Iconeect2026 Invited Speakers
Dr. Chandima Pathiranage
Associate Lecturer, Sheffield Hallam University
Dr. Pathiranage is an accomplished academic and researcher in Electrical Engineering with over 17 years of experience across higher education, applied research, and industry-focused engineering consultancy.
His expertise spans power electronics, high-power converters, renewable energy grid integration, battery and hybrid energy storage systems (BESS/HESS), electric vehicle (EV) charging infrastructure, smart grids, and intelligent control systems.
He holds a PhD in Robotics and Intelligent Systems and a Master of Engineering from Saga University, Japan, where he was awarded the prestigious JSME Miura Award. He also holds a BSc in Electrical Engineering from the University of Moratuwa, Sri Lanka.
Throughout his career, including extensive service as a Professor at the University of Moratuwa, a Visiting Professor at the University of Nottingham, and a Research Associate at Cardiff University, he has spearheaded research on high-impact projects. Most recently, his work with Cardiff University and National Grid UK focused on the interaction of megawatt heavy-duty eTruck charging networks with high-voltage transmission systems.
A Senior Member of IEEE and a Chartered Engineer, Dr. Pathiranage has published more than 85 peer-reviewed journal articles and international conference papers. He has served in key leadership roles, including President of the IEEE Robotics and Automation Society Sri Lanka Chapter, and continues to consult on advanced energy systems, microgrids, and power quality optimization.
Megawatt Charging Systems for Long-Haul Battery Electric Trucks in Great Britain: Challenges and Opportunities
Abstract
Decarbonisation of road freight is an important component of the transition towards net-zero transport. Heavy goods vehicles (HGVs) play a critical role in the movement of goods and materials, but their high vehicle mass, long operating distances and intensive utilisation result in significant energy consumption and greenhouse-gas emissions. Battery electric trucks (BETs) are emerging as a promising zero-tailpipe-emission alternative for heavy-duty transport.
However, the successful electrification of long-haul freight requires not only suitable vehicles and high-capacity batteries, but also a charging infrastructure capable of replenishing several hundred kilowatt-hours of energy within the limited stopping periods available during freight operations.
This talk presents an overview of Megawatt Charging Systems (MCS) for long-haul BETs, with particular emphasis on their deployment and interaction with the electricity network in Great Britain. The presentation first examines the operational characteristics of long-haul battery electric trucks, including battery capacity, energy consumption, state-of-charge behaviour and the influence of vehicle mass, driving speed, temperature and journey characteristics on energy demand. Different charging strategies, including depot, destination, public overnight and en-route charging, are discussed, with particular attention given to the need for megawatt-class en-route charging during relatively short driver rest periods.
The talk then introduces the architecture and technical characteristics of MCS, including high-power DC conversion, thermal management, vehicle–charger communication, protection requirements and emerging international standards. A modelling framework for estimating charging demand is presented, in which traffic characteristics, journey distance, battery state of charge, energy consumption and charging participation are used to generate individual and aggregated charging load profiles. The importance of stochastic and Monte Carlo approaches is highlighted because truck arrivals, journey characteristics and charging requirements vary significantly in practice.
The presentation further discusses MCS station sizing and strategic siting along major freight corridors, logistics hubs, motorway service areas and ports. Particular attention is given to the implications of multi-megawatt charging hubs for distribution and transmission networks, including transformer and feeder loading, voltage variation, network congestion, power quality and available connection capacity. Mitigation options such as battery energy storage systems, renewable generation, smart charging, dynamic power sharing, reactive-power support and network reinforcement are considered.
The talk concludes by examining the principal technical, economic and regulatory barriers to large-scale MCS deployment, together with opportunities in charging infrastructure, power electronics, energy storage, renewable integration, intelligent energy management and grid-support services. Although the presentation focuses on Great Britain, the challenges and solutions discussed are relevant to many countries planning for the electrification of heavy-duty road freight.
Dr. Imtiaz Ahmed
Associate Professor, Howard University, USA
Dr. Imtiaz Ahmed is an Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) at Howard University, Washington, DC, USA. Dr. Ahmed works in the areas of wireless communications, signal processing, and computer networks.
He finished his undergrad and Masters in Electrical and Electronic Engineering (EEE) from Bangladesh University of Engineering and Technology (BUET). He completed his Ph.D. in Electrical and Computer Engineering (ECE) from the University of British Columbia (UBC), Vancouver, BC, Canada in 2014. He then worked as a postdoctoral fellow at McGill University during 2014-15. Dr. Ahmed worked as a wireless systems engineer in Intel Corporation, San Diego, California for more than 3 years.
