Education:

• B.Sc. in Electrical and Computer Engineering, Isfahan University of Technology (IUT), Isfahan, Iran, 2021

Research Interests:

• Machine Learning
• Reinforcement Learning
• GPU-based real-time systems

Selected Publications:

• E. Ghoreishi, B. Abolhassani, Y. Huang, S. Acharya, W. Lou, and Y. T. Hou, “Cyrus: A DRL-Based Puncturing Solution to URLLC/eMBB Multiplexing in O-RAN, IEEE ICCCN 2024.”

Education:

• M.Sc. in Electrical Engineering , Virginia Polytechnic Institute and State University , Virginia, USA, 2023

Research Interests:

• Open Radio Access Network
• Software Define Radio
• Wireless Communication

Selected Publications:

• Tripathi, Asheesh, Jaswanth SR Mallu, Md Habibur Rahman, Abida Sultana, Aditya Sathish, Alexandre Huff, Mayukh Roy Chowdhury, and Aloizio Pereira Da Silva. "End-to-End O-RAN Control-Loop For Radio Resource Allocation in SDR-Based 5G Network." In MILCOM 2023-2023 IEEE Military Communications Conference (MILCOM), pp. 253-254. IEEE, 2023.

• Research Experience:

  • Graduate Research Assistant

Education:

• Ph.D. Candidate, Computer Engineering, Virginia Tech, Blacksburg, VA, USA, 2021 - Current
• M.S. Computer Engineering, Virginia Tech, Blacksburg, VA, USA, 2023
• B.S. (Summa cum laude), Electrical Engineering, McNeese State University, Lake Charles, LA, USA, 2020

Research Interests:

• Addressing channel uncertainty in wireless networks
• Spectrum sharing
• Real-time algorithm design
• Machine Learning

Selected Publications:

• S. Acharya, S. Li, Y. Wu, N. Jiang, W. Lou, and Y.T. Hou, “Rudra: Minimizing Spectrum Usage With Data Rate Guarantee in Next-G Communications,” Accepted at IEEE MILCOM 2024
• E. Ghoreishi, B. Abolhassani, Y. Huang, S. Acharya, W. Lou, and Y.T. Hou, “Cyrus: A DRL-based Puncturing Solution to URLLC/eMBB Multiplexing in O-RAN,” Proc. IEEE ICCCN, pp. 1-9, Kailua-Kona, HI, USA, 2024
• S. Acharya, S. Li, N. Jiang, Y. Wu, Y.T. Hou, W. Lou, and W. Xie, “Mitra: An O-RAN based Real-Time Solution for Coexistence between General and Priority Users in CBRS,” Proc. IEEE MASS, pp. 295-303, Toronto, Canada, 25-27 Sept. 2023

Research Experience:

• Handling uncertainty: I work on optimizing wireless network resources under channel uncertainty. My goal is to provide performance guarantees, such as interference protection for primary users in spectrum sharing (CBRS) and data rate guarantees for UEs in MU-MIMO settings. To model channel uncertainty, I explore a novel small-data approach, leveraging limited data samples to offer guarantees without assuming an underlying channel distribution.
• Real-time algorithms: I also focus on developing algorithms that meet real-time (wall-clock) requirements for 5G applications. For example, I design scheduling algorithms for 5G base stations that satisfy the 1 ms timing requirement under numerology 0, as mandated by the 5G standard.
• Additional experience: In addition, I collaborate closely with colleagues on projects related to satellite communication, applying Deep Reinforcement Learning (DRL) to eMBB/URLLC multiplexing, and enhancing URLLC for industrial automation.

Awards and Fellowships:

• Awarded CCI SWVA Cyber Innovation Scholar Fund 2024
• Chosen as CCI SWVA Cyber Innovation Scholar 2024
• Awarded Most Technically Enlightening Presentation at Inaugural CCI Graduate Student Summit 2023
• Awarded CCI SWVA Cyber Innovation Scholar Fund 2023
• Chosen as CCI SWVA Cyber Innovation Scholar 2023
• Summa cum laude, McNeese State University, Lake Charles, LA
• Won Design Award 2020 at VEXU Robotics Competition, Houston, TX, USA 2020

Education:

• Ph.D in Computer Science, Virginia Tech, Present
• M.Sc. in Network Engineering and Security, Jordan University of Science and Technology, Jordan, 2021
• B.Sc. in Network Engineering and Security, Jordan University of Science and Technology, Jordan, 2019

Research Interests:

• Network Security
• O-RAN and Wireless Security

Selected Publications:

• Al-Share Rama, Mohammad Shurman, and Abdallah Alma’aitah. A Collaborative Learning-Based Algorithm for Task Offloading in UAV-Aided Wireless Sensor Networks. The Computer Journal (2021).
• Al-Share, Rama A., Ahmed S. Shatnawi, and Basheer Al-Duwairi. Detecting and Mitigating Collusive Interest Flooding Attacks in Named Data Networking. IEEE Access (2022).

Work Experience:

• Junior BTEC Lecturer at Luminus Technical University College, Jordan, (10/2021 - 07/2024)
• Lead Internal Verifier at Luminus Technical University College, Jordan, (10/2023 - 07/2024)

Awards and Fellowships:

• Awarded an M.Sc. scholarship from Jordan University of Science and Technology to advancing research in Network Engineering and Security.

Education:

• B.Sc. in Electrical Engineering, Virginia Tech, Blacksburg, VA, 2023
• B.Sc. in Computer Engineering, Virginia Tech, Blacksburg, VA, 2023

Research Interests:

• Machine Learning
• Open Radio Access Network (ORAN)
• Wireless Networks

Selected Publications:

Work Experience:

• Software Engineer at Expedition Technology Inc

Research Experience:

• NASA Phase II Materials International Space Station Experiment (MISSE) project
• DoD Phase II Small Business Innovation Research (SBIR) project
• Conflict Detection for ORAN

Awards and Fellowships:

Education:

• Ph.D. in Electrical and Computer Engineering, Virginia Tech, Blacksburg, Virginia, U.S.A, 2024-Present
• M.S. in Electrical Engineering, Arizona State University, Tempe, Arizona, U.S.A, Jan.2022 - Dec. 2023
• B.Tech in Electronics and Communications Engineering, Sri Venkateswara University, Tirupati, India, 2013 - 2017

Research Interests:

• Wireless Communcations
• Next-G Cellular
• O-RAN
• Scheduler

Work Experience:

• Graduate Teaching Assistant (2024) -- ECE1004 Introduction to E.C.E Concepts, Virginia Tech, Blacksburg, VA, U.S.A
• Senior Software Engineer (Dec. 2020 - Dec. 2021) -- Altran (Samsung R&D Project), Bangalore, India
• Product Development Engineer (Sep. 2018 - Nov. 2020) -- WiSig Networks, Hyderabad, India
• Junior Research Engineer (July. 2017 - Sep. 2018) -- WiSig Networks, Hyderabad, India

Research Experience:

• Graduate Research Assistant, Wireless@VT (2024) -- Virginia Tech, Blacksburg, U.S.A (Aug 2024 - Present) -- Working on O-RAN Complaint Positioning xApp development

Education:

• B.Eng. in Computer Science, ShanghaiTech University, Shanghai, China, 2024

Research Interests:

• Reinforcement Learning
• Recommendation Systems
• Large Language Model
• Bandit Learning
• Online Machine Learning

Work Experience:

• Machine Learning Research Intern, Nanjing University, China, 2024

Research Experience:

• Revolutionizing Recommender Systems with LLMs: A Dual Approach of Re-ranking and Black-box Prompt Optimization

Awards and Fellowships:

• Outstanding Graduate Student
• Outstanding Student

Education:

• B.S. (Magna cum laude), Dual Degrees in Computer Science and Mathematics, The Pennsylvania State University, University Park, PA, 2022

Research Interests:

• Algorithms Design for Next-G Wireless Networks
• GPU-based Real-time Optimization
• Machine Learning
• Reinforcement Learning

Selected Publications:

• Zhao, J., Zhou, B., Butler, J. P., Bock, R. G., Portelli, J. P., and Bilén, S. G. IoT-based sanitizer station network: A facilities management case study on monitoring hand sanitizer dispenser usage. Smart Cities 2021, 4, 979-994.
• Liu, Q., Li, C., Shi, Y., Zhou, B., Hou, Y.T. and Lou, W. Pistis: A Scheduler to Achieve Ultra Reliability for URLLC Traffic in 5G O-RAN. IEEE Internet of Things Journal. 2024 Aug 1

Education:

• M.S. in Electrical Engineering, University of Southern California, Los Angeles, United States, 2021
• B.S. in Electrical Engineering, HUAZHONG University of Science and Technology, Wuhan, China, 2019

Research Interests:

• Neuromorphic Computing
• Digital Circuit Design
• FPGA Design

Selected Publications:

• Lin, C., Azmine, M. F., Liang, Y., & Yi, Y. (2024). Leveraging neuro-inspired AI accelerator for high-speed computing in 6G networks. Frontiers in Computational Neuroscience, 18, 1345644.
• Lin, C., Azmine, M. F., & Yi, Y. (2023, October). Accelerating Next-G Wireless Communications with FPGA-Based AI Accelerators. In 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD) (pp. 1-8). IEEE.
• Lin, C., Liang, Y., & Yi, Y. (2022, April). Fpga-based reservoir computing with optimized reservoir node architecture. In 2022 23rd International Symposium on Quality Electronic Design (ISQED) (pp. 1-6). IEEE.

Biography

Education:

• Ph.D. in Electrical and Computer Engineering, August 2023 - Present
• M.S in Electrical and Computer Engineering, January 2021 - August 2023
• B.Tech in Electronics and Communication Engineering, June 2015 - May 2019

Research Interests:

• Non-Terrestrial Networks
• 5G and Beyond Communications
• Dynamic Spectrum Sharing for Integrated Terrestrial- Non Terrestrial Networks
• Federated Learning
• O-RAN
• Machine Learning and AI
• Network Virtualization and Slicing

Publications:

• R. Chintalapati, N. D. Tripathi, M. Kumar and J. H. Reed, "Feasibility Analysis of Isolation-Based Spectrum Sharing Between Terrestrial and Non-Terrestrial Networks," 2024 IEEE Texas Symposium on Wireless and Microwave Circuits and Systems (WMCS), Waco, TX, USA, 2024, pp. 1-6, doi: 10.1109/WMCS62019.2024.10619038.
• R. Chintalapati, N. D. Tripathi and J. H. Reed, "V2XArcSim: Evaluation of Efficient and Scalable 5G-based V2X Infrastructure Architectures" 2024 IEEE Vehicular Technology Conference (VTC), Washington D.C, USA.

