Sandhya Aneja, From Data to Decisions: AI-Powered Optimization in Modern Communication Networks

Modern communication networks face growing challenges in performance, security, and resilience as superintelligence,the Internet of Things, and high-performance computing continue to evolve. This talk explores how AI-driven techniques can transform network data into actionable intelligence to improve efficiency, adaptability, and security. It highlights three interconnected research areas: intelligent congestion control, network traffic fingerprinting, and resilient computing systems. First, machine learning techniques, including Gated Recurrent Units (GRUs), are examined for identifying TCP congestion control protocols and analyzing network behavior. Second, deep learning approaches for browser and IoT device fingerprinting using encrypted traffic are discussed, emphasizing their applications in network security. Third, fault-tolerant and adaptive mechanisms for improving the reliability of heterogeneous computing environments are explored. The talk concludes by discussing emerging opportunities for integrating large language models, knowledge graphs, and intelligent decision-making to develop more efficient, secure, and self-adaptive communication networks. About the speaker: Dr Sandhya Aneja is an Assistant Professor of Computer Science at Marist College in Poughkeepsie, New York. Her primary areas of interest include Wireless Networks, High-Performance Computing, the Internet of Things, and Artificial Intelligence technologies—machine Learning, machine Translation, and deep Learning.