Currently, Dr. Ahmed is working on diversified topics in the areas of 5G and beyond 5G cellular communication systems. In particular, his research interests include artificial intelligence in physical layer design, intelligent reflecting surface (IRS) assisted communication network, cell-free massive MIMO, communications with THz bands, aerial terrestrial integrated network, digital twin for communication network, etc.
Digital Twin Assisted Next Generation Wireless Communications
Abstract
Digital twins (DTs) are emerging as a transformative technology for cyber-physical systems, providing virtual replicas that enable real-time monitoring, predictive modeling, and intelligent optimization of physical processes. However, designing DTs for wireless communication systems poses unique challenges because the wireless channel is inherently random and varies significantly across time, space, and frequency. Capturing both short-term fading dynamics and long-term spatial-temporal variations is essential to ensure that the DT remains accurate and useful for decision-making.
Moreover, a single DT model is often insufficient to capture the diverse physical layer functionalities required in modern networks. This talk will explore how ray-tracing tools, combined with AI-driven algorithms, can be used to build adaptive and high-fidelity channel twins that tackle channel randomness. It will further discuss how a multi-layer hierarchical DT framework, coordinated by large language models (LLMs), can integrate these components into a comprehensive and scalable physical layer DT solution for next-generation wireless networks.
Through these discussions, the talk will highlight how AI-driven digital twin technologies can transform wireless networks into intelligent, adaptive, and seamlessly connected systems, paving the way toward the sixth generation (6G) of cellular communications.
Laxmi Vanam
Data Analytics Specialist, Vanguard, USA
Laxmi Vanam is a technology and AI leader at Vanguard who enjoys turning complex data into clear, actionable insights that drive real-world decisions.
With over 17 years of experience, she is passionate about mentoring students through adplist, mentor collective programs and helping them build future-ready careers - blending strong technical skills with leadership, adaptability, and ethical thinking.
Building a Future Ready Career in AI and Data
Abstract
AI is reshaping careers in data and technology, making adaptability and strategic thinking as important as technical expertise. This session explores the skills professionals need to stay relevant, create meaningful business impact, and grow in an AI driven world, drawing on practical lessons from real world analytics, data modernization, and AI initiatives.
Ir. Ts. Dr. Maizatul Zolkapli, PhD., SMIEEE
Senior Lecturer, Universiti Teknologi MARA, Malaysia
Ir. Ts. Dr. Maizatul Zolkapli is a Senior Lecturer at the Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM), Malaysia. She is a registered Professional Engineer, Professional Technologist and IEEE Senior Member, with expertise in optical fibre sensing and semiconductor technology.
She has also served as the Electronic Engineering Discipline Coordinator at UiTM. Dr. Maizatul began her career in the semiconductor industry in 2001 as a Yield Engineer at Silterra Malaysia Sdn. Bhd. and later served as a Product Engineer at Freescale Semiconductor Malaysia Sdn. Bhd. She joined UiTM in 2009 and has over two decades of combined industry and academic experience.
Her research focuses on optical fibre-based sensors for water and environmental monitoring, as well as semiconductor device fabrication and characterization. She obtained her PhD in Electrical Engineering from UiTM, with research involving microfluidic transistors and fibre-optic sensing technologies. She has contributed through journal publications, conferences, workshops and research collaborations.
Dr. Maizatul is actively involved in IEEE professional and student activities. She has served as Secretary of the IEEE Malaysia Section and IEEE Electron Devices Society Malaysia Chapter, and currently serves as Counselor of the IEEE UiTM Student Branch. Her contributions to professional service and community engagement have been recognized through the IEEE EDS Malaysia Chapter Outstanding Volunteer Award (2021) and UiTM’s Special Award for Best Community Service Network (2024).
Advancing Fiber-Optic Oxygen Sensing Through Fluorescence and SPR
Abstract
Accurate oxygen monitoring is valuable in environmental, industrial, and biomedical applications. This work presents the development of oxygen sensors that combine fluorescence with surface plasmon resonance (SPR) on multimode plastic optical fibers. Three oxygen-sensitive dyes; Ru(dpp)₃²⁺, PtOEP, and PdTFPP are examined in dye-only and silver-coated, SPR-assisted configurations. As oxygen concentration increases, fluorescence intensity decreases through oxygen quenching. The SPR-assisted configurations also produce oxygen-dependent changes in the measured spectra. By comparing the responses of the three dyes, the study shows how dye selection and fiber coating influence sensor performance. It also discusses the potential of this approach for compact, flexible, and quasi-distributed oxygen sensing.
Md Aktaruzzaman, Ph.D.