Upcoming Works:

• R. Chintalapati, N. D. Tripathi and J. H. Reed, "Securing NTNs", Accepted to IEEE- Wiley.
• R. Chintalapati, N. D. Tripathi and J. H. Reed, "Following the Journey of Network Slicing in 5G and 5G Advanced: Essential Concepts, Key Operations, Lifecycle Management, and Future Enhancements", To be submitted.

  Work Experience:

  • Summer Research Intern - AT&T Labs Inc, June 2024 - August 2024
  • Developed an LLM-powered chatbot on Palantir to analyze and respond to questions about the performance of an end-to-end wireless system, using Ookla and Root Metrics data, and enhanced the chatbot to track and provide end-to-end packet routes for a given market.
  • Developed a python based root metrics simulator with the ability to simulate, evaluate, rank, and prioritize markets based on key performance indicators (KPIs).
  • Built interactive Power BI dashboards to analyze and visualize factors influencing total task times in a given market.

  Research Experience:

  • Spectrum Sharing Between Terrestrial and Non-Terrestrial Networks: Analyzed and proved the feasibility of reusing spectrum between indoor Terrestrial Network and outdoor NTN system.
  • Securing Non-Terrestrial Networks : Proposed how the current 5G mechanisms can be used to secure NTN systems. Additionally, proposed security mechanisms to secure interfaces that are most vulnerable to attacks.
  • Network Slicing in 5G and future Enhancements : Studied various aspects of network slicing such as standard architectures, operations and evolution, and proposed future QoS enhancements to network slicing.
  • 5G Vehicular Communication Candidate Architecture Analysis: Built a custom python simulator and Evaluated the performance of various 5G based infrastructure architectures, namely, Vanilla 5G, Roadside Unit and IAB based architectures on KPIs like resource utilization, end-to-end delay backhaul bandwidth utilization and amount of successful traffic transported from the user equipment to the application serve

Education:

• M.Sc. in Computer Science, University of Tehran, Tehran, Iran, 2017
• B.S. in Computer Science, Virginia Tech, Blacksburg, Virginia, 2024

Research Interests:

• Machine Learning
• Neuromorphic Computing
• Modulation Classification

Selected Publications:

• Rosen, Daniel and Rochez, Illa and McIrvin, Caleb and Lee, Joshua and D’Alessandro, Kevin and Wiecek, Max and Hoang, Nhan and Saffarini, Ramzy and Philips, Sam and Jones, Vanessa and Ivey, Will and Harris-Smart, Zavier and Harris-Smart, Zavion and Chin, Zayden and Johnson, Amos and Jones, Alyse M. and Headley, William C. RFRL Gym: A Reinforcement Learning Testbed for Cognitive Radio Applications. IEEE 2023 International Conference on Machine Learning and Applications. 2023 Dec 21.

Work Experience:

• Sofware Engineering (Present): CapitalOne
• Sofware Engineering Intern: CapitalOne
• Sofware Engineering Intern: Lockheed Martin

Research Experience:

• Undergraduate Research Assistant: Virginia Tech National Security Institute (VTNSI)

Education:

• M.Sc. in Electrical Engineering, The Pennsylvania State University, State College, PA, 2021

Research Interests:

• Online Machine Learning
• Online Convex Optimization
• Bandit Problems
• Bayesian Optimization
• Reinforcement Learning

Selected Publications:

• Duo Cheng, Xingyu Zhou, and Bo Ji. “Taming Heavy-Tailed Losses in Adversarial Bandits and the Best-of-Both-Worlds Setting.” In the Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS), 2024.
• Duo Cheng, Xingyu Zhou, and Bo Ji. “Understanding the Role of Feedback in Online Learning with Switching Costs.” In the Fortieth International Conference on Machine Learning (ICML), 2023.
• Duo Cheng*, Ruiquan Huang*, Cong Shen, and Jing Yang. “Cascading Bandits with Two-level Feedback.” In the IEEE International Symposium on Information Theory (ISIT), 2022.

Work Experience:

• Applied Scientist Intern at Amazon.com, Inc. from May/2024 to Aug/2024

Education:

• Ph.D. in Electrical Engineering, Virginia Tech, Blacksburg VA, USA December 2024
• Master of Science in Electrical Engineering, Virginia Tech, Blacksburg VA, USA 2021
• Bachelor of Science in Electrical Engineering, Virginia Tech, Blacksburg VA, USA May 2019

Research Interests:

• Machine Learning
• Neuromorphic Computing
• Artificial Intelligence
• Emerging Memory Technologies
• Autonomous Driving
• Analog IC Design

Selected Publications:

• F. Nowshin, Y. Huang, M. Sarkar, Q. Xia, Y. Yi, "MERRC: A Memristor Enabled Reconfigurable Reservoir Computing Architecture at the Edge,” in IEEE Transactions on Circuits and Systems I: Regular Papers, 2023.
• F. Nowshin, S. Sethi, Z. Dong and Y. Yi, “Advancing Anomaly Detection in Autonomous Vehicles through Reservoir Computing-based Temporal-Aware Learning”, In 2nd IEEE International Conference on Mobility: Operations, Services, and Technologies 2024.
• F. Nowshin, H. An, Y. Yi, “Towards Energy-Efficient Spiking Neural Networks: A Robust Hybrid CMOS-Memristive Accelerator,” in ACM Journal of Emerging Technologies in Computing Systems, 2024.
• F. Nowshin, L. Liu and Y. Yi, "Energy Efficient and Adaptive Analog IC Design for Delay-Based Reservoir Computing," 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS), Springfield, MA, USA, 2020.
• F. Nowshin and Y. Yi, "Memristor-based Deep Spiking Neural Network with a Computing-In-Memory Architecture," 2022 23rd International Symposium on Quality Electronic Design (ISQED), 2022, pp. 1-6.
• F. Nowshin, Y. Zhang, L. Liu and Y. Yi, "Recent Advances in Reservoir Computing With A Focus on Electronic Reservoirs," 2020 11th International Green and Sustainable Computing Workshops (IGSC).
• F. Nowshin and Y. Yi, “ReRAM-Based Neuromorphic Computing”, In: Iranmanesh, A. (eds) Frontiers of Quality Electronic Design (QED). Springer 2023.

Work Experience:

• Graduate Teaching Assistant at Virginia Tech, August 2019 – May 2020
  • Administered lab sessions for Electronics course
• Ericsson (Electrical Engineering Intern), May 2017 – August 2017
  • Solved network dimensioning problems and created a solution document to provide to customers
  • Monitored Traffic Channel Drop Rates and documented the reasons and possible solutions
  • Collaborated with the transmission department on SDH Mapping and Multiplexing

Research Experience:

• Memristor-based Spiking Neural Network: Developed a deep spiking neural network using memristor technology with a computing-in-memory architecture to enhance energy efficiency and computational speed.
• Memristor-based Reservoir Computing: Designed and implemented reservoir computing systems using memristor technology to improve computational efficiency and scalability in neuromorphic computing applications.
• Reservoir Computing for Autonomous Driving: Advanced anomaly detection and traffic forecasting in autonomous vehicles through reservoir computing-based temporal-aware learning.

Awards and Fellowships:

• Prasad Scholarship Award, Fall 2021
• Dean’s list with distinction, Fall 2015 – Spring 2019

Education:

• Ph.D. Student, Electrical Engineering, Virginia Tech, Blacksburg, VA, USA, 2024 - Current
• M.S. Information and Communication Engineering, ShanghaiTech University, Shanghai, China, 2024
• B.S. Information Engineering, South China University Of Technology, Guangzhou, Guangdong, China, 2021

Research Interests:

• Federated Learning
• Edge Computing
• Machine Learning

Selected Publications:

• F. Zhou, Z. Wang, Y. Shi, and Y. Zhou, ”Decentralized Satellite Federated Learning via Intra-and Inter-Orbit Communications” in Proc. IEEE Int. Conf. on Commun. Workshops (ICC Wcshps.), Denver, USA, June 2024.
• F. Zhou, X. Chen, H. Shan, and Y. Zhou, ”Adaptive Transceiver Design for Wireless Hierarchical Federated Learning,” in Proc. IEEE 98th Veh. Techno. Conf. (VTC2023-Fall), Hong Kong, China, Oct 2023.
• F. Zhou, Z. Wang, X. Luo, and Y. Zhou, ”Over-the-air computation assisted hierarchical personalized federated learning,” in Proc. IEEE Int. Conf. on Commun. (ICC), Rome, Italy, May 2023.
• L. Wu, G. Gao, J. Yu, F. Zhou, Y. Yang and T. Wang, "PDD: Partitioning DAG-Topology DNNs for Streaming Tasks," in IEEE Internet of Things J., vol. 11, no. 6, pp. 9258-9268, 2024,

Education:

• M.Sc. in Systems Engineering and Computer Science, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil, 2023
• B.Sc. in Computer Science, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil, 2020

Research Interests:

• Machine Learning
• Ultra Reliable and Low-Latency Communication

Work Experience:

• AI Specialist, Onereach.ai, June 2022 - August 2023
• Data Scientist, Twist Systems, September 2018 - June 2022

Research Experience:

• Graduate Research Assistant, Commonwealth Cyber Initiative, August 2023 - Current

Education:

• B.E. in Electrical and Electronics, BITS, Pilani, India
• M.Tech. in Communications and Signal Processing, IIIT, Delhi, India

Research Interests:

• Wireless Positioning
• Public Safety Networks
• Estimation Theory
• Radar Systems

Selected Publications:

