I am an assistant professor in the Knight Foundation School of Computing and Information Sciences (KFSCIS) at Florida International University (FIU). I obtained the Bachelor’s degree from University of Science and Technology of China (USTC) in 2017 and then received my PhD in Computer Science from Georgia Institute of Technology in 2022. My research interests are primarily centered on the intersection of machine learning and computing systems, including machine learning algorithm and system optimizations, deep learning, large language models (LLMs), edge AI, big data analytics and their real-world applications.

Several fully-funded Computer Science PhD positions are available. Feel free to contact me regarding PhD applications, internships, and visiting students/scholars.

Publications

You can also find my articles on Google Scholar. Students under my supervision are marked with *.

• ZipZap: Efficient Training of Language Models for Ethereum Fraud Detection
  Sihao Hu, Tiansheng Huang, Ka-Ho Chow, Wenqi Wei, Yanzhao Wu, Ling Liu
  ACM Web Conference 2024 (TheWebConf 2024)
  [code][video]

• Adaptive Deep Neural Network Inference Optimization with EENet
  Fatih Ilhan, Ka-Ho Chow, Tiansheng Huang, Selim Tekin, Wenqi Wei, Yanzhao Wu, Myungjin Lee, Ramana Kompella, Hugo Latapie, Gaowen Liu, and Ling Liu
  2024 IEEE/CVF Winter Conference on Applications of Computer Vision 2024 (WACV 2024)
  [paper][arXiv][code]

• Hierarchical Pruning of Deep Ensembles with Focal Diversity
  Yanzhao Wu, Ka-Ho Chow, Wenqi Wei, and Ling Liu
  ACM Transactions on Intelligent Systems and Technology
  [paper][arXiv][code]

• Demystifying Data Poisoning Attacks in Distributed Learning as a Service
  Wenqi Wei, Ka-Ho Chow, Yanzhao Wu, Ling Liu
  IEEE Transactions on Services Computing
  [paper]

• Privacy Risks Analysis and Mitigation in Federated Learning for Medical Images
  Badhan Chandra Das*, M. Hadi Amini, and Yanzhao Wu
  2023 International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2023)
  [paper][arXiv][code]

• Exploring Model Learning Heterogeneity for Boosting Ensemble Robustness
  Yanzhao Wu, Ka-Ho Chow, Wenqi Wei, and Ling Liu
  23rd IEEE International Conference on Data Mining (IEEE ICDM 2023)
  [paper][arXiv][code]

• Model Cloaking against Gradient Leakage
  Wenqi Wei, Ka-Ho Chow, Fatih Ilhan, Yanzhao Wu, and Ling Liu
  23rd IEEE International Conference on Data Mining (IEEE ICDM 2023)
  [paper][code]

• Rethinking Learning Rate Tuning in the Era of Large Language Models
  Hongpeng Jin*, Wenqi Wei, Xuyu Wang, Wenbin Zhang, and Yanzhao Wu
  2023 IEEE International Conference on Cognitive Machine Intelligence (IEEE CogMI 2023)
  [paper][arXiv][code]

• Amplifying Object Tracking Performance on Edge Devices
  Sanjana Vijay Ganesh, Yanzhao Wu, Gaowen Liu, Ramana Kompella, and Ling Liu
  2023 IEEE International Conference on Cognitive Machine Intelligence (IEEE CogMI 2023)
  [paper][TechReport][code]

• Invisible Watermarking for Audio Generation Diffusion Models
  Xirong Cao, Xiang Li, Divyesh Jadav, Yanzhao Wu, Zhehui Chen, Chen Zeng, and Wenqi Wei
  2023 IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (IEEE TPS 2023)
  [paper][arXiv][code]

• Securing Distributed SGD against Gradient Leakage Threats
  Wenqi Wei, Ling Liu, Jingya Zhou, Ka-Ho Chow, and Yanzhao Wu
  2023 IEEE Transactions on Parallel and Distributed Systems
  [paper][arXiv][code]

• STDLens: Securing Federated Learning Against Model Hijacking Attacks
  Ka-Ho Chow, Ling Liu, Wenqi Wei, Fatih Ilhan, and Yanzhao Wu
  2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023)
  [paper][arXiv][code]

