Meng Wang

My research spans theoretical machine learning, foundation models, signal processing, and power systems. The list below highlights recent publications first, followed by complete conference and journal publication lists. My publications can also be found on Google Scholar.

Selected Recent Publications

AI / Machine Learning

• Hongkang Li, Songtao Lu, Xiaodong Cui, Pin-Yu Chen, and Meng Wang, How Can Mamba Learn In-Context with Outliers and Generalize Provably?, in Proc. of 2026 International Conference on Machine Learning (ICML), July 2026. (acceptance rate: 26.6%)
• Mohammed Nowaz Rabbani Chowdhury, Kaoutar El Maghraoui, Hsinyu Tsai, Naigang Wang, Geoffrey W. Burr, Liu Liu, and Meng Wang, Efficient Quantization of Mixture-of-Experts with Theoretical Generalization Guarantees, in Proc. of the Fourteenth International Conference on Learning Representations (ICLR), April 2026. (acceptance rate: 28%)
• Jiawei Sun, Shuai Zhang, Hongkang Li, and Meng Wang, Contrastive Learning with Data Misalignment: Feature Purity, Training Dynamics and Theoretical Generalization Guarantees, in Proc. of the Thirty-ninth Annual Conference on Neural Information Processing Systems (NeurIPS), December 2025. (acceptance rate: 24.52%)
• Hongkang Li, Songtao Lu, Xiaodong Cui, Pin-Yu Chen, Meng Wang, Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis, in Proc. of 2025 International Conference on Learning Representations (ICLR), April 2025. (acceptance rate: 32.08%)
• Hongkang Li, Yihua Zhang, Shuai Zhang, Sijia Liu, Pin-Yu Chen, Meng Wang, When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers, in Proc. of 2025 International Conference on Learning Representations (ICLR), April 2025. (acceptance rate: 32.08%) Oral presentation
• Hongkang Li, Meng Wang, Songtao Lu, Xiaodong Cui, and Pin-Yu Chen, Training Nonlinear Transformers for Efficient In-Context Learning: A Theoretical Learning and Generalization Analysis, in Proc. of 2024 International Conference on Machine Learning (ICML), July 2024. (acceptance rate: 27.5%)
• Hongkang Li, Meng Wang, Tengfei Ma, Sijia Liu, Zaixi Zhang, and Pin-Yu Chen, What Improves the Generalization of Graph Transformer? A Theoretical Dive into Self-attention and Positional Encoding, in Proc. of 2024 International Conference on Machine Learning (ICML), July 2024. (acceptance rate: 27.5%)
• Mohammed Nowaz Rabbani Chowdhury, Meng Wang, Kaoutar El Maghraoui, Naigang Wang, Pin-Yu Chen, and Christopher Carothers, A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts, in Proc. of 2024 International Conference on Machine Learning (ICML), July 2024. (acceptance rate: 27.5%)
• Shuai Zhang, Heshan Devaka Fernando, Miao Liu, Keerthiram Murugesan, Songtao Lu, Pin-Yu Chen, Tianyi Chen, and Meng Wang, SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning, in Proc. of 2024 International Conference on Machine Learning (ICML), July 2024. (acceptance rate: 27.5%)

Power Systems / Signal Processing

• Yating Zhou, Shuai Zhang, and Meng Wang, Mid-Term Load Forecasting with Minimal Data: An In-Context-Learning-Aware Approach Using Large Language Models, accepted to IEEE Transactions on Power Systems, 2026.
• Jiawei Sun, Hongkang Li, and Meng Wang, Theoretical Learning Performance of Graph Networks: the Impact of Jumping Connections and Layer-wise Sparsification, in Transactions on Machine Learning Research, June 2025.
• Yating Zhou and Meng Wang, Empower Pre-trained Large Language Models for Building-level Load Forecasting, accepted to IEEE Transactions on Power Systems, 2025.
