Lei Yang
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Publications

Journal Publications

  • H. Hui, F. Lin, L. Meng, L. Yang, and X. Zhou, “Many-to-many matching based task allocation for dispersed computing,” Computing, (2023).

  • J. Li, D. Huang, L. Yang, and F. Lin, “AI service placement for multi-access edge intelligence systems in 6G,” IEEE Transactions on Network Science and Engineering, doi: 10.1109/TNSE.2022.3228815.

  • Y. Liu, A. Ghasemkhani, and L. Yang, “Drifting streaming peaks-over-threshold enhanced self-evolving neural networks for short-term wind farm generation forecast,” Future Internet, 15, no. 1, 2022.

  • A. Yazdi, H. Qin, C. B Jordan, L. Yang, and F. Yan, “Nemo: An open-sourced transformer-supercharged benchmark for fine-grained wildfire smoke detection,” Remote Sensing, 14(16):3979, 2022. <Code>

  • Y. Liu, L. Yang, A. Ghasemkhani, H. Livani, V. A. Centeno, P. Y. Chen, and J. Zhang, “Robust event classification using imperfect real-world PMU data,” IEEE Internet of Things Journal, accepted.

  • H. Hui, L. Yang, C. Gong, H. Xu, Z. Han, P. Shi, and F. Lin, “Affective computing model with impulse control in internet of things based on affective robotics” IEEE Internet of Things Journal, vol. 9, no. 21, pp. 20815-20832, Nov. 1, 2022.

  • R. Wu, S. D. Hamshaw, L. Yang, D. W. Kincaid, R. Etheridge, and A. Ghasemkhani, “Data imputation for multivariate time series sensor data with large gaps of missing data,” IEEE Sensors Journal, vol. 22, no. 11, pp. 10671-10683, June 1, 2022.

  • M. Xiao, W. Jin, M. Li, L. Yang, A. Thapa, P. Li, “Collusion-resistant worker recruitment in crowdsourcing systems,” IEEE Transactions on Mobile Computing, doi: 10.1109/TMC.2021.3071093.

  • W. Jin, M. Xiao, L. Guo, L. Yang, and M. Li, “ULPT: A user-centric location privacy trading framework for mobile crowd sensing,” IEEE Transactions on Mobile Computing, vol. 21, no. 10, pp. 3789-3806, Oct. 1, 2022.

  • M. Zhang, L. Yang, S. He, M. Li, and J. Zhang, “Privacy-preserving data aggregation for mobile crowdsensing with externality: An auction approach,” IEEE/ACM Transactions on Networking, vol. 29, no. 3, pp. 1046-1059, June 2021.

  • L. Zeng, X. Chen, Z. Zhou, L. Yang, J. Zhang, “CoEdge: Cooperative DNN inference with adaptive workload partitioning over heterogeneous edge devices,” IEEE/ACM Transactions on Networking, vol. 29, no. 2, pp. 595-608, April 2021.

  • A. Ghasemkhani, I. Niazazari, Y. Liu, H. Livani, V. Centeno, and L. Yang, “A regularized tensor completion approach for PMU data recovery,” IEEE Transactions on Smart Grid, vol. 12, no. 2, pp. 1519-1528, March 2021.

  • M.B. Mollah, J. Zhao, D. Niyato, K.Y. Lam, X. Zhang, A.M.Y.M. Ghias, L.H. Koh, and L. Yang, “Blockchain for future smart grid: A comprehensive survey," IEEE Internet of Things Journal, vol. 8, no. 1, pp. 18-43, Jan., 2021.

  • H. Qin, S. Zawad, Y. Zhou, S. Padhi, L. Yang, and F. Yan, “Reinforcement learning empowered MLaaS scheduling for serving intelligent internet of things,” IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6325-6337, 2020.

  • S. Yu, X. Chen, L. Yang, D. Wu, M. Bennisz, and J. Zhang, “Intelligent edge: Leveraging deep imitation learning for mobile edge computation offloading,” IEEE Wireless Communications, vol. 27, no. 1, pp. 92-99, 2020.

  • M. Zhang, J. Chen, S. He, L. Yang, X. Gong, and J. Zhang, “Privacy-preserving database assisted spectrum access for industrial Internet of things: A distributed learning approach,” IEEE Transactions on Industrial Electronics, vol. 67, no. 8, pp. 7094-7103, 2020.