• Selecting and Composing Learning Rate Policies for Deep Neural Networks
  Yanzhao Wu, Ling Liu
  2022 ACM Transactions on Intelligent Systems and Technology
  [paper][arXiv][code]

• Towards Deep Learning System and Algorithm Co-design
  Yanzhao Wu
  2022 Georgia Institute of Technology
  [PhD Dissertation]

• Transparent Network Memory Storage for Efficient Container Execution in Big Data Clouds
  Juhyun Bae, Ling Liu, Ka-Ho Chow, Yanzhao Wu, Gong Su, and Arun Iyengar
  2021 IEEE International Conference on Big Data (IEEE BigData 2021)
  [paper][code]

• Parallel Detection for Efficient Video Analytics at the Edge
  Yanzhao Wu, Ling Liu, and Ramana Kompella
  2021 IEEE International Conference on Cognitive Machine Intelligence (IEEE CogMI 2021)
  [paper][arXiv][code]

• RDMAbox : Optimizing RDMA for Memory Intensive Workload
  Juhyun Bae, Ling Liu, Yanzhao Wu, Gong Su, and Arun Iyengar
  2021 International Conference on Collaboration and Internet Computing (IEEE CIC 2021)
  Best Paper Award
  [paper][arXiv][code]

• Boosting Deep Ensemble Performance with Hierarchical Pruning
  Yanzhao Wu and Ling Liu
  21st IEEE International Conference on Data Mining (ICDM 2021)
  [paper][code]

• Learning Text-Image Joint Embedding for Efficient Cross-Modal Retrieval with Deep Feature Engineering
  Zhongwei Xie, Ling Liu, Yanzhao Wu, Lin Li, and Luo Zhong
  2021 ACM Transactions on Information Systems (TOIS)
  [paper][arXiv][code]

• Boosting Ensemble Accuracy by Revisiting Ensemble Diversity Metrics
  Yanzhao Wu, Ling Liu, Zhongwei Xie, Ka-Ho Chow, and Wenqi Wei
  2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021)
  [paper][code][video]

• Gradient-Leakage Resilient Federated Learning
  Wenqi Wei, Ling Liu, Yanzhao Wu, Gong Su, and Arun Iyenger
  41st IEEE International Conference on Distributed Computing Systems (ICDCS 2021)
  [paper][arXiv][code]

• Learning TFIDF Enhanced Joint Embedding for Recipe-Image Cross-Modal Retrieval Service
  Zhongwei Xie, Ling Liu, Yanzhao Wu, Lin Li, and Luo Zhong
  2021 IEEE Transactions on Services Computing (TSC)
  [paper][arXiv][code]

• Promoting High Diversity Ensemble Learning with EnsembleBench
  Yanzhao Wu, Ling Liu, Zhongwei Xie, Juhyun Bae, Ka-Ho Chow, and Wenqi Wei
  2020 IEEE International Conference on Cognitive Machine Intelligence (IEEE CogMI 2020)
  [paper][arXiv][code][video]

• Cross-Modal Joint Embedding with Diverse Semantics
  Zhongwei Xie, Ling Liu, Yanzhao Wu, Lin Li, and Luo Zhong
  2020 IEEE International Conference on Cognitive Machine Intelligence (IEEE CogMI 2020)
  [paper][code]

• Memory Abstraction and Optimization for Distributed Executors
  Semih Sahin, Ling Liu, Wenqi Cao, Qi Zhang, Juhyun Bae, and Yanzhao Wu
  2020 International Conference on Collaboration and Internet Computing (IEEE CIC 2020)
  [paper]

• Adversarial Deception in Deep Learning: Analysis and Mitigation
  Wenqi Wei, Ling Liu, Margaret Loper, Ka-Ho Chow, Mehmet Emre Gursoy, Stacey Truex, and Yanzhao Wu
  2020 IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (IEEE TPS 2020)
  [paper][code]

• Adversarial Objectness Gradient Attacks in Real-time Object Detection Systems
  Ka-Ho Chow, Ling Liu, Margaret Loper, Juhyun Bae, Mehmet Emre Gursoy, Stacey Truex, Wenqi Wei, and Yanzhao Wu
  2020 IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (IEEE TPS 2020)
  [paper][code][video]

