Yang (Cindy) Yi

http://www.yangyi.ece.vt.edu/

Research Interests:

• Very Large Scale Integrated (VLSI) Circuits and Systems, High Performance Computing, Computer Aided Design (CAD), Artificial Intelligence, and Emerging Nano-device
  

Research Topics:

• Neuromorphic Electronic Circuits Design and Automation for Brain-Inspired Computing System
• Three-dimensional Integrated Circuits Design and Analysis
  
• Artificial Intelligence, Machine Learning, and Cognitive Computing in Wireless Communications and Cybersecurity
• Integrated Circuit/Transceiver Design in Wireless/Cellular Networks, eHealth Systems, and Internet of Things
• Hardware Reliability and Variability Analysis in High Performance Computing Systems
• Interconnect Modeling and Simulation, Signal Integrity and Power Integrity

Teaching Interests:

• Digital Design
  
• VLSI Design
  
• Advanced Analog Integrated Circuit Design
• Artificial Intelligence 
• Neuromorphic computing

Recognition:

• Best Paper Award at IEEE Wireless and Optical Communication Conference 2020
• VT College of Engineering Faculty Fellow and ICTAS Junior Faculty Award (JFA) in 2019
• NSF CAREER Award in 2018
• IEEE Technical Committee on Green Communications & Computing Committee (TCGCC) in IEEE Global Communications Conference (GLOBECOM) in 2018
• IEEE International Symposium on Quality Electronic Design (ISQED) and IEEE Transmission, Access, and Optical Systems Technical Committee (TAOS) in International Conference on Communications (ICC) in 2018
• IEEE GLOBECOM in 2016
• IEEE Senior Member
  
• Miller Professional Development Award for Distinguished Research, 2016
• Best Paper Award, IEEE GLOBECOM, 2016
• United States Air Force (USAF) Faculty Fellowship, 2015, 2016
• National Science Foundation (NSF) EPSCoR First Award, 2015
• Miller Scholar Award, 2015
• IEEE VLSI-DAT in 2011
• IEEE EPEP in 2006

Publications in 2019:

• Google Scholar Profile
• List of entire publications
• S. Liu, L. Liu and Y. Yi, "Quantized Reservoir Computing for Spectrum Sensing with Knowledge Distillation," in IEEE Transactions on Cognitive and Developmental Systems, doi: 10.1109/TCDS.2022.3147789.
• L. Li, L. Liu, Z. Zhou and Y. Yi, "Reservoir Computing Meets Extreme Learning Machine in Real-Time MIMO-OFDM Receive Processing," in IEEE Transactions on Communications, doi: 10.1109/TCOMM.2022.3141399.
• H. -H. Chang et al., "Resource Allocation for D2D Cellular Networks with QoS Constraints: A DC Programming-based Approach," in IEEE Access, doi: 10.1109/ACCESS.2021.3132260.
• O. Shears, K. Bai, L. Liu and Y. Yi, "A Hybrid FPGA-ASIC Delayed Feedback Reservoir System to Enable Spectrum Sensing/Sharing for Low Power IoT Devices ICCAD Special Session Paper," 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD), 2021, pp. 1-9, doi: 10.1109/ICCAD51958.2021.9643536.
• H. Zheng, J. Anderson and Y. Yi, "Approaching the Area of Neuromorphic Computing Circuit and System Design," 2021 12th International Green and Sustainable Computing Conference (IGSC), 2021, pp. 1-8, doi: 10.1109/IGSC54211.2021.9651627.
• Q. Fan, J. Bai, H. Zhang, Y. Yi and L. Liu, "Delay-aware Resource Allocation in Fog-assisted IoT Networks Through Reinforcement Learning," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2021.3111079.
• B. Shang, Y. Yi and L. Liu, "Computing over Space-Air-Ground Integrated Networks: Challenges and Opportunities," in IEEE Network, vol. 35, no. 4, pp. 302-309, July/August 2021, doi: 10.1109/MNET.011.2000567.
• N. Mohammadi, J. Bai, Q. Fan, Y. Song, Y. Yi and L. Liu, "Differential Privacy Meets Federated Learning under Communication Constraints," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2021.3101991.
• H. An, Q. An and Y. Yi, "Realizing Behavior Level Associative Memory Learning Through Three-Dimensional Memristor-Based Neuromorphic Circuits," in IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 5, no. 4, pp. 668-678, Aug. 2021, doi: 10.1109/TETCI.2019.2921787.
• Hamedani, K., Liu, L., Jagannath, J., & Yi, Y. (2021, June). Adversarial Classification of the Attacks on Smart Grids Using Game Theory and Deep Learning. In Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning (pp. 13-18).
• Liu, S., Ha, D. S., Shen, F., & Yi, Y. (2021). Efficient neural networks for edge devices. Computers & Electrical Engineering, 92, 107121.
• J. Bai, H. Song, Y. Yi and L. Liu, "Multiagent Reinforcement Learning Meets Random Access in Massive Cellular Internet of Things," in IEEE Internet of Things Journal, vol. 8, no. 24, pp. 17417-17428, 15 Dec.15, 2021, doi: 10.1109/JIOT.2021.3081692.
• K. Bai, L. Liu and Y. Yi, "Spatial-Temporal Hybrid Neural Network With Computing-in-Memory Architecture," in IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 68, no. 7, pp. 2850-2862, July 2021, doi: 10.1109/TCSI.2021.3071956.
• H. An, M. S. Al-Mamun, M. K. Orlowski and Y. Yi, "A Three-dimensional (3D) Memristive Spiking Neural Network (M-SNN) System," 2021 22nd International Symposium on Quality Electronic Design (ISQED), 2021, pp. 337-342, doi: 10.1109/ISQED51717.2021.9424303.
• H. An, K. Bai and Y. Yi, "Three-dimensional Memristive Deep Neural Network with Programmable Attention Mechanism," 2021 22nd International Symposium on Quality Electronic Design (ISQED), 2021, pp. 210-215, doi: 10.1109/ISQED51717.2021.9424331.
• H. Zheng, N. Mohammadi, K. Bai and Y. Yi, "Low-power Analog and Mixed-signal IC Design of Multiplexing Neural Encoder in Neuromorphic Computing," 2021 22nd International Symposium on Quality Electronic Design (ISQED), 2021, pp. 154-159, doi: 10.1109/ISQED51717.2021.9424267.
