Dr. Wenqi Wei


Assistant Professor, ex-IBMer

Computer and Information Sciences Department

Fordham University

Email: wenqiwei@fordham.edu

Address: 610H, 113 West 60th street, New York City, NY 10023

New York is my campus, Fordham is my school.

News

Biography

I am currently a tenure-track assistant professor at Fordham University. I received my PhD in Computer Science from Georgia Institute of Technology in Spring 2022. I was fortunate to work with Professor Ling Liu in the Distributed Data Intensive Systems Lab (DiSL). After graduation, I spent some short but wonderful time at IBM Research - Almaden located at San Jose, California.

My research interest includes trustworthy AI systems, data privacy, responsible AI (fairness, accountability, transparency), data mining and analysis (for financial service, AIoT and misinformation), and machine learning service with particular focus on deep learning (centralized and distributed), graph learning (GNNs and representation learning), online learning (multi-armed bandits), and large language models (LLMs). You are welcome to visit my homepage for up-to-date research activities.

I lived in Atlanta, Georgia when I was a teenager, and attended Samuel M. Inman Middle School (now David T. Howard Middle School, home to MLK Jr.) and Henry W. Grady High School (now Midtown High School). I graduated from Inman with Awards for Achieving Highest Average in Science, Outstanding Achievement in ESOL, CRCT (Math & Science), and Honor Roll Certificate.

Research Projects

1. AI Robustness: Research on identifying and mitigating AI vulnerabilities including poisoning and backdoor at training phase and deceptions inputs (adversarial example and outlier) at inference phase. [XEnsemble project] [Security4AI vLab]

2. AI Privacy: Research on identifying privacy intrusion in AI systems and designing privacy-preserving machine learning solutions. [CPL attack] [AI-Privacy vLab]

3. AI Fairness: Research on eliminating algorithmic bias and improving accountability and transparency of AI systems.

4. Machine Learning System: Research on machine learning algorithm and system design with performance measurement (benchmarking) and model optimization (model compression and ensemble learning).

5. Data Mining and Machine Learning Service: Research on delivering AI/privacy/security solutions to data systems. Data mining with representation learning (graph embedding, graph neural networks and distributed data mining)/robust and privacy-preserving data analysis/financial service based on foundation model.

Experience

Jan. 2023 - now                          Tenure-Track Assistant Professor, Fordham University
May. 2022 - Jan. 2023                          Research Staff Member, IBM Research
Aug. 2017 - May. 2022                      RA in SCS, Georgia Tech
Fall 2019, 2020, 2021                       TA, CS6220 Big Data Systems, Georgia Tech
Spring 2019, 2022                             TA, CS6675 Advanced Internet Computing, Georgia Tech
Summer 2019, 2020, 2021             Research intern, IBM Research
Summer 2018                                   Research intern, Samsung Research America

Selected Publications

[Google Scholar]
- Wenqi Wei, and Ling Liu. "Gradient Leakage Attack Resilient Deep Learning", IEEE Transactions on Information Forensics and Security (TIFS), vol. 17, pp. 303-316, 2022.  [pdf]
- Mehmet Emre Gursoy, Ling Liu, Ka-Ho Chow, Stacey Truex, and Wenqi Wei, "An Adversarial Approach to Protocol Analysis and Selection in Local Differential Privacy", accepted by IEEE Transactions on Information Forensics and Security (TIFS), 2022.
- Wenqi Wei, and Ling Liu, "Robust Deep Learning Ensemble against Deception", IEEE Transactions on Dependable and Secure Computing (TDSC), 18(4), 1513-1527, 2021.  [pdf]
- Mehmet Emre Gursoy, Acar Tamersoy, Stacey Truex, Wenqi Wei, and Ling Liu, "Secure and Utility-Aware Data Collection with Condensed Local Differential Privacy", IEEE Transactions on Dependable and Secure Computing (TDSC), 18(5), 2365-2378, 2021.  [pdf]
- Jingya Zhou, Ling Liu, Wenqi Wei, and Jianxi Fan, "Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding", accepted by ACM Computing Surveys (CSUR). 2021.  [pdf]
- Wenqi Wei, Ling Liu, Yanzhao Wu, Gong Su, and Arun Iyengar. "Gradient-Leakage Resilient Federated Learning", IEEE International Conference on Distributed Computing Systems (ICDCS), Washington DC, USA. USA. July 2021. (virtual)  [pdf]
- Yanzhao Wu, Ling Liu, Zhongwei Xie, Ka-Ho Chow, and Wenqi Wei. "Boosting Ensemble Accuracy by Revisiting Ensemble Diversity Metrics", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, Tennessee. June 2021. (virtual)  [pdf]
- Wenqi Wei, Ling Liu, Margaret Loper, Ka-Ho Chow, Mehmet Emre Gursoy, Stacey Truex, and Yanzhao Wu. "A Framework for Evaluating Gradient Leakage Attacks in Federated Learning". European Symposium on Research in Computer Security (ESORICS), Guildford, UK. September 2020. (virtual)  [pdf]
- Mehmet Emre Gursoy, Ling Liu, Stacey Truex, Lei Yu, Wenqi Wei. "Utility-aware synthesis of differentially private and attack-resilient location traces", ACM Conference on Computer and Communications Security (CCS), Toronto, Canada. October 2018.  [pdf]