• Efficient Orchestration of Host and Remote Shared Memory for Memory Intensive Workloads
  Juhyun Bae, Gong Su, Arun Iyengar, Yanzhao Wu, and Ling Liu
  2020 International Symposium on Memory Systems (MEMSYS 2020)
  [paper][arXiv][code][video][slides]

• Understanding Object Detection Through an Adversarial Lens
  Ka-Ho Chow, Ling Liu, Mehmet Emre Gursoy, Stacey Truex, Wenqi Wei, and Yanzhao Wu
  2020 European Symposium on Research in Computer Security (ESORICS 2020)
  [paper][arXiv][code]

• A Framework for Evaluating Client Privacy Leakages in Federated Learning
  Wenqi Wei, Ling Liu, Margaret Loper, Ka-Ho Chow, Mehmet Emre Gursoy, Stacey Truex, and Yanzhao Wu
  2020 European Symposium on Research in Computer Security (ESORICS 2020)
  [paper][arXiv][code]

• Cross-Layer Strategic Ensemble Defense Against Adversarial Examples
  Wenqi Wei, Ling Liu, Margaret Loper, Ka-Ho Chow, Emre Gursoy, Stacey Truex, and Yanzhao Wu
  2020 International Conference on Computing, Networking and Communications (IEEE ICNC 2020)
  [paper][arXiv][code]

• Demystifying Learning Rate Policies for High Accuracy Training of Deep Neural Networks
  Yanzhao Wu, Ling Liu, Juhyun Bae, Ka-Ho Chow, Arun Iyengar, Calton Pu, Wenqi Wei, Lei Yu, and Qi Zhang
  2019 IEEE International Conference on Big Data (IEEE BigData 2019)
  [paper][arXiv][code]

• Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks
  Ka-Ho Chow, Wenqi Wei, Yanzhao Wu, and Ling Liu
  2019 IEEE International Conference on Big Data (IEEE BigData 2019)
  [paper][arXiv][code]

• Deep Neural Network Ensembles against Deception: Ensemble Diversity, Accuracy and Robustness
  Ling Liu, Wenqi Wei, Ka-Ho Chow, Margaret Loper, Emre Gursoy, Stacey Truex, and Yanzhao Wu
  16th IEEE International Conference on Mobile Adhoc and Sensor Systems (IEEE MASS 2019)
  [paper][arXiv][code]

• A Comparative Measurement Study of Deep Learning as a Service Framework
  Yanzhao Wu, Ling Liu, Calton Pu, Wenqi Cao, Semih Sahin, Wenqi Wei, and Qi Zhang
  2019 IEEE Transactions on Services Computing (TSC)
  [paper][arXiv][code]

• Memory Disaggregation: Research Problems and Opportunities
  Ling Liu, Wenqi Cao, Semih Sahin, Qi Zhang, Juhyun Bae, and Yanzhao Wu
  39th IEEE International Conference on Distributed Computing Systems (ICDCS 2019)
  [paper][code]

• Experimental Characterizations and Analysis of Deep Learning Frameworks
  Yanzhao Wu, Wenqi Cao, Semih Sahin, and Ling Liu
  2018 IEEE International Conference on Big Data (IEEE BigData 2018)
  [paper][code]

• Benchmarking Deep Learning Frameworks: Design Considerations, Metrics and Beyond
  Ling Liu, Yanzhao Wu, Wenqi Wei, Wenqi Cao, Semih Sahin, and Qi Zhang
  38th IEEE International Conference on Distributed Computing Systems (ICDCS 2018)
  [paper][code]

• CCAligner: a token based large-gap clone detector
  Pengcheng Wang, Jeffrey Svajlenko, Yanzhao Wu, Yun Xu, and Chanchal K. Roy
  40th International Conference on Software Engineering (ICSE 2018)
  [paper][code]

Research Experience

• Data-efficient Learning with DNN Ensembles
  Smart Decisions Team, Facebook, May 2021 – Aug 2021
  Mentor: Dr. Yin Huang
  Focus: Data Efficiency, Ensemble Learning
  Goal: Study the data efficiency of DNN ensemble models and design effective subsampling strategies to improve data efficiency for training ML models.

