GIKI, Topi, Pakistan | December 02-04, 2025

invited speakers

Click on a picture to view talk abstract.
Ahmed S. Khan, Ph.D.
Uvais Qidwai, Ph.D
MA Gondal, Ph.D
Junaid Zubairi, Ph.D


Title: Artificial Intelligence (AI) and the Fourth Industrial Revolution (4IR): Exploring Social and Ethical Implications
Dr. Ahmed S Khan
Professor of Electrical Engineering, Fulbright Specialist Scholar
Ex. Dean of the College of Engineering & Information Sciences
DeVry University, Addison, Illinois, USA
Abstract:
The emerging technologies of the Fourth Industrial Revolution (4IR) are reshaping the way we live, work, interact, and educate. Innovations such as Artificial Intelligence (AI), Big Data, the Internet of Things (IoT), Augmented Reality, Blockchain, Robotics, Drones, Nanotechnology, Genomics and Gene Editing, Quantum Computing, and Smart Manufacturing are driving rapid transformation across every sector of society. Unlike previous industrial revolutions—where change unfolded over centuries or decades—4IR is accelerating at an unprecedented pace. The time required to reshape global systems is now measured in months, not years. This shift marks a dramatic departure from:
Dr. Ahmed S Khan

1st Industrial Revolution: Steam and water-powered mechanization (centuries)
2nd Industrial Revolution: Mass production and electrical power (multiple decades)
3rd Industrial Revolution: Electronics and IT (decades)
Among these technologies, Artificial Intelligence (AI) stands out as the most transformative force in human history. It is essential that today’s and tomorrow’s stakeholders—policymakers, educators, and leaders—are equipped not only with technical knowledge of AI, but also with a deep understanding of its social, ethical, and unintended consequences. This awareness is critical to guiding its responsible use, identifying its failures, and shaping a humane and visionary future.
The talk will explore AI’s intended and unintended consequences through the following critical domains:
1. Ethics: Ensuring AI development aligns with moral principles and avoids reinforcing biases.
2. Privacy: Protecting user data and maintaining confidentiality in AI systems.
3. Transparency: Making AI decision-making processes understandable and accountable.
4. Public Trust: Building informed public confidence in AI technologies.
5. Safety: Designing AI systems that are secure and minimize risks.
6. Legal Frameworks: Establishing regulations that keep pace with technological advancement.
Engaging with these domains is essential for all stakeholders to responsibly navigate the complexities of AI adoption—maximizing its benefits while minimizing its risks.
Speaker Biography:
Dr. Ahmed S. Khan is a Fulbright Specialist Scholar selected by U.S. Department of State’s Bureau of Educational and Cultural Affairs (ECA). Dr. Khan has more than 40 years of progressively responsible experience in instruction (online and onsite), applied research, curriculum development, program and institutional accreditation (ABET & NCA/HLC), management, and supervision of academic programs at DeVry University. Dr. Khan held many academic positions that include Senior Processor, Chair, and Dean of the College of Engineering & Information, DeVry University, Addison, Illinois, USA. Dr. Khan also served as the National Curriculum Manager at the national headquarters of DeVry University, where he provided leadership by supervising and managing curriculum development and implementation of BSEEt, MSEE & MBA online & onsite programs at 25 DeVry campuses located in the United States and Canada. Dr. Khan received an MSEE from Michigan Technological University, an MBA from Keller Graduate School of Management, and his Ph.D. from Colorado State University. His research interests are in the areas of Nanotechnology, New Teaching & Learning Techniques, and Social and Ethical Implications of Technology. He is the author of many educational papers and presentations. He has authored/co-authored many technical books, including the Science, Technology & Society (STS) series of books (used globally in the academic programs of more than 200 Universities) that include Technology and Society: Issues for the 21st Century & Beyond, and Nanotechnology: Ethical and Social Implications, to stimulate, inspire, and provoke awareness of technology’s impact on society. Dr. Khan is a life senior member of the Institute of Electrical and Electronics Engineering (IEEE), and a life member of American Society of Engineering Education (ASEE). Dr. Khan also served as program evaluator for the accreditation agency ABET.


Title: Securing the Edge: An AI-Driven Detection of Intrinsic Cyber Threats via Current-Profiling in IoT Networks
Dr. Uvais Qidwai
Associate Professor of Computer Engineering
Qatar University, Qatar
Abstract:
With the rapid proliferation of IoT and edge computing in critical infrastructures—ranging from agriculture to smart surveillance—the attack surface has expanded, and so have the stealth and sophistication of cyber threats. Traditional signature-based detection systems often fall short in resource-constrained environments, failing to detect subtle, hardware-level manipulations. This talk presents a novel intrusion detection approach that utilizes profiling the current consumption in real-time combined with AI-based classification techniques to identify and respond to hardware intrinsic cyberattacks.
Dr. Uvais Qidwai
The presented work is a funded grant from Qatar Research Development and Innovation Council (QRDI) and evaluates a subset of targeted attacks in the forms of hardware trojans or hardware intrinsic attacks. Those that have been successfully implemented as part of the project, while more are being encoded, include Covert Channel Attacks (CCA), Power Depletion Attacks (PDA), Denial-of-Service Attack (DoSA), and Man-in-the-Middle Attack (MIMA)—deployed on an experimental testbed of ESP32 microcontrollers based IoT nodes and a Raspberry Pi 5-based edge node. The AI-driven intrusion detection system (IDS), analyzes current profiles and sensor data transmitted via UDP protocol to detect anomalies using a suite of ML classifiers such as Ensemble