  • R. Wu, L. Yang, C. Chen, S. Ahmad, S. M. Dascalu, and F. C. Harris, “MELPF version 1: Modeling error learning based post-processor framework for hydrologic models accuracy improvement,” Geoscientific Model Development, vol. 12, no. 9, pp. 4115-4131, 2019.

  • M. Jafari, A. Ghasemkhani, V. Sarfi, H. Livani, L. Yang, and H. Xu, “Biologically-inspired adaptive intelligent secondary control for microgrids under cyber imperfections,” IET Cyber-Physical Systems: Theory & Applications, vol. 4, pp. 341-352, 2019.

  • A. Ghasemkhani, L. Yang, and J. Zhang, “Learning-based demand response for privacy-preserving users,” IEEE Transactions on Industrial Informatics, vol. 15, no. 9, pp. 4988-4998, 2019.

  • X. An, X. Lv, L. Yang, X. Zhou, and F. Lin, “Node state monitoring scheme in fog radio access networks for intrusion detection,” IEEE Access, vol. 7, pp. 21879-21888, 2019.

  • M. Zhang, J. Chen, L. Yang, and J. Zhang, “Dynamic pricing for privacy-preserving mobile crowdsensing: A reinforcement learning approach,” IEEE Network, vol. 33, no. 2, pp. 160-165, 2019.

  • M. Zhang, L. Yang, X. Gong, S. He, and J. Zhang, “Wireless service pricing competition under network effect, congestion effect, and bounded rationality,” IEEE Transactions on Vehicular Technology, vol. 67, no. 8, pp. 7497 - 7507, 2018.

  • X. An, X. Zhou, X. Lv, F. Lin, and L. Yang, “Sample selected extreme learning machine based intrusion detection in fog computing and MEC,” Wireless Communications and Mobile Computing, vol. 2018, 10 pages, 2018.

  • X. Chen, X. Gong, L. Yang, and J. Zhang, “Amazon in the white space: Social recommendation aided distributed spectrum access,” IEEE/ACM Transactions on Networking, vol. 25, no. 1, pp. 536-549, 2017.

  • X. Chen, X. Gong, L. Yang, and J. Zhang, “Exploiting social tie structure for cooperative wireless networking: A social group utility maximization framework,” IEEE/ACM Transactions on Networking, vol. 24, no. 6, pp. 3593-3606, 2016.

  • L. Yang, M. He, V. Vittal, and J. Zhang, “Stochastic optimization based economic dispatch and interruptible load management with increased wind penetration,” IEEE Transactions on Smart Grid, vol. 7, no. 2, pp. 730-739, 2016.

  • L. Yang, M. He, J. Zhang, and V. Vittal, “Support vector machine enhanced Markov model for short-term wind power forecast,” IEEE Transactions on Sustainable Energy, vol. 6, no. 3, pp. 791-799, 2015.

  • L. Yang, Y. E. Sagduyu, J. Zhang and J. H. Li, “Deadline-aware scheduling with adaptive network coding for real-time traffic,” IEEE/ACM Transactions on Networking, vol. 23, no. 5, pp. 1430-1443, 2015.

  • L. Yang, X. Chen, J. Zhang, and H. V. Poor, “Cost-effective and privacy-preserving energy management for smart metering,” IEEE Transactions on Smart Grid, vol. 6, no. 1, pp. 486-495, 2015.

  • L. Yang, J. Zhang, and H. V. Poor, “Risk-aware day-ahead scheduling and real-time dispatch for electric vehicle charging,” IEEE Transactions on Smart Grid, vol. 5, no. 2, pp. 693-702, 2014.

  • M. He, L. Yang, J. Zhang, and V. Vittal, “A spatio-temporal analysis approach for short-term wind-farm generation forecast,” IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 1611-1622, 2014.

  • L. Yang, H. Kim, J. Zhang, M. Chiang, and C. W. Tan, “Pricing-based decentralized spectrum access control in cognitive radio networks,” IEEE/ACM Transactions on Networking, vol. 21, no. 2, pp. 522-535, 2013.

