Shiqiang Wang

Shiqiang Wang

Staff Research Scientist

IBM T. J. Watson Research Center, NY, USA

I am a Staff Research Scientist at IBM T. J. Watson Research Center, NY, USA. Before joining IBM, I received my Ph.D. from Imperial College London, United Kingdom, in 2015.

My research focuses on the theory and practice at the intersection of distributed computing, machine learning, networking, and optimization, currently aiming at addressing two important and broadly defined questions: 1. How to obtain high-quality data and knowledge? 2. How to make model training and inferenece efficient in distributed systems? My research has a broad range of applications including distributed data analytics, efficient model training and inference, edge-based artificial intelligence (Edge AI), and large language models (LLMs).

I was an early contributor to edge computing and federated learning, where my work has generated both academic and industrial impact. I received the IEEE Communications Society (ComSoc) Leonard G. Abraham Prize in 2021, IEEE ComSoc Best Young Professional Award in Industry in 2021, IBM Outstanding Technical Achievement Awards (OTAA) in 2019, 2021, 2022, and 2023, multiple Invention Achievement Awards from IBM since 2016, Best Paper Finalist of the IEEE International Conference on Image Processing (ICIP) 2019, and Best Student Paper Award of the Network and Information Sciences International Technology Alliance (NIS-ITA) in 2015.

I serve as an associate editor of the IEEE Transactions on Mobile Computing and IEEE Transactions on Parallel and Distributed Systems. I have also been actively organizing workshops at the intersection of edge computing and machine learning, and regularly participate in technical program committees (TPCs) of prominent conferences and review panels of research grants. In addition, I frequently collaborate with students and faculty members at academic institutions and have led multi-organizational research projects.

Feel free to drop me an email if you share common interests.

News

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Research

Machine Learning at the Edge

  • Focus on enabling machine learning in edge computing systems. Among the first to study federated learning. Developed foundational theory, algorithms, and systems for efficient model training from local data at distributed edge devices/servers, including federated learning and other techniques (e.g., coreset).
  • Highlights: resource-aware control [JSAC’19, INFOCOM’23], arbitrary client participation [NeurIPS’22, ICLR’24], adaptive model pruning [TNNLS’23], step size adaptation [ICLR’23], vertical data partitioning [ICML’22, ICML’23], hierarchical updates [AAAI’22], robust coreset construction [JSAC’20]

Placement and Scheduling in Edge Computing

  • Among the first to study edge computing. Developed foundational theory and algorithms for online decision making in edge computing and related systems/applications, for problems including service placement and workload scheduling.
  • Highlights: dynamic service migration based on Markov decision process [ToN’19], online decision making with inaccurate predictions [TPDS’17, NeurIPS’20], placement and scheduling with heterogeneous resource types [ICDCS’18, INFOCOM’19], live migration implementation [Wireless’18]

Applications

  • Developed machine learning algorithms and systems that run across mobile devices, edge, and cloud, for various application domains including foundation models / large language models (LLMs), distributed MLOps, data summarization and exploration, out-of-distribution detection, classification of environmental sounds, etc. These developments have been an essential part in many practical use cases at IBM.


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Publications

Note: Student co-authors (co-)mentored by me and named before me are underlined

Selected Recent Publications

All Publications

Shortcut to:  Book Chapters  |  Journal Papers  |  Conference Papers  |  Patents  |  Thesis  |  Technical Reports