Assistant Professor, Indiana University Kokomo, USA
Md Aktaruzzaman, Ph.D., is an Assistant Professor of Computer Science and Informatics at Indiana University Kokomo, USA. He received his Ph.D. from the University of Milan, Italy, where he was also a postdoctoral fellow. He later held a postdoctoral fellowship at the Indiana University School of Medicine, USA.
His research focuses on entropy-based analysis of physiological signals, particularly heart rate variability. He developed the parametric estimation of Sample Entropy and co-developed Bubble Entropy metrics. Recently he proposed Amplitude-Weighted Bubble Entropy - a robust entropy metric. He has published over 45 papers and serves as Associate Editor of Sleep Medicine and Disorders. He is a member of IEEE and AHA.
Complexity Analysis of Physiological Signals: Current Challenges and Future Perspectives
Abstract
Physiological systems such as the cardiovascular system are governed by many interacting feedback loops acting across time scales. Their output fluctuates in ways that are neither regular nor random. Disease and ageing erode this complexity, pushing dynamics toward rigid regularity, as in heart failure, or toward randomness, as in atrial fibrillation. Quantifying complexity has therefore become an important tool in physiological signal analysis. This talk reviews how entropy measures made complexity measurable, and where they fall short.
Approximate Entropy and Sample Entropy are widely used metrics for complexity (or irregularity measures), from neonatal sepsis monitoring to sleep staging and blood-pressure regulation in atrial fibrillation. However, their values depend strongly on the embedding dimension m and tolerance r, they become biased or undefined on short records, they are sensitive to artifacts, and their cost grows quadratically with record length. Bubble Entropy was proposed to address these limitations. It removes the tolerance r, is nearly insensitive to m, is always defined, and runs in near-linear time. However, because it uses only the rank order of samples, it is blind to amplitude. We then present Amplitude-Weighted Bubble Entropy (AWBE), which weights each bubble-sort swap by its magnitude while keeping Bubble Entropy's speed and freedom from r. On 5000 ECG beats, AWBE raised normal-versus-abnormal discriminative power from 0.692 to 0.815. A rank-preserving surrogate test confirmed that this gain comes from amplitude information that ordinal measures cannot capture. Future works include parameter free estimation of entropy, validating it across patients and conditions, establishing its theoretical properties, and deploying it in wearable devices toward complexity measures that are parameter-light, amplitude-aware and clinically useful.
Keywords: Entropy, Physiological Signals, Heart Rate Variability
Dr. Wordh Ul Hasan
Assistant Professor, Tuskegee University, USA
Dr. Wordh Ul Hasan is an Assistant Professor of Computer Science at Tuskegee University, Alabama, USA. He earned his Ph.D. in Computer Science from North Dakota State University, where his dissertation focused on a multi-agent conversational AI system for Alzheimer’s care and assistance.
His research lies at the intersection of artificial intelligence, healthcare, human–computer interaction, and well-being. He develops responsible and human-centered AI systems to support older adults, caregivers, underserved communities, and people seeking trustworthy health information. His work includes conversational AI, agentic systems, retrieval-augmented generation, multimodal technologies, and AI safety and evaluation.
Applications of Artificial Intelligence in Health and Well-Being
Abstract
Artificial intelligence is transforming health and well-being through earlier disease detection, personalized care, clinical decision support, remote health monitoring, and intelligent conversational assistants. This talk will introduce these applications using accessible real-world examples, including AI systems that support older adults, people living with Alzheimer’s disease, family caregivers, and individuals seeking reliable health information. It will also highlight emerging technologies such as generative AI, retrieval-augmented generation, and agentic AI.
Finally, the talk will discuss important challenges—including hallucinations, bias, privacy, trust, and equitable access—and explain why responsible, human-centered design is essential when developing AI for healthcare.
Ali Muzaffar
Engineering Manager, Privacy and AI Governance, Rivian & Volkswagen Group Technologies (RVT), USA
Ali Muzaffar is a technology leader specializing in privacy engineering, cybersecurity, Trust & Safety, and responsible AI governance, with more than a decade of experience across global technology and professional services organizations.
He currently serves as Engineering Manager in Privacy and AI Governance at Rivian & Volkswagen Group Technologies (RVT) (Joint venture between Rivian (USA) and Volkswagen (Germany)), where he is a founding member of the privacy function within the RV Tech Foundation team.
Previously, at Meta, Ali held leadership roles spanning Product Privacy, Trust & Safety, and AI governance. He led research privacy review and governance across Meta’s Central Strategy, Science, and Research organizations and contributed to major company initiatives involving Account Center, internationalization, social impact, responsible data use, and generative AI. His work included large-scale privacy and risk review programs and human-in-the-loop systems designed to translate complex privacy, safety, and governance requirements into practical product processes.
