Keynote Speakers

Design of Enumeration Algorithms: A Tool to Assist 4IR

Abstract

You are going to buy a new apartment where you are allowed to choose a floorplan of the apartment. The development company has an AI expert system which shows you a floorplan. If you do not like the floorplan, it shows you another floorplan. It would be great if they can show you all possible floorplans. It is an enumeration algorithm which can show you all possible floorplans. Given some properties of an object, an enumeration algorithm enumerates all objects with the properties. Enumeration algorithms play a crucial role in the Fourth Industrial Revolution (4IR), which is characterized by the fusion of physical, digital, and biological systems. In big data analysis enumeration algorithms help in systematically listing and analyzing large datasets which is crucial for extracting meaningful insights from big data. Enumeration algorithms are used in various Artificial Intelligence (AI) and Machine Learning (ML) applications to explore different model parameters, features, and structures, helping to enhance the accuracy and efficiency of predictive models. In the area of robotics and automation enumeration algorithms assist in path planning, task scheduling, and resource allocation for robots and automated systems, enabling them to perform complex tasks with precision. This talk explores the critical role of enumeration algorithms in advancing 4IR technologies, offering a comprehensive overview of their design principles and practical applications. We focus on the theoretical foundations of these algorithms, discussing their impact on problem-solving and decision-making processes. As working examples, we will show efficient algorithms to enumerate all distributions of objects to bins and an algorithm to enumerate all triangulations of a polygon. Attendees will gain insights into designing robust enumeration algorithms tailored to contemporary challenges, equipping them with the tools to harness the full potential of 4IR technologies.

Biography

Prof. Dr. Md. Saidur Rahman, a professor of Bangladesh University of Engineering and Technology (BUET) and a fellow of Bangladesh Academy of Sciences, is a renowned researcher in the field of graph algorithms and is regarded as an authority on graph drawing algorithms. He has more than 130 publications on algorithms and graph theory in internationally reputed journals and conferences. He has developed many efficient algorithms for finding drawings for planar graphs which have enormous applications in VLSI layout automation, software engineering, DNA recognition, etc. His graduate textbook “Planar Graph Drawing,” written with renowned computer scientist Professor Takao Nishizeki, is considered as the most valuable pioneering work in planar graph drawings. He has co-edited five volumes of LNCS series of Springer and served as guest editors for several reputed journals including Algorithmic and Theoretical Computer science. Currently he is in the editorial board of several international Journals including the Journal of Graph Algorithms and Applications and International Journal of Foundation of Computer Science. His undergraduate textbook “Basic Graph Theory” which has been published by Springer has received around 62,000 downloads within six years. Professor Rahman is leading an enthusiastic research group in Graph Drawing and Information Visualization Laboratory of CSE Department, BUET. He has supervised four Ph. D. theses and 27 M. Sc. Engg. theses. He is a recipient of “BAS Gold Medal 2003” in the junior group, “UGC Award 2004” and the prestigious “Funai Information Technology Award for Young Researchers 2004.” Prof. Rahman is a founder of the International Conference and Workshops on Algorithms and Computation (WALCOM) which has appeared as a very prestigious international venue of theoretical computer science over the years. Professor Rahman worked as a member of expert panels of various national and international bodies. Currently he is a member of Board of Accreditation for Engineering and Technical Education (BAETE), Bangladesh which is a full signatory of Washingtom Accord, and also a member of BAETE Evaluation Team (ET). In BUET administration, he showed excellent administrative capabilities as the Registrar, the Director of Institutional Quality Assurance Cell (IQAC) and Head of CSE Department of BUET.Professor Rahman served as the Chair of BUET ACM Chapter for two consecutive terms during 2016-2018. He served the IEEE Computer Society Bangladesh Chapter as the Educational Activity Coordinator in 2020 and as Vice-Chair (Technical) in 2021. He played the role of a founder of International Conference and Workshops on Algorithms and Computation (WALCOM) in 2007 which has appeared as a prestigious venue for theoretical computer science over the years.