• Yating Zhou and Meng Wang, Unifying Load Disaggregation and Prediction for Buildings with Behind-the-Meter Solar, accepted to IEEE Transactions on Power Systems, 2024.
• Hongkang Li, Shuai Zhang, Yihua Zhang, Meng Wang, Sijia Liu, and Pin-Yu Chen, How does promoting the minority fraction affect generalization? A theoretical study of one-hidden-layer neural network on group imbalance, accepted to IEEE Journal of Selected Topics in Signal Processing, February 2024.

Full List of Conference Publications

• Hongkang Li, Songtao Lu, Xiaodong Cui, Pin-Yu Chen, and Meng Wang, How Can Mamba Learn In-Context with Outliers and Generalize Provably?, in Proc. of 2026 International Conference on Machine Learning (ICML), July 2026. (acceptance rate: 26.6%)
• Mohammed Nowaz Rabbani Chowdhury, Kaoutar El Maghraoui, Hsinyu Tsai, Naigang Wang, Geoffrey W. Burr, Liu Liu, and Meng Wang, Efficient Quantization of Mixture-of-Experts with Theoretical Generalization Guarantees, in Proc. of the Fourteenth International Conference on Learning Representations (ICLR), April 2026. (acceptance rate: 28%)
• Haixu Liao, Yating Zhou, Songyang Zhang, Meng Wang, and Shuai Zhang, Theoretical Analysis of Contrastive Learning under Imbalanced Data: From Training Dynamics to a Pruning Solution, in Proc. of the Fourteenth International Conference on Learning Representations (ICLR), April 2026. (acceptance rate: 28%)
• Mugunthan Shandirasegaran, Hongkang Li, Songyang Zhang, Meng Wang, and Shuai Zhang, A Theoretical Analysis of Mamba’s Training Dynamics: Filtering Relevant Features for Generalization in State Space Models, in Proc. of the Fourteenth International Conference on Learning Representations (ICLR), April 2026. (acceptance rate: 28%)
• Yihua Zhang, Hongkang Li, Yuguang Yao, Aochuan Chen, Shuai Zhang, Pin-Yu Chen, Meng Wang, and Sijia Liu, Visual Prompting Reimagined: The Power of Activation Prompts, in Proc. of the Twenty-Ninth International Conference on Artificial Intelligence and Statistics (AISTATS), May 2026.
• Jiawei Sun, Shuai Zhang, Hongkang Li, and Meng Wang, Contrastive Learning with Data Misalignment: Feature Purity, Training Dynamics and Theoretical Generalization Guarantees, in Proc. of the Thirty-ninth Annual Conference on Neural Information Processing Systems (NeurIPS), December 2025. (acceptance rate: 24.52%)
• Hongkang Li, Songtao Lu, Xiaodong Cui, Pin-Yu Chen, Meng Wang, Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis, in Proc. of 2025 International Conference on Learning Representations (ICLR), April 2025. (acceptance rate: 32.08%)
• Hongkang Li, Yihua Zhang, Shuai Zhang, Sijia Liu, Pin-Yu Chen, Meng Wang, When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers, in Proc. of 2025 International Conference on Learning Representations (ICLR), April 2025. (acceptance rate: 32.08%) Oral presentation
• Hongkang Li, Meng Wang, Songtao Lu, Xiaodong Cui, and Pin-Yu Chen, Training Nonlinear Transformers for Efficient In-Context Learning: A Theoretical Learning and Generalization Analysis, in Proc. of 2024 International Conference on Machine Learning (ICML), July 2024. (acceptance rate: 27.5%)
• Hongkang Li, Meng Wang, Tengfei Ma, Sijia Liu, Zaixi Zhang, and Pin-Yu Chen, What Improves the Generalization of Graph Transformer? A Theoretical Dive into Self-attention and Positional Encoding, in Proc. of 2024 International Conference on Machine Learning (ICML), July 2024. (acceptance rate: 27.5%)
• Mohammed Nowaz Rabbani Chowdhury, Meng Wang, Kaoutar El Maghraoui, Naigang Wang, Pin-Yu Chen, and Christopher Carothers, A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts, in Proc. of 2024 International Conference on Machine Learning (ICML), July 2024. (acceptance rate: 27.5%)