Book Chapters
  1. S. Wang, T. Tuor, K. K. Leung, "Optimized federated learning in wireless networks with constrained resources," in Machine Learning and Wireless Communications, Y. Eldar, A. Goldsmith, D. Gündüz and H. V. Poor (Eds.), Cambridge University Press, 2022.
  2. G. Joshi, S. Wang, "Communication-efficient distributed optimization algorithms," in Federated Learning, H. Ludwig and N. Baracaldo (Eds.), Springer Nature, 2022.
Journal Papers (including magazines)
  1. C. Yu, S. Shen, S. Wang, K. Zhang, H. Zhao, "Communication-efficient hybrid federated learning for E-health with horizontal and vertical data partitioning," IEEE Transactions on Neural Networks and Learning Systems, accepted for publication, Apr. 2024. [DOI]
  2. B. Luo, W. Xiao, S. Wang, J. Huang, L. Tassiulas, "Adaptive heterogeneous client sampling for federated learning over wireless networks," IEEE Transactions on Mobile Computing, accepted for publication, Feb. 2024. [DOI]
  3. T. Castiglia, S. Wang, S. Patterson, "Flexible vertical federated learning with heterogeneous parties," IEEE Transactions on Neural Networks and Learning Systems, accepted for publication, Aug. 2023. [DOI]
  4. X. Liu, S. Wang, Y. Deng, A. Nallanathan, "Adaptive federated pruning in hierarchical wireless networks," IEEE Transactions on Wireless Communications, vol. 23, no. 6, pp. 5985 – 5999, Jun. 2024. [DOI]
  5. Y. Jiang, S. Wang, V. Valls, B. J. Ko, W.-H. Lee, K. K. Leung, L. Tassiulas, "Model pruning enables efficient federated learning on edge devices," IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 12, pp. 10374 – 10386, Dec. 2023. [DOI] [Code]
  6. S. Shen, Y. Han, X. Wang, S. Wang, V. C. M. Leung, "Collaborative learning-based scheduling for Kubernetes-oriented edge-cloud network," IEEE/ACM Transactions on Networking, vol. 31, no. 6, pp. 2950 – 2964, Dec. 2023. [DOI]
  7. C.-C. Chiu, X. Zhang, T. He, S. Wang, A. Swami, "Laplacian matrix sampling for communication-efficient decentralized learning," IEEE Journal on Selected Areas in Communications, vol. 41, no. 4, pp. 887 – 901, Apr. 2023. [DOI]
  8. Z. Chen, K. K. Leung, S. Wang, L. Tassiulas, K. Chan, D. Towsley, "Use coupled LSTM networks to solve constrained optimization problems," IEEE Transactions on Cognitive Communications and Networking, vol.9, no. 2, pp. 304 – 316, Apr. 2023. [DOI]
  9. A. Das, T. Castiglia, S. Wang, S. Patterson, "Cross-silo federated learning for multi-tier networks with vertical and horizontal data partitioning," ACM Transactions on Intelligent Systems and Technology, vol. 13, no. 6, Dec. 2022. [DOI]
  10. H. Lu, T. He, S. Wang, C. Liu, M. Mahdavi, V. Narayanan, K. Chan, S. Pasteris, "Communication-efficient k-means for edge-based machine learning," IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 10, pp. 2509 – 2523, Oct. 2022. [DOI]
  11. A. Imteaj, U. Thakker, S. Wang, J. Li, M. H. Amini, "A survey on federated learning for resource-constrained IoT devices," IEEE Internet of Things Journal, vol. 9, no. 1, pp. 1 – 24, Jan. 2022. [DOI]
  12. B. Luo, X. Li, S. Wang, J. Huang, L. Tassiulas, "Cost-Effective federated learning in mobile edge networks," IEEE Journal on Selected Areas in Communications, vol. 39, no. 12, pp. 3606 – 3621, Dec. 2021. [DOI]
  13. V. Farhadi, F. Mehmeti, T. He, T. La Porta, H. Khamfroush, S. Wang, K. Chan, K. Poularakis, "Service placement and request scheduling for data-intensive applications in edge clouds," IEEE/ACM Transactions on Networking, vol. 29, no. 2, pp. 779 – 792, Apr. 2021. [DOI]
  14. S. Wang, "Efficient deep learning," Nature Computational Science, vol. 1, pp. 181 – 182, Mar. 2021, invited News & Views paper. [DOI]
  15. H. Lu, M.-J. Li, T. He, S. Wang, V. Narayanan, K. Chan, "Robust Coreset Construction for Distributed Machine Learning," IEEE Journal on Selected Areas in Communications, vol. 38, no. 10, pp. 2400 – 2417, Oct. 2020 (acceptance rate of this special issue of the journal: 20.8%). [DOI] [Code]
  16. Y. Lin, T. He, S. Wang, K. Chan, S. Pasteris, "Looking glass of NFV: inferring the structure and state of NFV network from external observations," IEEE/ACM Transactions on Networking, vol. 28, no. 4, pp. 1477 – 1490, Aug. 2020. [DOI] [Code]
  17. S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, K. Chan, "Adaptive federated learning in resource constrained edge computing systems," IEEE Journal on Selected Areas in Communications, vol. 37, no. 6, pp. 1205 – 1221, Jun. 2019 (acceptance rate of this special issue of the journal: 13%, received the IEEE Communications Society Leonard G. Abraham Prize in 2021). [DOI] [Code]
  18. S. Wang, R. Urgaonkar, M. Zafer, T. He, K. Chan, K. K. Leung, "Dynamic service migration in mobile edge computing based on Markov decision process," IEEE/ACM Transactions on Networking, vol. 27, no. 3, pp. 1272 – 1288, Jun. 2019. [DOI] [Code]
  19. T. Zhao, I.-H. Hou, S. Wang, K. Chan, "ReD/LeD: an asymptotically optimal and scalable online algorithm for service caching at the edge," IEEE Journal on Selected Areas in Communications, vol. 36, no. 8, pp. 1857 – 1870, Aug. 2018. [DOI] [Code]
  20. A. Machen, S. Wang, K. K. Leung, B. J. Ko, T. Salonidis, "Live service migration in mobile edge clouds," IEEE Wireless Communications, vol. 25, no. 1, pp. 140 – 147, Feb. 2018. [DOI]
  21. T. He, E. N. Ciftcioglu, S. Wang, K. Chan, "Location privacy in mobile edge clouds: a chaff-based approach," IEEE Journal on Selected Areas in Communications, vol. 35, no. 11, pp. 2625 – 2636, Nov. 2017. [DOI]
  22. S. Wang, R. Urgaonkar, T. He, K. Chan, M. Zafer, K. K. Leung, "Dynamic service placement for mobile micro-clouds with predicted future costs," IEEE Transactions on Parallel and Distributed Systems, vol. 28, no. 4, pp. 1002 – 1016, Apr. 2017. [DOI]
  23. S. Wang, M. Zafer, K. K. Leung, "Online placement of multi-component applications in edge computing environments," IEEE Access, vol. 5, pp. 2514 – 2533, Feb. 2017. [DOI]
  24. F. Wang, L. Guo, S. Wang, Q. Song, A. Jamalipour, "Approaching single-hop performance in multi-hop networks: end-to-end known-interference cancellation (E2E-KIC)," IEEE Transactions on Vehicular Technology, vol. 65, no. 9, pp. 7606 – 7620, Sept. 2016. [DOI]
  25. R. Urgaonkar, S. Wang, T. He, M. Zafer, K. Chan, K. K. Leung, "Dynamic service migration and workload scheduling in edge-clouds," Performance Evaluation, vol. 91, pp. 205 – 228, Sept. 2015 (accepted directly through IFIP Performance 2015). [DOI]
  26. Q. Song, L. Guo, F. Wang, S. Wang, A. Jamalipour, "MAC-centric cross-layer collaboration: a case study on physical-layer network coding," IEEE Wireless Communications, vol. 21, no. 6, pp. 160 – 166, Dec. 2014. [DOI]
  27. Y. Huang, S. Wang, Q. Song, L. Guo, A. Jamalipour, "Synchronous physical-layer network coding: a feasibility study," IEEE Transactions on Wireless Communications, vol. 12, no. 8, pp. 4048 – 4057, Aug. 2013. [DOI]
  28. S. Wang, Q. Song, X. Wang, A. Jamalipour, "Distributed MAC protocol supporting physical-layer network coding," IEEE Transactions on Mobile Computing, vol. 12, no. 5, pp. 1023 – 1036, May 2013. [DOI]
  29. F. Wang, S. Wang, Q. Song, L. Guo, "Adaptive relaying method selection for multi-rate wireless networks with network coding," IEEE Communications Letters, vol. 16, no. 12, pp. 2004 – 2007, Dec. 2012. [DOI]
  30. Q. Song, Z. Ning, S. Wang, A. Jamalipour, "Link stability estimation based on link connectivity changes in mobile ad-hoc networks," Journal of Network and Computer Applications, vol. 35, no. 6, pp. 2051 – 2058, Nov. 2012. [DOI]