• G. Duggal, S. Dhungel, Dara Ron, Nishith Tripathi, R. Michael Buehrer, Jeffrey H. Reed, Vijay K. Shah, "Experimental Validation of a 3GPP Compliant 5G-Based Positioning System" ACM Wintech Washington DC, USA, 2024
• G. Duggal, R. M. Buehrer, H. S. Dhillon and J. H. Reed, "3D Positioning using a New Diffraction Path Model," 2024 IEEE International Conference on Communications Workshops (ICC Workshops), Denver, CO, USA, 2024, pp. 932-938
• Indranil Sinharoy, Aditya Dave, Gaurav Duggal, Vutha Va, Lianjun Li, Hao Chen, Abhishek Sehgal, "Data augmentation and synthesis of radar signatures for ML-based gesture recognition and activity detection from mocap trajectories," Proc. SPIE 13035, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II, 130351C (7 June 2024)
• G. Duggal, R. M. Buehrer, J. H. Reed and N. Tripathi, "Line-of-Sight Probability for Outdoor-to-Indoor UAV-Assisted Emergency Networks," ICC 2023 - IEEE International Conference on Communications, Rome, Italy, 2023
• G. Duggal, S. Vishwakarma, K. V. Mishra and S. S. Ram, "Doppler-Resilient 802.11ad-Based Ultrashort Range Automotive Joint Radar-Communications System," in IEEE Transactions on Aerospace and Electronic Systems, vol. 56, no. 5, pp. 4035-4048, Oct. 2020
• N. Pandey, G. Duggal and S. S. Ram, "Database Of Simulated Inverse Synthetic Aperture Radar Images For Short Range Automotive Radar," 2020 IEEE International Radar Conference (RADAR), Washington, DC, USA, 2020
• G. Duggal, S. Sundar Ram and K. V. Mishra, "Micro-Doppler and Micro-Range Detection via Doppler-resilient 802.11ad-Based Vehicle-to-Pedestrian Radar," 2019 IEEE Radar Conference (RadarConf), Boston, MA, USA, 2019

Work Experience:

• Engineer, Qualcomm , Hyderabad, India (2019-2021)
• Member of Technical Staff, Tonbo Imaging, India (2015-2016)
• Embedded Electronics Engineer, Ducere Technologies, India (2013-2015)

Research Experience:

• Research Intern, Samsung Research America, Plano, Texas, USA (SMI-lab) (2023)
• Research Intern, Samsung Research America, Plano, Texas, USA (SMI-lab) (2022)
• Research Intern, Hertzwell, Singapore (2018)
• Research Intern, Cranwell University, Shrivenham, United Kingdom (2012)

Awards and Fellowships:

• National Institute of Justice Graduate Research Fellowship $166,500 (2023-2026)

Education:

• M.S. in Computer Engineering, Arizona State University, Tempe, Arizona, 2023
• B.Tech in Electronics and Communications Engineering, PES University, Bangalore, India, 2019

Research Interests:

• NextG Cellular
• PHY Layer
• ORAN
• UWB

Selected Publications:

• S. R. Kudumala, A. K. Dubey, P. Gupta, S. Gupta and E. Sharma, "Hardware Impaired RIS Assisted Multipair FD Communication With Spatial Correlation," in IEEE Communications Letters, vol. 26, no. 9, pp. 2200-2204, Sept. 2022, doi: 10.1109 LCOMM.2022.3186482.

Work Experience:

• Teaching Assistant (2024) -- ECE5485 Networks and Protocols
• UWB Systems Engineer Intern (2023) -- NXP Semiconductors, Irvine, USA
• Cellular Firmware Intern (2022) -- Zeku, Inc., Palo Alto, USA

Research Experience:

• Research Assistant, Wireless@VT (2024) -- Developing a Radio Access Network (RAN)-based localization xApp for precise, real-time positioning of user equipment (UE), addressing applications with stringent time-sensitive requirements.
• Research Intern, IIT Roorkee (2021) -- Contributed to Designing and Performing Channel Estimation for a multi-pair full-duplex two-way communication system assisted by an Intelligent Reflective System-Aided Multiuser MIMO System with simulated hardware imperfections.

Education:

• M.Sc. in Electrical Engineering, Virginia Tech University, Blacksburg, VA, 2025
• B.Sc. in Electrical Engineering, Virginia Tech University, Blacksburg, VA, 2024

Research Interests:

• RF Circuit Design
• Antenna Design

Work Experience:

• Electrical Engineer Intern at CACI
• Electrical Engineer Intern at NASA
• Electrical Engineer Intern at Boeing
• Electrical Engineer Intern at Goodyear

Research Experience:

• Modeling Noise correlation with spacing of antenna arrays at VTNSI

Education:

• Ph.D. in Electrial and Computer Engineering, Virginia Tech, VA, USA, Sep. 2022 - Present
• Ph.D. in Computer Engineering, Texas A&M University, TX, USA, Aug. 2021 - Aug. 2022
• M.Tech. in Communications and Signal Processing, IIT Hyderabad, Hyderbad, India, 2016
• B.Tech. in Electronics and Communications Engineering, RGUKT, Kadapa, India, 2014

Research Interests:

• Wireless Communications
• Localization
• Massive MIMO

Selected Publications:

• H. K. Dureppagari, C. Saha, H. S. Dhillon, R. M. Buehrer, "UAV-Based 5G Localization for Emergency Response Using Signals of Opportunity", 2024 IEEE DySPAN, Washington, DC, USA, 2024, pp. 63-68.
• H. K. Dureppagari, C. Saha, H. S. Dhillon, R. M. Buehrer, "NTN-based 6G Localization: Vision, Role of LEOs, and Open Problems", IEEE Wireless Communications, vol. 30, no. 6, pp. 44-51, Dec. 2023.
• H. K. Dureppagari, H. S. Dhillon, R. M. Buehrer, "UAV-Aided Indoor Localization of Emergency Response Personnel", in Proc. IEEE/ION PLANS, Monterey, CA, Apr. 2023.
• H. K. Dureppagari, U. Dinesha, R. Wu, S. Ganji, W. Ko, S. Shakkottai, and D. Bharadia, "Realtime Intelligent Control for NextG Cellular Radio Access Networks", ACM MobiSys, Portland, OR, USA, Jun. 2022.
• P. R. Manne, K. R. Gudimetla, H. K. Dureppagari, S. Kumawat, S. Amuru, and K. Kuchi, "Uplink Coverage Enhancements for Extremely Large Cell Sites", EURASIP Journal on Wireless Communications and Networking, Jun. 2022.
• P. R. Manne, H. K. Dureppagari, S. Amuru, and K. Kuchi, "Design and implementation of beamformed physical downlink control channel for 4G massive MIMO systems", Ad Hoc Networks, Volume 111, Feb. 2021.
• P. R. Manne, H. K. Dureppagari, S. Amuru, and K. Kuchi, "Removing the PDCCH Bottleneck and Enhancing the Capacity of 4G Massive MIMO Systems,", International Conference on COMmunication Systems & NETworkS (COMSNETS), Bengaluru, India, Jan. 2020, pp. 237-244.

Work Experience:

• Lead Engineer at WiSig Networks Pvt Ltd, Hyderabad, India from Aug. 2020 - Aug. 2021
• Research Engineer at WiSig Networks Pvt Ltd, Hyderabad, India from Oct. 2017 - Jul. 2020
• Design Engineer at Analog Devices India Pvt Ltd, Bangalore, India from Aug. 2016 - Sep. 2017

Research Experience:

• Graduate Research Assistant, Virginia Tech, VA, USA from Sep. 2022 - Current
• Graduate Research Assistant, Texas A&M University, TX, USA from Aug. 2021 - Aug. 2022

Awards and Fellowships:

• Best Paper Award (Honorable Mention) at IEEE COMSNETS 2020

Education:

• B.E. in Electronic and Information Engineering, Xidian University, China, 2020

Research Interests:

• Wireless Communications
• Computer Vision
• Machine Learning
• Localization
• Detection/Estimation Theory
• Stochastic Process

Selected Publications:

• Hu, Haozhou and Dhillon, Harpreet S and Buehrer, R Michael. “Landmark-Based Localization Using Range Measurements: A Stochastic Geometry Perspective,” IEEE/IFIP WiOpt Workshops, 2023.
• Hu, Haozhou and Dhillon, Harpreet S and Buehrer, R Michael. “Stochastic geometry analysis of localizability in vision-based geolocation systems,” Proc., IEEE Asilomar, 2023.
• Hu, Haozhou and Dhillon, Harpreet S and Buehrer, R Michael. “Foundations of Vision-Based Localization: A New Approach to Localizability Analysis Using Stochastic Geometry,” arXiv preprint arXiv:2409.09525. 2024.

Work Experience:

• Research Intern, Samsung R&D Institute China - Beijing, China (2021)

Awards and Fellowships:

• National Scholarship (2018, 2017)
• Alumni Scholarship (2017)

Education:

• M.Sc. in Computer Engineering, Virginia Tech, Blacksburg, US, Aug 2023 - Present
• B.Tech. in Electronics Engineering, Veermata Jijabai Technological Institute, Mumbai, India, 2020

Research Interests:

• Machine Learning
• Computer Vision
• Radar Signal Processing

Selected Publications:

• A. Heddallikar, R. Pinto, H. Kothari, J. James, Y. S. Rao and T. Sajjad, "Initial Results of Ground based SAR Experiment in an Opencast Coal Mine for Slope Stability Monitoring," 2022 IEEE Microwaves, Antennas, and Propagation Conference (MAPCON), Bangalore, India, 2022, pp. 1720-1725, doi: 10.1109/MAPCON56011.2022.10047045.
• A. Dabi, H. Pethkar, R. Pinto and H. Kothari, "Low Power Signal Processing Implementation Using STM32 Controller for FMCW Radar," 2022 IEEE Microwaves, Antennas, and Propagation Conference (MAPCON), Bangalore, India, 2022, pp. 1698-1702, doi: 10.1109/MAPCON56011.2022.10047194.