AI and Cybersecurity: Gaps, Dead Ends and Opportunities

Abstract

While the industry benefited from the adoption of AI/ML in cybersecurity, fresh new batch of academics are still left with decade old dataset which is essentially a shadow of the past. In order to avoid leading brilliant minds to the dead end and irrelevance, what can be done? In this talk, we invite audience to brainstorm on possible source of challenges, as well as the fresh new field which remain largely unaddressed by the community.

Biography

Prof. Youki Kadobayashi has been leading the Laboratory for Cyber Resilience, Nara Institute of Science and Technology, Japan, where he works closely with research communities worldwide to further progress the state of cybersecurity. He also initiated cybersecurity education programs in academia as well as industry sectors in Japan, which resulted in a strong alumni network of more than 1,000 cybersecurity specialists.

Towards Data-Efficient and Interpretable Computer Vision: Advances in Few-Shot Learning and Explainable AI

Abstract

In the era of data-driven deep learning, the ability to learn effectively from limited examples and to explain model decisions remains a critical challenge across various sectors, including business, finance, healthcare, agriculture, smart cities, and cybersecurity. This talk explores recent advances at the intersection of Few-Shot Learning (FSL) and Explainable AI (XAI) in the field of computer vision, highlighting how models can be designed to be both data-efficient and interpretable, aimed at academics, industry professionals, and decision-makers. Besides, it will introduce some of my projects in FSL and XAI, demonstrating their applications in real-world automation scenarios. Additionally, the talk will address several pressing challenges and unsolved problems in the field that require further attention.

Biography

Prof Mohammad Shorif Uddin earned his PhD in Engineering from the Kyoto Institute of Technology, Japan, and holds a Master’s degree from Shiga University as well as a BSc in Engineering from BUET. He has been a faculty member of the Department of Computer Science and Engineering at Jahangirnagar University since 1992, where he has also served as Department Chair and Head of the ICT Cell. Since May 2024, he has been serving as the Vice Chancellor of Green University of Bangladesh. His research interests include computer vision, artificial intelligence, image security, and data science. He has authored over 250 publications with 6,000+ citations and holds two patents. He has undertaken postdoctoral research at leading institutions in Japan, Germany, China, and Singapore. In 2004, he gained international recognition for inventing the “Electronic Eye” and has since received multiple prestigious international awards. He has also coached ACM ICPC World Finalist teams. Prof Uddin is a Fellow of the Institution of Engineers, Bangladesh (IEB) and the Bangladesh Computer Society (BCS), a Senior Member of IEEE, and serves as an Associate Editor of IEEE Access.

AI-Driven Wearables and Decision Support Systems for Digital Health

Abstract

With the growing capabilities of AI, we now have the opportunity to move from reactive to proactive, real-time health monitoring. However, integrating AI with wearable and portable devices still faces key challenges, particularly in building robust, explainable, and clinically reliable systems. Dr Moni will discuss how my team and I have designed portable, AI-enabled, wearable devices capable of continuously monitoring physiological signals and supporting early diagnosis of chronic and acute conditions. These tools are supported by advanced machine learning algorithms that we have developed to analyse multimodal data, including imaging, EEG, ECG, and multi-omics data, to detect disease biomarkers and health patterns. Dr Moni will then present our work on real-time decision support systems that generate personalized insights and predict critical health conditions, including sleep apnoea, cardiovascular conditions, and mental health. Our goal is to transform digital health delivery by combining wearable technology, AI, and explainable models to enable scalable and personalized care, especially for remote and under-resourced settings. He will highlight how interdisciplinary collaboration is key to realising next-generation digital health innovations.