  31. S. Wang, Q. Song, X. Wang, A. Jamalipour, "Rate and power adaptation for analog network coding," IEEE Transactions on Vehicular Technology, vol. 60, no. 5, pp. 2302 – 2313, June 2011. [DOI]
Conference Papers (peer-reviewed, including workshops)
  1. W. Fang, D.-J. Han, E. Chen, S. Wang, C. Brinton, "Hierarchical federated learning with multi-timescale gradient correction," in the 38th Conference on Neural Information Processing Systems (NeurIPS), Dec. 2024 (acceptance rate: 25.8%).
  2. C.-C. Chiu, T. Nguyen, T. He, S. Wang, B.-S. Kim, K.-I. Kim, "Active learning for WBAN-based health monitoring," in ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc), Oct. 2024 (acceptance rate: 24.5%).
  3. H. Woisetschläger, A. Erben, S. Wang, R. Mayer, H.-A. Jacobsen, "A survey on efficient federated learning methods for foundation model training," in International Joint Conference on Artificial Intelligence (IJCAI), Aug. 2024.
  4. J. Wang, S. Wang, R.-R Chen, M. Ji, "A new theoretical perspective on data heterogeneity in federated optimization," in International Conference on Machine Learning (ICML), Jul. 2024 (acceptance rate: 27.5%).
  5. Y. Wang, S. Wang, S. Lu, J. Chen, "FADAS: towards federated adaptive asynchronous optimization," in International Conference on Machine Learning (ICML), Jul. 2024 (acceptance rate: 27.5%). [Code]
  6. H. Woisetschläger, A. Erben, B. Marino, S. Wang, N. D. Lane, R. Mayer, H.-A. Jacobsen, "Federated learning priorities under the European Union Artificial Intelligence Act," in Workshop on Generative AI and Law (GenLaw) in Conjunction with ICML 2024, Jul. 2024.
  7. D. Jhunjhunwala, N. Jali, G. Joshi, S. Wang, "Erasure coded neural network inference via Fisher averaging," in International Symposium on Information Theory (ISIT), Jul. 2024.
  8. H. Woisetschläger, A. Erben, S. Wang, R. Mayer, H.-A. Jacobsen, "Federated fine-tuning of LLMs on the very edge: the good, the bad, the ugly," in Workshop on Data Management for End-to-End Machine Learning (DEEM) in Conjunction with SIGMOD 2024, Jun. 2024.
  9. S. Wang, M. Ji, "A lightweight method for tackling unknown participation statistics in federated averaging," in International Conference on Learning Representations (ICLR), May 2024 (spotlight, 5% of submitted papers). [Code]
  10. D. Jhunjhunwala, S. Wang, G. Joshi, "FedFisher: leveraging Fisher information for one-shot federated learning," in International Conference on Artificial Intelligence and Statistics (AISTATS), May 2024. [Code]
  11. E. Chen, S. Wang, C. Brinton, "Taming subnet-drift in D2D-enabled fog learning: a hierarchical gradient tracking approach," in IEEE International Conference on Computer Communications (INFOCOM), May 2024 (acceptance rate: 19.6%).
  12. P. Han, S. Wang, Y. Jiao, J. Huang, "Federated learning while providing model as a service: joint training and inference optimization," in IEEE International Conference on Computer Communications (INFOCOM), May 2024 (acceptance rate: 19.6%).
  13. G. Xiong, G. Yan, S. Wang, J. Li, "DePRL: achieving linear convergence speedup in personalized decentralized learning with shared representations," in AAAI Conference on Artificial Intelligence, Feb. 2024 (acceptance rate: 23.7%).
  14. J. Park, D.-J. Han, J. Kim, S. Wang, C. Brinton, J. Moon, "StableFDG: style and attention based learning for federated domain generalization," in the 37th Conference on Neural Information Processing Systems (NeurIPS), Dec. 2023 (acceptance rate: 26.1%).
  15. T. Castiglia, Y. Zhou, S. Wang, S. Kadhe, N. Baracaldo, S. Patterson, "LESS-VFL: communication-efficient feature selection for vertical federated learning," in International Conference on Machine Learning (ICML), Jul. 2023 (acceptance rate: 27.9%).
  16. B. Luo, Y. Feng, S. Wang, J. Huang, L. Tassiulas, "Incentive mechanism design for unbiased federated learning with randomized client participation," in IEEE International Conference on Distributed Computing Systems (ICDCS), Jul. 2023 (acceptance rate: 18.9%) [DOI].
  17. H. Wang, S. Wang, Q. Ji, "Semantic attribution for explainable uncertainty quantification," in Workshop on Epistemic Uncertainty in Artificial Intelligence, in Conjunction with UAI 2023 (E-pi UAI), Aug. 2023.
  18. H. Wang, D. Joshi, S. Wang, Q. Ji, "Gradient-based uncertainty attribution for explainable Bayesian deep learning," in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2023 (acceptance rate: 25.8%). [DOI]
  19. D. Jhunjhunwala, S. Wang, G. Joshi, "FedExP: speeding up federated averaging via extrapolation," in International Conference on Learning Representations (ICLR), May 2023 (spotlight – notable-top-25%, top 25% of accepted papers, approximately top 8% of submitted papers). [Code]
  20. S. Wang, J. Perazzone, M. Ji, K. Chan, "Federated learning with flexible control," in IEEE International Conference on Computer Communications (INFOCOM), May 2023 (acceptance rate: 19.2%). [DOI] [Code]
  21. T. Castiglia, S. Wang, S. Patterson, "Self-supervised vertical federated learning," in Workshop on Federated Learning: Recent Advances and New Challenges, in Conjunction with NeurIPS 2022 (FL-NeurIPS’22), Dec. 2022.
  22. S. Wang, M. Ji, "A unified analysis of federated learning with arbitrary client participation," in the 36th Conference on Neural Information Processing Systems (NeurIPS), Nov.–Dec. 2022 (acceptance rate: 25.6%). [Code]
  23. T. Castiglia, A. Das, S. Wang, S. Patterson, "Compressed-VFL: communication-efficient learning with vertically partitioned data," in International Conference on Machine Learning (ICML), Jul. 2022 (acceptance rate: 21.9%). [Code]
  24. C. Yu, S. Shen, S. Wang, K. Zhang, H. Zhao "Efficient multi-Layer stochastic gradient descent algorithm for federated learning in E-health," in IEEE International Conference on Communications (ICC), May 2022. [DOI]
  25. J. Perazzone, S. Wang, M. Ji, K. Chan, "Communication-efficient device scheduling for federated learning using stochastic optimization," in IEEE International Conference on Computer Communications (INFOCOM), May 2022 (acceptance rate: 19.9%). [DOI]
  26. B. Luo, W. Xiao, S. Wang, J. Huang, L. Tassiulas, "Tackling system and statistical heterogeneity for federated learning with adaptive client sampling," in IEEE International Conference on Computer Communications (INFOCOM), May 2022 (acceptance rate: 19.9%). [DOI]
  27. J. Wang, S. Wang, R.-R. Chen, M. Ji, "Demystifying why local aggregation helps: convergence analysis of hierarchical SGD," in AAAI Conference on Artificial Intelligence, Feb.-Mar. 2022 (acceptance rate: 15.0%). [DOI]
  28. A. Feng, C. You, S. Wang, L. Tassiulas, "KerGNNs: interpretable graph neural networks with graph kernels," in AAAI Conference on Artificial Intelligence, Feb.-Mar. 2022 (oral presentation, oral acceptance rate: 4.6%, overall acceptance rate: 15.0%). [DOI] [Code]
  29. Z. Chen, K. K. Leung, S. Wang, L. Tassiulas, K. Chan, "Robust solutions to constrained optimization problems by LSTM networks," in IEEE MILCOM 2021, Nov.-Dec. 2021. [DOI]
  30. P. Han, J. Park, S. Wang, Y. Liu, "Robustness and diversity seeking data-free knowledge distillation," in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Jun. 2021. [DOI] [Code]
  31. K. Yu, Q. Li, D. Chen, M. Rahman, S. Wang, "PrivacyGuard: enhancing smart home user privacy," in ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), May 2021 (acceptance rate: 24.8%). [DOI] [Code]