Work Experience:

• Engineering Intern: Sensors at Rivian, Palo Alto, CA, May 2024 - Aug 2024
• Research Scientist, Society for Applied Microwave Electronics Engineering and Research, Mumbai, India, Nov 2020 - Nov 2022

Education:

• M.S. in Electrical Engineering, University of Southern California, Los Angeles, United States, 2020
• B.S. in Electrical Engineering, King Saud University, Riyadh, Saudi Arabia, 2016

Research Interests:

• Integrated Sensing and Communications (ISAC)
• Machine Learning

Work Experience:

• Faculty member, King Saud University, Riyadh, Saudi Arabia

Research Experience:

• Assistant researcher, Wireless@VT, Virginia tech

Education:

• M.Sc. in Computer Science, University of Brasília, Brasília, Brazil, 2021

Research Interests:

• Machine Learning
• Wireless Networks
• Green Networking

Selected Publications:

• A. Baron-Hyppolite, J. V. F. Abreu, J. F. Santos, L. A. DaSilva and J. Kibilda, "Adaptive Beam Management for Secure mmWave Communications using Software-Defined Radios," MILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM), Boston, MA, USA, 2023, pp. 243-244
• J. V. Fonseca Abreu, C. Ghedini Ralha and J. J. Costa Gondim, "Twitter Bot Detection with Reduced Feature Set," 2020 IEEE International Conference on Intelligence and Security Informatics (ISI), Arlington, VA, USA, 2020, pp. 1-6

Education:

• M.Sc. in Computer Science, University of Maryland, College Park, MD
• M.Sc. in Computer & Information Technology, Purdue University, West Lafayette, IN

Research Interests:

• Distributed Machine Learning
• O-RAN
• V2X
• Edge Intelligence

Selected Publications:

• Thornton CE, Allen E, Jones E, Jakubisin D, Templin F, Liu L. On the Role of 5G and Beyond Sidelink Communication in Multi-Hop Tactical Networks. IEEE MILCOM Workshops. 2023 Sep 28.

Work Experience:

• Senior Data Scientist, Booz Allen Hamilton
• Security Data Scientist, National Security Agency

Research Experience:

• Graduate Research Assistant, Virginia Tech, VA, USA
• Graduate Student Researcher (Project Dost), UMD, College Park, USA

Awards and Fellowships:

• GEM Fellowship

Education:

• M.Sc. in Electrical and Computer Engineering, University of Michigan, Ann Arbor, US, 2017

Research Interests:

• Machine Learning
• Deep Learning

Selected Publications:

• J. Xu, S. Jere, Y. Song, Y. Kao, L. Zheng, and L. Liu. "Learning at the Speed of Wireless: Online Real-Time Learning for AI-Enabled MIMO in NextG." in IEEE Communications Magazine, 2024.
• J. Xu, K. Said, L. Zheng, and L. Liu. "2D-RC: Two-Dimensional Neural Network Approach for OTFS Symbol Detection." in IEEE Transactions on Wireless Communications, 2024.
• J. Xu, L. Li, L. Zheng and L. Liu, "Detect to Learn: Structure Learning With Attention and Decision Feedback for MIMO-OFDM Receive Processing," in IEEE Transactions on Communications, vol. 72, no. 1, pp. 146-161, Jan. 2024.
• J. Xu, Z. Zhou, L. Li, L. Zheng and L. Liu, "RC-Struct: A Structure-Based Neural Network Approach for MIMO-OFDM Detection," in IEEE Transactions on Wireless Communications, vol. 21, no. 9, pp. 7181-7193, Sept. 2022.

Work Experience:

• Research Intern, Samsung NEON - STAR Labs, San Jose, California. May 2020 - Dec 2020.

Awards and Fellowships:

• Prasad Scholarship Award. Issued by the Bradley Department of Electrical and Computer Engineering, Virginia Tech, Aug 2021.

Education:

• M.S., Xi'an Jiaotong University, Shaanxi, China, 2017

Research Interests:

• Machine Learning
• O-RAN

Selected Publications:

• J. Dai, U. Saeed, Y. Wang, Y. Pan, H. Wang, K. Kornegay, and L. Liu, “Detection of Overshadowing Attack in 4G and 5G Networks”, IEEE/ACM Trans. Netw., 2024.
• J. Dai, L. Li, R. Safavinejad, S. Mahboob, H. Chen, V. Ratnam, H. Wang, J. Zhang, L. Liu, “O-RAN-enabled Intelligent Network Slicing to Meet Service-Level Agreement (SLA)”, IEEE Trans. Mobile Comput. (Major Revision), 2024.

Education:

• B.S in Electrical Engineering, Virginia Tech, Blacksburg, Virginia, 2024

Research Interests:

• Wireless Communications
• Sensing Systems
• Simulation of Communications Systems

Selected Publications:

• Esser. J, Jagoda. J, Winther. S, McEvoy. T, Vinod. T, VanOfferen. L, Cooper. D, Schroeder. K, " ContentCube: A 1U CubeSat to Test Display Screens in Space," Proceedings of the Small Satellite Conference, https://digitalcommons.usu.edu/smallsat/2024/all2024/158/

Work Experience:

• RF Engineering Intern, Leonardo DRS (2022, 2023)

Research Experience:

• inspireFly at Virginia Tech

Education:

• B.S. degree in Computer Science and Engineering, Southern University of Science and Technology (SUSTech), China, 2022

Research Interests:

• Age of Information
• Machine Learning

Selected Publications:

• Zhongdong Liu, Keyuan Zhang, Bin Li, Yin Sun, Y. Thomas Hou, and Bo Ji, "Learning-augmented Online Minimization of Age of Information and Transmission Costs", IEEE INFOCOM Age and Semantics of Information Workshop (ASoI Workshop), Vancouver, BC, Canada, 2024.
• Zhang, K., Liu, Z., Choi, N., & Ji, B, "Learning-Augmented Online Algorithm for Two-Level Ski-Rental Problem", Proceedings of the AAAI Conference on Artificial Intelligence, 38(18), pp. 20293-20300, 2024.
• K. Zhang, J. Bai and J. Liu, "Generating Game Levels of Diverse Behaviour Engagement," 2022 IEEE Conference on Games (CoG), Beijing, China, 2022, pp. 167-174.

Awards and Fellowships:

• CCI SWVA Cyber Innovation Scholarship, Commonwealth Cyber Initiative (CCI), 2023

Education:

• Bs. in Computer Engineering, Virginia Tech, Blacksburg, Virginia, 2024

Research Interests:

• Machine Learning
• Digital IC Design

Work Experience:

• Electrical Engineer at International Scientific Technologies

Awards and Fellowships:

• Bradley Fellowship

Education:

• Ph.D. in Electrial and Computer Engineering, Virginia Tech, VA, USA, Sep. 2023 - Present
• M.S in Electrical and Computer Engineering, Virginia Tech, VA, USA, 2023
• B.Tech. in Electronics and Communications Engineering, NIT Jaipur, India, 2019

Research Interests:

Wireless Communications, Distributed MIMO, Synchronization, Information Theory, Underlay Signalling

Selected Publications:

• K. S. Bondada et al., "Wireless Mobile Distributed-MIMO for 6G," 2024 IEEE Vehicular Technology Conference (VTC), Washington D.C, USA.
• K. S. Bondada et al., "Enhanced Non-preemptive Support of URLLC using Spread Spectrum Underlay Signalling," MILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM), Boston, MA, USA, 2023, pp. 298-303, doi: 10.1109/MILCOM58377.2023.10356263.
• K. S. Bondada, D. J. Jakubisin, N. Tripathi and J. H. Reed, "Bit Error Rate Analysis for 5G New Radio Interface Augmented by a Spread Spectrum Underlay," ICC 2024 - IEEE International Conference on Communications, Denver, CO, USA, 2024, pp. 5299-5304, doi: 10.1109/ICC51166.2024.10622869.
• K. S. Bondada et al., "Demonstration of 5G-Underlay Signal Co-existence," MILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM), Boston, MA, USA, 2023, pp. 227-232, doi: 10.1109/MILCOM58377.2023.10356273.

Work Experience:

• Product Development Engineer at WiSig Networks Pvt Ltd, Hyderabad, India from Jul. 2019 - May. 2021

Research Experience:

• Graduate Research Assistant, Virginia Tech, VA, USA from Jun. 2021 - Current

Awards and Fellowships:

• Best Demo Award, MILCOM 2024.

Education:

• B.S. in Data Science, Chongqing University, China, 2023

Research Interests:

• Multi-model Learning
• Large Language Models
• Egocentric Video Understanding
• Eye-tracking Analysis

Selected Publications:

• Lang Zhang and Fuyuan Xiao. Belief Renyi Divergence of Divergence and Its Application in Time Series Classification. IEEE Transactions on Knowledge and Data Engineering (TKDE). 2024 Feb 15.
• Lang Zhang and Fuyuan Xiao. Multi-channel EEG Signals Classification Via CNN and Multi-head Self-attention on Evidence Theory. Information Sciences (Inf. Sci.). 2023 Sep 12.
• Lang Zhang and Fuyuan Xiao. A Novel Belief χ^2 Divergence for Multisource Information Fusion and Its Application in Pattern Classification. International Journal of Intelligent Systems (IJIS). 2024 Feb 15.

Research Experience:

• Multi-modal Learning with Eye-tracking Information for Driver Distraction Detection, Sep 2023 – Oct 2024
• Belief Evidence Theory for Multisource Information Fusion, Sep 2021 – Jun 2023

Awards and Fellowships:

• CCI SWVA Cyber Innovation Scholarship, Commonwealth Cyber Initiative (CCI), 2023

Education:

• B.S. in Computer Engineering, Virginia Tech, Blacksburg, Virginia, United States, 2023

Research Interests:

• Analog IC Design

Education:

• B.S. in Electrical Engineering, Virginia Tech, Blacksburg, Virginia, 2019

Research Interests:

• Optimization Theory
• Reconfigurable Intelligent Surface
• Interference Mitigation
• Estimation Theory

Selected Publications:

• S. W. Ellingson, R. M. Buehrer, T. Anders, and X. Li, “New Results on Mitigation of Satellite Interference by Coherent Time-Domain Canceling,” in 2024 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM), 2024, pp. 276–276.
• X. Li, R. M. Buehrer, and S. W. Ellingson, “An Improved Weight Selection Algorithm for Interference Mitigation in Paraboloidal Reflector Antennas with Reconfigurable Rim Scattering,” in IEEE MILCOM, 2024.

