and Program (Co-)Chair for many international conferences, and a PC member for 150+ academic conferences. 
He is the founding researcher of Role-Based Collaboration and the creator of the E-CARGO model. His research monograph E-CARGO and Role-Based Collaboration can be found  https://www.wiley-vch.de/en/areas-interest/engineering/electrical-electronics-engineering-10ee/systems-engineering-management-10ee9/e-cargo-and-role-based-collaboration-978-1-119-69306-2. The accompanying codes can be downloaded from GitHub: https://github.com/haibinnipissing/E-CARGO-Codes. He has offered 30+ keynote and plenary speeches for international conferences and 90+ invited talks internationally. He has been granted more than $1M CAD from SSHRC, NSERC, IBM, DNDC, DRDC, and OPIC.
He was listed as “Most Influential Robotics Trailblazers, Making Wave in The Industry – 2024”, InsightsSuccess Magazine. He was the recipient of the best paper award in international collaboration from the 25th Int’l conf. on CSCWD, Hangzhou, China, 2022, the meritorious service award from IEEE SMC Society (2018), the chancellor’s award for excellence in research (2011) and two research achievement awards from Nipissing University (2006, 2012), the IBM Eclipse Innovation Grant Awards (2004, 2005), the Best Paper Award from the 11th ISPE Int’l Conf. on Concurrent Engineering (ISPE/CE2004), the Educator’s Fellowship of OOPSLA’03, a 2nd class National Award for Education Achievement (1997), and three 1st Class Ministerial Research Achievement Awards from China (1997, 1994, and 1991). 
His research interests include Collaboration/Complex Systems, Human-Machine Systems, Computational Social Systems, Collective Intelligence, Multi-Agent Systems, Software Engineering, and Distributed Intelligent Systems.

Speech Title: E-CARGO/RBC: Enabling Research Innovations in the Era of AI

Abstract: Role-Based Collaboration (RBC) is a computational methodology that uses roles as the primary underlying mechanism to facilitate collaboration activities. It consists of a set of concepts, principles, models, processes, and algorithms. In the AI (Artificial Intelligence) time, many AI tools, such as LLMs (Large Language Models), can help people accomplish many low-level intelligent tasks, such as coding and reporting. Many low-level routine jobs have high potential to be replaced by such LLMs. Traditional programmers need to master powerful high-level modelling tools to meet these new challenges. E-CARGO/RBC (Environments - Classes, Agents, Roles, Groups, and Objects /Role-Based Collaboration) is a modelling methodology, which helps people deal with complex problems by designing systematic strategies other than using low level programming skills.     
RBC is a computational methodology that uses roles as the primary underlying mechanism to facilitate collaboration activities. It consists of a set of concepts, principles, models, processes, and algorithms. RBC and its E-CARGO model have been developed to a powerful tool for investigating collaboration and complex systems. Related research has brought and will bring in exciting improvements to the development, evaluation, and management of systems including collaboration, services, clouds, productions, and administration systems. RBC and E-CARGO grow gradually into a strong fundamental methodology and model for exploring solutions to problems of complex systems including Collective Intelligence, Sensor Networking, Scheduling, Smart Cities, Internet of Things, Cyber-Physical Systems, and Social Simulation Systems.
E-CARGO assists scientists and engineering to formalize abstract problems, which originally are taken as complex problems, and finally points out solutions to such problems including programming. The E-CARGO model possesses all the preferred properties of a computational model. It has been verified by formalizing and solving significant problems in collaboration and complex systems, e.g., Group Role Assignment (GRA). With the help of E-CARGO, the methodology of RBC can be applied to solve various real-world problems. E-CARGO itself can be extended to formalize abstract problems as innovative investigations in research. On the other hand, the details of E-CARGO components are still open for renovations for specific fields to make the model easily applied. For example, in programming, we need to specify the primitive elements for each component of E-CARGO. When these primitive elements are well-specified, a new type of modelling/programming language can be developed and applied to solve general problems with software design and implementations. 
In this talk, the speaker examines the requirement of research on collaboration systems and technologies, discusses RBC and its model E-CARGO; reviews the related research achievements on RBC and E-CARGO in the past years; illustrates those problems that have not yet been solved satisfactorily; presents the fundamental methods to conduct research related to RBC and E-CRAGO and discover related problems; and analyzes their connections with other cutting-edge fields. This talk aims to inform the audience that E-CARGO is a well-developed model and has been investigated and applied in many ways. The speaker welcomes queries, reviews, studies, applications, and criticisms.
As case studies of E-CARGO, GRA and its related problem models are inspired by delving into the details of the E-CARGO components and the RBC process. GRA can help solve related collaboration problems with the help of programming and optimization platforms. All the related Java codes can be downloaded by GitHub: https://github.com/haibinnipissing/E-CARGO-Codes. The speaker welcomes interested researchers and practitioners to use these codes in their research and practice and contact the speaker if there are any questions about them.
As case studies of E-CARGO, GRA and its related problem models are inspired by delving into the details of the E-CARGO components and the RBC process. GRA can help solve related collaboration problems with the help of programming and optimization platforms. All the related Java codes can be downloaded by GitHub: https://github.com/haibinnipissing/E-CARGO-Codes. The speaker welcomes interested researchers and practitioners to use these codes in their research and practice and contact the speaker if there are any questions about them.