• Shuai Zhang, Heshan Devaka Fernando, Miao Liu, Keerthiram Murugesan, Songtao Lu, Pin-Yu Chen, Tianyi Chen, and Meng Wang, SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning, in Proc. of 2024 International Conference on Machine Learning (ICML), July 2024. (acceptance rate: 27.5%)
• Shuai Zhang, Meng Wang, Hongkang Li, Miao Liu, Pin-Yu Chen, Songtao Lu, Sijia Liu, Keerthiram Murugesan, Subhajit Chaudhury, On the Convergence and Sample Complexity Analysis of Deep Q-Networks with ε-Greedy Exploration, in Proc. of the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), New Orleans, December 2023. (acceptance rate: 26.1%)
• Mohammed Nowaz Rabbani Chowdhury, Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks, in Proc. of 2023 International Conference on Machine Learning (ICML), July 2023. (acceptance rate: 27.9%) Oral presentation
• Hongkang Li, Meng Wang, Sijia Liu, and Pin-Yu Chen, A Theoretical Understanding of Vision Transformers: Learning, Generalization, and Sample Complexity, in Proc. of the Eleventh International Conference on Learning Representations (ICLR), May 2023. (acceptance rate: 31.8%)
• Shuai Zhang, Meng Wang, Pin-Yu Chen, Sijia Liu, Songtao Lu, and Miao Liu, Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks, in Proc. of the Eleventh International Conference on Learning Representations (ICLR), May 2023. (acceptance rate: 31.8%)
• Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen and Jinjun Xiong, Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling, in Proc. of International Conference on Machine Learning (ICML), July 2022. (acceptance rate: 21.9%)
• Ming Yi and Meng Wang, Recent Results of Energy Disaggregation with Behind-the-Meter Solar Generation, in Proc. of the 11th Bulk Power Systems Dynamics and Control Symposium – IREP'2022, July 2022.
• Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen and Jinjun Xiong, How Does Unlabeled Data Improve Generalization in Self-training? A one-hidden-layer Theoretical Analysis, in Proc. of the Tenth International Conference on Learning Representations (ICLR), April 2022. (acceptance rate: 32.3%)
• Hongkang Li, Shuai Zhang, and Meng Wang, Learning and Generalization of One-hidden-layer Neural Networks, Going Beyond Standard Gaussian Data, in Proc. of the 56th Annual Conference on Information Sciences and Systems (CISS), Princeton, NJ, USA, 2022.
• Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen and Jinjun Xiong, Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Sparse Neural Networks, in Proc. of the Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS), 2021. (acceptance rate: 26%)
• Ren Wang, Kaidi Xu, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Chuang Gan, and Meng Wang, On Fast Adversarial Robustness Adaptation in Model-agnostic Meta-learning, in Proc. of International Conference on Learning Representations (ICLR), 2021. (acceptance rate: 28.7%)
• Ren Wang, Meng Wang, Jinjun Xiong, Quantized Higher-Order Tensor Recovery by Exploring Low-Dimensional Structures, in Proc. of Asilomar Conference on Signals, Systems and Computers, Pacific Grove, California, November 2020.
• Ren Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong and Meng Wang, Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases, in Proc. of European Conference on Computer Vision (ECCV), Glasgow, Scotland, August 2020. (acceptance rate: 26%)
• Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen and Jinjun Xiong, Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case, in Proc. of 2020 International Conference on Machine Learning (ICML), June 2020. (acceptance rate: 21.8%)
• Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen and Jinjun Xiong, Guaranteed Convergence of Training Convolutional Neural Networks via Accelerated Gradient Descent, in Proc. of 2020 54th Annual Conference on Information Sciences and Systems (CISS), Princeton, NJ, USA, 2020.
