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 for ASIC/FPGA
• 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.
• Azmine, Muhammad Farhan, Ruizhe Li, Gauri Sharma, and Yang Yi. "SpikeSpec: An On-chip Learning Neuromorphic Accelerator for Spectrum Sensing With Triplet-Boosting and Hardware Friendly Loss Function." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (2025).
• R. Li, M. F. Azmine, G. Sharma and Y. Yi, "Efficient Digital Architecture of Spiking Encoders for Neuromorphic Accelerators," 2025 26th International Symposium on Quality Electronic Design (ISQED), San Francisco, CA, USA, 2025, pp. 1-8, doi: 10.1109/ISQED65160.2025.11014384.

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. Computer Engineering, Chip-Scale Integration, Virginia Tech, Blacksburg VA, USA, 2024

Research Interests:

• Digital/VLSI Circuit Design
• Neuromorphic Hardware Design

Education:

• M.S. in Computer Engineering, Virginia Tech, Blacksburg, United States, 2025
• B.S. in Electrical and Electronics Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana, 2022

Research Interests:

• Neuromorphic Computing
• Designing Reliable and Accelerated Circuits for ML Optimization

Education:

• M.S. in Computer Engineering, Virginia Tech, 2025
• B.S. in Computer Science, Virginia Tech, 2023

Research Interests:

• Machine Learning
• Wireless Communications

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, Chunxiao, Ummay Sumaya, Muhammad Farhan, and Yang Yi. “FPGA-based High-speed Reservoir Computing for MIMO Channel Prediction.” INTERNATIONAL CONFERENCE ON COMPUTING, NETWORKING AND COMMUNICATIONS (ICNC), 2026.
• Lin, C., Yi, Y. (2025). Enhancing FPGA CAD Flow with AI-Powered Solutions. In: Iranmanesh, A., Sayadi, H. (eds) AI-Enabled Electronic Circuit and System Design. Springer, Cham.
• 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:

• M.Sc. in Electrical Engineering, Virginia TEch, Blacksburg, VA, 2025

Research Interests:

• Machine Learning
• Distributed Communication Systems
• Integrated Sensing and Communications
• Distributed Sensing

Selected Publications:

• E. Allen, K. Said, R. Calderbank and L. Liu, "MIMO Precoding at the Speed of Wireless: Precoder Prediction for MIMO-OTFS Systems," 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall)
• J. Sloop, E. Allen, C. E. Thornton, L. Liu, F. Templin and D. J. Jakubisin, "A Demonstration of 5G Sidelink-Enabled MANETs Within CORE/EMANE Framework," MILCOM 2024

Work Experience:

• Air Force Research Lab - Rome NY Summer 2022
• Naval Research Lab - Washington DC Summer 2024
• Naval Research Lab - Washington DC Summer 2025

Research Experience:

• USRP Radios
• Realtime Communications Testbeds
• Distributed Network Emulation
• MIMO Communications

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. in Communications and Information Systems, Xidian University, China, 2024
• B.Sc. in Communication and Engineering, Xidian University, China, 2018

Research Interests:

• Machine Learning
• Spectrum sensing
• Large Language Models

Selected Publications:

• H. Nan, R. Li, X. Zhu, J.Ma, and D. Niyato, “An Effcient Data-driven Traffc Prediction Framework for Network Digital Twin,” IEEE Network, 2024.
• H. Nan, R. Li, X. Zhu, J. Ma, and K. Xue, “Spatio-Temporal Identity MultiGraph Convolutional Network for Traffc Prediction in the Metaverse,” IEEE Journal on Selected Areas in Communications, 2024, 42(3): 669-679.
• H. Nan, X. Zhu, and J. Ma, “MSTL-GLTP: A Global–Local Decomposition and Prediction Framework for Wireless Traffc,” IEEE Internet of Things Journal, 2023, 10(6): 5024-5034.