  32. B. Luo, X. Li, S. Wang, J. Huang, L. Tassiulas, "Cost-effective federated learning design," in IEEE International Conference on Computer Communications (INFOCOM), May 2021 (acceptance rate: 19.9%). [DOI]
  33. Y. Han, S. Shen, X. Wang, S. Wang, V. C. M. Leung, "Tailored learning-based scheduling for kubernetes-oriented edge-cloud system," in IEEE International Conference on Computer Communications (INFOCOM), May 2021 (acceptance rate: 19.9%). [DOI] [Journal Version] [Code]
  34. S. Pasteris, T. He, F. Vitale, S. Wang, M. Herbster, "Online learning of facility locations," in the 32nd International Conference on Algorithmic Learning Theory (ALT), Mar. 2021.
  35. T. Tuor, S. Wang, B. J. Ko, C. Liu, K. K. Leung, "Overcoming noisy and irrelevant data in federated learning," in the 25th International Conference on Pattern Recognition (ICPR), Jan. 2021. [DOI] [Code]
  36. Y. Jiang, S. Wang, V. Valls, B. J. Ko, W.-H. Lee, K. K. Leung, L. Tassiulas, "Model pruning enables efficient federated learning on edge devices," in Workshop on Scalability, Privacy, and Security in Federated Learning (SpicyFL) in Conjunction with NeurIPS 2020, long talk (acceptance rate: approximately 12.5%), Dec. 2020. [Journal Version] [Code]
  37. S. Wang, J. Li, S. Wang, "Online algorithms for multi-shop ski rental with machine learned advice," in the 34th Conference on Neural Information Processing Systems (NeurIPS), Dec. 2020 (acceptance rate: 20.1%). [Code]
  38. P. Han, S. Wang, K. K. Leung, "Adaptive gradient sparsification for efficient federated learning: an online learning approach," in IEEE International Conference on Distributed Computing Systems (ICDCS), Nov. 2020 (acceptance rate: 18.0%). [DOI] [Code]
  39. H. Lu, T. He, S. Wang, C. Liu, M. Mahdavi, V. Narayanan, K. Chan, S. Pasteris, "Communication-efficient k-means for edge-based machine learning," in IEEE International Conference on Distributed Computing Systems (ICDCS), Nov. 2020 (acceptance rate: 18.0%). [DOI]
  40. T. Inoue, P. Vinayavekhin, S. Morikuni, S. Wang, T. H. Trong, D. Wood, M. Tatsubori, R. Tachibana, "Detection of anomalous sounds for machine condition monitoring using classification confidence," in Detection and Classification of Acoustic Scenes and Events (DCASE) Workshop 2020, Nov. 2020.
  41. H. Lu, C. Liu, T. He, S. Wang, K. Chan, "Sharing models or coresets: a study based on membership inference attack," in International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2020 (FL-ICML'20), long talk, Jul. 2020.
  42. H. Lu, C. Liu, S. Wang, T. He, V. Narayanan, K. Chan, S. Pasteris, "Joint coreset construction and quantization for distributed machine learning," in IFIP Networking, Jun. 2020 (acceptance rate: 27.5%).
  43. Y. Lin, T. He, S. Wang, K. Chan, "Waypoint-based topology inference," in IEEE International Conference on Communications (ICC), Jun. 2020. [DOI]
  44. P. Han, S. Wang, K. K. Leung, "Capacity analysis of distributed computing systems with multiple resource types," in IEEE Wireless Communications and Networking Conference (WCNC), May 2020. [DOI]
  45. A. Feraudo, P. Yadav, V. Safronov, D. A. Popescu, R. Mortier, S. Wang, P. Bellavista, J. Crowcroft, "CoLearn: enabling federated learning in MUD compliant IoT edge networks," in International Workshop on Edge Systems, Analytics and Networking (EdgeSys), in conjunction with ACM EuroSys, Apr. 2020. [DOI]
  46. H. Lu, M.-J. Li, T. He, S. Wang, V. Narayanan, K. Chan, "Robust coreset construction for distributed machine learning," in IEEE Global Communications Conference (GLOBECOM), Dec. 2019. [DOI] [Journal Version]
  47. J.-W. Ahn, K. Grueneberg, B. J. Ko, W.-H. Lee, E. Morales, S. Wang, X. Wang, D. Wood, "Acoustic anomaly detection system: demo abstract," in ACM Conference on Embedded Networked Sensor Systems (SenSys), Nov. 2019. [DOI]
  48. T. Inoue, P. Vinayavekhin, S. Wang, D. Wood, A. Munawar, B. Ko, N. Greco, R. Tachibana, "Shuffling and Mixing Data Augmentation for Environmental Sound Classification," in Detection and Classification of Acoustic Scenes and Events (DCASE) Workshop, Oct. 2019. [DOI]
  49. A. Baughman, E. Morales, G. Reiss, N. Greco, S. Hammer, S. Wang, "Detection of tennis events from acoustic data," in ACM Workshop on Multimedia Content Analysis in Sports (MMSports), in conjunction with ACM International Conference on Multimedia (ACM Multimedia), Oct. 2019. [DOI]
  50. W.-H. Lee, B. J. Ko, S. Wang, C. Liu, K. K. Leung, "Exact incremental and decremental learning for LS-SVM," in the 26th IEEE International Conference on Image Processing (ICIP), Sept. 2019 (best paper finalist, top 20 out of 2,065 submitted papers). [DOI]
  51. C. Liu, X. He, T. Chanyaswad, S. Wang, P. Mittal, "Investigating statistical privacy frameworks from the perspective of hypothesis testing," in Privacy Enhancing Technologies Symposium (PETS), Jul. 2019 (acceptance rate: 22.3%). [DOI]
  52. T. Tuor, S. Wang, K. K. Leung, B. J. Ko, "Online collection and forecasting of resource utilization in large-scale distributed systems," in IEEE International Conference on Distributed Computing Systems (ICDCS), Jul. 2019 (acceptance rate: 19.6%). [DOI]
  53. B. J. Ko, S. Wang, T. He, D. Conway-Jones, "On data summarization for machine Learning in multi-organization federations," in Workshop on Distributed Analytics InfraStructure and Algorithms for Multi-Organization Federations (DAIS), Jun. 2019. [DOI]
  54. D. Conway-Jones, T. Tuor, S. Wang, K. K. Leung, "Demonstration of federated learning in a resource-constrained networked environment," in IEEE International Conference on Smart Computing (SMARTCOMP), Jun. 2019. [DOI]
  55. S. Vhaduri, T. Van Kessel, B. J. Ko, D. Wood, S. Wang, T. Brunschwiler, "Nocturnal cough and snore detection in noisy environments using smartphone-microphones," in IEEE International Conference on Healthcare Informatics (ICHI), Jun. 2019. [DOI]
  56. Y. Lin, T. He, S. Wang, K. Chan, S. Pasteris, "Multicast-based weight inference in general network topologies," in IEEE International Conference on Communications (ICC), May 2019. [DOI]
  57. S. Pasteris, S. Wang, M. Herbster, T. He, "Service placement with provable guarantees in heterogeneous edge computing systems," in IEEE International Conference on Computer Communications (INFOCOM), Apr. 2019 (acceptance rate: 19.7%). [DOI]
  58. Y. Lin, T. He, S. Wang, K. Chan, S. Pasteris, "Looking glass of NFV: inferring the structure and state of NFV network from external observations," in IEEE International Conference on Computer Communications (INFOCOM), Apr. 2019 (acceptance rate: 19.7%). [DOI] [Journal Version]
  59. V. Farhadi, F. Mehmeti, T. He, T. La Porta, H. Khamfroush, S. Wang, K. Chan, "Service placement and request scheduling for data-intensive applications in edge clouds," in IEEE International Conference on Computer Communications (INFOCOM), Apr. 2019 (acceptance rate: 19.7%). [DOI] [Journal Version]
  60. S. Pasteris, F. Vitale, K. Chan, S. Wang, M. Herbster, "MaxHedge: maximising a maximum online," in International Conference on Artificial Intelligence and Statistics (AISTATS), Apr. 2019.
  61. T. Tuor, S. Wang, K. K. Leung, K. Chan, "Distributed machine learning in coalition environments: overview of techniques," in the 21st International Conference on Information Fusion (FUSION), July 2018. [DOI]