• Genevieve de Mijolla, Stavros Konstantinopoulos, Pengzhi Gao, Joe H. Chow, and Meng Wang, An Evaluation of Low-Rank Matrix Completion Algorithms for Synchrophasor Missing Data Recovery, Power Systems Computation Conference (PSCC), June 2018.
• Pengzhi Gao and Meng Wang, Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), April 2018.
• Shuai Zhang, Yingshuai Hao, Meng Wang, and Joe H. Chow, Multi-Channel Missing Data Recovery by Exploiting the Low-rank Hankel Structures, IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Curaçao, Dutch Antilles, December 2017.
• Meng Wang, Joe H. Chow, Pengzhi Gao, Yingshuai Hao, Wenting Li, and Ren Wang, Recent Results of PMU Data Analytics by Exploiting Low-dimensional Structures, The 10th Bulk Power Systems Dynamics and Control Symposium – IREP'2017, Espinho, Portugal, August 2017.
• Wenting Li, Meng Wang, and Joe H. Chow, Fast Event Identification through Subspace Characterization of PMU Data in Power Systems, IEEE Power & Energy Society General Meeting, Chicago, IL, July 2017.
• Pengzhi Gao, Ren Wang, Meng Wang, and Joe H. Chow, Low-rank Matrix Recovery from Quantized and Erroneous Measurements: Accuracy-preserved Data Privatization in Power Grids, in Proc. of Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, November 2016.
• Pengzhi Gao, Meng Wang, Joe Chow, Matthew Berger, and Lee M. Seversky, Matrix Completion with Columns in Union and Sums of Subspaces, in Proc. of IEEE Global Conference on Signal and Information Processing (GlobalSIP), Orlando, FL, December 2015.
• Yingshuai Hao, Meng Wang, and Joe Chow, Likelihood Analysis of Cyber Data Injection Attacks to Power Systems, in Proc. of IEEE Global Conference on Signal and Information Processing (GlobalSIP), Orlando, FL, December 2015.
• Yao Xie, Meng Wang, and Andrew Thompson, Sketching for Sequential Change-Point Detection, in Proc. of IEEE Global Conference on Signal and Information Processing (GlobalSIP), Orlando, FL, December 2015.
• M. Wang, J. H. Chow, P. Gao, X. T. Jiang, Y. Xia, S. G. Ghiocel, B. Fardanesh, G. Stefopoulos, Y. Kokai, N. Saito, and M. P. Razanousky, A Low-Rank Matrix Approach for the Analysis of Large Amounts of Synchrophasor Data, in Proc. of Hawaii International Conference on System Sciences, Kauai, Hawaii, January 2015. Runner-up for Best Paper in Electric Energy Systems Track.
• M. Wang, P. Gao, S. G. Ghiocel, J. H. Chow, B. Fardanesh, G. Stefopoulos, and M. P. Razanousky, Identification of "Unobservable" Cyber Data Attacks on Power Grids, in Proc. of IEEE SmartGridComm, Venice, Italy, November 2014.
• P. Gao, M. Wang, S. G. Ghiocel, and J. H. Chow, Modeless Reconstruction of Missing Synchrophasor Measurements, in Proc. of IEEE Power & Energy Society General Meeting, Washington, DC, July 2014. Selected in Best Conference Paper Sessions.
• Q. Wang, X. Zhang, M. Wang, and K. Boyer, Learning Room Occupancy Patterns from Sparsely Recovered Light Transport Models, in Proc. of IEEE International Conference on Pattern Recognition, Stockholm, Sweden, August 2014.
• Q. Wang, X. Shen, M. Wang, and K. Boyer, Label Consistent Fisher Vectors, in Proc. of IEEE International Conference on Pattern Recognition, Stockholm, Sweden, August 2014.
• M. Wang, W. Xu, and R. Calderbank, Compressed Sensing with Corrupted Participants, in Proc. of IEEE ICASSP, Vancouver, Canada, May 2013.