• K. Bai, C. Thiem, N. McDonald, L. Loomis and Y. Yi, "Toward Intelligence in Communication Networks: A Deep Learning Identification Strategy for Radio Frequency Fingerprints," 2021 22nd International Symposium on Quality Electronic Design (ISQED), 2021, pp. 204-209, doi: 10.1109/ISQED51717.2021.9424319.
• K. Hamedani, L. Liu and Y. Yi, "Energy Efficient MIMO-OFDM Spectrum Sensing Using Deep Stacked Spiking Delayed Feedback Reservoir Computing," in IEEE Transactions on Green Communications and Networking, vol. 5, no. 1, pp. 484-496, March 2021, doi: 10.1109/TGCN.2020.3046725.
• K. Bai, L. Liu, Z. Zhou and Y. Yi, "Detection Through Deep Neural Networks: A Reservoir Computing Approach for MIMO-OFDM Symbol Detection," 2020 IEEE/ACM International Conference On Computer Aided Design (ICCAD), 2020, pp. 1-7.
• H. -H. Chang, L. Liu and Y. Yi, "Deep Echo State Q-Network (DEQN) and Its Application in Dynamic Spectrum Sharing for 5G and Beyond," in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2020.3029711.
• 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), 2020, pp. 1-8, doi: 10.1109/IGSC51522.2020.9290858.
• Wu, Xiaolong, Yi, Yang, Tian, Dave, & Li, Jiajia. Generic, Sparse Tensor Core for Neural Networks. United States.
• An, H., Ha, D. S., & Yi, Y. (2020, September). Powering next-generation industry 4.0 by a self-learning and low-power neuromorphic system. In Proceedings of the 7th ACM International Conference on Nanoscale Computing and Communication (pp. 1-6).
• 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), 2020, pp. 592-595, doi: 10.1109/MWSCAS48704.2020.9184677.
• An, Q., Bai, K., Liu, L., Shen, F., & Yi, Y. (2020). A unified information perceptron using deep reservoir computing. Computers & Electrical Engineering, 85, 106705.
• H. An, M. S. Al-Mamun, M. K. Orlowski, L. Liu and Y. Yi, "Robust Deep Reservoir Computing Through Reliable Memristor With Improved Heat Dissipation Capability," in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 40, no. 3, pp. 574-583, March 2021, doi: 10.1109/TCAD.2020.3002539.
• Hamedani, K., Liu, L., Hu, S., Ashdown, J., Wu, J., & Yi, Y. (2019). Detecting dynamic attacks in smart grids using reservoir computing: A spiking delayed feedback reservoir based approach. IEEE Transactions on Emerging Topics in Computational Intelligence, 4(3), 253-264.
• Hamedani, K., Liu, L., Hu, S., Ashdown, J., Wu, J., & Yi, Y. (2019). Detecting dynamic attacks in smart grids using reservoir computing: A spiking delayed feedback reservoir based approach. IEEE Transactions on Emerging Topics in Computational Intelligence, 4(3), 253-264.
• K. Bai, Y. Yi, Z. Zhou, S. Jere and L. Liu, "Moving Toward Intelligence: Detecting Symbols on 5G Systems Through Deep Echo State Network," in IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 10, no. 2, pp. 253-263, June 2020, doi: 10.1109/JETCAS.2020.2992238.
• L. Li, L. Liu, J. Zhang, J. D. Ashdown and Y. Yi, "Reservoir Computing Meets Wi-Fi in Software Radios: Neural Network-based Symbol Detection using Training Sequences and Pilots," 2020 29th Wireless and Optical Communications Conference (WOCC), 2020, pp. 1-6, doi: 10.1109/WOCC48579.2020.9114937.
• L. Li et al., "Accelerating Model-Free Reinforcement Learning With Imperfect Model Knowledge in Dynamic Spectrum Access," in IEEE Internet of Things Journal, vol. 7, no. 8, pp. 7517-7528, Aug. 2020, doi: 10.1109/JIOT.2020.2988268.
• Hamedani, K., Liu, L., Liu, S., He, H., & Yi, Y. (2020, April). Deep spiking delayed feedback reservoirs and its application in spectrum sensing of MIMO-OFDM dynamic spectrum sharing. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 02, pp. 1292-1299).
