06/2024 Yanning is awarded the 2024 Samueli School of Engineering Early Career Faculty Excellence in Research Award
05/2024 Yanning started serving as an associate editor for the IEEE Transactions on Signal Processing
04/2024 Our paper "Filtering as Rewiring for Bias Mitigation on Graphs" O. D. Kose, G. Mateos, Y. Shen was accepted by SAM 2024
03/2024 Our collaborative project on "Graph-based intelligent transportation system" with Bolei Zhou was funded by the Samueli Foundation
02/2024 New paper on fairness-aware Graph Generative Model "FairWire: Fair Graph Generation" O. D. Kose, Y. Shen is available!
02/2024 Constanza joined the group as a visiting student from the Universitat Politècnica de Catalunya, Barcelona Tech. Welcome!
01/2024 Our paper "Budgeted Online Model Selection and Fine-Tuning via Federated Learning," P. M. Ghari, and Y. Shen has been accepted by the Transactions on Machine Learning Research. Congratulations to Pouya!
01/2024 Our new preprint "Long-term Fairness For Real-time Decision Making: A Constrained Online Optimization Approach" R. Du, D. Muthirayan, P. P Khargonekar, Y. Shen is available. Congratulations to Ruijie for the new work.
01/2024 Our paper "Fairness-aware Optimal Graph Filter Design" O. D. Kose, G. Mateos, Y. Shen is accepted by the IEEE Journal of Selected Topics in Signal Processing. Congratulations to Deniz!
12/2023 Yanning gave a talk on "Demystifying and Mitigating Unfairness for Machine Learning over Graphs" at the ECE department of the University of Maryland, Colege Park.
11/2023 Yanning organized a special session on "Machine Learning over Graphs" in Asilomar 2023
10/2023 Ruijie finished his MS degree and is officially a PhD student! Congratulations to Ruijie!
09/2023 Erfan joined the group as an MS/PhD student. Welcome!
08/2023 New paper "Fairness-Aware Graph Filter Design" is accepted by Asilomar 2023
07/2023 Two papers were accepted at the 62nd IEEE Conference on Decision and Control (CDC).
06/2023 New collaborative paper "Analysis of Relative Error in Perturbation Monte Carlo Simulations of Radiative Transport" published in the Journal of Biomedical Optics.
05/2024 Yanning gave a talk on "Demystifying and Mitigating Unfairness for Machine Learning over Graphs" at the ECE Department at the University of Michigan, Ann Arbor.
05/2023 Our tutorial "Fairness in Graph Machine Learning: Recent Advances and Future Prospectives" is accepted by KDD 2023!
04/2023 New paper with Deniz "Fast&Fair: Training Acceleration and Bias Mitigation for GNNs" accepted by the Transactions on Machine Learning Research.
03/2023 Yanning receive the 2023 CORCL Research Award
03/2023 New paper "Demystifying and Mitigating Bias for Node Representation Learning" O. D. Kose and Y. Shen accepted by IEEE Transactions on Neural Networks and Learning Systems. Congratulations Deniz!
02/2023 Yanning was invited to give a talk at ITA about "fairness aware learning over graphs" in San Diego, CA.
01/2023 New paper "Online Learning with Uncertain Feedback Graphs," P.M. Ghari and Y. Shen accepted by IEEE Transactions on Neural Networks and Learning Systems. Congratulations Pouya!
01/2023: New paper "Graph-Aided Online Multi-Kernel Learning," P.M. Ghari and Y. Shen is published in the Journal of Machine Learning Research!
12/2022: I am honored to be selected as a Newkirk Faculty Fellow! I look very much forward to collaborating with researchers at the Newkirk Center for Science & Society.
11/2022: I am thrilled to be selected as one of the honorees of MIT Technology Review Innovators under 35 Asia Pacific
10/2022: I organized a special session on "Learning over Graphs " at Asilomar 2022.
09/2022: New paper "Personalized Online Federated Multi-Kernel Learning" P. M. Ghari and Y. Shen* accepted by NeurIPS 2022! Congratulations to Pouya. This work is supported by NSF ECCS 2207457.
08/2022: Thanks NSF for supporting our project "Distributed and Quantized Kernel-based Learning over Interconnected Sensing Systems"!
08/2022: Three papers with my students were presented at EUSIPCO. Congratulations to Deniz and Pouya for their new work, and Ruijie for his first publication!
"Fairness-aware User Classification in Power Grids," R. Du and Y.Shen*
“Fairness-aware Adaptive Network Link Prediction,” O. D. Kose and Y. Shen*
“Graph-Assisted Communication-Efficient Federated Learning,” P. M. Ghari and Y. Shen*
07/2022: New work with Jiaxuan and Yezi "Explaining Dynamic Graph Neural Networks via Relevance Backpropagation" is available!
07/2022: I am honored to be awarded the Hellman Fellowship Award!
06/2022: I was invited to give a talk at the IEEE SPS SAM Webinar!
05/2022: I am thrilled to receive a Google Research Scholar Award in the area of Machine Learning and Data Mining!
05/2022: New work with Deniz "FairNorm: Fair and Fast Graph Neural Network Training" available!
05/2022: "Fairness-aware Graph Contrastive Learning" O. D. Kose and Y. Shen* accepted by IEEE Transactions on Signal and Information Processing over Networks.
04/2022: New work "Multiple Kernel Representation Learning on Networks" is accepted by Transactions on Knowledge and Data Engineering
03/2022: I am honored to receive the 2022 CORCL Research Award
02/2022: New work "Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network" accepted by IEEE Transactions on Neural Networks and Learning Systems!
02/2022 New work with Pouya available "Graph-Assisted Communication-Efficient Ensemble Federated Learning," P. M. Ghari, Y.Shen*
01/2022: Two papers with my students got accepted by ICASSP. Congratulations to Deniz and Pouya!