• X. Li, H. Yao, M. Wang, and S. C. Liew, ADMOT: Compressive Sensing Techniques for Channel Monitoring in Multiple Access Networks, in Proc. of IEEE ICASSP, Vancouver, Canada, May 2013.
• M. Wang, W. Xu, E. Mallada and A. Tang, Sparse Recovery with Graph Constraints: Fundamental Limits and Measurement Construction, in Proc. of IEEE INFOCOM, Orlando, Florida, March 2012.
• M. Wang, X. Meng, and L. Zhang, Consolidating Virtual Machines with Dynamic Bandwidth Demand in Data Centers, in Proc. of IEEE INFOCOM Mini-Conference, Shanghai, China, April 2011.
• W. Xu, M. Wang and A. Tang, On State Estimation with Bad Data Detection, in Proc. of IEEE CDC-ECC, Orlando, Florida, December 2011.
• M. Wang, W. Xu, and A. Tang, The Limits of Error Correction with lp Decoding, in Proc. of IEEE ISIT, Austin, Texas, June 2010.
• M. Wang and A. Tang, Conditions for a Unique Non-negative Solution to an Underdetermined System, in Proc. of Allerton Conference, Monticello, Illinois, September 2009.
• M. Wang, C. Tan, A. Tang, and S. Low, How Bad is Single-Path Routing, in Proc. of IEEE Globecom, Honolulu, Hawaii, November 2009.
• A. Anandkumar, M. Wang, L. Tong, and A. Swami, Prize-Collecting Data Fusion for Cost-Performance Tradeoff in Distributed Inference, in Proc. of IEEE INFOCOM, Rio de Janeiro, Brazil, April 2009.
• M. Wang, F. Li, Y. Liu, L. Huang, and M. Sakane, Distributed Parallel Operation of Modified Deadbeat Controlled UPS Inverters, in Proc. of IEEE PESC, Orlando, Florida, June 2007.

Full List of Journal Publications

• Soroush Vahedi, Junbo Zhao, and Meng Wang, Behind-the-Meter PV Disaggregation with Copula-Constrained Bayesian Variational Autoencoder and Feature Clustering, accepted to IEEE Transactions on Smart Grid, 2026.
• Yating Zhou, Shuai Zhang, and Meng Wang, Mid-Term Load Forecasting with Minimal Data: An In-Context-Learning-Aware Approach Using Large Language Models, accepted to IEEE Transactions on Power Systems, 2026.
• Jiawei Sun, Hongkang Li, and Meng Wang, Theoretical Learning Performance of Graph Networks: the Impact of Jumping Connections and Layer-wise Sparsification, in Transactions on Machine Learning Research, June 2025.
• Yating Zhou and Meng Wang, Empower Pre-trained Large Language Models for Building-level Load Forecasting, accepted to IEEE Transactions on Power Systems, 2025.
• Yating Zhou and Meng Wang, Unifying Load Disaggregation and Prediction for Buildings with Behind-the-Meter Solar, accepted to IEEE Transactions on Power Systems, 2024.
• Hongkang Li, Shuai Zhang, Yihua Zhang, Meng Wang, Sijia Liu, and Pin-Yu Chen, How does promoting the minority fraction affect generalization? A theoretical study of one-hidden-layer neural network on group imbalance, accepted to IEEE Journal of Selected Topics in Signal Processing, Special Issue on AI in Signal & Data Science – Toward Explainable, Reliable, and Sustainable Machine Learning, February 2024.
• Ming Yi, Meng Wang, Tianqi Xiong, and Dongbo Zhao, Bayesian High-Rank Hankel Matrix Completion for Nonlinear Synchrophasor Data Recovery, accepted to IEEE Transactions on Power Systems, March 2023.
• Ming Yi, Meng Wang, Evangelos Farantatos and Tapas Barik, Bayesian Robust Hankel Matrix Completion with Uncertainty Modeling for Synchrophasor Data Recovery, ACM SIGENERGY Energy Informatics Review, 2022.