Work Experience:

• Postdoctoral Research Fellow, College of ECE, George Mason University, Fairfax, Virginia, United States Nov 2024 – Nov 2025

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:

• B.Tech. in Electronics Engineering, K.J. Somaiya College of Engineering, Mumbai, India, 2023
• M.Sc. in Electrical and Computer Engineering, Virginia Tech, Virginia, USA, 2025

Research Interests:

• Digital & RTL design for ASIC/SoC
• Memory Subsystem Design
• Chip design & physical-aware implementation

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 for Cellular Networks
• 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”, in IEEE/ACM Trans. Netw., 32(6):4615-4628, 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)," in Trans. Mobile Comput., 24(2):890–906, 2025, doi: 10.1109/TMC.2024.3476338.​
• J. Dai, R. Zhao, F. Rezazadeh, L. Zheng, H. Wang, and L.Liu, “Learning Low-Dimensional Representation for O-RAN Testing via Transformer-ESN”, in Proc. IEEE 22nd Int. Conf. Mobile Ad-Hoc Smart Syst. (MASS), 2025.​​
• S. Mahboob, J. Dai, A. Soysal, and L. Liu, “Transforming Future 6G Networks via O-RAN-Empowered NTNs,” IEEE Commun. Mag., 63(3):76–82, 2025, doi: 10.1109/MCOM.001.2400104.

Education:

• M.Sc. in Communication Engineering, Nanyang Technological University(NTU), Singapore, 2024-2025
• B.Eng. in Electronic and Electrical Engineering, University of Electronic Science and Technology of China (UESTC),2020-2024

Research Interests:

• Machine Learning with wireless

Research Experience:

• MMWave Radar Recognition with Machine Learning
• Power Allocation Optimization for Power-Domain NOMA

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:

• Phd in Electrical Engineering, Virginia Tech, 2017

Research Interests:

• Channel Estimation and Signal Processing
• Massive MIMO and Distributed MIMO
• Integrated Sensing and Communication
• Waveforms Design for NextG communications
• Explainable Machine Learning

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 11.
• 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:

• Alcatel-Lucent Enterprise (Bell Labs)

Research Experience:

• Conducting research on novel Physical Layer Waveforms: OTFS, DPSS-spread OFDM, AFDM
• Pioneering research on the explainability of the fundamental underlying mechanism of echo-state networks (ESNs) and its suitability for communication

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

Research Interests:

• Spiking Neural Network

Education:

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

Research Interests:

• Analog IC Design

Education:

• BFA in Art and Visual Technology (New Media), George Mason University, Fairfax, VA, 2021

Research Interests:

• Machine Learning
• Neuromorphic Computing

Education:

• B.S. in Computer Engineering, Minor in Computer Science, Virginia Commonwealth University, Richmond, Virginia, US 2025

Research Interests:

• CPU/GPU Architecture and Verification
• FPGA/SoC Design and Verification
• Machine Learning
• Embedded Devices

Selected Publications:

• Publication here

Work Experience:

• Work Experience 1

Research Experience:

• Research Experience 1
• Research Experience 2

Awards and Fellowships:

• Award 1

Education:

• B.S. Computer Engineering, Chip-Scale Integration, Virginia Tech, Blacksburg VA, USA, 2024

Research Interests:

• Neuromorphic Computing
• Digital/VLSI Circuit Design

Education:

• M.Sc. in Electrical Engineering, Michigan Technological University, Houghton, Michigan, 49931
• M.Sc. in Physics, Michigan Technological University, Houghton, Michigan, 49931

Research Interests:

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

Selected Publications:

• Rahman M, Hassan Z, Reed J, and Liu L. Non-Terrestrial Network (NTN): A Novel Alternate Fractional Programming for the Downlink Channels Power Allocation. IEEE MILCOM 2023.
• Rahman M, Hassan Z, Reed J, and Liu L. Joint Interference Management and Traffic Offloading in Integrated Non-Terrestrial and Terrestrial Networks. IEEE TCOM 2025
• Rahman M, Tripathi N, Reed J, and Liu L. Developing an NTN Architecture for End to End Performance Evaluation. IEEE MILCOM 2025.
• Rahman M, Tripathi N, Reed J, and Liu L. Machine Learning and Smartphone Data-Assisted BER Prediction in nLOS Mission-Critical Environments. IEEE MILCOM 2025.
• Rahman M, Tripathi N, Reed J, and Liu L. ML-Based Downlink Throughput Prediction and Feature Importance Analysis in a Realistic LTE Testbed Using COTS Smartphone. ICNC 2025.

