Keynote Speakers

Prof. Zhu Han, FIEEE, FAAAS, FACM

Moores Professor, Electrical Engineering,
Houston University, USA

Title

Generative-AI Diffusion Models for Structured Distribution Evolution in Wireless and Energy Systems

Abstract

Diffusion models have rapidly evolved from image generators into a general framework for modeling complex data distributions. This talk presents three complementary directions that extend diffusion models beyond conventional generation and toward system-level intelligence. First, we revisit the reverse diffusion process through the lens of mean-field games, where denoising is reformulated as an optimal population-level transport problem between noise and data distributions. This perspective connects diffusion generation with controlled distribution evolution, saddle-point optimization, and coupled HJB–FPK dynamics. Second, we show how latent diffusion inpainting can reconstruct UAV radio maps from sparse and partial observations. By combining UAV-based sensing, spatio-temporal latent diffusion, and radio map reconstruction, diffusion models become a tool for recovering hidden wireless environments under limited coverage and dynamic sensing constraints. Third, we introduce socio-aware diffusion for residential load data generation, where social attributes guide controllable generation for disadvantaged or data-scarce communities. Through global-personalized multi-step diffusion and fairness-oriented adversarial training, the model generates realistic and diverse electricity-consumption patterns while improving downstream forecasting and reducing data imbalance. Together, these works highlight a broader view of diffusion models: not merely as sample generators, but as controllable and conditional for reasoning about evolving distributions in large-scale infrastructure systems, including wireless networks and smart grids.

Biography

Zhu Han received the B.S. degree in electronic engineering from Tsinghua University, in 1997, and the M.S. and Ph.D. degrees in electrical and computer engineering from the University of Maryland, College Park, in 1999 and 2003, respectively. From 2000 to 2002, he was an R&D Engineer of JDSU, Germantown, Maryland. From 2003 to 2006, he was a Research Associate at the University of Maryland. From 2006 to 2008, he was an assistant professor at Boise State University, Idaho. Currently, he is a John and Rebecca Moores Professor in the Electrical and Computer Engineering Department as well as the Computer Science Department at the University of Houston, Texas. Dr. Han is an NSF CAREER award recipient of 2010, and the winner of the 2021 IEEE Kiyo Tomiyasu Award. He has been an IEEE fellow since 2014, an AAAS fellow since 2020, ACM fellow since 2024, an IEEE Distinguished Lecturer from 2015 to 2018, and an ACM Distinguished Speaker from 2022-2025.

Prof. Muhammad M. Hussain, FIEEE, APS, and IoP

Professor, Elmore School of Electrical & Computer Engineering,
Purdue University, USA

Title

RIVER: Reimagining Intelligence through Value Creation, Emerging Technologies, and Resilient Ecosystems

Abstract

The next technological revolution will not be defined by artificial intelligence alone, nor by advances in semiconductor devices, robotics, biotechnology, or electronics in isolation. Its transformative impact will emerge from our ability to connect these technologies into intelligent systems that solve consequential human, industrial, and societal problems.

This talk draws upon two decades of research spanning unconventional electronics, flexible and stretchable systems, heterogeneous integration, sensing and actuation, edge intelligence, and emerging semiconductor technologies, and connects those experiences to a much larger experiment now underway in Bangladesh through Silicon River. A recurring lesson is that breakthrough innovation rarely begins with abundant resources. It begins by identifying an important problem, questioning established assumptions, creating with what is available, and building the missing pieces along the way.

I introduce RIVER: Reimagining Intelligence through Value Creation, Emerging Technologies, and Resilient Ecosystems, a framework for moving from scientific possibility to scalable impact. RIVER connects talent → knowledge → research → intellectual property → products → companies → customers → manufacturing → economic value, while treating AI and machine intelligence as accelerators across the entire chain rather than isolated destinations.

Bangladesh presents an unusual testbed for this philosophy. A large young population, expanding engineering talent, global diaspora, competitive cost structure, and emerging semiconductor ambition coexist with significant limitations in infrastructure, capital, advanced manufacturing, and established deep-tech institutions. Rather than treating these constraints as reasons to wait, they can become design parameters for building a new kind of innovation ecosystem.

The talk will argue that emerging economies need not reproduce the historical pathways of today's technology leaders. Through distributed intelligence, virtual and shared infrastructure, AI-enabled design, digital twins, advanced and legacy semiconductor technologies, intelligent testing and assembly, AIoT, and globally connected research and workforce networks, they may create entirely new pathways from ideas to products and from talent to economic power.