• Zhou, Z., Liu, L., Chandrasekhar, V., Zhang, J., & Yi, Y. (2020, April). Deep reservoir computing meets 5G MIMO-OFDM systems in symbol detection. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 01, pp. 1266-1273).
• B. Shang, V. Marojevic, Y. Yi, A. S. Abdalla and L. Liu, "Spectrum Sharing for UAV Communications: Spatial Spectrum Sensing and Open Issues," in IEEE Vehicular Technology Magazine, vol. 15, no. 2, pp. 104-112, June 2020, doi: 10.1109/MVT.2020.2980020.
• S. Liu, Y. Liang, V. Gan, L. Liu and Y. Yi, "Accurate and Efficient Quantized Reservoir Computing System," 2020 21st International Symposium on Quality Electronic Design (ISQED), 2020, pp. 364-369, doi: 10.1109/ISQED48828.2020.9136986.
• Q. An, K. Bai, M. Zhang, Y. Yi and Y. Liu, "Deep Neural Network Based Speech Recognition Systems Under Noise Perturbations," 2020 21st International Symposium on Quality Electronic Design (ISQED), 2020, pp. 377-382, doi: 10.1109/ISQED48828.2020.9136978.
• Z. Zhou, L. Liu, S. Jere, J. Zhang and Y. Yi, "RCNet: Incorporating Structural Information Into Deep RNN for Online MIMO-OFDM Symbol Detection With Limited Training," in IEEE Transactions on Wireless Communications, vol. 20, no. 6, pp. 3524-3537, June 2021, doi: 10.1109/TWC.2021.3051317.
• B. Shang, S. Liu, S. Lu, Y. Yi, W. Shi and L. Liu, "A Cross-Layer Optimization Framework for Distributed Computing in IoT Networks," 2020 IEEE/ACM Symposium on Edge Computing (SEC), 2020, pp. 440-444, doi: 10.1109/SEC50012.2020.00067.
• S. Liu, L. Liu and Y. Yi, "Quantized Reservoir Computing on Edge Devices for Communication Applications," 2020 IEEE/ACM Symposium on Edge Computing (SEC), 2020, pp. 445-449, doi: 10.1109/SEC50012.2020.00068.
• Gan, V. M., Liang, Y., Li, L., Liu, L., & Yi, Y. (2021). A Cost-Efficient Digital ESN Architecture on FPGA for OFDM Symbol Detection. ACM Journal on Emerging Technologies in Computing Systems (JETC), 17(4), 1-15.
• H. Song, L. Liu, J. Ashdown and Y. Yi, "A Deep Reinforcement Learning Framework for Spectrum Management in Dynamic Spectrum Access," in IEEE Internet of Things Journal, vol. 8, no. 14, pp. 11208-11218, 15 July15, 2021, doi: 10.1109/JIOT.2021.3052691.
• H. An, M. S. Al-Mamun, M. K. Orlowski, L. Liu and Y. Yi, "Three-dimensional Neuromorphic Computing System with Two-layer and Low-variation Memristive Synapses," in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, doi: 10.1109/TCAD.2021.3061481.
• H. Song, J. Bai, Y. Yi, J. Wu and L. Liu, "Artificial Intelligence Enabled Internet of Things: Network Architecture and Spectrum Access," in IEEE Computational Intelligence Magazine, vol. 15, no. 1, pp. 44-51, Feb. 2020, doi: 10.1109/MCI.2019.2954643.
• H. Chang, H. Song, Y. Yi, J. Zhang, H. He, and L. Liu ‘‘Distributive Dynamic Spectrum Access Through Deep Reinforcement Learning: A Reservoir Computing-Based Approach,’’ IEEE Internet of Things Journal, vol. 6, no. 2, pp. 1983-1948, April 2019.
•  H. Chen, L. Liu, H. S. Dhillon, and Y. Yi, ‘‘QoS-Aware D2D Cellular Networks with Spatial Spectrum Sensing: A Stochastic Geometry View,’’ IEEE Transactions on Communications, vol. 67, issue 5, pp. 3651-3664, May 2019.
• H. An, Q. An, and Y. Yi, ‘‘Realizing Behavior Level Associative Memory Learning Through Three-Dimensional Memristor-Based Neuromorphic Circuits,’’ IEEE Transactions on Emerging Topics in Computational Intelligence, Early Access, Jul. 2019.
•  R. Atat, L. Liu, J. Wu, J. Ashdown, and Y. Yi, ‘‘Green massive traffic offloading for cyber-physical systems over heterogeneous cellular networks,’’ Mobile Networks and Applications, vol. 24, issue 4, pp. 1364-1372, Aug. 2019.