Biography

Prof. Dr. Mohammad Ali Moni is a leading global scientist in AI and Digital Health Technology. He completed his PhD in AI and Digital Health at the University of Cambridge and has worked at world-renowned universities including Oxford, Cambridge, and the University of Sydney. He is currently the Director of the AI and Digital Health Centre and serves as an Advisor of Commonwealth Rural Digital Health Initiatives. Prof. Moni has published 450+ journal articles, receiving 47,000+ citations with an h-index of 91. He is the recipient of numerous prestigious awards and fellowships, including the

IoT for Controlled Environments: Building Smart Plant Factory Infrastructures

Abstract

The new Agriculture paradigm is getting a paradigm shift under stress from booming urban population, climate change, and limited land for crops, so controlled environmental systems such as plant factory labs based on precision technology have been developed to ensure year-round production of crops with efficient use of resources, irrelevant of seasonal and geographical limitations. The IoT acts as a key enabler of this transformation through the integration of sensor networks, smart actuators, and cloud analytics over agricultural infrastructures to build resilient ecosystems, in which temperature, humidity, light intensity, carbon dioxide, and nutrient flows are monitored in real time, thus allowing precise intervention in crop growth conditions. Pairing those capabilities with advanced data analytics and AI-driven decision support, IoT ensures the best use of resources, highest yields, reduced operational costs, predictive maintenance, supply chain transparency, and methods that serve global food security and climate resilience goals. This keynote, spotlighting case studies, research findings, and future development avenues, elucidates how IoT-enabled smart infrastructures stand to transform plant factories and offer a working model for sustainable urban agriculture and resilient food systems.

Biography

Dr. Muhammad Raisuddin Ahmed is a Associate Professor (SL) at the Military Technology College in Muscat, Oman, affiliated with the University of Portsmouth's Oman campus. With a rich academic and research background, Dr. Ahmed has held positions as a Teaching Fellow (Lecturer) at the University of Canberra and a research officer at the Australian National University in Australia. He has an impressive educational profile, having completed a Ph.D. at the University of Canberra, a Master of Engineering Studies in Telecommunication and a Master of Engineering Management from the University of Technology, Sydney, Australia and a Bachelor of Engineering (Hons) in Electronics Majoring in Telecommunications from Multimedia University in Malaysia. Dr. Ahmed has authored numerous papers in the fields of Wireless Sensor Networks, Internet of Things, Machine Learning and Artificial Intelligence, Distributed Wireless Communication, and Antenna. His research contributions have been published in high-impact journals and conferences, reflecting his dedication to advancing knowledge and innovation in these domains.

Multi-Agent Vision-Language Models for Data Visualization and Analytics

Abstract

Recent advances in Vision-Language Models (VLMs) and agent-based AI have enabled new ways of supporting data visualization and analytics. In this talk, I will explore how multi-agent frameworks and GUI agents can tackle tasks ranging from visualization generation to interactive data exploration. I will introduce Text2Vis, a benchmark for generating multimodal visualizations from natural language queries, DataNarrative, a multi-agent framework for crafting coherent data stories that blend text and charts, and DashboardQA, the first benchmark to evaluate GUI agents on interactive dashboards. Together, these works illustrate how multi-agent approaches can refine answers, visualization code, and interface interactions to support complex analytical workflows. I will conclude with future directions for building collaborative, trustworthy, and human-centered AI agents that make data visualization and analytics more accessible.

Biography

Enamul Hoque Prince is an Associate Professor and the Director of the School of Information Technology at York University. Previously, he was a postdoctoral fellow in Computer Science at Stanford University. His research addresses the challenges of the information overload problem using an interdisciplinary lens, combining information visualization and human-computer interaction with natural language processing. Since his research is uniquely positioned at the intersection of information visualization, NLP, and HCI, he regularly publishes in top venues in each of these areas including IEEE Vis, ACL, EMNLP, CHI, IUI, and UIST. He serves as an Area Chair for the ACL Rolling Review (2021-) and as a program committee member (2018-) for the IEEE Vis. His research has been funded by NSERC Canada, Canada Foundation for Innovation, and National Research Council Canada, among others.