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:

• Spectrum Sensing
• Non Terrestrial Networks
• Deep Learning
• Reservoir Computing
• O-RAN

Selected Publications:

• S. Mahboob and L. Liu, "Revolutionizing Future Connectivity: A Contemporary Survey on AI-Empowered Satellite-Based Non-Terrestrial Networks in 6G," in IEEE Communications Surveys & Tutorials, vol. 26, no. 2, pp. 1279-1321, Secondquarter 2024, doi: 10.1109/COMST.2023.3347145.
• S. Mahboob, J. Dai, A. Soysal and L. Liu, "Transforming Future 6G Networks via O-RAN-Empowered NTNs," in IEEE Communications Magazine, vol. 63, no. 3, pp. 76-82, March 2025, doi: 10.1109/MCOM.001.2400104.

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)
• Graduate Research Intern:
  • Keysight Technologies (May 2024 - August 2024)

Research Experience:

• DQN-based satellite handover strategy with reservoir computing optimizing the number of handovers on ns-3.
• Spatial spectrum mapping for radio map estimation using graph reservoir computing.
• 4G and 5G USRP-based hardware testbeds with srsRAN and OAI protocols stacks.

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:

• B.S. Electrical Engineering, University of Maryland College Park, 2019
• M.Eng. Electrical Engineering, University of Maryland College Park, 2022

Research Interests:

• Neuromorphic Computing
• Data Science
• Neural Networks
• Wireless Communications

Selected Publications:

• S. Pramanik, M. Fan, Y. Lan, “Machine Learning of in-situ Temperature Reconstruction from Metalnanoparticle Thermometry on Transmission Electron Microscopy, Microscopy and Microanalysis 2022” (Machine Learning of In situ Temperature Reconstruction from Metal-nanoparticle Thermometry on Transmission Electron Microscopy | Microscopy and Microanalysis | Oxford Academic (oup.com))
• A. Shamaki, C. B.Ogueri, S. Alomary, M. Zondode, M. Ndaw, J. Hou, S. Pramanik, S. Pokharel, S. Tadesse, S-C. Liou, “Photovoltaic amorphous feroxyhyte nanostructures synthesized by atmospheric AC microplasma”, 2024 Nanotechnology 35 085601 (Photovoltaic amorphous feroxyhyte nanostructures synthesized by atmospheric AC microplasma - IOPscience)
• C. Ogueri, A. Nanor, T. Fowlkes, S. Tadesse, K.Sze, S. Pramanik, G. Severn, and Y. Lan, Abstract: KW81.00034: UV vis and Infrared Spectroscopic Investigation of Feroxyhite Nanomaterial Synthesis by AC Atmospheric Microplasma (UV-vis and Infrared Spectroscopic Investigation of Feroxyhite Nanomaterial Synthesis by AC Atmospheric Microplasma - Astrophysics Data System (harvard.edu))

Research Experience:

• Research Position Morgan State University: June -August 2021

Education:

• B.Sc in Electrical Engineering, Iran University of Science and Technology, Tehran, Iran, 2018
• M.Sc in Electrical Engineering, Ommunication Systems branch, Sharif University of Technology, Tehran, Iran 2021

Research Interests:

• Wireless Communications
• Statistical Learning Analysis and Theory
• Reinforcement Learning
• Distributed MIMO Systems

Selected Publications:

• KA Said, Y Liang, U Saeed, R Safavinejad, KS Bondada, E Allen, ... . Mobile Distributed MIMO (MD-MIMO) for NextG: Mobility Meets Cooperation in Distributed Arrays. arXiv preprint arXiv:2504.12244. 2025.
• J Dai, L Li, R Safavinejad, S Mahboob, H Chen, VV Ratnam, H Wang, ... . O-ran-enabled intelligent network slicing to meet service-level agreement (sla). IEEE Transactions on Mobile Computing. 2024.
• R Safavinejad, HH Chang, L Liu. Deep reinforcement learning for dynamic spectrum access: Convergence analysis and system design. IEEE Transactions on Wireless Communications. 2024.
• HH Chang, N Mohammadi, R Safavinejad, Y Yi, L Liu. Dyna-ESN: Efficient Deep Reinforcement Learning for Partially Observable Dynamic Spectrum Access. IEEE Transactions on Wireless Communications. 2024.
• S Jere, R Safavinejad, L Zheng, L Liu. Channel equalization through reservoir computing: A theoretical perspective. IEEE Wireless Communications Letters 12 (5), 774-778. 2023.
• S Jere, R Safavinejad, L Liu. Theoretical foundation and design guideline for reservoir computing-based MIMO-OFDM symbol detection. IEEE Transactions on Communications 71 (9), 5169-5181. 2023.

Work Experience:

• Interim Engineering Intern, Qualcomm, Summer 2023

Research Experience:

• Developed and analyzed novel **Deep Reinforcement Learning (DRL)** frameworks, including Dyna-ESN, for solving complex resource allocation and dynamic spectrum access (DSA) problems in wireless networks, with a focus on convergence analysis.
• Investigated the theoretical foundations and design guidelines for applying **Reservoir Computing (RC)** techniques to physical layer signal processing, specifically for high-performance channel equalization and robust MIMO-OFDM symbol detection.
• Conducted systems research on **Next-Generation (NextG) Network Architectures**, by designing mobility-aware cooperation protocols for Mobile Distributed MIMO (MD-MIMO).

Awards and Fellowships:

Education:

• B.S. in Engineering, James Madison University, Harrisonburg, VA, 2024

Research Interests:

• Autonomous UAV Mission Planning
• Machine Learning
• Digital Twin Modeling

Work Experience:

• Embedded Engineer Intern, Two Six Technologies, June 2025 - present
• Software Engineer Intern, GDIT, June 2023 - May 2025

Research Experience:

• Hardware Engineer Researcher, James Madison University, Sept 2024 - May 2025

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.
• M. R. Sarkar, S. S. Chowdhury, J. S. Walling and C. Y. Yi, "An In-Memory Power Efficient Computing Architecture with Emerging VGSOT MRAM Device," 2024 IEEE International Symposium on Circuits and Systems (ISCAS), Singapore, Singapore, 2024, pp. 1-5, doi: 10.1109/ISCAS58744.2024.10557835.
• M. R. Sarkar, S. Salekin Chowdhury, J. S. Walling and C. Yang Yi, "Phase Signal Classification with Reservoir Computing," 2025 IEEE 34st Microelectronics Design & Test Symposium (MDTS), Albany, NY, USA, 2025, pp. 1-6, doi: 10.1109/MDTS64924.2025.11177059.
• M. R. Sarkar, S. S. Chowdhury, G. S. Kumar and C. Y. Yi, "SARHD: A 6T-SRAM based Side-Channel Attack Resilient Brain-Inspired Hyperdimensional Computing Architecture," 2025 IEEE 34st Microelectronics Design & Test Symposium (MDTS), Albany, NY, USA, 2025, pp. 1-6, doi: 10.1109/MDTS64924.2025.11177062.

Work Experience:

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

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

Summer Internship:

• Analog Design Intern at MACOM Technology Solutions (May 2025 - August 2025)
• 100 Gb/s PAM4 Transimpedance Amplifier (TIA): Achieved a BER of 10⁻⁵ at −12 dBm sensitivity and 10⁻¹² at 7 dBm overload.
• Low-Noise TIA for LiDAR: Reduced input-referred flicker noise by approximately 43%, improving receiver sensitivity.