• Orlem L. D. Santos, Daniel Dotta, Meng Wang, Joe H. Chow, Ildemar C. Decker, Performance Analysis of a DNN Classifier for Power System Events using an Interpretability Method, accepted to International Journal of Electrical Power and Energy Systems, 2021.
• Ming Yi and Meng Wang, Bayesian Energy Disaggregation at Substations with Uncertainty Modeling, accepted to IEEE Transactions on Power Systems, 2021.
• Meng Wang, Joe H. Chow, Denis Osipov, Stavros Konstantinopoulos, Shuai Zhang, Evangelos Farantatos, and Mahendra Patel, Review of Low-Rank Data-Driven Methods Applied to Synchrophasor Measurement, IEEE Open Access Journal of Power and Energy, 2021.
• Wenting Li, Ming Yi, Meng Wang, Yishen Wang, Di Shi, and Zhiwei Wang, Real-time Energy Disaggregation at Substations with Behind-the-Meter Solar Generation, accepted to IEEE Transactions on Power Systems, October 2020.
• Ren Wang, Meng Wang, Jinjun Xiong, Tensor Recovery from Noisy and Multi-Level Quantized Measurements, EURASIP Journal on Advances in Signal Processing, 2020, 41.
• Shuai Zhang, Meng Wang, Jinjun Xiong, Sijia Liu, Pin-Yu Chen, Learning One-hidden-layer Convolutional Neural Networks via Accelerated Gradient Descent with Generalizability Guarantees, accepted to IEEE Transactions on Neural Networks and Learning Systems, June 2020.
• Ren Wang, Meng Wang, Jinjun Xiong, Achieve Data Privacy and Clustering Accuracy Simultaneously Through Quantized Data Recovery, EURASIP Journal on Advances in Signal Processing, 2020, 22.
• Stavros Konstantinopoulos, Genevieve M. De Mijolla, Joe H. Chow, Hanoch Lev-Ari, and Meng Wang, Synchrophasor Missing Data Recovery via Data-Driven Filtering, IEEE Transactions on Smart Grid, 11(5): 4321–4130, 2020.
• Yingshuai Hao, Meng Wang, Joe H. Chow, Modeless Streaming Synchrophasor Data Recovery in Nonlinear Systems, accepted to IEEE Transactions on Power Systems, August 2019.
• Yang Cao, Andrew Thompson, Meng Wang, and Yao Xie, Sketching for Sequential Change-Point Detection, accepted to EURASIP Journal on Advances in Signal Processing, July 2019.
• Wenting Li, Deepjyoti Deka, Michael Chertkov, and Meng Wang, Real-time Fault Localization in Power Grids with Convolutional Neural Networks, accepted to IEEE Transactions on Power Systems, May 2019.
• Wenting Li and Meng Wang, Identifying Overlapping Successive Events Using a Shallow Convolutional Neural Network, accepted to IEEE Transactions on Power Systems, April 2019.
• Shuai Zhang and Meng Wang, Correction of Corrupted Columns in Robust Matrix Completion by Exploiting the Hankel Structure, accepted to IEEE Transactions on Signal Processing, February 2019.
• Ren Wang, Meng Wang, and Jinjun Xiong, Data Recovery and Subspace Clustering from Quantized and Corrupted Measurements, accepted to IEEE Journal of Selected Topics in Signal Processing, Special Issue on Robust Subspace Learning and Tracking: Theory, Algorithms, and Applications, August 2018.
• Yingshuai Hao, Meng Wang, Joe H. Chow, Evangelos Farantatos, and Mahendra Patel, Model-less Data Quality Improvement of Streaming Synchrophasor Measurements by Exploiting the Low-Rank Hankel Structure, accepted to IEEE Transactions on Power Systems, June 2018.
• Pengzhi Gao, Ren Wang, Meng Wang, and Joe H. Chow, Low-rank Matrix Recovery from Noisy, Quantized and Erroneous Measurements, accepted to IEEE Transactions on Signal Processing, March 2018.