  62. T. He, H. Khamfroush, S. Wang, T. La Porta, S. Stein, "It's hard to share: joint service placement and request scheduling in edge clouds with sharable and non-sharable resources," in IEEE International Conference on Distributed Computing Systems (ICDCS), July 2018 (acceptance rate: 20%). [DOI]
  63. S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, K. Chan, "When edge meets learning: adaptive control for resource-constrained distributed machine learning," in IEEE International Conference on Computer Communications (INFOCOM), Apr. 2018 (acceptance rate: 19.2%). [DOI] [Journal Version] [Code of Journal Version]
  64. T. Tuor, S. Wang, T. Salonidis, B. J. Ko, K. K. Leung, "Demo abstract: distributed machine learning at resource-limited edge nodes," in IEEE International Conference on Computer Communications (INFOCOM), Apr. 2018. [DOI]
  65. T. Tuor, S. Wang, K. K. Leung, B. J. Ko, "Understanding information leakage of distributed inference with deep neural networks: Overview of information theoretic approach and initial results," in Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX, Apr. 2018. [DOI]
  66. D. Wood, S. Wang, T. Salonidis, D. Conway-Jones, B. J. Ko, G. White, "Distributed analytics for audio sensing applications," in Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX, Apr. 2018. [DOI]
  67. S. Pasteris, S. Wang, C. Makaya, K. Chan, M. Herbster, "Data distribution and scheduling for distributed analytics tasks," in Workshop on Distributed Analytics InfraStructure and Algorithms for Multi-Organization Federations (DAIS), Aug. 2017. [DOI]
  68. T. He, E. N. Ciftcioglu, S. Wang, K. Chan, "Location privacy in mobile edge clouds," in Proc. of the 37th IEEE International Conference on Distributed Computing Systems (ICDCS), short paper, Jun. 2017. [DOI] [Journal Version]
  69. D. Verma, B. J. Ko, S. Wang, X. Wang, G. Bent, "Audio analysis as a control knob for social sensing," in Proc. of the 2nd International Workshop on Social Sensing (SocialSens'17), Apr. 2017. [DOI]
  70. S. Wang, J. Ortiz, "Non-negative matrix factorization of signals with overlapping events for event detection applications," in Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Mar. 2017. [DOI]
  71. B. J. Ko, J. Ortiz, T. Salonidis, M. Touma, D. Verma, S. Wang, X. Wang, D. Wood, "Demo abstract: acoustic signal processing for anomaly detection in machine room environments," in Proc. of ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys) 2016. [DOI]
  72. A. Machen, S. Wang, K. K. Leung, B. J. Ko, T. Salonidis, "Poster: migrating running applications across mobile edge clouds," in Proc. of ACM International Conference on Mobile Computing and Networking (MobiCom) 2016. [DOI] [Journal Version]
  73. I.-H. Hou, T. Zhao, S. Wang, K. Chan, "Asymptotically optimal algorithm for online reconfiguration of edge-clouds," in Proc. of ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc) 2016 (acceptance rate: 18.7%). [DOI] [Journal Version] [Code]
  74. S. Wang, K. Chan, R. Urgaonkar, T. He, K. K. Leung, "Emulation-based study of dynamic service placement in mobile micro-clouds," in Proc. of IEEE MILCOM 2015, Oct. 2015. [DOI]
  75. R. Urgaonkar, S. Wang, T. He, M. Zafer, K. Chan, K. K. Leung, "Dynamic service migration and workload scheduling in edge-clouds," in Proc. of IFIP International Symposium on Computer Performance, Modeling, Measurements and Evaluation (Performance) 2015, Oct. 2015 (acceptance rate: 28.40%). [DOI]
  76. Y. Yang, S. Wang, Q. Song, L. Guo, A. Jamalipour, "Double auction and negotiation for dynamic resource allocation with elastic demands," in Proc. of IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) 2015, Aug. – Sept. 2015. [DOI]
  77. S. Wang, R. Urgaonkar, K. Chan, T. He, M. Zafer, K. K. Leung, "Dynamic service placement for mobile micro-clouds with predicted future costs," in Proc. of IEEE International Conference on Communications (ICC) 2015, Jun. 2015. [DOI] [Journal Version]
  78. F. Wang, L. Guo, S. Wang, Y. Yu, Q. Song, A. Jamalipour, "Almost as good as single-hop full-duplex: bidirectional end-To-end known interference cancellation," in Proc. of IEEE International Conference on Communications (ICC) 2015, Jun. 2015. [DOI]
  79. S. Wang, R. Urgaonkar, M. Zafer, T. He, K. Chan, K. K. Leung, "Dynamic service migration in mobile edge-clouds," in Proc. of IFIP Networking 2015, May 2015 (acceptance rate: 23.27%). [DOI] [Journal Version] [Code of Journal Version]
  80. S. Wang, R. Urgaonkar, T. He, M. Zafer, K. Chan, K. K. Leung, "Mobility-induced service migration in mobile micro-clouds," in Proc. of IEEE MILCOM 2014, Oct. 2014. [DOI]
  81. L. Zhang, Y. Yu, F. Huang, Q. Song, L. Guo, S. Wang, "Deadline-aware adaptive packet scheduling and transmission in cooperative wireless networks," in Proc. of IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) 2014, Sept. 2014. [DOI]
  82. F. Wang, Q. Song, S. Wang, L. Guo, "Rate and power adaptation for physical-layer network coding with M-QAM modulation," in Proc. of IEEE International Conference on Communications (ICC) 2014, Jun. 2014. [DOI]
  83. S. Wang, L. Le, N. Zahariev, K. K. Leung, "Centralized rate control mechanism for cellular-based vehicular networks," in Proc. of IEEE Global Communications Conference (GLOBECOM) 2013, Dec. 2013. [DOI]
  84. F. Huang, S. Wang, Q. Song, L. Guo, A. Jamalipour, "Joint encoding and node-pair grouping for physical-layer network coding," in Proc. of IEEE Global Communications Conference (GLOBECOM) 2013, Dec. 2013. [DOI]
  85. F. Wang, Q. Song, S. Wang, L. Guo, A. Jamalipour, "MAC protocol supporting physical-layer network coding with overhearing," in Proc. of IEEE Global Communications Conference (GLOBECOM) 2013, Dec. 2013. [DOI]
  86. S. Wang, G.-H. Tu, R. Ganti, T. He, K. K. Leung, H. Tripp, K. Warr, Murtaza Zafer, "Mobile micro-cloud: application classification, mapping, and deployment," in Annual Fall Meeting of the ITA, Oct. 2013.
  87. S. Wang, Q. Song, L. Guo, A. Jamalipour, "Constellation mapping for physical-layer network coding with M-QAM modulation," in Proc. of IEEE Global Communications Conference (GLOBECOM) 2012, Dec. 2012. [DOI]
  88. Y. Huang, Q. Song, S. Wang, A. Jamalipour, "Phase-level synchronization for physical-layer network coding," in Proc. of IEEE Global Communications Conference (GLOBECOM) 2012, Dec. 2012. [DOI]
  89. Y. Huang, Q. Song, S. Wang, A. Jamalipour, "Symbol error rate analysis for M-QAM modulated physical-layer network coding with phase errors," in Proc. of IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) 2012, Sept. 2012. [DOI]
  90. S. Wang, Q. Song, J. Feng, X. Wang, "Predicting the link stability based on link connectivity changes in mobile ad hoc networks," in Proc. of IEEE International Conference on Wireless Communications, Networking and Information Security (WCNIS) 2010, vol. 2, pp. 409 – 414, 2010.
  91. S. Wang, M. Wang, H. Hong, Y. Ma, "Environmental monitoring system based on sensor networks using multi-channel MAC protocol," in Proc. of International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM) 2009, pp. 1 – 4, 2009.
  92. S. Wang, M. Wang, Y. Ma, H. Hong, "Series connected buck-boost type solar power converter based on microcontroller," in Proc. of IEEE International Conference on Mechatronics and Automation (ICMA) 2009, pp. 2642 – 2646, 2009.
  93. S. Wang, Y. He, Z. Liu, H. Wu, "Personalized web based English learning system using artificial neural networks," in Proc. of International Conference on Computer Science & Education (ICCSE) 2009, pp. 1263 – 1268, 2009.
Patents