“Fairness-aware Selective Sampling on Attributed Graphs,” O. D. Kose, Y. Shen*
“Graph-Assisted Communication-Efficient Federated Learning,” P. M. Ghari, Y. Shen*
11/2022: New work with Deniz available "Fair Node Representation Learning via Adaptive Data Augmentation," Ö. D. Köse and Y. Shen*.
11/2021: I was invited to give a lightning talk on "Personalised Online Federated Model Selection with Graph Feedback" at Google Workshop on Federated Learning and Analytics.
11/2021: I started serving as an Editor for Signal Processing Journal, Elsevier.
11/2021: I organized a special session on "Machine Learning with Graphs " at Asilomar 2021.
10/2021: I was invited to give a talk on "Fairness-aware learning over Graphs" at the University of Rochester ECE department seminar!
10/2021: I was invited to give a talk on "Adaptive Online Scalable Learning with Graph Feedback "at the UCI CS seminar!
09/2021: New paper "Distributed and Quantized Online Multiple Kernel Learning" accepted!
09/2021: I am co-organizing the 2021 Women in IoT Workshop--AI on Edge!
06/2021: Our project is selected as one of the two winners of Microsoft Academic Grants for AI Research!
06/2021: New work with Pouya "Online Learning with Uncertain Feedback Graphs" submitted to IEEE Transactions on Neural Networks and Learning Systems!
06/2021: First work with Oyku on fairness-aware learning over graphs "Fairness-aware Node Representation Learning" is available!
06/2021: New work "Multiple Kernel Representation Learning on Networks" submitted to TKDE!
04/2021: Congratulations to Oyku Deniz Kose and Jiaxuan Xie for being awarded the Samueli Endowed Fellowship
02/2021: New work with Pouya "Graph-Aided Online Multi-Kernel Learning" submitted to Journal of Machine Learning Research!
01/2021: New paper "Online Multi-hop Information based Kernel Learning over Graphs" accepted!
10/2020: I am very excited to co-organize the One World Signal Processing Seminar Series --Machine Learning for signal processing.
09/2020: Welcome New members Oyku Deniz Kose and Jiaxuan Xie to join our group!
08/2020: New paper "Graph-aided Online Learning with Expert Advice" accepted.
06/2020: New paper "Online Multi-Kernel Learning with Graph-Structured Feedback" is accepted by Intl. Conf. on Machine Learning (ICML 2020). Congratulations to Pouya on his first paper!
06/2020: New paper "Differentially Private Nonlinear Canonical Correlation Analysis" is presented at the Sensor Array and Multichannel Signal Processing Workshop.
03/2020: New paper "Topology Identification of Directed Graphs via Joint Diagonalization of Correlation Matrices" accepted by IEEE Transactions on Signal and Information Processing over Networks
03/2020: New paper "Graph-based Learning under Perturbations via Total Least-Squares" accepted by IEEE Trans. on Signal Processing
02/2020: New paper "Graph-aided Online Learning with Expert Advice" submitted
02/2020: I am invited to give a talk about "Privacy-aware Online Nonlinear Function Learning over Graphs" at ITA, San Diego, CA
01/2020: I am teaching a new course EECS 298 Network Science in Winter 2019
10/2019: I am invited to give a talk at Physical Sciences Machine Learning Nexus
10/2019: New paper "Nonlinear Structural Vector Autoregressive Models for Inferring Effective Brain Network Connectivity" was accepted by IEEE Trans. on Signal Processing
07/2019: New paper "Topology Identification of Directed Graphs via Joint Diagonalization of Correlation Matrices" was submitted to IEEE Transactions on Signal and Information Processing over Networks
06/2019: I graduate from the University of Minnesota, Twin cities with Ph. D degree, and join the University of California, Irvine as an Assistant Professor!
04/2019: New paper "Scalable Learning with Privacy over Graphs'' was accepted by IEEE Data Science Workshop
02/2019: Our paper on online learning adaptive to unknown dynamics "Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics" appeared in the Journal of Machine Learning Research (JMLR). [pdf]
02/2019: New paper "Online Graph-Adaptive Learning with Scalability and Privacy" was accepted by IEEE Trans. on Signal Processing. [pdf]
02/2019: New paper "Semi-Blind Inference of Topologies and Dynamical Processes over Graphs" was accepted for publication at IEEE Trans. on Signal Processing. [pdf]
02/2019: I was invited to give an oral presentation at ITA 2019 Graduation Day
11/2018: Our tutorial proposal on "Learning Nonlinear and Dynamic Connectivity and Processes over Graphs" was accepted by ICASSP 2019
10/2018: I co-presented a tutorial talk on "Resilient and Scalable Interactive Learning" at MILCOM October 28-31, 2018, Los Angeles, USA
10/2018: Attended the Asilomar Conference on Signals, Systems, and Computers
08/2018: New paper on adaptive learning for IoT "Heterogeneous Online Learning for "Thing-Adaptive" Low-Latency Fog Computing in IoT" was accepted for publication at IEEE Internet-of-Things Journal. [pdf]
06/2018: New paper "Topology Identification and Learning over Graphs: Accounting for Nonlinearities and Dynamics" was accepted for publication by Proceedings of the IEEE. [pdf]
06/2018: New paper "Canonical Correlation Analysis of Datasets With a Common Source Graph" was accepted by IEEE Trans. on Signal Processing.
04/2018: Our paper on interactive learning in nonstationary environments was presented at AISTATS 2018.[pdf]
12/2017: Our paper was in the finalist for the best student paper award for CAMSAP 2017.
11/2017: I am selected as Rising Stars in EECS by Stanford University.
10/2017: Our paper was in the finalist for Best Student Paper award for Asilomar 2017.