• Shuai Zhang, Yingshuai Hao, Meng Wang, and Joe H. Chow, Multi-Channel Hankel Matrix Completion through Nonconvex Optimization, accepted to IEEE Journal of Selected Topics in Signal Processing, Special Issue on Signal and Information Processing for Critical Infrastructures, March 2018.
• Wenting Li, Meng Wang, and Joe H. Chow, Real-time Event Identification through Low-dimensional Subspace Characterization of High-dimensional Synchrophasor Data, accepted to IEEE Transactions on Power Systems, January 2018.
• Junbo Zhao, Lamine Mili, and Meng Wang, A Generalized False Data Injection Attacks Against Power System Nonlinear State Estimator and Countermeasures, accepted to IEEE Transactions on Power Systems, January 2018.
• Pengzhi Gao, Meng Wang, Joe H. Chow, Matthew Berger, and Lee M. Seversky, Missing Data Recovery for High-dimensional Signals with Nonlinear Low-dimensional Structures, accepted to IEEE Transactions on Signal Processing, 2017.
• Yingshuai Hao, Meng Wang, and Joe H. Chow, Likelihood Analysis of Cyber Data Attacks to Power Systems with Markov Decision Processes, accepted to IEEE Transactions on Smart Grid, 2016.
• Pengzhi Gao, Meng Wang, Joe H. Chow, Scott G. Ghiocel, Bruce Fardanesh, George Stefopoulos, and Michael P. Razanousky, Identification of Successive "Unobservable" Cyber Data Attacks in Power Systems, IEEE Transactions on Signal Processing, 2016, 64(21): 5557–5570.
• Pengzhi Gao, Meng Wang, Scott G. Ghiocel, Joe H. Chow, Bruce Fardanesh, and George Stefopoulos, Missing Data Recovery by Exploiting Low-dimensionality in Power Systems Synchrophasor Measurements, IEEE Transactions on Power Systems, 2016, 31(2): 1006–1013.
• Meng Wang, Weiyu Xu, Enrique Mallada, and Ao Tang, Network Tomography via Sparse Recovery, IEEE Transactions on Information Theory, 61(2): 1028–1044, 2015.
• Weiyu Xu, Meng Wang, and Ao Tang, Sparse Recovery with Nonlinear Measurements for Bad Data Detection in Power Networks, IEEE Transactions on Signal Processing, 61(24): 6175–6187, 2013.
• Meng Wang, Chee Wei Tan, Weiyu Xu, and Ao Tang, Cost of Not Splitting in Routing: Characterization and Estimation, IEEE/ACM Transactions on Networking, 19(6): 1849–1859, 2011.
• Meng Wang, Weiyu Xu, and Ao Tang, On the Performance of Sparse Recovery via ell_p-minimization (0 <= p <= 1), IEEE Transactions on Information Theory, 57(11): 7255–7278, 2011.
• Meng Wang, Weiyu Xu, and Ao Tang, A Unique "Nonnegative" Solution to an Underdetermined System: from Vectors to Matrices, IEEE Transactions on Signal Processing, 59(3): 1007–1016, 2011.
• M. Wang, F. Li, L. Huang, and S. Makoto, A Robust Deadbeat Control Method for UPS Inverters, Advanced Technology of Electrical Engineering and Energy, 26(4): 31–35, 2007.
• M. Wang, Y. Liu, F. Li, and L. Huang, Progressively Converging Deadbeat Control for UPS Inverters, Advanced Technology of Electrical Engineering and Energy, 26(1): 47–50, 2007.
• M. Wang, Y. Liu, Z. Jiang, and L. Huang, Second-harmonic Compensation Method and Capacitor-voltage Time-sharing Control Scheme for a Double Conversion UPS Rectifier, Advanced Technology of Electrical Engineering and Energy, 25(2): 29–33, 2006.

Last updated: May 2026.