Keynote Speakers

Keynote Speakers at ICCCN 2026

July 27, 2026 (Mon)
Nexus of AI and Cybersecurity
Prasant Mohapatra, University of South Florida

Abstract: Artificial Intelligence (AI) is rapidly transforming the technological landscape, redefining how systems learn, reason, and operate at scale. At the same time, cybersecurity has emerged as a critical global challenge, with cyberattacks growing in frequency, sophistication, and economic impact. This talk explores the powerful and complex intersection between AI and cybersecurity, highlighting both the opportunities and emerging risks in this evolving domain. AI offers unprecedented capabilities to strengthen cybersecurity defenses. However, these same capabilities are increasingly being weaponized by adversaries. Attackers are leveraging AI to automate cyberattacks, enhance social engineering, exploit vulnerabilities, and scale malicious operations, thereby intensifying the asymmetry between defenders and attackers. The talk delves into the emerging paradigm of Generative AI and Agentic AI systems, which introduce new security challenges. The session will also present mitigation strategies, including secure system design, runtime monitoring, governance frameworks, and the integration of traditional cybersecurity principles with AI-specific safeguards. Finally, the talk reflects on the future coexistence of real and artificial worlds, emphasizing the need for robust, ethical, and adaptive security frameworks. As AI continues to evolve faster than traditional systems, building resilient, trustworthy, and human-centric cybersecurity solutions will be essential.

Biography: Professor Prasant Mohapatra serves as the Provost and Executive Vice President and Interim Vice President for Research & Innovation at the University of South Florida. He is also a Distinguished University Professor at the Bellini College of Artificial Intelligence, Cybersecurity, and Computing. Prior to joining USF, he served as the Vice Chancellor for Research at University of California, Davis. Dr. Mohapatra received an Outstanding Research Faculty Award at the University of California, Davis. He also received multiple HP Labs Innovation awards. He is a Fellow of the IEEE, a Fellow of AAAS, and a Fellow of the National Academy of Inventors. He has been inducted into the Academy of Science, Engineering and Medicine of Florida and the Pan American Academy of Engineering. Dr. Mohapatra’s research interests are in the areas of artificial intelligence and cybersecurity. He has published more than 400 papers in reputed conferences and journals on these topics. Dr. Mohapatra’s research has been funded through collaborative grants totaling about 90 million US dollars from the National Science Foundation, US Department of Defense, US Army Research Labs, Intel Corporation, Siemens, Panasonic Technologies, Hewlett Packard, Raytheon, ARM Research, Bosch, and EMC Corporation.


July 28, 2026 (Tue)
Adaptive Application and Offloading for Video Analytics and Communications
Hang Liu, The Catholic University of America and The U.S. National Science Foundation

Abstract: Video analytics is a popular computer vision task with many applications, spanning from surveillance, autonomous driving, and AR/VR to industrial automation, smart cities, and remote healthcare. Particularly, semantic communication has recently attracted considerable research interest as a new intelligent paradigm that focuses on transmitting the meaning or intent of information. It can drastically reduce network bandwidth usage and transmission latency. With assistance of generative AI, semantic communication offers greater flexibility by enabling data modifications and modality conversion during communications, e.g., generating images with different object orientations or translating text to videos. However, it understands and extracts relevant data semantics and features at the sender, while the receiver interprets and reconstructs a meaningful version of the original message and performs a specific task with the features, which may cause not only data compression but also information loss and distortion. In addition, computation is shipped to the sender and receiver. Resource-constrained mobile or Internet of Things (IoT) devices may suffer from processing burden and battery draining, leading to high latency and low accuracy. The sender and receiver also need to share external knowledge and context for semantic communication, and the semantic significance of data varies across different tasks and over time at the receiving end. They result in additional overheads and challenges to maintain the quality of service. This talk will discuss the tradeoffs between on-device semantic information/feature coding and transmission of raw compressed video. The impacts of video configurations and video analytics pipelines are considered. The approaches for adaptation of communication strategies and distributed machine learning actions are explored.

Biography: Hang Liu is a professor of Computer Science at the Catholic University of America. He is currently on leave to serve as a Program Director with the Directorate for Computer and Information Science and Engineering (CISE) at the U.S. National Science Foundation (NSF). Prior to joining Catholic University in 2013, he had more than a decade of research and development experience in networking industry and worked in senior research and management positions at several companies. He also served as the Site Director of NSF IUCRC Broadband Wireless Access and Applications Center (BWAC) from 2016 to 2024. Dr. Liu has published more than 150 papers in leading journals and conferences, and received several best paper awards. He holds over 50 granted U.S. patents. He has also made many contributions to the IEEE 802 wireless standards and 3GPP standards. He has served on the Editorial Boards of multiple IEEE journals and the Technical Program Committees of numerous IEEE international conferences in wireless communications and networking, mobile computing, Internet, multimedia, and IoT areas. Hang Liu received his Ph.D. degree in Electrical Engineering from the University of Pennsylvania. He is an IEEE Fellow.


July 29, 2026 (Wed)
Principles of Machine Learning Theories and Network Applications
Nageswara S. V. Rao, Oak Ridge National Laboratory

Abstract: Machine learning (ML) computations of increasing sophistication and complexity are being developed to solve complex, data-driven problems in diverse areas. Their output is often subject to undesirable phenomena such as overfitting and hallucinations that are hard to detect, resulting in their lower scientific rigor and confidence. We propose the concept of ML-solvability by combining the theories of learnability, computing and logic, which characterizes the model space, the learning algorithm that estimates a model using samples, and the inference algorithm that utilizes the model. It provides insights into the applicability and generalization of ML codes, and the possibility of incomplete and unsound inferences if the underlying problem is not ML-solvable. We describe a framework for ML-solvability and generalization analyses based on a combination of laws that govern system or network and information laws that characterize the learning processes. We briefly describe the uses of smooth and non- smooth laws to develop or analyze ML solutions in problems in systems and networks. We illustrate solutions to two problems in networking: (i) estimation of concave-convex throughput profiles of data transport networks, and (ii) converting inaccurate network measurement from digital twins to match those of physical and cloud networks and their testbed emulations.

Biography: Nagi Rao is a Corporate Fellow at Oak Ridge National Laboratory where he joined in 1993. He received PhD from Louisiana State University, ME from Indian Institute of Science, Bangalore, and BTech from National Institute of Technology, Warangal, India. His research interests are high-performance and quantum networking, rigorous machine learning methods, and information fusion. He is a Life Fellow of IEEE and Fellow of International Society of Information Fusion.