Monte Carlo Track Structure Simulations of High-Z Metallic Nanorods for Modeling Radiosensitization Effects in Radiotherapy

Abstract

Radiotherapy is a major cancer treatment, however, its efficacy is hindered by limited tumor selectivity and radioresistance in hypoxic regions. Nanoparticle radiosensitization (NPRS) using high atomic number metallic nanoparticles offers a strategy to enhance localized radiation effects. While gold nanospheres dominate current research, nanorods offer unique advantages, such as tunable plasmonic properties, enhanced cellular uptake, and high surface area-to-volume (SA:V) ratios. This study investigates high-Z nanorods to understand how their characteristics can influence radiosensitization mechanisms. Key goals included assessing secondary electron emission, dose enhancement, ROS generation, and DNA damage via detailed Monte Carlo simulations, building a computational framework using Monte Carlo simulations for NPRS optimization. Using TOPAS code, track-structure simulations were conducted to investigate four influencing parameters: (i) geometry, (ii) elemental composition (Au, Pt, Hf, Gd, Ce), (iii) surface coatings, and (iv) spatial distribution of nanorods in tumor vasculature system. Simulations at 50 and 100 kVp modeled superficial radiotherapy, particularly for shallow-sited tumors such as melanoma. Gold nanorods showed DERs comparable to nanospheres, but with geometry-driven changes in low-energy Auger-Meitner electron emission. Interestingly, higher SA:V ratios decreased secondary electron yield. Gold and platinum nanorods provided the highest DERs within 300 nm, outperforming hafnium, gadolinium, and cerium. However, beyond 1 µm distance from the nanorod, differences between the nanomaterials faded. Surface coatings reduced DERs by 1–7%, with CTAB having the minimum impact on physical dose enhancement. However, thicker coating layer enhanced ROS production due to spectral shifts in emitted secondary electrons. Vascular modeling revealed that nanorods embedded in the microvessel wall produced the highest DERs (up to 10 at 50 mg Au/g under 50 kVp), with dose enhancement damage observed up to 10–15 µm from the vessel wall. This work demonstrates that nanorods are effective radiosensitizers, with geometry offering distinct advantages over nanospheres. Surface coatings modulate physical and chemical effects, emphasizing the need for balanced design. Vascular targeting may overcome tumor radioresistance without requiring cellular uptake. Overall, this study provides a detailed computational framework linking nanoparticle design to radiobiological impact, guiding future experimental and clinical translation.

Biography

Professor Dr. Mayeen Uddin Khandaker graduated from the University of Chittagong in 1996. He did MSc in Nuclear Physics from the same University in 1997. He achieved a brilliant result (1st class 1st position) in both the BSc (Physics) and MSc (Physics) examinations. Professor Mayeen was conferred a Ph.D. in Radiation and Nuclear Physics by Kyungpook National University, South Korea, in February 2008. Professor Mayeen is currently working as a Professor of Applied Physics and Radiation Technology at the Faculty of Engineering and Technology, Sunway University, Malaysia. Previously, he worked as a Lecturer of Physics at the International Islamic University Chittagong, Bangladesh, as an Assistant Professor of Physics at the American International University-Bangladesh, an Associate Professor of Physics at the University of Malaya, Malaysia. He also worked as a Nuclear Scientist at the International Atomic Energy Agency (Austria), a pre-doctoral and post-doctoral researcher at the Korea Atomic Energy Research Institute (Korea), and a short-term post-doctoral research fellow at the France Atomic Energy Agency (CEA), Saclay, Paris, France. At present, he has more than 24 years of teaching experience at the university level, whereas his primary responsibility is to teach various physics courses to undergraduate and postgraduate students. Prof. Mayeen is an elected fellow of the Bangladesh Academy of Sciences. He is a life member of the Bangladesh Medical Physics Society and a voting member of the International Radiation Physics Society. At present, he is a senate member of Sunway University and an Adjunct Professor at Korea University (South Korea). A prolific author with over 756 Web of Science/Scopus-indexed papers, Professor Mayeen's work has been cited over 21050 times, with an h-index of 68. He is recognized by Elsevier-Stanford University among the top 2% most highly cited researchers globally since 2020, a recipient of the 'Excellence in Research-2024' by Sunway University, and holds editorial roles in leading international journals, including Radiation Physics and Chemistry (Elsevier). He also served as one of the Chief Scientific Investigators for a CRP on theranostic radionuclide production assigned by the IAEA.