Work Experience:

• Test Engineer, MediaTek, Bridgewater, NJ, September 2019 - August 2021
• Test Engineer, Samsung Electronics, Berkeley Heights, NJ, September 2021 - July 2022

Education:

• Post Graduate Diploma in Advanced Computing, C-DAC Hyderabad, Telangana, India, 2017
• M.Sc. in Computer Science, University of Allahabad, Uttar Pradesh, India, 2014

Research Interests:

• Wireless Communications
• Machine Learning

Selected Publications:

• R. Chintalapati, N. D. Tripathi, M. Kumar and J. H. Reed, "Feasibility Analysis of Isolation-Based Spectrum Sharing Between Terrestrial and Non-Terrestrial Networks," 2024 IEEE Texas Symposium on Wireless and Microwave Circuits and Systems (WMCS), Waco, TX, USA, 2024 Aug 06

Patents:

• M Medithe, M Kumar, N Petrit, "Method, device, and computer program product for anomaly detection" - US Patent Grant Number: 11716240, 2023
• M Elsakhawy, M Kumar, A Elagamy, H Sharma, V Gala, "Method, device, and computer program product for anomaly detection and root cause analysis" - US Patent Grant Number: 11777786, 2023
• M MEDITHE, S CHINNAM, M Kumar, P NAHI, "Neighbor relation conflict prediction" - US Patent App. US17/542242, 2023
• M Kumar, R Davda, A Nurul, S Gupta, "Systems and methods for X2 interface link detection and construction" - US Patent App. PCT/US2023/025555, 2023

Work Experience:

• Section Manager, Rakuten Mobile Inc, Tokyo, Japan : Jan 2020 - Jul 2023
• Product Development Engineer, WiSig Networks, Hyderabad, India : Nov 2017 - Nov 2019

Research Experience:

• Graduate Research Assistant, Virginia Tech, Blacksburg, USA (Aug 2024 - Present)
• Graduate Research Assistant, Virginia Tech, Blacksburg, USA (Aug 2023 - Dec 2023)

Education:

• M.Sc. in Electrical Engineering, Virginia Tech, Blacksburg, Virginia, 2022
• B.Sc. in Electrical Engineering, Virginia Tech, Blacksburg, Virginia, 2021

Research Interests:

• 5/6G Connectivity
• Software Defined Radio
• Metasurface phased array applications
• Metamaterial antenna applications

Selected Publications:

• Evans, M, “UAV based measurement opportunities and evaluation for 5/6G connectivity of autonomous vehicles,” Virginia Tech, 2022. https://vtechworks.lib.vt.edu/items/0d2cec3f-885e-4bec-92a9-6ed0f2944b1f
• Evans, M., Talty, T., Dietrich, C., and Gomez, X., "5G Network Connectivity Automated Test and Verification for Autonomous Vehicles Using UAVs," SAE Technical Paper 2022-01-0145, 2022, https://doi.org/10.4271/2022-01-0145.

Work Experience:

• Antenna Engineer at Intellian (2024 - Present)
• Antenna Systems Engineer at HTL (2022 - 2024)
• Graduate Teaching Assistant at Virginia Tech (2021 - 2022)
• RF Engineer Intern at HTL (2021)

Research Experience:

• UAV Leveraged Connectivity for 5/6G Networks
• Cost Efficient Antenna Measurement Opportunities for Student and Research Applications

Awards and Fellowships:

• Cum Laude (M.Sc and B.Sc)

Education:

• Ph.D. in Electrical and Computer Engineering, Virginia Tech, VA, USA. (Jan, 2023 – Present)
• M.Sc. in Electronics Engineering, Kookmin University, Seoul, South Korea. (Mar, 2020 – Feb, 2022)

Research Interests:

• Open Radio Access Network.
• Machine Learning & Deep Learning.
• AI/ML Services Automation.
• DRL for Resource Management.
• Energy Consumption Optimization.
• Channel Estimation.

Selected Publications:

• M. H. Rahman, A. Jain, A. D. Silva, S. Singh, M. R. Chowdhury, H. S. Dhillon, and M. Hong "Leveraging O-RAN SC AI/ML Framework & Non-RT RIC for AI-Driven Network Slice QoS Optimization" in Proc. IEEE Globecom Workshops, Cape Town, South Africa, 2024.
• M. H. Rahman, M.R. Chowdhury, A. Sultana, A. Tripathi, and A. P. Da Silva, "Deep Learning Based Uplink Power Allocation in Multi-Radio Dual Connectivity Heterogeneous Wireless Networks" in Proc. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Valencia, Spain, 2024.
• A. Tripathi, J. S. R. Mallu, M. H. Rahman, A. Sultana, A. Sathish, A. Huff, M. R. Chowdhury, and A. P. Da Silva, “End-To-End ORAN Control-Loop for Radio Resource Allocation in SDR-Based 5G Network” in Proc. MILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM) – DEMOS, Boston, USA, 2023.
• M. H. Rahman, M. Z. Chowdhury, I. B. K. Y. Utama, and Y. M. Jang, "Channel Estimation for Indoor Massive MIMO Visible Light Communication System with Deep Residual Convolutional Blind Denoising Network,” IEEE Trans. Cogn. Commun. Netw., vol. 9, no. 3, pp. 683-694, June 2023.
• M. H. Rahman, M. Z. Chowdhury, I. B. K. Y. Utama, and Y. M. Jang, "Channel Estimation for Indoor Massive MIMO Visible Light Communication System with Deep Residual Convolutional Blind Denoising Network,” IEEE Trans. Cogn. Commun. Netw., vol. 9, no. 3, pp. 683-694, June 2023.
• M. Shahjalal, M. H. Rahman, M. M. Alam, M. Z. Chowdhury and Y. M. Jang, "DRL-Assisted Dynamic Subconnected Hybrid Precoding for Multi-Layer THz mMIMO-NOMA System," IEEE Transactions on Vehicular Technology, vol. 73, no. 9, pp. 12950-12961, Sept. 2024.
• A. Chakma, S. S. Alam, M. H. Rahman and Y. M. Jang, "Deep Decoder CsiNet for FDD Massive MIMO System," IEEE Wireless Communications Letters, vol. 12, no. 12, pp. 2073-2077, Dec. 2023.

Research Experience:

• Graduate Research Assistant, Virginia Tech. (Jan, 2023 – Present)
• Graduate Research Assistant, Kookmin University. (Mar, 2020 – Feb, 2022)

Awards and Fellowships:

• Graduate Research Assistantship, Bradley Department of Electrical and Computer Engineering Department, Virginia Tech.
• Best Demo Award, IEEE Military Communications Conference-2023, Boston, MA, USA.
• Graduate Research Assistantship, Wireless Communications and Artificial Intelligence Lab, Kookmin University.
• Academic Excellence Award, Dept. of Electronics Engineering, Kookmin University.

Education:

• M.Sc. in Electrical Engineering, Michigan Technological University, Houghton, MI, 2016
• M.Sc. in Physics, Michigan Technological University, Houghton, MI, 2016
• B.Sc. in Electrical Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh

Research Interests:

• Non-Terrestrial Network (NTN)
• ORAN
• Machine Learning

Selected Publications:

• M. Rahman, Z. Hassan, J. H. Reed, and L. Liu, “Non-Terrestrial Network (NTN): a Novel Alternate Fractional Programming for the Downlink Channels Power Allocation,” IEEE MILCOM, 2023, pp. 963–968.
• M. Rahman, Z. Hassan, J. H. Reed, and L. Liu, “Joint Interference Management and Traffic Offloading in Integrated Terrestrial and Non-Terrestrial Networks,” IEEE TCOM, 2024

Work Experience:

• Work Experience 1

Research Experience:

• Research Experience 1
• Research Experience 2

Awards and Fellowships:

• Award 1

Education:

• M.Sc. in Electrical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA, 2021

Research Interests:

• Non Terrestrial Networks
• O-RAN
• Machine Learning

Selected Publications:

• S. Mahboob, K. Kar and J. Chakareski, "Decentralized Collaborative Video Caching in 5G Small-Cell Base Station Cellular Networks," 2021 19th International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt), 2021, pp. 1-8, doi: 10.23919/WiOpt52861.2021.9589569.

Work Experience:

• Graduate Research Assistant:
  • Wireless@VT (January 2022 - Present)
  • Networking Lab, RPI (May 2021 - Decemeber 2021)
• Graduate Teaching Assistant:
  • ECE, Virginia Tech (January 2022 - December 2022)
  • ECSE, RPI (August 2019 - May 2021)

Research Experience:

• Devised a close-to-optimal algorithm for minimizing overall video delivery delay across a set of 5G small-cell base stations (SBS) and generated caching performance results through simulations on real network traces
• Working on spectrum-aware DRL based efficient handover schemes for LEO satellites in Non-Terrestrial Networks

Education:

• M.Sc. in Computer Engineering (CPE), Virginia Tech, Blacksburg, USA, 2024
• B.Sc. in Electrical & Electronics Engineering (EE), Bangladesh University of Engineering & Technology, Dhaka, Bangladesh, 2017

Research Interests:

• Machine Learning chip design
• Neuromorphic Accelerator
• Hardware Accelerator design
• RTL design & Optimization
• FPGA implementation & optimization

Selected Publications:

• Lin, Chunxiao, Muhammad Farhan Azmine, and Yang Yi. "Accelerating Next-G Wireless Communications with FPGA-Based AI Accelerators." 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD). IEEE, 2023.
• Lin, Chunxiao, Muhammad Farhan Azmine, Yibin Liang, and Yang Yi. "Leveraging neuro-inspired AI accelerator for high-speed computing in 6G networks." Frontiers in Computational Neuroscience 18 (2024): 1345644.

Work Experience:

• Graduate Teaching Assistant for course ECE 2544 - Fundamentals of Digital Systems (3C) for Spring 2023, Virginia Tech
• Graduate Teaching Assistant for course ECE 3544 - Digital Design I (3C) for Fall 2022, Virginia Tech

Research Experience:

• Graduate Research Assistant (GRA) at Multifunctional Integrated Circuits and Systems (MICS) from Summer 2023~ to present.