• 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:

• Ph.D. in Computer Science, Yonsei University, Seoul, South Korea, 2025
• B.S. in Computer Science and Engineering, Yonsei University, Seoul, South Korea, 2018

Research Interests:

• On-device AI
• Mobile immersive computing
• Energy-aware mobile systems

Selected Publications:

• S. Park, M. Kim, H. Park, J. Lee, J. Kim, H. Cha. "EOS: Energy-Optimized Super-Resolution on Mobile Devices for Live 360-Degree Videos." The 31st Annual International Conference on Mobile Computing and Networking (ACM MobiCom 2025). 2025.
• G. Kim*, S. Park*, J. Lee, C. Jung, H. Jun, H. Cha. "Vega: Fully Immersive Mobile Volumetric Video Streaming with 3D Gaussian Splatting." The 31st Annual International Conference on Mobile Computing and Networking (ACM MobiCom 2025). 2025.
• C. Jung, J. Lee, G. Kim, J. Kim, S. Park, H. Cha. "ARIA: Optimizing Vision Foundation Model Inference on Heterogeneous Mobile Processors for Augmented Reality." The 23rd Annual International Conference on Mobile Systems, Applications and Services (ACM MobiSys 2025). 2025.
• S. Park, J. Lee, Y. Choi, H. Cha. "Vulture: Cross-Device Web Experience with Fine-Grained Graphical User Interface Distribution." IEEE Conference on Computer Communications (IEEE INFOCOM 2024)." 2024.
• S. Park*, Y. Cho*, H. Jun, J. Lee, H. Cha. "OmniLive: Super-Resolution Enhanced 360° Video Live Streaming for Mobile Devices." The 21st Annual International Conference on Mobile Systems, Applications and Services (ACM MobiSys 2023)." 2023.
• S. Park, J. Lee, H. Cha. "Crow API: Cross-device I/O Sharing in Web Applications." IEEE Conference on Computer Communications (IEEE INFOCOM 2023). 2023.

Awards and Fellowships:

• Best Paper Award, ACM MobiSys 2025

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.Tech. in Electrical Engineering, VJTI, Mumbai, India, 2022

Research Interests:

• Digital IC Design
• FPGAs

Education:

• Master's in Computer Science, Virginia Tech, 2025 (expected)

Research Interests:

• Spiking Neural Networks
• Deep Learning

Education:

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

Research Interests:

• Analog IC Design
• ASIC Design

Work Experience:

• Analog Design Engineer, ST Microelectronics Audio Division Group
• Graduate Research Assistant, Virginia Tech

Research Experience:

• Meta: AFE of MEMS scanning and SMI based Eye Tracking Project
• BIO EM Image Segmentation and Tracking projects

Education:

• M.tech in Electrical Engineering (Vlsi &Embedded system) Diat- Pune,2020

Research Interests:

• Domain specific accelerators
• Digital circuits
• Neuromorphic computing

Work Experience:

• FPGA validation Engineer,INTEL 2020-2024
• Senior silicon validation engineer,AMD 2024-2025

Education:

• Ph.D. in Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, Ongoing
• M.S. in Computer Science, Auburn University, Auburn, AL, 2021
• B.Tech. in Electronics and Communication Engineering, Uttar Pradesh Technical University, India, 2018

Research Interests:

• Statistical Modeling of Complex Multidimensional Random Fields
• Compression Limits for High-Dimensional Scientific Data
• Statistical Reconstruction and High-Dimensional Kriging
• Real-Time XR Systems and Edge-Assisted Processing
• Token-Level Semantics and Structure in Modern AI Models
• Adversarial Machine Learning

Selected Publications:

• Sinha S, Soysal A. Adversarial Attacks and Defenses for Wireless Signal Classifiers using CDI-aware GANs. IEEE International Conference on Communications (ICC). Aug 2024.
• Sinha S, Soysal A. Channel Aware Adversarial Attacks are Not Robust. IEEE Military Communications Conference (MILCOM) Workshops. Nov 2023.

