I am an Assistant Professor in the AI Thrust of the Information Hub at The Hong Kong University of Science and Technology (Guangzhou). The AI Thrust head is Prof. Lei Chen. Additionally, I hold an affiliated position as an Assistant Professor at The Hong Kong University of Science and Technology in Hong Kong. My primary research focuses on the design and analysis of networked control systems integrating concepts from distributed optimization, networked control theory, machine learning, and privacy preservation. My goal is to develop novel algorithms that enable secure, scalable, and energy efficient integration of interconnected systems across smart grids, cyber-physical systems, Internet of Things, and autonomous systems. 

I received the B.Sc., M.Sc and Ph.D. degrees in Electrical Engineering from the Department of Electrical and Computer Engineering, University of Cyprus in 2010, 2012 and 2018 respectively. My supervisor was Prof. Christoforos N. Hadjicostis. My Ph.D. thesis focused on distributed control and coordination of multi agent networks, entitled ''Distributed Weight Balancing in Directed Topologies''. After completing my PhD, I took up a position as a Research Lecturer at KIOS Research and Innovation Center of Excellence from November 2018 to January 2020. Then, I served as a postdoctoral researcher at KTH Royal Institute of Technology from February 2020 to March 2023. I worked under the supervision of Prof. Karl Henrik Johansson. Following that, I transitioned to Boston University, where I held a postdoctoral researcher position from April 2023 until March 2024 working under Prof. Ioannis Paschalidis. 

Apostolos I. Rikos (M'16) is an Assistant Professor at the Artificial Intelligence Thrust of the Information Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China. He is also affiliated with the Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong. He received his B.Sc., M.Sc., and Ph.D. degrees in Electrical Engineering from the Department of Electrical and Computer Engineering, University of Cyprus in 2010, 2012, and 2018, respectively. In 2018, he joined the KIOS Research and Innovation Center of Excellence in Cyprus, where he was a Research Lecturer. In 2020, he joined the Division of Decision and Control Systems of KTH Royal Institute of Technology as a Postdoctoral Researcher. In 2023, he joined the Department of Electrical and Computer Engineering, Division of Systems Engineering, at Boston University as a Postdoctoral Associate. His research interests are in the area of distributed optimization and learning, distributed network control and coordination, privacy and security, and algorithmic design.

Network control systems have become a pivotal area of research and application in the modern technological landscape. These systems serve as the backbone for orchestrating and managing the intricate web of interconnected devices in today’s technological landscape. Their importance is underscored by the rapid proliferation of interconnected devices, driven by advancements in the Internet of Things (IoT) and wireless communication technologies. Network control systems find critical applications across a diverse array of domains, ranging from optimizing cyber-physical systems and enhancing traffic and transportation networks to bolstering military and medical systems. Their role in efficiently controlling interconnected devices and optimizing the overall network performance, positions them as pivotal components in addressing the multifaceted challenges of our interconnected world. 

Within the realm of network control, a pivotal focus is on the design of efficient algorithms that can navigate the complex landscape of memory usage, energy efficiency, and computational complexity. The design of efficient algorithms is crucial for network control systems to ensure optimized performance, resource conservation, and adaptability in managing memory, energy, and computational complexity. In this context, two primary paradigms, centralized and distributed control algorithms, play a critical role. Centralized control entails the coordination of control decisions from a single, central authority, which can optimize global objectives. In distributed control, on the other hand, control decisions are made autonomously by individual nodes or agents within the network. These agents collaborate to optimize local objectives while adhering to a set of global constraints. This approach harnesses the power of decentralized decision-making, mitigating the resource burden and enhancing scalability. Efficient algorithms in both contexts are paramount for enabling network control systems to navigate the challenges of contemporary interconnected environments effectively. Their proficiency in efficiently managing memory, conserving energy, and taming computational complexity ensures the smooth operation and adaptability of networked systems in an ever-evolving technological landscape. Overall, as the interconnectedness of devices and systems continues to underpin technological advancements, the development of such algorithms remains integral to their success and growth. 

My research focuses on the development and analysis of efficient algorithms for network control. This topic is of critical importance in today’s interconnected world. With the exponential growth of networked devices, there is an increasing need for effective strategies that can ensure efficient operation among agents within the network. My current focus is on exploring four key areas: distributed and centralized optimization, distributed learning, distributed unsupervised learning, privacy preservation, distributed coordination.