Yanzhao Wu

Assistant Professor

Dr. Yanzhao Wu is an Assistant Professor in the Knight Foundation School of Computing and Information Sciences at Florida International University. He obtained his bachelor’s degree from University of Science and Technology of China in 2017 and then received his Ph.D. degree in Computer Science from Georgia Institute of Technology in 2022. His research interests are primarily centered on the intersection of machine learning and computing systems, including machine learning algorithm and system optimizations, deep learning, edge AI, and big data analytics. His work has been published in top venues, including CVPR, ICSE, IEEE ICDCS, IEEE ICDM, IEEE TSC, and ACM TOIS, and won the IEEE CIC 2021 best paper award. He also serves as a reviewer for top conferences and journals, such as ICDE, WWW, CVPR, ECCV, AAAI, IEEE ICDCS, IEEE TKDE, and ACM TOIT.

Honors and Awards
  • IEEE CIC Best Paper Award, December 2021
  • ICDM 2021 Student Attendance Award, December 2021
  • College of Computing Student Travel Award, December 2020
  • Outstanding Graduate Award (USTC), April 2017
  • Fourth Place for 2016 ISC Student Cluster Competition, June 2016
Research and Educational Interests
  • Systems for Machine Learning
  • Machine Learning for Systems
  • Ensemble Learning
  • Big Data Systems & Analytics
  • Edge AI Systems
Background Education

2022 Ph.D., Computer Science, Georgia Institute of Technology
2017 B.E., Computer Science, University of Science and Technology of China

Professional Activities

Program Committee: AAAI 2023, ICDCS 2023

Conference Reviewer: ICDE 2018, UCC 2018, BDCAT 2018, ICDCS 2019, WWW 2021, DCIS 2022, CVPR 2022, ECCV 2022, CVPR 2023

Journal Reviewer: IEEE TKDE, ACM TOIT, JISA, Digital Communications and Networks, Computers & Security, Information Sciences, Knowledge-Based Systems

Professional Experience
  • Assistant Professor, the Knight Foundation School of Computing and Information Sciences, Florida International University (Dec 2022 – present)
  • Research Scientist, Meta Platforms (Facebook). (May 2022 – Dec 2022)
  • Intern, Facebook (Summer 2020, Summer 2021)
  • Intern, IBM Research (Summer 2018, Summer 2019)
Selected Publications
  1. Y. Wu and L. Liu, “Selecting and Composing Learning Rate Policies for Deep Neural Networks,” ACM Transactions on Intelligent Systems and Technology, Nov. 2022, doi: 10.1145/3570508.
  2. Z. Xie, L. Liu, Y. Wu, L. Zhong, and L. Li, “Learning Text-image Joint Embedding for Efficient Cross-modal Retrieval with Deep Feature Engineering,” ACM Transactions on Information Systems, vol. 40, no. 4, pp. 1–27, Oct. 2022, doi: 10.1145/3490519.
  3. Y. Wu, L. Liu, C. Pu, W. Cao, S. Sahin, W. Wei, and Q. Zhang, “A Comparative Measurement Study of Deep Learning as a Service Framework,” in IEEE Transactions on Services Computing, vol. 15, no. 1, pp. 551-566, 1 Jan.-Feb. 2022, doi: 10.1109/TSC.2019.2928551.
  4. Y. Wu and L. Liu, “Boosting Deep Ensemble Performance with Hierarchical Pruning,” 2021 IEEE International Conference on Data Mining (ICDM), Auckland, New Zealand, 2021, pp. 1433-1438, doi: 10.1109/ICDM51629.2021.00184.
  5. Y. Wu, L. Liu, Z. Xie, K. -H. Chow and W. Wei, “Boosting Ensemble Accuracy by Revisiting Ensemble Diversity Metrics,” 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 2021, pp. 16464-16472, doi: 10.1109/CVPR46437.2021.01620.
  6. W. Wei, L. Liu, M. Loper, K.-H. Chow, M. E. Gursoy, S. Truex, and Y. Wu, “A framework for evaluating client privacy leakages in federated learning,” in Computer Security – ESORICS 2020. Springer International Publishing, 2020, pp. 545–566.
  7. Y. Wu, L. Liu, J. Bae, K.-H. Chow, A. Iyengar, C. Pu, W. Wei, L. Yu, and Q. Zhang, “Demystifying Learning Rate Policies for High Accuracy Training of Deep Neural Networks,” 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019, pp. 1971-1980, doi: 10.1109/BigData47090.2019.9006104.
  8. P. Wang, J. Svajlenko, Y. Wu, Y. Xu and C. K. Roy, “CCAligner: A Token Based Large-Gap Clone Detector,” 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE), Gothenburg, Sweden, 2018, pp. 1066-1077, doi: 10.1145/3180155.3180179.
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