Patent applications and granted patents that have been published in public are listed below. Recent applications that have not been published remain confidential and are not included in this list. Note: Some items list inventors in alphabetical order (by last name or first name).

  1. T. Castiglia, Y. Zhou, N. Baracaldo, S. Kadhe, S. Wang, S. Patterson. Feature selection in vertical federated learning, US20240242087A1, Jan. 2023, filed.
  2. A. Mukherjee, G. Kollias, T. Salonidis, S. Wang. Sketched and clustered federated learning with automatic tuning, US20240070531A1, Jan. 2023, filed.
  3. C. Liu, W.-H. Lee, S. Wang, S. Calo, D. Verma. Drone-assisted communications network, US20240107330A1, Sept. 2022, filed.
  4. S. Wang, T. Castiglia, N. Baracaldo, S. Patterson, R. Xu, Y. Zhou. Vertical federated learning with secure aggregation, US20230401439A1, Jun. 2022, filed.
  5. T. Castiglia, S. Wang, S. Patterson. Semi-supervised vertical federated learning, US20230342655A1, Apr. 2022, filed.
  6. D. Verma, S. Chakraborty, S. Wang, A. Vega, H. Yueksel, A. Verma, P. Bose, J. K. Radhakrishnan. Data subset selection for federated learning, US20230281518A1, Mar. 2022, filed.
  7. A. Das, T. Castiglia, S. Patterson, S. Wang. Vertical federated learning with compressed embeddings, US12033074B2, May 2021, granted.
  8. H. Yueksel, B. Kingsbury, K. Varshney, P. Bose, D. Verma, S. Wang, A. Vega, A. Verma, S. Chakraborty. Input-encoding with federated learning, US20220343218A1, Apr. 2021, filed.
  9. S. Wang, S. Chakraborty, N. Desai, D. Freimuth, W.-H. Lee, C. Liu. Federated ensemble learning from decentralized data with incremental and decremental updates, US20220121999A1, Oct. 2020, filed.
  10. W.-H. Lee, C. Liu, S. Wang, B. J. Ko, Y. Jiang. Incremental and decentralized model pruning in federated machine learning, US11842260B2, Sept. 2020, granted.
  11. S. Wang, G. Kollias, T. Salonidis. Federated machine learning using locality sensitive hashing, US11620583B2, Sept. 2020, granted.
  12. G. Kollias, T. Salonidis, S. Wang. Tensor comparison across a distributed machine learning environment, US11954611B2, Aug. 2020, granted.
  13. S. Wang, T. Tuor, C. Liu, F. Le. Adaptive asynchronous federated learning, US11574254B2, Apr. 2020, granted.
  14. D. Verma, R. Raghavendra, B. J. Ko, M. Srivatsa, N. Desai, R. Ganti, S. Wang, S. Chakraborty. Base station beam management based on terminal transmit data indication, US11296771B2, Mar. 2020, granted.
  15. D. Verma, R. Raghavendra, B. J. Ko, M. Srivatsa, N. Desai, R. Ganti, S. Wang, S. Chakraborty. Cost effective delivery of network connectivity to remote areas, US11228961B2, Mar. 2020, granted.
  16. T. Tuor, S. Wang, C. Liu, B. J. Ko, W.-H. Lee. Federated learning of clients, US11461593B2, Nov. 2019, granted.
  17. P. Novotny, S. Wang, Q. Zhang, V. Ramakrishna. Optimization of delivery of blocks, US11689616B2, May 2019, granted.
  18. C. Liu, S. Wang, W.-H. Lee, S. Calo. Leveraging correlation across agents for enhanced distributed machine learning, US11521091B2, May 2019, granted.
  19. M. Srivatsa, S. Wang, J. Rosenkranz, S. Chakraborty, B. J. Ko. Determining value of corpora for machine learning using coresets, US11526800B2, May 2019, granted.
  20. S. Wang, I. Manotas, B. J. Ko, K. Grueneberg. User adapted data presentation for data labeling, US11132623B2, Oct. 2018, granted.
  21. S. Wang, T. Salonidis. Collaborative distributed machine learning, US11521090B2, Aug. 2018, granted.
  22. S. Wang, T. Tuor, T. Salonidis, C. Makaya, B. J. Ko. Distributed machine learning at edge nodes, US11836576B2, Apr. 2018, granted.
  23. M. Beigi, S. Calo, D. Verma, S. Wang, D. Wood. Flexible and self-adaptive classification of received audio measurements in a network environment, US10121109B2, Apr. 2017, granted.
  24. L. Le, N. Zahariev, S. Wang. Adaptive rate control for cellular-based vehicular networks, US10044589B2, Jan. 2013, granted.
Thesis
  1. S. Wang, Dynamic service placement in mobile micro-clouds, Ph.D. Thesis, Imperial College London, 2015.
Technical Reports
  1. T. Inoue, P. Vinayavekhin, S. Morikuni, S. Wang, T. H. Trong, D. Wood, M. Tatsubori, R. Tachibana, "Detection of anomalous sounds for machine condition monitoring using classification confidence," in Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge 2020, Jun. 2020 (ranked 4th out of 40 teams in DCASE Challenge 2020, Task 2).
  2. A. Imteaj, U. Thakker, S. Wang, J. Li, M. H. Amini, "Federated learning for resource-constrained IoT devices: Panoramas and state-of-the-art," Feb. 2020.
  3. J. Park, S. Wang, A. Elgabli, S. Oh, E. Jeong, H. Cha, H. Kim, S.-L. Kim, M. Bennis, "Distilling on-device intelligence at the network edge," Aug. 2019.
  4. T. Inoue, P. Vinayavekhin, S. Wang, D. Wood, N. Greco, R. Tachibana, "Domestic activities classification based on CNN using shuffling and mixing data augmentation," in Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge 2018, Sept. 2018 (ranked first in DCASE Challenge 2018, Task 5).
  5. S. Wang, Q. Song, K. Wu, F. Wang, L. Guo, "End-to-End Known-Interference Cancellation (E2E-KIC) with Multi-Hop Interference," Mar. 2016.
  6. F. Wang, L. Guo, S. Wang, Q. Song, A. Jamalipour, "Approaching single-hop performance in multi-hop networks: end-to-end known-interference cancellation (E2E-KIC) – long version," Sept. 2015.
  7. S. Wang, R. Urgaonkar, M. Zafer, T. He, K. Chan, K. K. Leung, "Supplementary materials for dynamic service migration in mobile edge-clouds," Mar. 2015.