Education:

• B.S. in Electrical Engineering, Virginia Tech, Blacksburg, Va, 2023
• B.S. in Mathematics, Virginia Tech, Blacksburg, Va, 2023

Research Interests:

• Stochastic Geometry
• Radar Detection

Work Experience:

• InPhase Research Signal Processing Intern June-August 2024
• Rincon Signal Processing Intern June-August 2023

Awards and Fellowships:

• Bradley Fellow

Nima Mohammadi

Education:

• Ph.D. in Computer Engineering, Virginia Polytechnic Institute and State University, USA, Ongoing
• M.Sc. in Computer Science, University of Tehran, Tehran, Iran, 2016

Research Interests:

• Radio Resource Allocation
• Privacy-Aware Machine Learning
• Dynamic Spectrum Access
• Distributed Systems
• Differential Privacy
• Neuromorphic Computing
• Deep Learning
• Explainable AI
• Anomaly Detection

Selected Publications:

• Mohammadi N, Bai J, Fan Q, Song Y, Yi Y, Liu L. Differential Privacy Meets Federated Learning Under Communication Constraints. IEEE Internet of Things Journal. 2021 Aug.
• Chang H, Mohammadi N, Safavinejad R, Yi Y, Liu L. Dyna-ESN: Efficient Deep Reinforcement Learning for Partially Observable Dynamic Spectrum Access. IEEE Transactions on Wireless Communications. Accepted for publication in Nov 2024.
• Liu S, Mohammadi N, Yi Y. Quantization-Aware Training of Spiking Neural Networks for Energy-Efficient Spectrum Sensing on Loihi Chip. IEEE Transactions on Green Communications and Networking. 2023 Nov.
• Zhou Z, Bai K, Mohammadi N, Yi Y, Liu L. Making Intelligent Reflecting Surfaces More Intelligent: A Roadmap Through Reservoir Computing. IEEE Network Magazine. 2022.
• Emamjomeh A, Goliaei B, Torkamani A, Ebrahimpour R, Mohammadi N, Parsian A. Protein-Protein Interaction Prediction by Combined Analysis of Genomic and Conservation Information. Genes & Genetic Systems. 2014.
• Ahangi A, Karamnejad M, Mohammadi N, Ebrahimpour R, Bagheri N. Multiple Classifier System for EEG Signal Classification with Application to Brain-Computer Interfaces. Neural Computing and Applications. 2013 Oct.
• Ebrahimpour R, Sadeghnejad N, Arani SA, Mohammadi N. Boost-Wise Pre-Loaded Mixture of Experts for Classification Tasks. Neural Computing and Applications. 2013 May.

Conference Publications and Presentations:

• Mohammadi N, Liu L, Yi Y. Policy-Based Fully Spiking Reservoir Computing for Multi-Agent Distributed Dynamic Spectrum Access. Presented at IEEE ICC'22 - GCSN Symposium.
• Zheng H, Mohammadi N, Bai K, Yi Y. Low-Power Analog and Mixed-Signal IC Design of Multiplexing Neural Encoder in Neuromorphic Computing. Presented at ISQED 2021.

Under Review:

• Mohammadi N, Liu L, Song Y, Yi Y. Energy-Efficient Dynamic and Spatiotemporal Spectrum Access Using Spiking Reservoir Computing. Submitted to IEEE Transactions on Wireless Communications.
• Chang H, Rayala S, Mohammadi N, Chen H, Zhang J, Liu L. Multi-Agent Deep Reinforcement Learning Based Resource Allocation. Submitted to IEEE/ACM Transactions on Networking.

Work Experience:

• Graduate Research Assistant, Virginia Polytechnic Institute and State University, 2020 - Present
• Software Lead, Electronics Dept., Kaveh Industrial Group, 2018 - 2019
• Software Developer, Ideano Pishro Shargh Inc., 2013 - 2018
• Embedded System Developer, Ideano Pishro Shargh Inc., 2010 - 2013

Research Experience:

• Mobile Distributed MIMO: Vehicular channel modeling and GNU Radio implementation for NCJT scheme. Part of a $9.2 million DoD-funded project.
• SCISRS: Developed ML/RL algorithms for anomaly detection in RF environments. Funded by IARPA.

Miscellaneous:

• President of Student Leadership Council, Wireless@VT, 2023
• Vice President of Student Leadership Council, Wireless@VT, 2022

Education:

• Ph.D. in Computer Science, Virginia Tech, Present
• M.Sc. in Computer Science, The George Washington University, 2020
• B.Sc. in Information Technology, King Abdulaziz University, 2017

Research Interests:

• Network Security
• System Security
• Programmable Data Planes (e.g., P4, eBPF)
• AI for Security (e.g., LLM for Security)

Selected Publications:

• O. Bajaber, B. Ji and P. Gao, "P4Control: Line-Rate Cross-Host Attack Prevention via In-Network Information Flow Control Enabled by Programmable Switches and eBPF," in IEEE Symposium on Security and Privacy (S&P), 2024

Awards and Fellowships:

• Davenport Leadership Fellowship, Virginia Tech, Sep 2024
• IEEE S&P Travel Grant, Apr 2024
• CCI SWVA Cyber Innovation Scholarship, Commonwealth Cyber Initiative (CCI), 2023-2025
• Bitshares Fellowship, Virginia Tech, Aug 2023
• IEEE ICNP Travel Grant, Oct 2022

Education:

• B.S. in Electrical Engineering, Virginia Tech, Blacksburg, VA, 2024

Research Interests:

• Wireless Sensor Networks
• Spectrum Sensing/Prediction

Work Experience:

• PNT DSP Intern - Johns Hopkins University Applied Physics Laboratory, Summer 2024
• Wireless Systems Intern - Shared Spectrum Company, Summer 2023
• Software Engineering Intern - NSWC Dahlgren, Summer 2021, 2022

Research Experience:

• Graduate Research Assistant, August 2024-Present

Awards and Fellowships:

• Bradley Fellowship

Education:

• B.S in Electrical Engineering, Virginia Tech, Blacksburg, Virginia, 2024

Research Interests:

• Wireless Communications

Work Experience:

• Digital Signal Processing Intern at CACI International (2023, 2024)

Education:

• M.Sc. (obtained) and Direct Ph.D. in Electrical Enginnering, Virginia Tech, Blacksburg, USA, Expected graduation: May 2025.
• B. Tech. in Electronics and Communication Engineering (GPA 9.6/10.0, Ranked 1st among 95), National Institute of Technology, Durgapur, India, 2014-2018.

Research Interests:

• MIMO
• Reconfigurable intelligent surfaces
• Optimization theory
• Beam management
• Probability theory
• Machine learning

Selected Publications:

• A. Pradhan, J. K. Devineni, H. S. Dhillon, and A. F. Molisch, “Intelligent surface optimization in terahertz under two manifestations of molecular re-radiation,” in Proc., IEEE GLOBECOM, 2021.
• A. Pradhan, M. A. Abd-Elmagid, H. S. Dhillon, and A. F. Molisch, “Robust optimization of ris in terahertz under extreme molecular re-radiation manifestations,” IEEE Trans. on Wireless Commun., 2023.
• A. Pradhan, H. S. Dhillon, F. Tufvesson, and A. F. Molisch, “Stochastic geometry analysis of a new gscm with dual visibility regions,” in IEEE PIMRC, 2023.
• A. Pradhan, J. K. Devineni, A. F. Molisch, and H. S. Dhillon, “Novel los β − γ thz channel unifying molecular re-radiation manifestations,” IEEE Trans. on Vehicular Technology, 2024.
• A. Pradhan and H. S. Dhillon, “A probabilistic reformulation technique for discrete ris optimization in wireless systems,” IEEE Trans. on Wireless Commun., 2023.
• A. Pradhan, I. Alamzadeh, M. F. Imani, and H. S. Dhillon, “RIS-Aided Coexistence in Wireless Networks Using Angular Information,” Nature Scientific Reports, 2024, to appear.

Work Experience:

• Research Intern in NXP Semiconductors, San Jose, California, May 2024 - August 2024
• Graduate Research Assistant in Virginia Tech, August 2019 - Present
• Research Intern in Indraprastha Indian Institute of Technology Delhi (IIIT-D), India, May 2018 - October 2018

Education:

• Master's in Computer Engineering, Virginia Tech, Blacksburg, USA. Aug 2023 - Present
• Bachelor's in Computer Engineering, National Institute of Technology, Kurukshetra, India. July 2014 - July 2018

Research Interests:

• Wireless Communications
• ORAN
• Non Terrestrial Networks
• Multimedia Broadcast services in Cellular Networks
• Optimization in Cellular Networks

Selected Publications:

• Connor Mcpeak, Rakesh Nagampally, Hanchao Yang, Nishith Tripathi, Gustave Anderson, Jeffery Reed, Yaling Yang, Daniel Jakubisin. Enhancing 5G Spectrum Agility Through O-RAN xApp Initiated Handovers. MILCOM 2024.
• Hanchao Yang, Connor Mcpeak, Rakesh Nagampally, Nishith Tripathi, Gustave Anderson, Jeffery Reed, Yaling Yang, Daniel Jakubisin. Agile 5G Networks: Advance Traffic Steering xAPP for Interference Mitigation. MILCOM 2024.

Work Experience:

• Engineering Intern - Lynk Global Inc, Falls Church, VA, USA. [May 2024 - Aug 2024]
• Senior Engineer - WiSig Networks Pvt Ltd, Hyderabad, India. [June 2022 - July 2023]
• Product Development Engineer - WiSig Networks Pvt Ltd, Hyderabad, India. [Aug 2020 - June 2022]
• Project Associate - Indian Institute of Technology, Hyderabad R&D, India. [Sep 2018 - Aug 2020]

Research Experience:

• Graduate Research Assistant - Virginia Tech National Security Institute (VTNSI), USA. [Dec 2023 - Present]

Education:

• Integrated Dual Degree (BTech. + MTech.), IIT-BHU, Varanasi, India, 2017
• MASc., University of Waterloo, Waterloo, Canada, 2021

Research Interests:

• Spiking Neural Networks, Neuromorphic Computing
• Computational Neuroscience, AI/ML

Selected Publications:

• Gaurav, Ramashish, Terrence C. Stewart, and Yang Yi. "Reservoir based spiking models for univariate Time Series Classification." Frontiers in Computational Neuroscience 17 (2023): 1148284.
• Gaurav, Ramashish, Terrence C. Stewart, and Yang Cindy Yi. "Spiking reservoir computing for temporal edge intelligence on loihi." 2022 IEEE/ACM 7th Symposium on Edge Computing (SEC). IEEE, 2022.