Work Experience:

• Visiting Student, Argonne National Laboratory, Lemont, Illinois. June 2025 - Present.
• Graduate Research and Teaching Assistant, Virginia Tech, Virginia, VA. August 2021 - Present.
• Graduate Research and Teaching Assistant, Auburn University, Auburn, AL. August 2019 - May 2021.
• Co-Founder, Pulse Prognostics, Uttar Pradesh, India, Nov 2016 - March 2018.

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:

• B.S. in Computer Engineering, Virginia Tech, Blacksburg, Virginia, USA, 2024

Research Interests:

• RTL Design
• FPGAs

Work Experience:

• Akuna Capital - FPGA Hardware Intern - Summer 2025
• Apple - GPU RTL Design Intern - Spring 2025
• Texas Instruments - Digital IC Design Intern - Summer 2024

Awards and Fellowships:

• Boeing Graduate Scholar

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 in Wireless Communication
• High-Preformance Reservoir Computing
• MIMO-OFDM Receive Processing
• FPGA-based Machine Learning Accelerator for Wireless Application

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:

• B.E. in Electrical Engineering, National University of Science and Technology, Islamabad, Pakistan, 2019

Research Interests:

• Distributed/Massive MIMO
• Machine Learning for Wireless Systems
• Physical Layer Security

Selected Publications:

• K. Said, Y. Liang, U. Saeed, R. Safavinejad, K. S. Bondada, E. Allen, N. Mohammadi, B. Pimentel, B. Kelley, J. Reed, N. Tripathi, M. Buehrer, Y. Yi, D. Jakubisin, L. Liu, “Mobile Distributed MIMO (MD-MIMO) for NextG: Mobility Meets Cooperation in Distributed Arrays”, IEEE Wireless Comm. Mag., 2025
• U. Saeed, R. Calderbank, K. Zeng, E. Bentley, L. Huie-Seversky, K. Said and L. Liu, “Key Generation and Secrecy Analysis using OTFS for TDD Systems”, IEEE Trans. on Wireless Comm., vol. 24, no. 8, pp. 6275-6289, Aug. 2025

Education:

• M.Sc. in Computer Engineering, Virginia Tech, Blacksburg, USA, 2025-2027
• B.Tech in Electrical & Computer Engineering, Anna University, Chennai, India, 2019-2023

Research Interests:

• Design Verification
• Digital Design
• Brain Inspired Computing

Work Experience:

• Amadeus (Software Engineer C++)

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:

• Deep Learning
• CSI Compression

Selected Publications:

• Song, Yifei.et al., “Miniature UAV Empowered Reconfigurable Energy Harvesting Holographic Surfaces in THz Cooperative Networks,” in 2025 Internet of Things Journal, doi: 10.1109/JIOT.2025.3619874
• 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.
• 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, N, Liu, L, Song, Yifei, and Y. Yi, "Energy-Efficient Dynamic and Spatiotemporal Spectrum Access via Spiking Reservoir Computing," in IEEE Transactions on Wireless Communications, doi: 10.1109/TWC.2025.3591117.
• ] Xu, Jiarui, … Song, Yifei. et al. “Towards AI-Enabled NextG Air Interface: Online Real-Time Machine Learning for MIMO Operations,” in 2025 IEEE Wireless Communications Magazine, vol. 63, no. 1, pp. 92-98, January 2025, doi: 10.1109/MCOM.001.2300529.
• 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, 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.
• 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.
• 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:

• Summer Intern at Futurewei, Schaumburg, IL (2022, 2023, 2024, 2025)

Research Experience:

• Research Experience 1
• Research Experience 2

Awards and Fellowships:

• Award 1

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:

• 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:

  • Wireless Communications
  • Machine Learning
  • Nonlinear Neural Modeling
  • ORAN

  Work Experience:

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

  Awards and Fellowships:

  • Bradley Fellow
  • Best Poster Award IEEE PECI 2023