  • D. Qian, O. Yagan, L. Yang, J. Zhang and K. Xing, “Diffusion of real-time information in overlaying social-physical networks: network coupling and clique structure,” Networking Science, vol. 3, no. 1-4, pp. 43-55, 2013.

  • L. Yang, Y. E. Sagduyu, J. Zhang, and J. H. Li, “Distributed stochastic power control in ad-hoc networks: a nonconvex case,” EURASIP Journal on Wireless Communications and Networking, 2012:231, doi:10.1186/1687-1499-2012-231.

  • L. Yang, W. Pei, T. Li, Y.-M. Chen, and Z. He, “Topological self-similar networks introduced by diffusion-limited aggregation mechanism,” Chinese Physics Letters, 2008; 25, 1153.

Conference Publications

  • S. M. Sajjadi Mohammadabadi, L. Yang, F. Yan, and J. Zhang, “Communication-efficient training workload balancing for decentralized multi-agent learning,” in IEEE ICDCS 2024.

  • R. Li, C. Huang, X. Qin, and L. Yang, “AoI-delay tradeoff in mobile edge caching: A mixed-order drift-plus-penalty method,” in IEEE International Conference on Communications (ICC), 2024.

  • G. Wang, H. Qin, S. Ade Jacobs, X. Wu, C. Holmes, Z. Yao, S. Rajbhandari, O. Ruwase, F. Yan, L. Yang, and Y. He. “ZeRO: Extremely efficient collective communication for giant model training,” in ICLR 2024.

  • J. Cardoza-Aguilar, C. Milbourn, Y. Zhang, L. Yang, S. Dascalu and F. Harris, “A holistic approach for single-cell data trajectory inference using chromosome physical location and ensemble random walk,” in The International Conference on Information Technology - New Generations (ITNG), Las Vegas, Nevada, USA, April 2024.

  • X. Ma, F. Yan, L. Yang, I. Foster, M. Papka, Z. Liu, and R. Kettimuthu, “MalleTrain: Deep neural networks training on unfillable supercomputer nodes,” in ACM/SPEC ICPE 2024, South Kensington, London, UK, May 2024.

  • A. Ghasemkhani, R. Sanjeev Haridas, S. M. Sajjadi Mohammadabadi, and L. Yang, “Feature collusion attack on PMU data-driven event classification,” in 2024 IEEE PES Innovative Smart Grid Technologies (ISGT), Washington DC, USA, Feb. 2024.

  • O. Tawose, L. Yang, and D. Zhao, “TopoCommit: A topological commit protocol for cross-ledger transactions in scientific computing,” in IEEE Cluster 2023, Santa Fe, New Mexico, USA, Nov, 2023.

  • S. M. Sajjadi Mohammadabadi, Y. Liu, A. Canafe, and L. Yang, “Towards distributed learning of PMU data: A federated learning based event classification approach,” in 2023 IEEE PES General Meeting, Orlando, FL, USA, July, 2023.

  • O. Tawose, J. Dai, L. Yang, and D. Zhao, “Toward efficient homomorphic encryption for outsourced databases through parallel caching,” in ACM SIGMOD 2023, Seattle, WA, USA, June, 2023.

  • A. Ghasemkhani, Y. Liu, and L. Yang, “Real-time event detection using rank signatures of real-world PMU data,” in 2022 IEEE PES General Meeting, Denver, CO, USA, July, 2022.

  • Y. Liu and L. Yang, “Weakly supervised event classification using imperfect real-world PMU data with scarce labels,” in 2022 IEEE PES General Meeting, Denver, CO, USA, July, 2022. (Best Paper Award)

  • A. Canafe, Y. Liu, L. Yang, and H. Livani, “DCCA enhanced forced oscillation frequency detection using real-world PMU data,” in 2022 IEEE Texas Power and Energy Conference (TPEC), College Station, TX, 2022.

  • O. Tawose, B. Li, L. Yang, F. Yan, and D. Zhao, “Topological modeling and parallelization of multidimensional data on microelectrode arrays,” in IEEE International Parallel & Distributed Processing Symposium 2022 (IPDPS 2022), Virtual, May, 2022.