Prof. Nikola K Kasabov, Auckland University of Technology, New Zealand (IEEE Life Fellow)

Biography: Professor Nikola K Kasabov is a Life Fellow of IEEE, Fellow of the Royal Society of New Zealand, Fellow of the INNS College of Fellows, DVF of the Royal Academy of Engineering UK, Fellow of the Asia-Pacific AI Association (AAIA) . He has a Masters and a PhD degrees from TU Sofia and Doctor Honoris Causa from Obuda University, Budapest. He is the Founding Director of KEDRI and Professor of Аuckland University of Technology, New Zealand. He is а Visiting Professor at IICT Bulgarian Academy of Sciences and Dalian University, China. He is the Director of Knowledgeengineering.ai and member of the advisory board of Conscium.com. Kasabov is Past President of the Asia Pacific Neural Network Society (APNNS) and the International Neural Network Society (INNS).  Since 2022 he established the N3BG group in Bulgaria. Kasabov has published more than 700 works in neural networks and brain-inspired computation. More information of Prof. Kasabov can be found in: https://academics.aut.ac.nz/nkasabov and https://www.knowledgengineering.ai.

Speech Title: TBD

Abstract: TBD

Prof. Teh Ying Wah, University of Malaya, Malaysia

Biography: Professor Dr. Teh Ying Wah is a distinguished computer scientist and data mining expert with over 35 years of experience. Renowned for his exceptional leadership, expertise, and contributions to the field, he has demonstrated unwavering dedication to advancing computer science and data mining. Professor Teh began his career in 1988 as an entry-level computer programmer and has since risen to become a Professor of Data Mining at the Faculty of Computer Science and Information Technology, University of Malaya. He earned his academic qualifications from Oklahoma City University and the University of Malaya. Over his illustrious career, he has authored more than 90 academic papers published in prestigious journals such as Information Fusion and the International Journal of Information Management. His research interests span diverse areas, including data warehousing, data mining, deep learning, IoT, activity recognition, wearable sensors, multivariate time series, edge computing, task scheduling, data streams, mobile computing, speaker verification, language recognition, clustering algorithms, MapReduce, stock market analysis, and sentiment analysis. His work is highly cited, with an impressive H-index and citation count across databases such as Web of Science, Scopus, and Google Scholar.
Professor Teh has supervised numerous students across all levels of study and has been teaching data mining since 2002. Many of his former students have gone on to become successful data scientists and Ph.D. graduates, contributing to leading companies such as IBM, Amazon Web Services, and Google. He has secured over RM one million in research funding from public, international, and private grants and has successfully completed two commercial data science projects for Petronas GTD and Air Liquide. His professional affiliations include serving as an Associate Editor for Human-Media Interaction - Frontiers in Psychology and as a reviewer for several high-impact journals. He is also an Expert Advisory Panel member for the Master of Science (Data Science) program at UTP, a Programme Advisory Panel member for the Bachelor of Business (Hons) in Business Analytics at TARUC, and an external assessor for Swinburne University of Technology’s Bachelor of Computer Science program. Additionally, he serves as an external assessor for the Programme Master Sciences (Computer and Information Engineering) at IIUM and as a technical assessor for the Swiss National Science Foundation. Professor Teh’s contributions to academia, research, and the data science industry have established him as a leading authority in his field. His expertise, vision, and dedication have not only advanced the field but also inspired the next generation of data scientists andresearchers.

Speech Title: Modern Data Mining: Unleashing the Power of LLMs, SLMs, and Generative AI for Deeper Insights and Smarter Decision-Making 

Abstract: With the rapid advancements in large language models (LLMs), small language models (SLMs), and generative AI (GenAI), modern data mining has transcended traditional analytical methods to achieve unprecedented accuracy, scalability, and automation. These AI-driven technologies are revolutionizing data processing, enabling intelligent pattern recognition, contextual understanding, and autonomous decision-making across industries such as healthcare, finance, cybersecurity, and smart cities.
This keynote will delve into how LLMs and SLMs are reshaping the landscape of data mining by enhancing text analytics, knowledge discovery, and contextual reasoning. It will also explore the role of Generative AI in data augmentation, synthetic data generation, and AI-driven automation, providing robust solutions for incomplete, biased, or noisy datasets. Furthermore, we will discuss challenges such as hallucinations in AI-generated insights, model interpretability, and ethical concerns related to bias and misinformation.
By presenting cutting-edge applications and real-world case studies, this session will highlight how AI-powered data mining—driven by LLMs, SLMs, and Generative AI—can unlock deeper insights, automate complex workflows, and enhance decision-making capabilities. Attendees will gain insights into emerging trends and research directions, fostering collaboration and innovation to shape the future of intelligent and ethical AI-powered data mining.