Work Experience:

• MTS-3 at Nutanix, India (2017-2019)

Awards and Fellowships:

• Pratt Fellowship (2024)
• International Master’s Award of Excellence (2019)

Education:

• Ph.D. in Electrical Engineering, Virginia Tech, Blacksburg VA, USA/ 2022-Present
• B.Sc. in Electrical and Electronic Engineering, AUST, Dhaka, Bangladesh/ 2017

Research Interests:

• SRAM and MRAM (STT, SOT) based Hyperdimensional Computing (HDC)
• In-Memory Computing

Selected Publications:

• M. R. Sarkar, M. M. A. Bappy, M. M. Azmir, D. M. Rashid and S. I. Hasan, ”VG-SOT MRAM Design and Performance Analysis,” 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communi- cation Conference (IEMCON), Vancouver, BC, Canada, 2021, pp. 0715- 0719, doi: 10.1109/IEMCON53756.2021.9623147.
• M. R. Sarkar and C. Y. Yi, ”An In-Memory Computing Architecture Utilizing Energy-Efficient VGSOT MRAM Device,” in IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 71, no. 7, pp. 3258-3262, July 2024, doi: 10.1109/TCSII.2024.3359993.

Work Experience:

• Graduate Research Assistant at Virginia Tech, August 2022 – Present/li>
• Physical Design Engineer at Intel, May 2024 – August 2024/li>
• Technical Manager, Globalfoundries DTCO (ODC) - Circuit & System Design Department, Ulkasemi Pvt. Ltd., May 2017 – August 2022/li>
• Graduate Teaching Assistant at Virginia Tech, August 2022 – May 2023/li>

Research Experience:

• SRAM based Hyperdimensional Computing (HDC) Architecture design. This computing system is capable of classifying spatio-temporal data. Highly power efficient for edge computing devices.
• STT and SOT MRAM based in-memory computing. Performing logical operation while reading data from memory for lateral CPU computation eliminating memory bottleneck of Von-Neumann computing.

Awards and Fellowships:

• NA

Education:

• B.S. in Computer engineering, Virginia Tech, 2022

Research Interests:

• Machine Learning
• Spiking reservoir computing
• Digital IC Design

Work Experience:

• Worked as embedded vision intern in Lattice semiconductor at San Jose, CA on summer 2023
• Worked as graduate teaching assistant for ECE 2544, ECE 3544, ECE 4514 at VT

Research Experience:

• Implemented and verified a liquid state machine in a wireless application.
• Hardware implementation of different types of spiking encoder for time-series data processing

Education:

• M.Sc. in Electrical and Electronic Engineering, American International University - Bangladesh (AIUB), Dhaka, Bangladesh, 2021

• B.Sc. in Electrical and Electronic Engineering, American International University - Bangladesh (AIUB), Dhaka, Bangladesh, 2019

Research Interests:

• Analog Mixed-Signal Integrated Circuits
• Neuromorphic Computing
• AI Hardware Accelerator
• In-Memory Computing

Selected Publications:

• Chowdhury, S. S., Sarkar, M. R. & Yi, C. Y. Energy-Efficient Reconfigurable MRAM-Enabled Delay Feedback Reservoir Computing at the Edge. [Submitted in IEEE TCAS-II]. 2024
• Sarkar, M. R. et al. An In-Memory Power Efficient Computing Architecture with Emerging VGSOT MRAM Device in 2024 IEEE International Symposium on Circuits and Systems (ISCAS) (2024), 1–5.
• Chowdhury, S. S. & Arifin, F. The Effects of the Substrate Doping Concentrations on 6H-SiC Nano-Scale ggNMOS ESD Protection Device in 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST) (2021), 329–333.

Work Experience:

• Senior Engineer (Analog IC Design) at Ulkasemi Limited August 2019 - July 2023

• Graduate Research Assistant:
  • MICS@VT (May 2024 - Present)
• Graduate Teaching Assistant:
  • ECE, Virginia Tech (August 2023 - May 2024)

Research Experience:

• Next-generation low-power, high-performance neuromorphic computing solutions.
• RF Integrated Circuits (RFIC) Design.
• Image Classification AI Chip using Delay Feedback Reservoir Computing (DFR) in GF22FDX Technology.

Awards and Fellowships:

• Chancellor's Award (B.Sc), Dr. Anwarul Abedin's Leadership Award (B.Sc), Dean's Award (B.Sc), Summa Cum Laude (M.Sc and B.Sc).

Education:

• B.S. in Electrical Engineering (Summa Cum Laude), Virginia Tech, Blacksburg, Virginia, 2023

Research Interests:

• Wireless Communication
• Channel Sounding
• RF Propagation Characteristics

Selected Publications:

• S. Frietchen, D. J. Jakubisin and A. J. Michaels, "Semi-Automation of Wideband Channel Sounding Transmitter," 2024 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), Washington, DC, USA, 2024, pp. 145-150, doi: 10.1109/DySPAN60163.2024.10632753.

Work Experience:

• Digital Signal Processing Intern - CACI International Inc. [May 2021 - Aug. 2023]
• Undergraduate Lab Assistant - Circuits and Devices - Virginia Tech [Jan. 2021 - May 2021]

Research Experience:

• Graduate Research Assistant - Virginia Tech National Security Institute [Aug. 2023 -]
• Undergraduate Research Scholar - Virginia Tech National Security Institute [Sept. 2020 - May 2023]

Awards and Fellowships:

• Bradley Research Fellowship
• Major Design Experience Best Project in Track
• Pratt Engineering Scholarship

Education:

• B.S. in Electrical Engineering, Virginia Tech, Blacksburg, Virginia, 2023

Research Interests:

• Detection/Estimation Theory
• RF Machine Learning
• Beamforming & Array Theory

Selected Publications:

• S. B. Brown, H. S. Dhillon, Improving Receiver Detection Performance Through NLOS/LOS Vision, in Proc., IEEE MILCOM, Washington, DC, Oct. 2024. [Accepted]
• S. Brown, S. Young, A. Wagennecht, D. Jakubisin, C. Thornton, A. Orndorff, W. Headley, Aircraft Radar Altimeter Interference Mitigation Through a CNN-Layer Only Denoising Autoencoder Architecture, in Proc., IEEE MILCOM, Washington, DC, Oct. 2024. [Accepted]

Work Experience:

• Communications Engineer Intern - The Johns Hopkins University Applied Physics Laboratory [May 2024 - Aug. 2024]
• Radar Engineer Intern - MIT Lincoln Laboratory [May 2023 - Aug. 2023]
• Software Engineer Intern - MITRE [May 2021 - Aug. 2022]

Research Experience:

• Graduate Research Assistant - Virginia Tech National Security Institute [Aug. 2023 -]

Awards and Fellowships:

• 2023 Johnston Fellowship Recipient

Education:

• M.S. in Electrical Engineering, University of California, Los Angeles (UCLA), 2016
• B.Eng. (Hons.) in Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 2014.

Research Interests:

• Wireless Communications
• Machine Learning
• Domain Knowledge-aided Learning Strategy Design for Online Real-Time Physical Layer applications

Selected Publications:

• S. Jere, L. Zheng, K. Said and L. Liu, “Universal Approximation of Linear Time-Invariant (LTI) Systems through RNNs: Power of Randomness in Reservoir Computing,” in IEEE Journal of Selected Topics in Signal Processing, doi: 10.1109/JSTSP.2024.3387274.
• S. Jere, R. Safavinejad, L. Zheng and L. Liu, “Channel Equalization Through Reservoir Computing: A Theoretical Perspective,” in IEEE Wireless Communications Letters, vol. 12, no. 5, pp. 774-778, May 2023.
• S. Jere, K. Said, L. Zheng and L. Liu, “Towards Explainable Machine Learning: The Effectiveness of Reservoir Computing in Wireless Receive Processing,” MILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM), Boston, MA, USA, 2023, pp. 667-672.
• S. Jere, R. Safavinejad and L. Liu, “Theoretical Foundation and Design Guideline for Reservoir Computing-Based MIMO-OFDM Symbol Detection,” in IEEE Transactions on Communications, vol. 71, no. 9, pp. 5169-5181, Sept. 2023.

Work Experience:

• Wireless AI/ML Research Intern, Intel Labs (2021, 2022)
• Product and Platform Development Engineer, Qualcomm Technologies, Inc. (2016 - 2019)

Research Experience:

• Graduate Research Assistant (Wireless@VT)
• Graduate Student Researcher (NESL, UCLA)

Awards and Fellowships:

• IEEE Communications Society (ComSoc) Student Travel Grant

Education:

• B. Tech in Electronics Engineering, Sardar Patel Institute of Technology, Mumbai, India, 2022
• M.S. in Computer Engineering, Virginia Tech, Blacksburg, USA, 2014

Research Interests:

• Machine Learning
• Spiking Neural Networks
• FPGA Programming and Digital Design
• Robotics and Internet of Things
• System Administration and Linux Programming

Work Experience:

• Siemens - Factory Automation, 2022
• Viavi Solutions - Rotational Engineer, FPGA Design, 2024

Education:

• M.S. in Electronics and Communication Engineering, IIIT, Hyderabad, India
• B.Tech. in Electronics and Communication Engineering, NIT, Silchar, India

Research Interests:

• O-RAN
• 5G and Beyond Communication
• Software-Defined Radios
• Machine Learning
• Internet of Things

Selected Publications:

• Deb S., Dwivedi A.K., Chaudhari S., Rajan K.S. Spatial Factor Analysis of Mobile IoT Data: A Case Study on PM Across India. IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2022.
• Deb S., Reddy C.R., Chaudhari S., Vemuri K., Rajan K.S. IoT Network Based Analysis of Variations in Particulate Matter due to COVID-19 Lockdown. IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). 2021.