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Awards

(Selected)

  • IEEE Communications Society Leonard G. Abraham Prize in 2021, for the best paper published in the IEEE Journal on Selected Areas in Communications in the previous three years
  • IEEE Communications Society Best Young Professional Award in Industry in 2021
  • IBM Outstanding Technical Achievement Awards (OTAA) in 2019, 2021, 2022, and 2023
  • Multiple IBM Invention Achievement Awards in 2016 – 2023
  • IBM Culture Catalyst Award in 2023, for promoting growth-oriented team culture
  • Best Paper Finalist of the IEEE International Conference on Image Processing (ICIP) 2019
  • Best Student Paper Award of Network and Information Sciences International Technology Alliance (NIS ITA) in 2015
  • Top Reviewer (top 11%) of Conference on Neural Information Processing Systems (NeurIPS) 2022
  • Highlighted Reviewer (top 9%) of International Conference on Learning Representations (ICLR) 2022
  • Outstanding Reviewer (top 10%) of International Conference on Machine Learning (ICML) 2021 and 2022
  • Exemplary Reviewer of the IEEE Transactions on Communications in 2017 (top 2%) and 2022 (top 5%)

Services

Panelist/Reviewer for Grant Proposals

  • National Science Foundation (NSF) Panelist, reviewed grant proposals for the NSF and participated in panel discussion
  • Reviewer for Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant

Associate Editor

  • IEEE Transactions on Parallel and Distributed Systems (2022 – present)
  • IEEE Transactions on Mobile Computing (2021 – present)
  • IEEE Transactions on Computational Social Systems (2021 – present)
  • IEEE Access (2017 – 2020)

Program Chair

  • IEEE International Conference on Edge Computing and Communications (IEEE EDGE) 2024

Track/Area Chair

  • Area Chair of the Annual Conference on Neural Information Processing Systems (NeurIPS) in 2024
  • Area Chair of the AAAI Conference on Artificial Intelligence (AAAI) in 2025, 2024 and 2022
  • Track Chair of the IEEE International Conference on Distributed Computing Systems (ICDCS) in 2024 (federated learning, analytics, and deployment track) and 2023 (edge computing track)
  • Workshop Chair of the IEEE International Conference on Computer Communications (INFOCOM) 2025
  • Work-in-Progress and Demo Chair of the IEEE International Conference on Smart Computing (SMARTCOMP) in 2019

Technical Program Committee (TPC) Member/Reviewer

  • International Conference on Machine Learning (ICML) 2024, 2023, 2022, 2021
  • Annual Conference on Neural Information Processing Systems (NeurIPS) 2023, 2022, 2021
  • International Conference on Learning Representations (ICLR) 2025, 2024, 2023, 2022
  • Annual Conference on Machine Learning and Systems (MLSys) 2025
  • IEEE International Conference on Distributed Computing Systems (ICDCS) 2022, 2021, 2019
  • IFIP Networking 2022, 2021, 2020, 2019
  • IEEE Global Communications Conference (Globecom) 2024, 2023, 2022, 2019, 2018, 2017, 2016
  • IEEE International Conference on Communications (ICC) 2024, 2023, 2022, 2021, 2020, 2019
  • IEEE Wireless Communications and Networking Conference (WCNC) 2024, 2023, 2022, 2021
  • IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2023
  • International Conference on Artificial Intelligence and Statistics (AISTATS) 2021
  • International Joint Conference on Artificial Intelligence (IJCAI) 2020

Workshop Organization

  • Chair (Lead) of Good-Data'25 - Workshop on Preparing Good Data for Generative AI: Challenges and Approaches at AAAI 2025
  • Chair (Lead) of FL@FM-NeurIPS'23 – International Workshop on Federated Learning in the Age of Foundation Models at NeurIPS 2023
  • Co-Chair of the Workshop on Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities at ICML 2023
  • Chair (Lead) of FL-NeurIPS'22 – International Workshop on Federated Learning: Recent Advances and New Challenges at NeurIPS 2022
  • Steering Committee Member of AIChallengeIoT’22 – 4th International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things at ACM SenSys 2022
  • Steering Committee Member of AIChallengeIoT’21 – 3rd International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things at ACM SenSys 2021
  • Chair (Lead) of FL-ICML'21 – International Workshop on Federated Learning for User Privacy and Data Confidentiality at ICML 2021
  • Co-Chair of EMDL'21 - 5th International Workshop on Embedded and Mobile Deep Learning at ACM MobiSys 2021
  • Co-Chair of the 2nd Workshop on Edge Machine Learning for 5G Mobile Networks and Beyond at IEEE ICC 2021
  • Chair (Lead) of AIChallengeIoT’20 – 2nd International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things at ACM SenSys 2020
  • Chair (Lead) of FL-ICML'20 – International Workshop on Federated Learning for User Privacy and Data Confidentiality at ICML 2020
  • Founding Chair (Lead) of AIChallengeIoT’19 – 1st International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things at ACM SenSys 2019
  • Founding General Co-Chair of FL-IJCAI'19 – International Workshop on Federated Learning for User Privacy and Data Confidentiality at IJCAI 2019

Journal Reviewer for the IEEE Journal on Selected Areas in Communications, IEEE Transactions on Cloud Computing, IEEE Transactions on Communications, IEEE Transactions on Mobile Computing, IEEE Transactions on Network Science and Engineering, IEEE/ACM Transactions on Networking, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Signal Processing, IEEE Transactions on Vehicular Technology, IEEE Transactions on Wireless Communications, Journal of Parallel and Distributed Computing, Nature Computational Science, Proceedings of the IEEE, etc.

