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:

• 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.

Education:

• M.Sc. in Computer Science, University of Tehran, Tehran, Iran, 2017

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.
• Zhou Z, Bai K, Mohammadi N, Yi Y, Liu L. Making Intelligent Reflecting Surfaces More Intelligent: A Roadmap Through Reservoir Computing. IEEE Network. 2022 Apr 1;36(2):175-80.

Work Experience:

• Sofware Engineering Intern: CapitalOne
• Sofware Engineering Intern: Lockheed Martin

Research Experience:

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

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:

• 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 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:

• MENG in Electrical and Computer Engineering, University of The West Indies BSc. Electrical-Power Engineering 2019

Research Interests:

• Power System Analysis
• Transmission Systems
• Renewable Energy
• Sustainability

Work Experience:

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:

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

Research Interests:

• Analog IC Design

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

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:

• 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:

• 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 and Mixed Signal IC Design
• Neuromorphic Computing

Selected Publications:

• Chowdhury, Shirazush Salekin, and Farhadur Arifin. "The effects of the substrate doping concentrations on 6H-SiC nano-scale ggNMOS ESD protection device." 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST). IEEE, 2021.

Work Experience:

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

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

Research Experience:

• Hardware Implementation of Reservoir Computing. Tape-out a chip in 22nm process.

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:

• 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:

• 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:

• 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:

• 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:

• 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