Work Experience:

• Integration Engineer (Circuit Switched and Packet Switched Core Networks), Ericsson India, August 2015 - August 2017

Research Experience:

• Graduate Research Assistant, Wireless@VT, August 2024 - present
• Graduate Research Assistant, Commonwealth Cyber Initiative, August 2023 - July 2024
• Research Assistant, Center of Excellence in IoT, Signal Processing and Communication Research Center, IIIT Hyderabad, August 2019 - April 2022

Awards and Fellowships:

• iHub Data M.S. Fellowship
• NEC Undergraduate Scholarship

Education:

• B.S. in Electrical Engineering, University of California, Davis, CA, United States, 2021
• M.S. in Computer Engineering, Virginia Tech, VA, United States, 2024

Research Interests:

• Analog and Layout Design
• Machine Learning

Work Experience:

• Analog Design Engineer at ST Microelectronics
• Research Assistant at VT CBIL Lab

Education:

• B.S. in Computer Engineering, Virginia Tech, Blacksburg, Virginia, 2023

Research Interests:

• Distributed Multistatic Radar
• Inter-node synchronization
• Online/offline learning

Work Experience:

• US Army Research Laboratory - Signal Processing and RF Fellowship, Summer 2024

Research Experience:

• Interference detection and cancellation using Offline ML, Fall 2021 - Summer 2023
• Distributed Multistatic Radar with Lowered Synchronization Requirements, Summer 2023 - Ongoing

Education:

• B.Sc. in Computer Engineering, Virginia Tech, Blacksburg, Virginia, 2025

Research Interests:

• Machine Learning
• FPGA

Work Experience:

• Software Engineering Intern - Clark Associates [Summer 2023 & 2024]
• Hardware Engineering Intern - Sechan Electronics [Summer 2022]

Awards and Fellowships:

• ECE 2804 Integrated Design Project Best In Course Award

Education:

• M.Sc. in Electrical and Computer Engineering, University of Florida, Gainesville, Florida, 2023
• BSc. in Electrical and Electronics Engineering, Bangladesh University of Engineering and Technology, Bangladesh, 2017

Research Interests:

• Machine Learning
• Wireless Communication
• FPGA-based Machine Learning Accelerator

Selected Publications:

• Publication 1

Work Experience:

• RTL design and verification Engineer, BJIT Inc., Tokyo, Japan, 2018-2021

Research Experience:

• Graduate Research Assistant, Wireless@VT, Summer 2024
• Graduate Research Assistant, Florida Institute of Cyber Security, 2021-2023

Awards and Fellowships:

• Award 1

Education:

• BEE in Electrical Engineering, National University of Science and Technology (NUST), Islamabad, Pakistan, 2019

Research Interests:

• Physical Layer Security
• Machine Learning
• Orthogonal Time Frequency Space (OTFS)

Selected Publications:

• U. Saeed, L. Liu, K. Zeng and R. Calderbank, "Securing Wireless Channels: Reliable Shared Secret Extraction through OTFS," 2022 IEEE Conference on Communications and Network Security (CNS), Austin, TX, USA, 2022
• Y. Liang, U. Saeed and L. Liu, "Resilient Machine Learning for NextG Wireless Systems Under Smart Jamming," ICC 2024 - IEEE International Conference on Communications, Denver, CO, USA, 2024

Research Experience:

• Research Assistant - Virginia Tech (2022-Current)

Education:

• M.Sc. in Electrical Engineering, University of Hong Kong, Hong Kong, China
• B.Sc. in Electrical Engineering, Beijing Jiaotong University, Beijing, China

Research Interests:

• Machine Learning
• Chiplets based Heterogeneous Integration Design

Selected Publications:

• Wang, Xiaomeng, and Hui, Shu-Yuen Ron. Pinched hysteresis loops and symmetry. IET Science, Measurement & Technology 11, no. 1 (2017): 134-140.
• Wang, Xiaomeng, and Hui, Shu-Yuen Ron. Graphical modelling of pinched hysteresis loops of memristor. Measurement & Technology memristors.” IET Science, Measurement & Technology 11, no. 1 (2017): 86-96.

Work Experience:

• Graduate Teaching Assistant, Virginia Tech, VA, USA

Research Experience:

• Graduate Research Assistant, Virginia Tech, VA, USA

Awards and Fellowships:

Education:

• M.Sc. in Electrical Engineering, University of Washington, Seattle, WA, 2020
• B.Sc. in Electrical Engineering, University of Connecticut, Storrs, CT, 2017

Research Interests:

• CSI Compression/Prediction
• Spectrum Sensing/Prediction
• Dynamic Spectrum Sharing
• Machine/Deep Learning

Selected Publications:

• Xu, Jiarui, … Song, Yifei. et al. “Towards AI-Enabled NextG Air Interface: Online Real-Time Machine Learning for MIMO Operations,” in 2024 IEEE Wireless Communications Magazine
• Sheen, Baoling, Song, Yifei. et al., “Learning-based CSI Look Up Table: A Novel Vector Quantization Approach for High Accuracy CSI Reconstruction,” in 2024 IEEE International Conference on Communications (ICC), Denver, CO, USA, 2024, pp. 2561-2566, doi: 10.1109/ICC51166.2024.10622182.
• Sheen, Baoling, Song, Yifei. et al., “Learning-based CSI Look Up Table: A Novel Vector Quantization Approach for High Accuracy CSI Reconstruction,” in 2024 IEEE International Conference on Communications (ICC), Denver, CO, USA, 2024, pp. 2561-2566, doi: 10.1109/ICC51166.2024.10622182.
• Jalali, Jalal, Roa, Juan, Song, Yifei. et al., “Fast Best Beam Prediction and Overhead Reduction for 6G Networks: A Deep Learning Approach,” in 2024 IEEE Vehicular Technology Conference (VTC2024-Spring), Singapore, Singapore, 2024, pp. 01-06, doi: 10.1109/VTC2024-Spring62846.2024.10683207.
• Song, Yifei. et al., “Variable Code Size Autoencoder (VCSA) Meets CSI Compression in Model Generalization,” in 2024 International Conference on Computing, Networking and Communications (ICNC), Big Island, HI, USA, 2024, pp. 209-214, doi: 10.1109/ICNC59896.2024.10556024.
• Jere, Shashank, Song, Yifei. et al., “Distributed Learning Meets 6G: A Communication and Computing Perspective.” in IEEE Wireless Communications, vol. 30, no. 1, pp. 112-117, February 2023, doi: 10.1109/MWC.006.2200252.
• Chang, H. -H., Song, Yifei, Doan, T. T. and Liu, L., "Federated Multi-Agent Deep Reinforcement Learning (Fed-MADRL) for Dynamic Spectrum Access," in IEEE Transactions on Wireless Communications, doi: 10.1109/TWC.2022.3233436.
• Song, Yifei, Chang, H. -H., and Liu, L. "Federated Dynamic Spectrum Access through Multi-Agent Deep Reinforcement Learning," in GLOBECOM 2022 - 2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 2022, pp. 3466-3471, doi: 10.1109/GLOBECOM48099.2022.10001688.
• Mohammadi, Nima, …Song, Yifei. et al., "Differential Privacy Meets Federated Learning Under Communication Constraints," in IEEE Internet of Things Journal, vol. 9, no. 22, pp. 22204-22219, 15 Nov.15, 2022, doi: 10.1109/JIOT.2021.3101991.

Work Experience:

• Research Intern at Futurewei – Schaumburg, IL May 2024–August 2024
• Research Intern at Futurewei – Schaumburg, IL May 2023–August 2023
• Research Intern at Futurewei – Schaumburg, IL May 2022–August 2022
• Research Intern at Tactual Labs – Redmond, WA Oct 2020–Dec 2020
• Electrical Engineer at Strain Measurement Devices – Wallingford, CT July 2017 –March 2018

Education:

• M.S., Electrical Engineering, Virginia Tech, Blacksburg, VA

Research Interests:

• Wireless communications, digital signal processing, and software-defined radio
• Machine learning, neuromorphic computing, and intelligent systems
• Lower-power ML hardware, SoC architecture, VLSI design

Research Experiences:

• Multifunctional Integrated Circuits and Systems group (MICS) – A low-power hybrid neural processing architecture for mobile edge intelligent computing; Unified training and inference algorithms for efficient deep neural networks; Low-power machine learning accelerators for edge computing applications; Neuromorphic computing algorithms and hardware implementation.
• Wireless @ Virginia Tech – Information theoretical interpretation of deep neural networks; Machine learning based MIMO detection algorithms and real-time implementation; Software-defined radio (SDR) prototyping platforms for B5G wireless systems.

Selected Publications:

• Y. Liang, U. Saeed and L. Liu, "Resilient Machine Learning for NextG Wireless Systems Under Smart Jamming," IEEE ICC 2024, Denver, CO, USA, 2024.
• Y. Liang, L. Li, et al., “Real-time Machine Learning for Symbol Detection in MIMO-OFDM Systems”, IEEE INFOCOM 2022 - IEEE Conference on Computer Communications, 2022.

Awards and Fellowships:

• Winner of the 2020 Qualcomm Innovation Fellowship, 2020-2021
• 2019-2020 Pratt Scholarship Award, ECE, Virginia Tech

Education:

• B.S. in Electrical Engineering, Rose-Hulman Institute of Technology, Terre Haute Indiana, United States, 2019

Research Interests:

• Analog IC Design
• MEMS

Selected Publications:

Work Experience:

Research Experience:

Awards and Fellowships:

Education:

• B.S. in Computer Engineering, Virginia Tech, Blacksburg, Virginia, United States, 2024
• B.S. in Electrical Engineering, Virginia Tech, Blacksburg, Virginia, United States, 2024
• B.S. in Mathematics, Virginia Tech, Blacksburg, Virginia, United States, 2024

Research Interests:

• Wireless Communications

Research Experience:

• Research Experience 1
• Research Experience 2

Education:

• M.Sc. in Electrical Engineering, Boston University, Boston, USA, 2018
• Bachelor of Engineering & Arts, Dalian University of Technology, Dalian, China, 2016

Research Interests:

• Machine Learning
• Analog & Mixed Signal IC Design

Selected Publications:

• H. Zheng, K. J. Bai and Y. Yi, "Enabling a New Methodology of Neural Coding: Multiplexing Temporal Encoding in Neuromorphic Computing," in IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 31, no. 3, pp. 331 342, March 2023, doi: 10.11 09/TVLSI.2023.3234514.
• H. Zheng, J. Xu, L. Liu and Y. Yi, "SNNOT: Spiking Neural Network With On-Chip Training for MIMO-OFDM Symbol Detection," in IEEE Transactions on Green Communications and Networking, doi: 10.1109/TGCN.2024.3393854.

Research Experience:

• Spiking Multiplexing Temporal Encoding – Explored the advantages of multiplexing temporal encoding, Designed and prototyped the multiplexing temporal ISI-Phase encoder.
• Spike-Timing-Dependent Plasticity – Explored different STDP rules and the integrated circuit implementations of them, Designed the triplet-based reconfigurable STDP circuits.

Education:

• B.S. in Electrical Engineering, Rose-Hulman Institute of Technology, Indiana, United States of America, 2023

• Research Interests:

  • ORAN
  • Machine Learning

  Work Experience:

  • mmWave Engineer Intern, Texas Instruments - Dallas, Texas. June 2023 - August 2023

  Awards and Fellowships:

  • Bradley Fellow
  • Best Poster Award PECI 2023