Extracting commuting patterns in railway networks through matrix decompositions. - Shashank Jere; Justin Dauwels; Muhammad Tayyab Asif; Nikola Mitrovic; Andrzej Cichocki; Patrick Jaillet (ICARCV, 2014)

Learning with Knowledge of Structure: A Neural Network-Based Approach for MIMO-OFDM Detection. - Zhou Zhou 002; Shashank Jere; Lizhong Zheng; Lingjia Liu 001 (ACSSC, 2020)

RCNet: Incorporating Structural Information into Deep RNN for MIMO-OFDM Symbol Detection with Limited Training. - Zhou Zhou 002; Lingjia Liu 001; Shashank Jere; Jianzhong Zhang 002; Yang Yi 002 (CoRR, 2020)

Federated Learning in Mobile Edge Computing: An Edge-Learning Perspective for Beyond 5G. - Shashank Jere; Qiang Fan; Bodong Shang; Lianjun Li 001; Lingjia Liu 001 (CoRR, 2020)

Learning for Integer-Constrained Optimization through Neural Networks with Limited Training. - Zhou Zhou 002; Shashank Jere; Lizhong Zheng; Lingjia Liu 001 (CoRR, 2020)

Moving Toward Intelligence: Detecting Symbols on 5G Systems Through Deep Echo State Network. - Kangjun Bai; Yang Yi 002; Zhou Zhou 002; Shashank Jere; Lingjia Liu 001 (IEEE J. Emerg. Sel. Topics Circuits Syst., 2020)

Edge Intelligence for Beyond-5G through Federated Learning. - Shashank Jere; Yang Yi 002 (SEC, 2021)

Federated Dynamic Spectrum Access. - Yifei Song; Hao-Hsuan Chang; Zhou Zhou 002; Shashank Jere; Lingjia Liu 001 (CoRR, 2021)

RCNet: Incorporating Structural Information Into Deep RNN for Online MIMO-OFDM Symbol Detection With Limited Training. - Zhou Zhou 002; Lingjia Liu 001; Shashank Jere; Jianzhong Zhang 002; Yang Yi 002 (IEEE Trans. Wirel. Commun., 2021)

Anonymous Jamming Detection in 5G with Bayesian Network Model Based Inference Analysis. - Ying Wang 113; Shashank Jere; Soumya Banerjee 001; Lingjia Liu 001; Sachin Shetty; Shehadi Dayekh (HPSR, 2022)

Error bound characterization for reservoir computing-based OFDM symbol detection. - Shashank Jere; Hussein Metwaly Saad; Lingjia Liu 001 (ICC, 2022)

Towards Explainable Machine Learning: The Effectiveness of Reservoir Computing in Wireless Receive Processing. - Shashank Jere; Karim A. Said; Lizhong Zheng; Lingjia Liu 001 (MILCOM, 2023)

Universal Approximation of Linear Time-Invariant (LTI) Systems through RNNs: Power of Randomness in Reservoir Computing. - Shashank Jere; Lizhong Zheng; Karim A. Said; Lingjia Liu 001 (CoRR, 2023)

Distributed Learning Meets 6G: A Communication and Computing Perspective. - Shashank Jere; Yifei Song; Yang Yi 002; Lingjia Liu 001 (IEEE Wirel. Commun., 2023)

Channel Equalization Through Reservoir Computing: A Theoretical Perspective. - Shashank Jere; Ramin Safavinejad; Lizhong Zheng; Lingjia Liu 001 (IEEE Wirel. Commun. Lett., 2023)

Performance Analysis and Optimization for Layer-Based Scalable Video Caching in 6G Networks. - Junchao Ma; Lingjia Liu 001; Bodong Shang; Shashank Jere; Pingzhi Fan (IEEE/ACM Trans. Netw., 2023)

Theoretical Foundation and Design Guideline for Reservoir Computing-Based MIMO-OFDM Symbol Detection. - Shashank Jere; Ramin Safavinejad; Lingjia Liu 001 (IEEE Trans. Commun., 2023)

Towards xAI: Configuring RNN Weights using Domain Knowledge for MIMO Receive Processing. - Shashank Jere; Lizhong Zheng; Karim A. Said; Lingjia Liu 001 (CoRR, 2024)

Bayesian Inference-Assisted Machine Learning for Near Real-Time Jamming Detection and Classification in 5G New Radio (NR). - Shashank Jere; Ying Wang 113; Ishan Aryendu; Shehadi Dayekh; Lingjia Liu 001 (IEEE Trans. Wirel. Commun., 2024)

Learning at the Speed of Wireless: Online Real-Time Learning for AI-Enabled MIMO in NextG. - Jiarui Xu; Shashank Jere; Yifei Song; Yi-Hung Kao; Lizhong Zheng; Lingjia Liu 001 (IEEE Commun. Mag., 2025)