  • H. Qin, S. Rajbhandari, O. Ruwase, F. Yan, L. Yang, and Y. He, “SimiGrad: Fine-grained adaptive batching for large scale training using gradient similarity measurement,” in Proceedings of the Neural Information Processing Systems 2021 (NeurIPS 2021), Virtual, December, 2021 (Acceptance rate: 2371/9122=26%).

  • MD Kamran Chowdhury Shisher, H. Qin, L. Yang, F. Yan, and Y. Sun, “The age of correlated features in supervised learning based forecasting,” in IEEE INFOCOM Age of Information Workshop, May 10, 2021.

  • I. Niazazari, H. Livani, A. Ghasemkhani, Y. Liu, and L. Yang, “Event cause analysis in distribution networks using synchro waveform measurements,” in 2020 North American Power Symposium (NAPS), April 11, 2021.

  • I. Niazazari, Y. Liu, A. Ghasemkhani, S. Biswas, H. Livani, L. Yang, and V. A. Centeno, “PMU-data-driven event classification in power transmission grids,” in 2021 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT), February 2021.

  • A. Ghasemkhani, Y. Liu, and L. Yang, “Low-rank tensor completion for PMU data recovery,” in 2021 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT), February 2021.

  • Y. Liu, A. Ghasemkhani, L. Yang, J. Zhao, J. Zhang, and V. Vittal, “Seasonal self-evolving neural networks based short-term wind farm generation forecast,” in Proc. IEEE SmartGridComm, November 11-13, 2020.

  • A. Yazdi, X. Lin, L. Yang, and F. Yan, “SEFEE: Lightweight storage error forecasting in large scale enterprise storage systems,” in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC 2020), Atlanta, GA, USA, Nov, 2020 (Acceptance rate: 68/380=18%).

  • A. Ghasemkhani, A. Darvishi, I. Niazazari, A. Darvishi, H. Livani, and L. Yang, “Deepgrid: Robust deep reinforcement learning-based contingency management,” in 2020 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT), February 2020.

  • H. Qin, S. Zawad, Y. Zhou, L. Yang, D. Zhao, and F. Yan, “Swift deep learning serving scheduling: A region based reinforcement learning approach,” in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC 2019), Denver, CO, USA, Nov, 2019 (Acceptance rate: 78/344=22%).

  • V. Sarfi, A. Ghasemkhani, I. Niazazari, H. Livani, L. Yang, “Decentralized dynamic state estimation with bimodal Gaussian mixture measurement noise,” in 51st North American Power Symposium (NAPS), 2019.

  • M. Jafari, V. Sarfi, A. Ghasemkhani, H. Livani, L. Yang, H. Xu, “Adaptive intelligent secondary control of microgrids using a biologically-inspired reinforcement learning,” in 2019 IEEE Power & Energy Society General Meeting, Atlanta, GA, USA, 2019.

  • L. Yang, M. Zhang, S. He, M. Li, and J. Zhang, “Crowd-empowered privacy-preserving data aggregation for mobile crowdsensing,” in ACM Mobihoc 2018, Los Angeles, USA, 2018. (Acceptance rate 16.9%)

  • B. Hutchins, A. Reddy, W. Jin, M. Zhou, M. Li, and L. Yang, “Beat-PIN: A user authentication mechanism for wearable devices through secret beats,” in ACM ASIACCS 2018, Incheon, Korea, 2018. (Acceptance rate 20%)

  • W. Jin, M. Li, L. Guo, and L. Yang, “DPDA: A differentially private double auction scheme for mobile crowd sensing,” in Proc. IEEE CNS 2018, Beijing, China, 2018.

  • A. Ghasemkhani and L. Yang, “Reinforcement learning based pricing for demand response,” in Proc. IEEE ICC 2018 Workshop-ICT4SG, Kansas, USA, 2018.

  • M. Jafari, V. Sarfi, A. Ghasemkhani, H. Livani, L. Yang, and H. Xu “Adaptive neural network based intelligent secondary control for microgrids,” in Proc. IEEE TPEC 2018, College Station, TX, 2018.

  • A. Ghasemkhani, V. Sarfi, L. Yang, and H. Livani, “Decentralized dynamic state estimation with missing and delayed PMU measurements,” in Proc. IEEE PES T&D 2018, Denver, USA, 2018.