• High performance Object Detection on Edge Devices
  Distributed Data Intensive Systems Lab, Georgia Tech, Aug 2020 – May 2021
  Supervisor: Prof. Ling Liu
  Focus: Deep Learning, Edge AI
  Goal: Design and implement an efficient framework for supporting various object detection models and achieving high performance on multiple edge devices.

• Pipeline Parallelism for Deep Learning Recommendation Models
  AI System SW/HW Co-Design Team, Facebook Research, May 2020 – Aug 2020
  Mentor: Dheevatsa Mudigere
  Focus: Deep Learning, Pipeline Parallelism
  Goal: Apply pipeline parallelism into Facebook deep learning recommendation models to accelerate distributed recommendation model training.
  Achievement: PipeDLRM - an open-sourced software package built on top of DLRM and PyTorch.

• High Accuracy and Robust Ensemble of Deep Neural Networks
  Distributed Data Intensive Systems Lab, Georgia Tech, Aug 2019 – May 2020
  Supervisor: Prof. Ling Liu
  Focus: Deep Learning, Edge AI
  Goal: Design and implement an ensemble framework to improve deep neural network accuracy and optimize inference robustness on GPUs and edge devices.
  Achievement: EnsembleBench - a holistic framework for promoting high diversity ensemble learning.

• A Performance Study of Deep Learning with the High-performance Storage System
  Storage Systems Research Group, IBM Research, May 2019 - July 2019
  Mentors: Dr. Daniel Waddington, Dr. Luna Xu
  Focus: Storage Systems, Deep Learning Frameworks
  Achievement: Conducting a comprehensive performance analysis of the high-performance storage system with different storage backends, such as persistent memory and SSD, with popular deep learning workloads.

• Semi-automatic Hyperparameter Tuning for Training Deep Neural Networks
  Distributed Data Intensive Systems Lab, Georgia Tech, Aug 2018 – May 2019
  Supervisor: Prof. Ling Liu
  Focus: Deep Learning Training, Performance Optimization
  Goal: Accelerate deep learning training and improve the training efficiency via semi-automatic hyper-parameter tuning.
  Achievement: LRBench - a semi-automatic learning rate tuning tool to enhance the deep neural network training efficiency and accuracy.

• Accelerating Deep Learning with Direct-to-GPU Storage
  Storage Systems Research Group, IBM Research, May 2018 – Aug 2018
  Mentors: Amit Warke, Dr. Daniel Waddington
  Focus: Storage Systems, Deep Learning Frameworks
  Achievement: Integrating the Direct-to-GPU storage system into Caffe to obtain over 2$\times$ performance improvement by reducing the overhead of data transmission.

• Experimental Analysis and Optimization of Deep Learning Frameworks
  Distributed Data Intensive Systems Lab, Georgia Tech, Aug 2017 – May 2018
  Supervisor: Prof. Ling Liu
  Focus: Deep Learning Systems, Performance Analysis
  Goal: Analyze the hyper-parameters and basic components of Deep Learning and optimize Deep Learning Frameworks by tuning data-related and hardware-related parameters. Achievement: GTDLBench - a performance benchmark of deep learning frameworks to measure and optimize mainstream deep learning frameworks.

Teaching

• Florida International University
  • Spring 2023: CAP4630/CAP5602: Artificial Intelligence (Intro to AI)
  • Fall 2023: CAP5602: Introduction to Artificial Intelligence
  • Spring 2024: CAP4630: Artificial Intelligence

TA for:

• Georgia Institute of Technology
  • CS6220 Big Data Systems and Analytics (Fall 2021)
  • CS6675/CS4675 Advanced Internet Computing Systems and Application Development (Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022)
  • CS6235/CS4220 Embedded Systems and Real-Time Systems (Fall 2018)
• University of Science and Technology of China
  • CS1001A Computer Programming A (Fall 2015)

Guest lectures for:

• Georgia Institute of Technology
  • CS6220 Big Data Systems and Analytics
    • Fall 2019: GTDLBench & LRBench
    • Fall 2020: Emulab
    • Fall 2021: Google Colab
  • CS6675/CS4675 Advanced Internet Computing Systems and Application Development
    • Spring 2019: Emulab
    • Spring 2020: Emulab
    • Spring 2021: AWS + Google Colab
    • Spring 2022: Emulab + Google Colab

