CCI xG Testbed Team
Aloizio P. DaSilva,
CCI xG Testbed director
DaSilva's primary area of research is in wireless networking communication, focusing on NFV, SDN, and SDR on large-scale testbed experimentation, as well as radio access networks and core networks. DaSilva is also research faculty at the Bradley Department of Electrical and Computer Engineering at Virginia Tech.
Md. Habibur Rahman
Rahman is a Ph.D. student in electrical engineering at Virginia Tech, and a graduate research assistant at CCI.
His research focuses on the intersection of modeling centralized and decentralized machine learning (ML) or deep learning (DL) methods and next-generation wireless communication networks. He is examining designing and integrating optimized ML/DL based intelligence in the Open Radio Access Network (O-RAN) architecture and software-defined radios.
He has an M.Sc. degree in electronics engineering from Kookmin University, and a B.Sc. degree in electrical and electronic engineering from the Khulna University of Engineering & Technology.
Jaswanth Sai Reddy
Reddy, a masters student in Computer Engineering at Virginia Tech, is a graduate research assistant at CCI, where he works on the Open Radio Access Network (O-RAN).
He received his B.Eng. in electronics and communication engineering from VIT University in India.
He previously worked as a network engineer at Accenture in India, and also as a senior network engineer in Alethea Communications Technologies.
Sathish, a masters student in Computer Engineering at Virginia Tech, is a graduate research assistant at CCI.
After completing his Bachelor of Technology degree at the National Institute of Technology Karnataka in India, he worked at Qualcomm India as a senior engineer in Wireless Local-Area Network (WLAN) system software engineering, where he implemented the protocol stack for Wi-Fi 6, 6E and Wi-Fi 7 standards for wireless LAN.
He is currently conducting research in 5G and Beyond-5G radio access networks, focusing on the challenges in adopting the Open Radio Access Network (O-RAN) architecture and software-defined radios.
Sultana is a master's student in electrical engineering at Virginia Tech and a graduate research assistant at the Commonwealth Cyber Initiative.
Her research focus is on Near Real Time Open Radio Access Networks (O-RAN).
She has a BSc from Chittagong University of Engineering and Technology in electronics and telecommunications engineering.
Tripathi is a PhD student at Virginia Tech and a graduate research assistant at CCI, where he’s working on software-defined radio system-based citizens broadband radio service network development.
He holds a B.Tech degree in electronics and telecommunications from Bharati Vidyapeeth College of Engineering, and an MS in electrical engineering from the University of North Carolina, Charlotte. His master's thesis is on radio-frequency energy harvesting in wireless sensor networks.
His research interests include O-RAN-based NextG wireless networks, software-defined radio-based citizens broadband radio service networks, and machine learning.
His previous experience includes working as a senior engineer in system integration and testing at Verana Networks, and lead engineer in access QA at Parallel Wireless.
His research interests include SDR-based CBRS networks, O-RAN-based NextG wireless networks, and machine learning.
Deb holds a B.Tech degree in electronics and communication engineering (ECE) from the National Institute of Technology Silchar, and an MS in ECE from the International Institute of Information Technology (IIT) Hyderabad.
His previous experience includes a position as a research assistant at IIT on “IoT Enabled Smart Cities: Pollution, Health, and Governance.” He was also an integration engineer and network planning/provisioning engineer at Ericsson.
Mayukh Roy Chowdhury
Chowdhury received a B. Tech. degree in Electronics and Communication Engineering from West Bengal University of Technology, Kolkata, India, an M.Tech degree in Communication Systems Engineering from Indian Institute of Technology (IIT) Patna, India, and a Ph.D. in Electrical Engineering from the Indian Institute of Technology (IIT) Delhi, New Delhi, India.
His thesis is Resource Efficient Strategies for Massive Machine Type Communication (mMTC) in 5G. He has worked in 6G Lab, Samsung Research and Development Institute and TCS Innovation Lab.
His research interests include AI-driven radio resource management for cellular networks, applied machine learning in 5G, and next-generation wireless networks, reinforcement learning, AI on edge for smart IoT systems, random access for massive machine type communication in 5G, resource efficiency in communication networks.
Harshit Sai Teja Doddi
Doddi is a master's student in Computer Science and Information at Virginia Tech, and an intern at the Commonwealth Cyber Initiative, where he works with the NextG Testbed.
His role is to deploy and develop the next-generation wireless communication testbed.
Doddi has a bachelor’s degree in computer science and engineering from Vellore institute of Technology University.
His research focus is in artificial intelligence and machine learning in cloud computing.
Fahim Bashar is an intern working with the NextG Testbed at the Commonwealth Cyber Initiative in Arlington, Va.
His role is to help deploy cloud solutions to the testbed, support proof-of-concept development, and maintain the CCI webpage,
He has a bachelor’s degree in computer science from Temple University.
Kibilda is Research Associate Professor with the Commonwealth Cyber Initiative and Bradley Department of Electrical and Computer Engineering at Virginia Tech. His research focuses on modelling and technology design for Next G mobile networks, using tools of stochastic geometry, AI, optimization, and computer modelling.
Santos, a research assistant professor, is a 5G Testbed and AI Assurance Researcher with the Commonwealth Cyber Initiative (CCI) at Virginia Tech. His experience includes developing software-defined radio systems, implementing radio virtualization mechanisms, and bridging SDR with SDN in support of programmable end-to-end communication networks.