  • M. Zhang, L. Yang, X. Gong, and J. Zhang, “Privacy-preserving crowdsensing: privacy valuation, network effect, and profit maximization,” in Proc. IEEE GLOBECOM 2016, Washington, DC, USA, 2016.

  • M. Zhang, L. Yang, X. Gong, and J. Zhang, “Impact of network effect and congestion effect on price competition among wireless service providers,” in Proc. IEEE CISS 2016, Princeton, NJ, USA, 2016.

  • M. Zhang, L. Yang, D. Shin, X. Gong, and J. Zhang, “Privacy-preserving database assisted spectrum access: A socially-aware distributed learning approach” in Proc. IEEE GLOBECOM 2015, San Diego, California, USA, 2015.

  • L. Yang, M. He, V. Vittal, J. Zhang, “Stochastic optimization based economic dispatch and interruptible load management with distributional forecast of wind farm generation,” in Proc. IEEE Conference on Decision and Control (CDC), Los Angeles, CA, USA, 2014.

  • S. Lakshminarayana, L. Yang, H. V. Poor, T. Q.S. Quek, and J. Zhang, “Risk-aware energy procurement with renewable energy and storage,” in Proc. IEEE SmartGridComm, Venice, Italy, 2014.

  • L. Yang, X. Chen, J. Zhang, and H. V. Poor, “Optimal privacy-preserving energy management for smart meters,” in Proc. IEEE INFOCOM 2014, Toronto, Canada 2014. (Acceptance rate 19.4%)

  • X. Chen, X. Gong, L. Yang, and J. Zhang, “A social group utility maximization framework with applications in database assisted spectrum access,” in Proc. IEEE INFOCOM 2014, Toronto, Canada 2014. (Acceptance rate 19.4%) (Best Paper Award Runner-up)

  • L. Yang, J. Zhang, and H. Vincent Poor, “Risk-aware scheduling and real-time charging for plug-in electric vehicles,” in INFORMS annual meeting 2013, Minneapolis, MN, USA, 2013 (invited).

  • D. Shin, J. Koo, L. Yang, X. Lin, S. Bagchi, and J. Zhang, “Low-complexity secure protocols to defend cyber-physical systems against network isolation attacks,” in IEEE Conference on Communications and Network Security 2013, Washington, D.C. USA, 2013.

  • M. He, L. Yang, J. Zhang, and V. Vittal, “Spatio-temporal analysis for smart grids with wind generation integration,” in ICNC 2013, San Diego, California, USA, 2013 (invited).

  • L. Yang, J. Zhang and D. Qian, “Risk-aware day-ahead scheduling and real-time dispatch for plug-in electric vehicles,” in Proc. IEEE GLOBECOM 2012, Anaheim, California, USA, 2012.

  • D. Qian, O. Yagan, L. Yang and J. Zhang, “Diffusion of real-time information in social-physical networks,” in Proc. IEEE GLOBECOM 2012, Anaheim, California, USA, 2012.

  • L. Yang, Y. E. Sagduyu, and J. H. Li, “Adaptive network coding for scheduling real-time traffic with hard deadlines,” in ACM MobiHoc 2012, Hilton Head Island, South Carolina, USA, 2012. (Acceptance rate 20%)

  • L. Yang, S. Murugesan, and J. Zhang, “Real-time scheduling over Markovian channels: when partial observability meets hard deadlines,” in Proc. IEEE GLOBECOM 2011, Houston, Texas, USA, 2011.

  • L. Yang, S. Murugesan and J. Zhang, “Packet scheduling with strict deadline constraints: the multi-level 'exploitation vs exploration’ trade-off and the optimality of the greedy policy,” in Information Theory and Applications Workshop 2011, San Diego, CA, Feb. 2011 (invited).

  • L. Yang, Y. E. Sagduyu, J. Zhang, and J. H. Li, “Distributed power control for ad-hoc communications via stochastic nonconvex utility optimization,” in Proc. IEEE ICC 2011, Kyoto, Japan, 2011.

  • L. Yang, H. Kim, J. Zhang, M. Chiang, and C. W. Tan, “Pricing-based spectrum access control in cognitive radio networks with random access,” in Proc. IEEE INFOCOM 2011, Shanghai, China, Apr. 2011. (Acceptance rate 15.96%)