Students

• Hongpeng Jin (PhD Student, Fall 2023 - Present)
• Badhan Chandra Das (PhD Student, Co-advised with Prof. M. Hadi Amini, Spring 2023 - Present)

Talks

• IEEE CogMI 2023, Atlanta, GA, USA, Nov. 1-3, 2023
• IEEE CogMI 2021, Virtual Conference, Dec. 13-15, 2021
• IEEE ICDM 2021, Auckland, New Zealand, Dec. 7-10, 2021
• IEEE/CVF CVPR 2021, Nashville, TN, USA, June 19–25, 2021
• IEEE CogMI 2020, Atlanta, GA, USA, Dec. 1-3, 2020
• IEEE BigData 2019, Los Angeles, CA, USA, Dec. 9-12, 2019
• IEEE BigData 2018, Seattle, WA, USA, Dec. 10-13, 2018
• Southern Data Science Conference 2018, Atlanta, GA, USA, Apr. 13-14, 2018

Professional Activities

• Program Committee: AAAI 2023, ICDCS 2023, IJCAI 2023, IEEE ISI 2023, AAAI 2024, SDM 2024, CCGRID 2024
• Conference Reviewer: ICDE 2018, UCC 2018, BDCAT 2018, ICDCS 2019, WWW 2021, CVPR 2022, ECCV 2022, CVPR 2023, KDD 2023, ICCV 2023, WACV 2024, WWW 2024, CVPR 2024
• Journal Reviewer: IEEE TKDE, IEEE TPAMI, IEEE TIFS, IEEE TSC, ACM TOIT, Journal of Information Security and Applications, Digital Communications and Networks, Computers & Security, Information Sciences, Knowledge-Based Systems, The Journal of Supercomputing, Neural Networks, Frontiers of Computer Science, Image and Vision Computing, e-Prime, Expert Systems with Applications, Future Generation Computer Systems, Journal of Network and Computer Applications, Image and Vision Computing, Computer Vision and Image Understanding, SoftwareX, Computers and Electrical Engineering, Journal of Industrial Information Integration, Artificial Intelligence in Medicine, Neurocomputing
• NSF Panelist

Open-source Projects

• HeteRobust: Exploring model learning heterogeneity for boosting ensemble robustness.
• EVA: Fast edge video analytics through multi-model multi-device parallelism.
• HQ-Ensemble: Efficient ensemble pruning via focal ensemble diversity.
• DP-Ensemble: Leveraging FQ-diversity metrics to identify high diversity ensemble teams with high performance effectively.
• PipeDLRM: Using pipeline parallelism for training deep learning recommendation models.
• EnsembleBench: A set of tools for building high-quality ensembles for machine learning and deep learning models.
• LRBench: A semi-automatic learning rate tuning tool to improve the deep neural network training efficiency and accuracy.
• GTDLBench: A performance benchmark of deep learning frameworks to measure and optimize mainstream deep learning frameworks.
• Comanche: Accelerating deep learning with Direct-to-GPU storage with a modified Caffe and DeepBench.
• CCAligner: A token based code clone detector for detecting large-gap copy-and-paste source codes.
• PRISM: Building the LTS and Game model checkers for PRISM, a widely applied model checker for system analysis.

Awards & Scholarship

• FIU STEM Transformation Institute Faculty Fellow, 2023-2024
• IEEE CIC Best Paper Award, December 2021
• ICDM 2021 Student Attendance Award, December 2021
• College of Computing Student Travel Award, December 2020
• Qualified for Men’s Singles in 2020 NCTTA South Regional Championships
• Outstanding Graduate Award (USTC), April 2017
• Fourth Place for 2016 ISC Student Cluster Competition, June 2016
• Excellent Student Scholarship (Top 3%, USTC), 2015-2016
• Leadership Scholarship, 2014-2015
• The Third Prize for Electromagnetism Paper Competition, June 2014

Non-academic

• Sports: When I’m not working in the lab, you may find me running on the court or playing table tennis.
• Literature & music: I love reading novels and listening to music.

Contact

• Email: yawu@fiu.edu | yanzhaowumail@gmail.com