• C. Zhao, Q. An, K. Bai, B. Wysocki, C. Thiem, L. Liu, and Y. Yi, ‘‘Energy Efficient Temporal Spatial Information Processing Circuits Based on STDP and Spike Iteration,’’ IEEE Transactions on Circuits and Systems II: Express Briefs, Early Access, Oct. 2019.
•  K. Bai, Q. An, L. Liu, and Y. Yi, ‘‘A Training-Efficient Hybrid-Structured Deep Neural Network With Reconfigurable Memristive Synapses,’’ IEEE Transactions on Very Large Scale Integration (VLSI) Systems            , Early Access, Oct. 2019.
•  K. Hamedani, L. Liu, S. Hu, J. Ashdown, J. Wu, and Y. Yi, ‘‘Detecting Dynamic Attacks in Smart Grids Using Reservoir Computing: A Spiking Delayed Feedback Reservoir Based Approach,’’ IEEE Transactions on Emerging Topics in Computational Intelligence, Early Access, Oct. 2019.
• M. Liu, L. Liu, H. Song, Y. Hu, Y. Yi, and F. Gong, ‘‘Signal Estimation in Underlay Cognitive Networks for Industrial Internet of Things,’’ IEEE Transactions on Industrial Informatics, Early Access, Nov. 2019.
• H. Song, J. Bai, Y. Yi, J. Wu, L. Liu, “Artificial Intelligence Enabled Internet of Things: Network Architecture and Spectrum Access,” Accepted, IEEE Computational Intelligence Magazine (CIM), 2019.
• Q. An, K. Bai, Y, Yi, “A Unified Information Perceptron using Deep Reservoir Computing
  Journal: Computers and Electrical Engineering,” Accepted, Computers & Electrical Engineering - Journal – Elsevier, 2019.
• F. Shen, J. Roccosalvo, J. Zhang, Y. Yi, Y. Ji, K. Guo, A. M. Kok, and, Y. Han, “STEM Education Enrichment in NYC,” in 16th International Conference on Information Technology-New Generations (ITNG 2019), pp. 277-282, May 2019.
• H. Song, L. Liu, H. Chang, J. Ashdown, Y. Yi, “Deep Q-Network Based Power Allocation Meets Reservoir Computing in Distributed Dynamic Spectrum Access Networks,” in IEEE INFOCOM 2019-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) pp. 774-779, April 2019,.
• K. Bai, Q.  An, Y. Yi, “Deep-DFR: A Memristive Deep Delayed Feedback Reservoir Computing System with Hybrid Neural Network Topology,” in DAC '19 Proceedings of the 56th Annual Design Automation Conference 2019, June 2019.
• C. Zhao, L. Liu, Y. Yi, “Design and Analysis of Real Time Spiking Neural Network Decoder for Neuromorphic Chips,” in ICONS '19 Proceedings of the International Conference on Neuromorphic Systems, Jul. 2019.
• K. Bai, S. Liu, Y. Yi, “High speed and energy efficient deep neural network for edge computing,” in SEC '19 Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, pp. 347-349, Nov. 2019.
• K. Hamedani, S. Liu, Y. Yi, “Deep Spiking Delayed Feedback Reservoirs and Its Application in Spectrum Sensing of MIMO-OFDM Dynamic Spectrum Sharing,” Accepted, Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) 
• Z. Zhou, L. Liu, J. Zhang, J. Ashdown, Y. Yi, “Deep Reservoir Computing Meets 5G MIMO-OFDM Systems in Symbol Detection,” Accepted, Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) 
• S. Liu, V. Gan, Y, Liang, Y. Yi, “Accurate and Efficient Quantized Reservoir Computing System,” Accepted, 2020 21th International Symposium on Quality Electronic Design (ISQED).
• H. Song, L. Liu, H. Chang, J. Ashdown, Y. Yi, “Maximizing System Throughput in D2D Networks using Alternative DC Programming,” Accepted, IEEE Global Communications Conference (GLOBECOM), 2019.
• H. An, K. Bai, and Y. Yi, “The roadmap to realize memristive three-dimensional neuromorphic computing system,” in Advances in MemristorNeural Networks-Modeling and Applications. London, U.K.: IntechOpen, 2019.
• K. Bai and Y. Yi, "Opening the “Black Box” of Silicon Chip Design in Neuromorphic Computing," in Bio-Inspired Technology: IntechOpen, 2019.