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Lingjia Liu, Ph.D.


Profile Lingjia Liu, Ph.D.
Professor, Bradley Department of Electrical and Computer Engineering
Director, Wireless@Virginia Tech
Virginia Tech, Blacksburg, VA, USA

Elected Member, Executive Committee
National Spectrum Consortium

439 Durham Hall
1145 Perry Street, Blacksburg, VA, USA
Phone: (540) 231-7243
Email: ljliu AT vt dot edu

Research Interests:

  • Machine learning for Wireless
    • Real-time online learning-based wireless system design and hardware prototyping
    • Incorporating communication structure knowledge in learning strategy design
    • Accelerating deep reinforcement learning for spectrum access and user/resource scheduling
  • Signal Process., Commun. & Netw. for 5G & Beyond
    • 3D mmWave massive FD-MIMO
    • Waveform design for 6G (e.g., MIMO-OTFS)
    • Computing and communication co-design for non-terrestrial networks (NTN)
    • Network slicing using open radio access network (ORAN) platform for prototyping and system design
  • Security and Privacy
    • Anomaly detection for smart grids
    • Adversarial learning for spectrum sharing
    • Differential privacy and federated learning for massive IoT networks
    • Anomaly and jamming detection for 5G networks

Bio:

Lingjia Liu received the Bachelor of Science (B.S.) degree with the highest honor in Electronic Engineering Department from Shanghai Jiao Tong University, Shanghai, China, and completed his Doctor of Philosophy (Ph.D.) degree at Texas A&M University in Electrical and Computer Engineering. He spent the summer of 2007 and spring of 2008 in the Mitsubishi Electric Research Laboratory. Prior to joining the ECE Department at Virginia Tech (VT), he was an Associate Professor in the EECS Department at the University of Kansas (KU). He spent 3+ years working in the Standards & Mobility Innovation Lab of Samsung Research America (SRA) where he received Global Samsung Best Paper Award twice (in 2008 and 2010 respectively). He was a technical leader and a leading 3GPP RAN1 standard delegate from Samsung on downlink MIMO, Coordinated Multipoint (CoMP) transmission/reception, device-to-device (D2D) communications, and Heterogeneous Networks (HetNets). He was elected as the New Faces of Engineering 2011 by the Diversity Council of the National Engineers Week Foundation. From 2013 to 2017, he has been selected as U.S. Air Force Research Laboratory (AFRL)/Air Force Office of Scientific Research (AFOSR) summer faculty fellow. In 2015, he received the Miller Professional Development Award for Distinguished Research at KU. In 2021, he received College of Engineering Dean’s Award for Excellence in Research at VT.

Lingjia Liu is a senior member of IEEE. He is currently serving as an Associate Editor of the IEEE Trans. Neural Netw. and Learning Syst. (TNNLS). He was an Editor of IEEE Trans. Wireless Commun. (TWireless) from 2012 to 2017, an Editor of the IEEE Trans. Commun. (TCom) from 2015 to 2017. He has been serving as the Technical Program Committee Chair of 7 consecutive IEEE GLOBECOM Workshops on Emerging Technologies for 5G ('12-'18). He served as the Vice-Chair, Americas of the IEEE Technical Committee on Green Communications & Computing (TCGCC) from 2017 to 2019. Currently, he is an Elected Member of Executive Committee of National Spectrum Consortium and an Elected Member of the IEEE Signal Processing Society SPCOM Technical Committee.

Lingjia Liu has 200+ publications including 3 book chapters, 90+ journal publications, 5 editorials, and 100+ conference papers. He has numerous technical contributions to major 4G standards including both 3GPP LTE/LTE-Advanced and IEEE 802.16m. He has 20+ granted U.S. patents with 20+ pending applications, and 10+ essential intellectual property rights (IPRs) in major 4G standards. His research receives including 8 Best Paper Awards. Currently, his research is sponsored by many programs from various agencies including NSF/DARPA Real-Time Machine Learning (RTML) Program, NSF/Intel Partnership on Machine Learning for Wireless Networking Systems (MLWiNS) Program, DARPA Open, Programmable, Secure 5G (OPS-5G) Program, AFRL/AFOSR University Center of Excellence (UCoE) on Machine Learning for Waveform Design, and IARPA Securing Compartmented Information with Smart Radio Systems (SCISRS) Program. His research efforts have been supported in part by over $125 M in research funding, with Lingjia Liu serving as the principal investigator (PI) on more than $14 M federal research grants (personal share $8+ M).

What's New:

Available positions: research assistant positions are available for self-motivated Ph.D. students who are interested in conducting research on the topic of signal processing, machine learning and networking for Fall 2023 (click for details).

[Grant] January 12, 2023: “RINGS: Learning-Enabled Ground and Air Integrated Networks (GAINs)” has been awarded by the National Science Foundation (NSF). This is a collaborative project with a total budget of $800K. Dr. Lingjia Liu is serving as the PI of the project while Prof. Yuejie Chi from ECE@CMU and Prof. Robert Calderbank from CS/ECE@Duke are serving as the Co-PIs of the project. See news on VTX for more details.
[Grant] January 6, 2023: “Efficient target recognition and classification” has been awarded. Dr. Lingjia Liu is serving as the Co-PI from Virginia Tech. The total budget of the project is $100,000.
[Paper Acceptance] December 29, 2022: Our paper, Channel Equalization Through Reservoir Computing: A Theoretical Perspective, has been accepted to IEEE Wireless Commun. Lett. (WCL) Congratulations Shashank and Ramin! This is a collaborative work with Prof. Lizhong Zheng from EECS@MIT.
[Paper Acceptance] December 22, 2022: Our paper, Federated Multi-Agent Deep Reinforcement Learning (Fed-MADRL) for Dynamic Spectrum Access, has been accepted to IEEE Trans. Wireless Commun. (TWireless) Congratulations Hao-Hsuan and Yifei!
[Best Paper Finalist] November 16, 2022: Our paper, Low-Complexity Channel Matrix Calculation for OTFS Systems With Fractional Delay and Doppler, was recommended to be the Best Paper Award Finalist at MILCOM 2022. This is a collaborative work with Prof. Robert Calderbank from CS/ECE@Duke.
[Paper Acceptance] November 07, 2022: Our paper, Distributed Learning Meets 6G: A Communication and Computing Perspective, has been accepted to IEEE Wireless Commun. Mag. (WCM) Congratulations Shashank and Yifei!
[Paper Acceptance] October 21, 2022: Our paper, Performance Analysis and Optimization for Layer-Based Scalable Video Caching in 6G Networks, has been accepted to IEEE Trans. Netw. (TNET) Congratulations Shashank!


More news can be found HERE.

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start.txt · Last modified: 2023/01/25 16:24 by lingjialiu