Prof. Dewan Farid, Dean of School of Science & Engineering, Southeast University, Bangladesh

Biography: Prof. Dr. Dewan Md. Farid is the Dean of School of Science and Engineering, and Professor of Computer Science and Engineering at Southeast University. Before joining Southeast he worked for over a decade as the Professor of Computer Science and Engineering at United International University. He is an IEEE Senior Member and Member ACM. Prof. Farid worked as the Postdoctoral Fellow/Staff at the following research labs/groups: (1) Computational Intelligence Group (CIG), Department of Computer Science and Digital Technology, University of Northumbria at Newcastle, UK in 2013, (2) Computational Modelling Lab (CoMo) and Artificial Intelligence Research Group, Department of Computer Science, Vrije Universiteit Brussel, Belgium in 2015-2016, and (3) Decision and Information Systems for Production systems (DISP) Laboratory, IUT Lumière – Université Lyon 2, France in 2020. Prof. Farid was a Visiting Faculty at the Faculty of Engineering, University of Porto, Portugal in June 2016. He holds a PhD in Computer Science and Engineering from Jahangirnagar University in 2012. Part of his PhD research has been done at ERIC Laboratory, University Lumière Lyon 2, France by Erasmus-Mundus ECW eLink PhD Exchange Program. His PhD was fully funded by Ministry of Science & Information and Communication Technology, Government of the People's Republic of Bangladesh and European Union (EU) eLink project. Prof. Farid has published 153 peer-reviewed scientific articles, including 38 highly esteemed journals like Expert Systems with Ap­plications, IEEE Access, Journal of Theoretical Biology, Journal of Neuroscience Methods, Bioinformatics, Scientific Reports (Nature), Proteins and so on in the field of Machine Learning, Data Mining, Data Science and Big Data. Prof. Farid received the following awards: (1) Dr. Fatema Rashid Best Paper Award (2nd Position) for the paper titled “KNNTree: A new method to ameliorate k-nearest neighbour classification using decision tree” in 3rd International Conference on Electrical Computer and Communication Engineering (ECCE 2023), CUET, Chittagong, Bangladesh, (2) Best Paper Award for the paper titled "Layered ensemble learning for effective binary classification" in 2nd International Conference on Emerging Technology in Data Mining and Information Security (IEMIS 2020), Kolkata, India, (3) JuliaCon 2019 Travel Award for attending Julia Conference at the University of Maryland, Baltimore, USA, and (4) United Group Research Award 2016 in the field of Science and Engineering. He received the following research funds as Principal Investigator: (1) a2i Innovation Fund of Innov-A-Thon 2018 (Ideabank ID No.: 12502) from a2i-Access to Information Program – II, Information and Communication Technology (ICT) Division, Government of the People’s Republic of Bangladesh, and (2) Project Code: UIU/IAR/01/2021/SE/23 received from Institute for Advanced Research (IAR), United International University. Prof. Farid received the following Erasmus Mundus scholarships: (1) LEADERS (Leading mobility between Europe and Asia in Developing Engineering Education and Research) to undertake a staff level mobility at the Faculty of Engineering, University of Porto, Portugal in 2015, (2) cLink (Centre of excellence for Learning, Innovation, Networking and Knowledge) for pursuing Postdoc at University of Northumbria at Newcastle, UK in 2013, and (3) eLink (east west Link for Innovation, Networking and Knowledge exchange) for pursuing Ph.D. at University Lumière Lyon 2, France in 2009. Prof. Farid also received Senior Fellowship I and II awards by National Science & Information and Communication Technology (NSICT), Ministry of Science & Information and Communication Technology, Government of the People's Republic of Bangladesh respectively in 2008 and 2011 for pursuing Ph.D. at Jahangirnagar University. He visited 19 countries for attending international conferences, research and higher education. Prof. Farid delivered several invited/keynote talks including an invited research talk at Data to AI Group (DAI), Laboratory for Information and Decision Systems (LIDS), Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, USA.   

Speech Title: Deep Transfer Learning for Vehicle Classification and Detection: Tools and Techniques

Abstract: Machine Learning especially Deep Learning methods are employing for Image Processing and Classification in many real-life applications e.g. Vehicle classification and detection. Vehicle classification and detection has been a field of application for deep learning and image processing which play a very important role in intelligent transport management and AI-assisted driving systems. Deep computational modelling is the foundation of Artificial Intelligence (AI) and solves problems that would prove impossible or difficult by human or statistical standards. This talk will address the challenges and issues of applying Machine Learning, Deep Learning, and Transfer Learning in vehicle classification and detection system to detect and classify low-speed and high-speed vehicles. We will discuss about the 11 pre-trained deep convolutional neural network (CNN) models: YOLOv8 Classify, MobileNetV2, GoogLeNet, AlexNet, ResNet-50, SqueezeNet, VGG19, DenseNet-121, Xception, InceptionV3, and NASNetMobile in this talk. Finally, the talk will be concluded by discussing a system that can detect and classify low-speed and high-speed vehicles, while facing challenges that include issues with image annotation tools like poor label visibility, lack of error checking, and limited guidance, as well as difficulties in setting up the NVIDIA Jetson Nano embedded device for efficient model deployment.

 

The Keynot Speaker list above is continuously updated.