Biography

I am a Research Scientist at Meta. Previously, I completed my PhD degree in EE at University of Southern California in 2024, supervised by Prof. Konstantinos Psounis. During my PhD study at USC, I also received two Master’s Degree of Science (EE and CS). My research interests focus on enhancing the privacy and security of ML systems, with applications in online advertising, recommendation systems, and large foundation models.

Education

• University of Southern California, 08/2019-05/2024
  • Degree: PhD
  • Major: Electrical Engineering
  • GPA: 3.9/4.0
• University of Southern California, 08/2019-12/2022
  • Degree: MS
  • Major: Computer Science
  • GPA: 3.9/4.0
• University of Southern California, 08/2019-05/2021
  • Degree: MS
  • Major: Electrical Engineering
  • GPA: 4.0/4.0
• Beijing Institute of Technology, 09/2015-06/2019
  • Degree: Bachelor
  • Major: Automation (Xu Teli Elite Class)
  • GPA: 92.31/100 (rank: 1/17)

Research Experience

• 08/19/2019-05/08/2024: Research Asistant
  • NetPD Lab, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California
  • Research interests: Machine learning and data privacy with applications to computer networks
  • Supervisor: Prof. Konstantinos Psounis
  • Publications: NDSS (1), PETS (2), AAAI (1) etc.
• 03/05/2019-06/30/2019: Research Internship
  • AST Lab, Department of Computer Science, ETH Zurich,
  • Research interests: LLVM IR and sanitizers
  • Supervisor: Prof. Zhendong Su
  • Publications: OSDI (1).
• 01/01/2017 - 03/04/2019: Research Assistant
  • National Key Laboratory of Complex System Intelligent Control and Decision, Beijing Institute of Technology
  • Research interests: Deep reinforcement learning, mobile crowdsensing and control theory
  • Supervisor: Prof. Yuanqing Xia, Prof. Yufeng Zhan
  • Publications: INFOCOM (1), TMC (1), TC(1), etc.

Work Experience

• 05/13/2024 - Present: Research Scientist
  • Meta
• 05/15/2023 - 07/28/2023: Applied Scientist Internship
  • AGI, Amazon
  • Research on toxic content detection using LLMs
• 05/16/2022 - 08/19/2022: Applied Scientist Internship
  • Alexa AI, Amazon
  • Research on zero-shot cross-lingual named entity recognition
• 05/17/2021 - 08/20/2021: Applied Scientist Internship
  • Alexa AI, Amazon
  • Research on detecting personal identifiable information
• 05/18/2020 - 08/21/2020: Applied Scientist Internship
  • Alexa AI, Amazon
  • Research on heuristics for effective quantization

See my LinkedIn for more details.

Conference Publications (* means equal contributions)

• Jiang Zhang, Rohan Sequeira, Konstantinos Psounis. SpinML: Customized Synthetic Data Generation for Private Training of Specialized ML Models [C]. PETS, 2025(2).
• Jiang Zhang, Qiong Wu, Yiming Xu, Cheng Cao, Zheng Du, Konstantinos Psounis. Efficient Toxic Content Detection by Bootstrapping and Distilling Large Language Models [C]. AAAI, 2024.
• Jiang Zhang, Hadi Askari, Konstantinos Psounis, Zubair Shafiq. No Video Left Behind: A Utility-Preserving Obfuscation Approach for YouTube Recommendations [C]. PETS, 2023(4).
• Ahmed Roushdy Elkordy*, Jiang Zhang*, Yahya H. Ezzeldin, Konstantinos Psounis, Salman Avestimehr. How Much Privacy Does Federated Learning with Secure Aggregation Guarantee? [C]. PETS, 2023(1).
• Jiang Zhang, Konstantinos Psounis, Muhammad Haroon, Zubair Shafiq. HARPO: Learning to Subvert Online Behavioral Advertising [C]. NDSS, 2022.
• Jiang Zhang, Shuai Wang, Manuel Rigger, Pingjia He, and Zhendong Su. SanRazor: Reducing Redundant Sanitizer Checks in C/C++ Programs [C]. OSDI, 2021.
• Yufeng Zhan, Jiang Zhang. An Incentive Mechanism Design for Efficient Edge Learning by Deep Reinforcement Learning Approach [C]. INFOCOM, 2020.

Journal Publications

• Evita Bakopoulou, Mengwei Yang, Jiang Zhang, Konstantinos Psounis, Athina Markopoulou. Location Leakage in Federated Signal Maps. IEEE Transactions on Mobile Computing, 2023.
• Jiang Zhang, Lillian Clark, Matthew Clark, Konstantinos Psounis, Peter Kairouz. Privacy-Utility Trades in Crowdsourced Signal Map Obfuscation [J]. Computer Networks, 2022.
• Yufeng Zhan, Song Guo, Peng Li, Jiang Zhang. A deep reinforcement learning based offloading game in edge computing [J]. IEEE Transactions on Computers, 2020.
• Jiang Zhang, Yuanqing Xia, Ganghui Shen. A Novel Learning-based Global Path Planning Algorithm for Planetary Rovers [J]. Neurocomputing, 2019.
• Yufeng Zhan, Chi Harold Liu, Yinuo Zhao, Jiang Zhang, Jian Tang. Free Market of Multi-Leader Multi-Follower Mobile Crowdsensing: An Incentive Mechanism Design by Deep Reinforcement Learning [J]. IEEE Transactions on Mobile Computing, 2019.
• Yufeng Zhan, Jiang Zhang, Peng Li, Yuanqing Xia. Crowdtraining to Mobile Edge Devices in Industry Internet of Things by Deep Reinforcement Learning Approach [J]. IEEE Network, 2019.

See my Google Scholar for more details.

Skills

• Programming languages: C, C++, Matlab, Python, LLVM.
• Development environments or tools: Ubuntu, Tensorflow, PyTorch, Spark, Ray, Docker, Selenium.

Awards and Honors

• USC Annenberg Fellowship, 2019
• Meritorious Winner of the MCM (top 9%, global), 2017
• First Scholarship of BIT (five semesters, top 10%), 2015-2018
• First Prize of the China Undergraduate Mathematics Competition (Non Mathematical Speciality, rank 20 in Beijing), 2016
• National Scholarship (top 5%), 2016