Learning, LDA, Decision Trees, k-NN, and SVM, for comparison and selection of best technique. The results demonstrate high detection accuracy and low false positive rates, validating the approach for practical deployment in real-time, mission-critical IoT environments. By deploying machine learning classifiers, including Ensemble Learning (Bagging and LPBoost), the system effectively classified cyberattacks such as Covert Channel Attack (CCA), Power Depletion Attack (PDA), Denial-of-Service Attack (DoSA), and Man-in-the-Middle Attack (MIMA). The classification results indicate that ensemble learning techniques (Bagging and LPBoost) provided the highest accuracy, with Bagging achieving 99.78% accuracy and LPBoost reaching 98.03% accuracy, demonstrating their robustness in real-time threat detection scenarios.
Speaker Biography:
Uvais Qidwai received his Ph.D(EE). from the University of Massachusetts–Dartmouth USA in 2001. He taught in the EECS Department at Tulane University in New Orleans USA as Assistant Professor, and was in-charge of the Robotics lab as well as a research member of Missile Defense Center, during June 2001 to June 2005. His current affiliation (since September 2005) is with the Department of Computer Science & Engineering at Qatar University, Qatar where he is Associate Professor of Computer Engineering at present. His research interests include Smart system design and AI-based embedded systems and techniques in Robotics applied to healthcare and industrial applications. He has participated in several government- and industry-funded projects in the United States, Saudi Arabia, Qatar, UAE, Singapore, Malaysia, and Pakistan. He has published over 150 papers in reputable journals and conference proceedings, and has been granted two US and one GCC patents.


Title: Shaping a Sustainable Future: Harnessing Laser-Driven Synthesis of Advanced Materials for Renewable Energy Generation, Energy Storage, and Green Hydrogen

Dr. M. A. Gondal
Professor of Physics
IRC-Hydrogen & Energy Storage and K.A.CARE Energy Research and Innovation Center King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia
Abstract:
The ability to tailor the properties of materials by modifying their size, structure, and composition is crucial for the development of functional materials. Nanoscale materials, in particular, exhibit unique physical, chemical, and optical characteristics that make them ideal building blocks for advanced applications. Our research group has developed a simple, environmentally friendly, and versatile method - Pulsed Laser Ablation in Liquids (PLAL) - to synthesize functional nanocomposite materials with tailored properties.
Dr. M. A. Gondal
The PLAL process involves the irradiation of precursor materials in a liquid medium with a pulsed laser beam, enabling precise control over particle size and structure through photo-induced fragmentation, chemical reactions, and defect engineering. This keynote speech will highlight our recent advances in the PLAL-based synthesis of advanced functional materials and their applications in sustainable energy and environmental remediation.

Selected examples from our research group's work will be presented, demonstrating the potential of this technique to drive innovation in these critical area.

*The author is thankful to KFUPM for supporting this work under project ##INHT2513


Title: Digital Twin for the Manhattan Grid: A Simulation Framework for Traffic Management Research
Dr. Junaid Zubairi
Contributors:
Megan Johnson, Junaid Zubairi (presenter), Sahar Idwan, Wael Etaiwi and Syed Haider, Smart City Research Group, State University of New York at Fredonia, USA.

Abstract:
Our research group is working on traffic management in smart cities. The negative effects of traffic congestion in major cities include waste of fuel, waste of time and adverse effects on the temperament of drivers resulting in accidents. A study estimated the public health cost of congestion across 83 U.S. cities to be nearly $43 billion. We have modeled the digital twin of a portion of Manhattan grid and implemented hierarchical routing, reactive congestion control and route planning for emergency vehicles for reduction in disaster response time. In this presentation, we are going to discuss the simulation platform, configuration parameters and load effects on the grid. We address the traffic management issues including multi-modal traffic integration, data fusion from heterogeneous sources and traffic capacity planning for future alterations and lane/road closures due to scheduled events or sudden mishaps.
Dr. Junaid Zubairi
Prof.
SUNY Fredonia, NY, USA 
Speaker Biography:
Dr. Junaid Ahmed Zubairi received his BE (Electrical Engineering) from NED University of Engineering, Pakistan and MS and Ph.D. (Computer Engineering) from Syracuse University, USA. He worked in Space Research Commission and then joined various institutions in Pakistan and Malaysia where he was engaged in research and curriculum and lab development. In 1999, he accepted a position in State University of New York at Fredonia where currently he is SUNY Distinguished Professor and department chair in the computer science department. He has won many awards and grants including AURAK Presidential award for exceptional academic service, SUNY Chancellor's award for excellence in research and creative activities, Kasling Memorial Lecture award, SUNY Distinguished Professorship, NSF I-CORPS award ($50k), Malaysian Government IRPA research award ($62k), NSF MACS grant ($400k), multiple SUNY scholarly incentive awards and AURAK UAE grant. His research interests include network traffic engineering, network applications and smart city and IoT applications. He has edited two books on network applications and security and has numerous peer reviewed publications including book chapters, journal articles and papers in conference proceedings. He can be reached at zubairi at fredonia.edu.