Biography

Muhammad Mustafa Hussain is a Professor of Electrical and Computer Engineering at Purdue University and a Fellow of IEEE and APS. His pioneering research spans advanced CMOS technology, flexible and stretchable electronics, heterogeneous integration, emerging devices, biosensing and therapeutics, and intelligent systems. His innovations have received recognition including Scientific American’s Top 10 World Changing Ideas, CES Best Innovation Award, and Edison Gold Award. He has pioneered scalable semiconductor education through vFabLab and the Semiconductor 101 series, reaching 30,000+ learners worldwide. He is also Chief Architect of Silicon River and BEAR for Bangladesh, spearheading an ambitious effort to build a globally competitive semiconductor and deep-tech innovation ecosystem.

Prof. Latifur Khan, FIEEE, AAAS, BCS, IET

Department of Computer Science
Director Big Data Analytics and Management Lab
The University of Texas at Dallas, USA

Title

AI for X and Cybersecurity for AI: Advancing Intelligent and Trustworthy AI*

Abstract

In this presentation, I will discuss two complementary directions: AI for X, applying AI to domains such as cybersecurity and political conflict, and cybersecurity for AI.

For AI for cybersecurity, we use Large Language Models (LLMs) and active learning to automatically extract cyberattack techniques from CVE and Cyber Threat Intelligence (CTI) reports and map them to the MITRE ATT&CK framework. This curated knowledge can help security analysts better understand and respond to emerging threats.

For AI for political conflict, we developed ConfliBERT, a domain-specific language model for analyzing conflict and political violence. Publicly available through GitHub and Hugging Face, ConfliBERT has been downloaded more than 14,000 times and demonstrates strong performance in tasks such as classification and question answering.

For cybersecurity for AI, we developed Tri-Layer, a defense against knowledge-poisoning attacks in Retrieval-Augmented Generation (RAG) systems. Tri-Layer validates retrieved documents across semantic, structural, and factual dimensions, forwarding only trusted information to the LLM.

Biography

Dr. Latifur Khan is currently a full Professor (tenured) in the Computer Science department at the University of Texas at Dallas, USA where he has been teaching and conducting research since September 2000. He received his Ph.D. degree in Computer Science from the University of Southern California (USC) in August of 2000. In addition, he received his bachelor degree in Computer Science and Engineering (CSE) from Bangladesh University of Engineering and Technology (BUET) with first class honors.

Dr. Khan is a fellow of IEEE, AAAS, IET, BCS, and an ACM Distinguished Scientist. He has received prestigious awards including the IEEE Technical Achievement Award for Intelligence and Security Informatics, IEEE Big Data Security Award, and IBM Faculty Award (research) 2016. Dr. Khan has published over 300 papers in premier journals and prestigious conferences. Currently, Dr. Khan’s research focuses on big data management and analytics, data mining and its application to cyber security, and complex data management including geospatial data and multimedia data. His research has been supported by grants from NSF, NIH, the Air Force Office of Scientific Research (AFOSR), DOE, NSA, IBM, and HPE. More details can be found at www.utdallas.edu/~lkhan.

Sheikh M. Shariful Islam, FESC

Professor, Medical AI and Digital Health
Department of Nutritional Sciences,
College of Health and Human Sciences,
Texas Tech University, USA

Title

Innovations in digital health and AI for improving cardiometabolic health

Abstract

Cardiometabolic diseases, including cardiovascular disease, diabetes, obesity, and hypertension, remain leading causes of morbidity and mortality worldwide. Rapid advances in digital health and artificial intelligence offer new opportunities to improve prevention, early detection, and long-term management of these conditions. This presentation will highlight recent innovations in wearable technologies, artificial intelligence, mobile health applications, remote monitoring, and digital therapeutics that enable more personalized, proactive, and scalable care. Drawing on examples from current research and real-world implementation, the talk will discuss how digital health tools can enhance risk prediction, support behavior change, improve clinical decision-making, and expand access to care across diverse populations. Challenges related to engagement, equity, privacy, and implementation will also be considered, along with future directions for digitally enabled cardiometabolic health care.

Biography

Professor Shariful Islam, MBBS, MPH, PhD, FESC, is Professor in the Department of Nutritional Sciences at Texas Tech University, USA, and Director of Digital Health and Artificial Intelligence Lab in the Nutrition and Metabolic Health Initiative. He is also Honorary Professor at Ludwig-Maximilians-Universität München, Germany, and Bangladesh University of Health Sciences.

His work focuses on digital health, artificial intelligence, nutrition, diabetes, cardiovascular disease, and implementation research, with a strong emphasis on translating evidence into clinical practice and health policy. His research spans guideline-informed digital interventions, wearable and smartphone-based monitoring, AI-driven prediction models, and health systems innovation for chronic disease prevention and management.

Professor Islam has published more than 430 peer-reviewed papers, with over 232,000 citations and an h-index of 122. He is ranked among the leading global researchers in mHealth, medical AI, digital health, and global public health.

He has led or co-led major funded programs in digital health, AI, chronic disease, and population health, and has contributed to global initiatives through the World Health Organization, the International Society of Hypertension, and other international bodies. He currently serves in several leadership and editorial roles across digital health, cardiometabolic health, and implementation science.