wzljerry / wzljerry.github.io Goto Github PK

Department of Computer Science, Purdue University,

625 W Michigan St, Indianapolis, IN 46202

Email: wang5327 AT purdue DOT edu, jerrywang AT haviai DOT com, jerrywzl08 AT gmail DOT com

Google Scholar|ResearchGate|CV|Blog|Twitter|LinkedIn

I am expected to graduate in December 2024.

I am Zhilin Wang, currently in my final year as a Ph.D. candidate in the Department of Computer Science at Purdue University West Lafayette, Indiana, USA. I'm also affiliated with CERIAS at Purdue. I obtained my bachelor's degree at Nanchang University in June 2020 and commenced my doctoral journey at Purdue in January 2021. I am co-advised by Prof. Qin Hu and Prof. Snehasis Mukhopadhyay. I've actively contributed as a reviewer for prestigious academic journals and conferences such as IEEE TPDS, IEEE IoTJ, Elsevier JNCA, IEEE TCCN, and IEEE ICC. Additionally, I was a member of the Technical Program Committee (TPC) for the IEEE ICC'22 Workshop. My research interests are briefly described below:

• Decentralized Machine Learning. Implementing blockchain to achieve decentralized federated learning focuses on addressing the challenges of resource allocation on local and edge devices and straggler mitigation in a hierarchical system.
• Robust Machine Learning. Revealing the vulnerabilities of existing security mechanisms in federated learning and proposing lightweight and general schemes to protect industrial federated learning systems.
• Efficient Mobile Edge Computing. Designing efficient resource-sharing schemes to facilitate computational resource allocation on mobile edge servers.
• Large-scale Distributed Optimization. Designing efficient and robust distributed algorithms to solve large-scale optimization problems.
• Security & Privacy in Large Language Models. Toward improving the security and privacy protection of large language models during training and application.

In general, my research focuses on the system design, network optimization, and security protection of AI systems. If you are interested in my research, please directly email me.

• [02/2024]: I published an AI-powered tool, Anton, to help users generate personalized resumes.
• [12/2023]: Our work, Resource Optimization for Blockchain-based Federated Learning in Mobile Edge Computing, was accepted by IEEE Internet of Things Journal!
• [12/2023]: I passed my Ph.D. preliminary exam!
• [10/2023]: I published a Python package, xiezhi-ai, which can be used to do one-dimensional data anomaly detection!
• [03/2023]: Our paper, Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning, was accepted to IEEE Transactions on Parallel and Distributed Systems!

• Ph.D., Purdue University, 01/2021-12/2024(expected)
• B.S., Nanchang University, 09/2016-06/2020

• Co-founder and AI Team Leader, Havi Lab, Beijing, China, 04/2024-present.

  As the co-founder of the Havi AI Team, I spearheaded the creation of this research group to innovate AI solutions for the recruitment industry. Our focus is on developing technologies that streamline the hiring process, improving how companies identify and attract the right talent.

• Co-founder and AI Team Leader, Leago AI, CA, USA, 10/2023-present.

  Leago AI is a startup specializing in the legal area. Our goal is to streamline the creation of legal documents, making the process of accessing legal services faster and more efficient for our users, ultimately saving them valuable time.

• Teaching Assistant, Purdue University, 01/2024-present.

• Research Assistant, Purdue University, 09/2021-12/2023.

• Student Tutor, Purdue University, 05/2022-09/2022.

1. Anton. AI-powered Resume Generation Tool.[link]

2. xiezhi-ai. The first one-dimensional anomaly detection tool. [Link]

3. HFL. A benchmark of hierarchical federated learning based on TensorFlow. [Code]

4. RL-based Knapsack Problem Solver. We provide a learning-based solution to multiple knapsack problems, which can get the approximate optimal solutions in polynomial time. [Code]

5. Blockchain-based FL. A user-friendly and robust blockchain-based federated learning framework in MEC will be applied to facilitate research and practical applications. [Code]

6. Correlated Equbirum Optimizer. An approximation method is provided. [Code]

1. [IEEE TPDS]：Wang Z, Hu Q, Li R, et al. Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning[J], 2023. [Link] [PDF]

2. [IEEE IoTJ]：Wang Z, Hu Q, Xiong Z. Resource Optimization for Blockchain-based Federated Learning in Mobile Edge Computing[J], 2023. [Link]. [PDF].

3. [Elsevier HCC]: Wang Z, Hu Q, Wang Y, et al. Transaction Pricing Mechanism Design and Assessment for Blockchain[J]. High-Confidence Computing, 2021: 100044. [Link] [Code] [PDF]

4. [IEEE IoTJ]: Hu Q, Wang Z, Xu M, et al. Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing[J]. IEEE Internet of Things Journal, 2021. [Link] [PDF] [NSF CRII]

5. [IEEE IoTJ]: Peng C, Hu Q, Wang Z, et al. "Online Learning based Fast-Convergent and Energy-Efficient Device Selection in Federated Edge Learning." IEEE Internet of Things Journal (2022). [Link] [PDF]

1. [IEEE MASS'22]: Wang Z, Qin Hu, et al. Blockchain-based Edge Resource Sharing for Metaverse. IEEE MASS 2022. [Link] [Code] [PDF]

2. [IEEE WCNC'22]: Wang Z, Qiao Kang, Xinyi Zhang, Qin Hu, Defense Strategies Toward Model Poisoning Attacks in Federated Learning: A Survey, IEEE WCNC 2022. [Link] [PDF]

3. [IEEE ICBC'20]: Hu Q, Nigam Y, Wang Z, et al. A Correlated Equilibrium based Transaction Pricing Mechanism in Blockchain[C]//2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC). IEEE, 2020: 1-7. [Link] [Code] [PDF]

The following papers are on arXiv or submitted to journals and conferences:

1. Wang Z, Hu Q, blockchain-based Federated Learning: A Comprehensive Survey. [Link] [PDF]. Will be submitted to IEEE Communications Surveys & Tutorials.
2. Wang Z, Hu Q et al. Straggler Mitigation and Latency Optimization in Blockchain-based Hierarchical Federated Learning. [Link]. [PDF]. Submitted to IEEE TMC.
3. Li S, Hu Q, and Wang Z*. PoFEL: Energy-efficient Consensus for Blockchain-based Hierarchical Federated Learning. Submitted to IEEE TMC.
4. Wang Z, Hu Q et al. Can We Trust the Similarity Measurement in Federated Learning? Submitted to IEEE TIFS, Major Revision.
5. Wang Z, Hu Q. A Robust federated learning algorithm. Will be submitted to S&P '25.

The students below are all from the CS department at Purdue. They performed excellently during the time they worked with me.

1. Garrett Sanders (Graduated in May 2022)

2. Minh Khuat (Graduated in May 2022)

3. Samuel Sibhatu

4. Richard Ekwenibe

5. Xinyi Zhang (Graduated in May 2023)

1. Arushi Pandit

2. Akshita Gupta

3. Joe Kang (Graduated in Dec 2022)

4. Deepak Chinthada

Spring 2024: CSCI 59000/49000 Wireless And Mobile Security

Fall 2024: CSCI 45900 Capstone Project

1. 2024/5/21, Falcon Talent, How GenAI can Influence the Recruitment Industry?