Invited Talks

(This list does not include paper presentations.)

  • Machine Learning from Imbalanced Data Sources. Seminar at the Department of Electrical and Computer Engineering, Seminar at Yale University, USA, Oct. 2024.
  • How Do System Dynamics Affect the Convergence of Federated Learning? Invited Talk at the 60th Annual Allerton Conference on Communication, Control, and Computing, Sept. 2024.
  • Machine Learning from Imbalanced Data Sources. Seminar at the School of Electrical and Computer Engineering, Purdue University, USA, Sept. 2024.
  • Towards Distributed MLOps: Theory and Practice. Seminar at the the Department of Software Technology, Delft University of Technology (TU Delft), the Netherlands, May 2024.
  • Towards Distributed MLOps: Theory and Practice. Seminar at the Chair for Decentralized Information Systems and Data Management, Technical University of Munich, Germany, May 2024.
  • Towards Distributed MLOps: Theory and Practice. Seminar at the Department of Computer Science, Stony Brook University, USA, Apr. 2024.
  • How to Make Federated Learning Work with Challenging Client Participation Patterns? Seminar at Federated Learning One World (FLOW), Feb. 2024.
  • Towards Distributed MLOps: Theory and Practice. Seminar at Brookhaven National Laboratory, USA, Feb. 2024.
  • Towards Distributed MLOps: Theory and Practice. Guest Lecture at the Department of Computer Science, Colorado School of Mines, USA, Nov. 2023.
  • Towards Distributed MLOps: Theory and Practice. Keynote Talk at the 22nd IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), Nov. 2023.
  • Towards Distributed MLOps: Theory and Practice. Invited Talk at the 5th Buffalo Day for 5G and Wireless Internet of Things, Oct. 2023.
  • Towards Distributed MLOps: Theory and Practice. Seminar at the Department of Electrical and Computer Engineering, University of Miami, USA, Jun. 2023.
  • The Role of Learning in Distributed MLOps. Invited Talk at the Federated Learning Systems (FLSys) Workshop in Conjunction with MLSys, Jun. 2023.
  • Towards Distributed MLOps: Theory and Practice. Colloquium at the Department of Computer Science, Rensselaer Polytechnic Institute, USA, Apr. 2023.
  • Federated Learning in “Day 2” Operations with Dynamic Resources. Guest Lecture at the Department of Computer Science and Technology, University of Cambridge, UK, Feb. 2023.
  • Understanding the Effect of Arbitrary Participation in Federated Learning. Seminar at the School of Electrical Engineering and Computer Science, Louisiana State University, USA, Nov. 2022.
  • Adaptive Control for Federated Learning at the Edge. Keynote Talk at the Caching, Computing and Delivery in Wireless Networks (CCDWN) Workshop in Conjunction with WiOpt, Sept. 2022.
  • Understanding the Effect of Arbitrary Participation in Federated Learning. YINS/EE Seminar at Yale University, USA, Jun. 2022.
  • Edge AI for Future Autonomous Systems. Invited Talk at the EPSRC Trustworthy Autonomous Systems-Security Node Workshop, Mar. 2022.
  • From Federated Learning to Edge AI. Panel Talk at the IEEE Consumer Communications & Networking Conference (CCNC) Panel on “Distributed and Federated Learning for Consumer and Industrial IoT”, Jan.~2022.
  • Efficient Federated Learning: Current Solutions and Open Challenges. Keynote Talk at IUCC\slash CIT\slash DSCI\slash SmartCNS, Dec. 2021.
  • Sustainable Edge AI. Panel Talk at the Applied AI Panel of the Center of Excellence in Wireless & Information Technology (CEWIT) Conference, Nov. 2021.
  • My Journey in the Past 10 Years. Talk at the Communications and Signal Processing (CSP) Alumni Day, Imperial College London, UK, Sept. 2021.
  • Towards Robust and Efficient Federated Learning. Invited Talk at the International Workshop on Federated and Transfer Learning for Data Sparsity and Confidentiality in Conjunction with IJCAI, Aug. 2021.
  • Edge AI: A Practical View. Panel Talk at the Workshop on Wireless Networking Innovations for Mobile Edge Learning in Conjunction with IEEE ICC, Jun. 2021.
  • How to Make Federated Learning Self-Adaptive? Colloquium at the Department of Computer and Information Science, University of Oregon, USA, Apr. 2021.
  • Sustainable Edge Intelligence. Panel Talk at the IEEE ISPA/BDCloud/SocialCom/SustainCom/IUCC Joint Plenary Panel on “Artificial Intelligence of Things (AIoT) and Smart Edge Computing”, Dec. 2020.
  • Federated Machine Learning in Resource-Constrained Edge Computing Systems. Invited Lecture at the School of Computing and Information Sciences, Florida International University, USA, Oct. 2019.
  • Federated Machine Learning in Resource-Constrained Edge Computing Systems. Seminar at the Institute of Computer Science, University of Goettingen, Germany, Sept. 2019.
  • Federated Machine Learning in Resource-Constrained Edge Computing Systems. Seminar at the Department of Computer Science, Vrije Universteit Amsterdam, the Netherlands, Sept. 2019.
  • Federated Machine Learning in Resource-Constrained Edge Computing Systems. YINS/EE Seminar at Yale University, USA, Oct. 2018.
  • Dynamic Service Placement in Mobile Micro-Clouds. Seminar at the Internet and Mobile Computing Laboratory, Department of Computing, Hong Kong Polytechnic University, Hong Kong, Jan. 2016.
  • Dynamic Service Placement in Mobile Micro-Clouds. Seminar at the Networks and Services Research Laboratory (NSRL), Department of Electronic & Electrical Engineering, University College London, UK, Nov. 2015.

Short Bio

Shiqiang Wang is a Staff Research Scientist at IBM T. J. Watson Research Center, NY, USA. He received his Ph.D. from Imperial College London, United Kingdom, in 2015. His research focuses on the intersection of distributed computing, machine learning, networking, and optimization, currently emphasizing on quality and efficiency aspects related to distributed data and models, which has a broad range of applications including distributed data analytics, efficient model training and inference, edge-based artificial intelligence (Edge AI), and large language models (LLMs). He has made foundational contributions to edge computing and federated learning that generated both academic and industrial impact. Dr. Wang serves as an associate editor of the IEEE Transactions on Mobile Computing and IEEE Transactions on Parallel and Distributed Systems. He has also been actively organizing workshops at the intersection of edge computing and machine learning, and regularly participates in technical program committees (TPCs) of prominent conferences and review panels of research grants. He received the IEEE Communications Society (ComSoc) Leonard G. Abraham Prize in 2021, IEEE ComSoc Best Young Professional Award in Industry in 2021, IBM Outstanding Technical Achievement Awards (OTAA) in 2019, 2021, 2022, and 2023, multiple Invention Achievement Awards from IBM since 2016, Best Paper Finalist of the IEEE International Conference on Image Processing (ICIP) 2019, and Best Student Paper Award of the Network and Information Sciences International Technology Alliance (NIS-ITA) in 2015. He is a senior member of the IEEE.