Earlier in his career, Ali spent 6 years at Deloitte, where he served as a Manager in Cybersecurity and Technology Risk and worked with multiple Fortune 100 companies on cybersecurity, technology risk, privacy, and governance.
Beyond industry, Ali is an IEEE Senior Member, chaired multiple committees on AI Ethics and Standards development, Young Professional committee, and currently chairs the IEEE Society on Social Implications of Technology (SSIT) Education Committee’s K-12 STEM initiatives and leads IEEE RAISE (Responsible AI Student Education), an international initiative focused on responsible AI education and preparing the next generation for an AI-driven world. He is passionate about bridging industry and academia, advancing responsible innovation, and helping the next generation of engineers build technologies that benefit people and society.
Engineering Trustworthy AI: From Research to Real-World Systems — Privacy, Security, Human Oversight, and Responsible AI at Scale
Abstract
As artificial intelligence moves from research laboratories into everyday products and services, engineers face an increasingly important challenge: how do we build intelligent technologies that are not only powerful and innovative, but also trustworthy?
Drawing on more than a decade of experience across technology, cybersecurity, privacy, and Trust & Safety, this talk explores how privacy engineering, cybersecurity, responsible AI governance, and human oversight can be integrated into the design and deployment of AI systems at scale.
The talk will examine practical industry challenges, including responsible data use, privacy-preserving research, human-in-the-loop AI review systems, risk-based governance, and the responsible deployment of generative AI. It will also consider how engineering decisions shape the safety, privacy, and trustworthiness of technologies used at global scale and how organizations can translate responsible AI principles into practical systems, processes, and safeguards.
The session will conclude by looking at the role of the next generation of engineers and researchers in shaping AI, with an emphasis on combining technical innovation with ethical responsibility, critical thinking, and human-centered design.
Dr. Ford Lumban Gaol
Professor of Computer Science, Binus University, Indonesia
Dr. Ford Lumban Gaol is currently Professor of Computer Science at Binus University. He is the Director of the Binus Graduate Program and Head of the Department of Doctor of Computer Science at Binus University. He was also the Research Interest Group Leader for Advance System in Computational Intelligence & Knowledge Engineering at Binus University.
Dr. Ford is the Chair of the IEEE Computer Society Indonesia Chapter and also the IIAI ASEAN President. He is a Research Fellow at RCAISS in the Advanced Institute of Industrial Technology, Tokyo Metropolitan Public University Corporation, Japan, and also serves on the Advisory Board of Shridhar University, India. Dr. Ford is a Visiting Professor at Sam Houston Texas University, USA (2024–2030) and an Adjunct Professor at Algoma University, Canada (since 2025).
He is a Fellow of the Alliance for AI & Humanity (AAIH), Singapore. Dr. Ford is a Senior Member of IEEE, IEEE Computer Society, IEEE Education Society, a Member of the Indonesian Mathematical Society (IndoMS), a Professional Member of The Association for Computing Machinery (ACM), The International Association of Engineers (IAENG), and the Indonesia Society for Bioinformatics.
Dr. Ford was the Vice Chair of the IEEE Indonesia Section and former Chair of the ACM Indonesia. He has served as a PhD Supervisor for 7 Doctor of Management Science and 25 Doctor of Computer Science students, as well as more than 50 Master Students. He has 490 Scopus documents, with 1,860 citations and an H-Index of 20. He has authored more than 40 books that can be explored on Amazon.
Dr. Ford holds a B.Sc. in Mathematics, a Master of Computer Science, and a Doctor in Computer Science from the University of Indonesia, Indonesia, in 1997, 2001, and 2009, respectively.
Symbiotic Intelligence: IoT in the Era of Intelligent Systems
Abstract
In this talk will examine the evolving dynamic between human intelligence and artificial systems in the era of advanced AI, machine learning, and autonomous technology. It explores the transition from traditional human-operated tools to symbiotic, highly autonomous "human-plus-machine" ecosystems. The future of intelligent systems relies on responsible integration that augments human capabilities rather than displacing human judgment. Sustainable progress requires building frameworks that prioritize human-centered design, ethical oversight, and continuous learning to ensure technology serves to expand human potential.
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The goal of iCONEECT 2026 is to bring together leading academic scientists, researchers, and professionals from around the world to exchange innovative ideas, share knowledge, and explore recent advancements in the broad domains of Electrical Engineering, Computer Science, Artificial Intelligence, Communication, Robotics, Networking, Optimization, and Emerging Technologies. The conference will serve as a premier platform for presenting cutting-edge research, fostering interdisciplinary collaboration, and discussing future directions that will shape the next generation of systems and technologies.
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