Intelligent and Autonomous Systems Lab

Intelligent and Autonomous Systems Lab

Computer Science Department, Donald Bren School of Information and Computer Sciences, University of California, Irvine

Yoshitomo Matsubara

Ph.D. (Computer Science)

Research Interests:

  • Machine learning
  • Computer vision
  • Natural language processing
  • Information retrieval
  • Split computing

Email: yoshitom@uci.edu

Website: https://yoshitomo-matsubara.net/


  • 2022 – Ph.D. in Computer Science, University of California, Irvine, USA
  • 2016 – Master of Applied Informatics, University of Hyogo, Japan
  • 2014 – B.E. in Computer and Information Science, National Institute of Technology, Akashi College, Japan


  1. Y. Matsubara, N. Chiba, R. Igarashi, and Y. Ushiku, “Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery,” Journal of Data-centric Machine Learning Research (DMLR), 2024
    [PDF] [Video] [Preprint] [Code] [Dataset 1] [Dataset 2] [Dataset 3] [Dataset 4] [Dataset 5] [Dataset 6]
  2. F. Lalande, Y. Matsubara, N. Chiba, T. Taniai, R. Igarashi, Y. Ushiku, “A Transformer Model for Symbolic Regression towards Scientific Discovery,” NeurIPS 2023 AI for Science Workshop, 2023
    [PDF] [Code]
  3. Y. Matsubara, R. Yang, M. Levorato and S. Mandt, “SC2 Benchmark: Supervised Compression for Split Computing,” Transactions on Machine Learning Research, 2023
    [PDF] [Video] [Preprint] [Code]
  4. S. Gupta, Y. Matsubara, A. Chadha and A. Moschitti, “Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages,” Findings of the Association for Computational Linguistics: ACL 2023, 2023
    [Amazon Science] [Preprint] [Dataset 1] [Dataset 2]
  5. Y. Matsubara, L. Soldaini, E. Lind and A. Moschitti, “Ensemble Transformer for Efficient and Accurate Ranking Tasks: an Application to Question Answering Systems,” Findings of the Association for Computational Linguistics: EMNLP 2022, pp 7259–7272, 2022
    [PDF] [Amazon Science] [Preprint] [Code]
  6. Y. Matsubara, N. Chiba, R. Igarashi and Y. Ushiku, “SRSD: Rethinking Datasets of Symbolic Regression for Scientific Discovery,” NeurIPS 2022 AI for Science Workshop, 2022
    [PDF] [Code] [Dataset 1] [Dataset 2] [Dataset 3]
  7. Y. Matsubara, M. Levorato and F. Restuccia, “Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges,” ACM Computing Surveys (CSUR), Volume 55, Issue 5, Article No.: 90, pp 1–30, 2022
    [PDF] [Preprint]
  8. Y. Matsubara, D. Callegaro, S. Singh, M. Levorato and F. Restuccia, “BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing,” Proceedings of 2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 337-346, 2022
    [PDF] [Preprint] [Code]
  9. Y. Matsubara, R. Yang, M. Levorato and S. Mandt, “Supervised Compression for Resource-Constrained Edge Computing Systems,” Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 2685-2695, 2022
    [PDF + Supp] [Preprint] [Code]
  10. Y. Matsubara, “torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation,” Third Workshop on Reproducible Research in Pattern Recognition at ICPR ’20, pp. 24-44, 2021
    [PDF] [Preprint] [Code]
  11. T. Hossain*, R. L. Logan IV*, A. Ugarte*, Y. Matsubara*, S. Young and S. Singh, “COVIDLies: Detecting COVID-19 Misinformation on Social Media,” (Best Paper AwardProceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, 2020
    [PDF] [OpenReview] [Demo]
  12. D. Callegaro, Y. Matsubara and M. Levorato, “Optimal Task Allocation for Time-Varying Edge Computing Systems with Split DNNs,” GLOBECOM 2020 – 2020 IEEE Global Communications Conference, pp. 1-6, 2020
  13. Y. Matsubara, D. Callegaro, S. Baidya, M. Levorato and S. Singh, “Head Network Distillation: Splitting Distilled Deep Neural Networks for Resource-constrained Edge Computing Systems,” IEEE Access, Vol. 8, pp. 212177-212193, 2020
    [PDF] [Code]
  14. W. Schallock, D. Agress*, Y. Du*, D. Dua*, L. Hu*, Y. Matsubara* and S. Singh, “ZOTBOT: Using Reading Comprehension and Commonsense Reasoning in Conversational Agents,” 3rd Proceedings of Alexa Prize (Alexa Prize 2019), 2020
  15. Y. Matsubara and M. Levorato, “Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks,” 2020 25th International Conference on Pattern Recognition (ICPR), pp. 2272-2279, 2021
    [PDF] [Supp] [Preprint] [Code]
  16. Y. Matsubara and M. Levorato, “Split Computing for Complex Object Detectors: Challenges and Preliminary Results,” Proceedings of the 4th International Workshop on Embedded and Mobile Deep Learning (EMDL ’20), pp. 7-12, 2020
    [PDF] [Preprint] [Code]
  17. Y. Matsubara and S. Singh, “Citations Beyond Self Citations: Identifying Authors, Affiliations, and Nationalities in Scientific Papers,” Proceedings of the 8th Workshop on Mining Scientific Publications (WOSP ’20), pp. 9-20, 2020
    [PDF] [Code]
  18. Y. Matsubara, T. Vu and A. Moschitti, “Reranking for Efficient Transformer-based Answer Selection,” Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1577-1580, 2020
    [PDF] [Amazon Science]
  19. Y. Matsubara, S. Baidya, D. Callegaro, M. Levorato and S. Singh, “Distilled Split Deep Neural Networks for Edge-Assisted Real-Time Systems,” Proceedings of the 2019 Workshop on Hot Topics in Video Analytics and Intelligent Edges (HotEdgeVideo ’19), pp. 21-26, 2019
    [PDF] [Code]