Full Publication list

*** with 28 papers (17 in conferences and 11 in journals) and 1 patent published as of today.
*** CCF B or higher are highlighted with bold.
[C17] Wenqi Wei, Mu Qiao, Eric Butler, and Divyesh Jadav. "Graph Representation Learning based Vulnerable Target Identification in Ransomware Attack.", IEEE International Conference on Big Data (Big Data), Osaka, Japan, December 2022.
[C16] Stacey Truex, Ling Liu, Emre Gursoy, Wenqi Wei, and Ka-Ho Chow. "The TSC-PFed Architecture for Privacy-Preserving FL", IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (IEEE TPS), virtual, USA. December 2021.  [pdf]
[C15] Yanzhao Wu, Ling Liu, Zhongwei Xie, Ka-Ho Chow, and Wenqi Wei. "Boosting Ensemble Accuracy by Revisiting Ensemble Diversity Metrics", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, Tennessee. June 2021. (virtual)  [pdf]
[C14] Wenqi Wei, Ling Liu, Yanzhao Wu, Gong Su, and Arun Iyengar. "Gradient-Leakage Resilient Federated Learning", IEEE International Conference on Distributed Computing Systems (ICDCS), Washington DC, USA. USA. July 2021. (virtual)  [pdf]
[C13] Wenqi Wei, Ling Liu, Margaret Loper, Ka-Ho Chow, Mehmet Emre Gursoy, Stacey Truex, and Yanzhao Wu. "Cross-layer Strategic Ensemble Defense against Adversarial Examples", International Conference on Computing, Networking and Communications (ICNC), Big Island, Hawaii, USA. February 2020.  [pdf]
[C12] Wenqi Wei, Ling Liu, Margaret Loper, Ka-Ho Chow, Mehmet Emre Gursoy, Stacey Truex, and Yanzhao Wu. "A Framework for Evaluating Gradient Leakage Attacks in Federated Learning", European Symposium on Research in Computer Security (ESORICS), Guildford, UK. September 2020. (virtual)  [pdf]
[C11] Ka-Ho Chow, Ling Liu, Mehmet Emre Gursoy, Stacey Truex, Wenqi Wei, and Yanzhao Wu. "Understanding Object Detection Through An Adversarial Lens", European Symposium on Research in Computer Security (ESORICS), Guildford, UK. September 2020. (virtual)  [pdf]
[C10] Stacey Truex, Ling Liu, Ka-Ho Chow, Mehmet Emre Gursoy, and Wenqi Wei. "LDP-Fed: federated learning with local differential privacy". ACM International Workshop on Edge Systems, Analytics and Networking (EdgeSys), Heraklion, Crete, Greece. April 2020 (Best paper). (virtual)  [pdf]
[C9] Wenqi Wei, Ling Liu, Margaret Loper, Ka-Ho Chow, Mehmet Emre Gursoy, Stacey Truex, and Yanzhao Wu, "Adversarial Deception in Deep Learning: Analysis and Mitigation", IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS), Atlanta, Georgia, USA. December 2020. (virtual)  [pdf][previous arxiv]
[C8] Ka-Ho Chow, Ling Liu, Margaret Loper, Mehmet Emre Gursoy, Stacey Truex, Wenqi Wei and Yanzhao Wu, "Adversarial Objectness Gradient Attacks on Real-time Object Detection Systems", IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS), Atlanta, Georgia, USA. December 2020. (virtual)  [pdf]
[C7] Yanzhao Wu, Ling Liu, Zhongwei Xie, Juhyun Bae, Ka-Ho Chow, and Wenqi Wei, "Promoting High Diversity Ensemble Learning with EnsembleBench", IEEE International Conference on Cognitive Machine Intelligence (CogMI), Atlanta, Georgia, USA. December 2020. (virtual)  [pdf]
[C6] Ka-Ho Chow, Wenqi Wei, Yanzhao Wu, and Ling Liu, “Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks”, IEEE International Conference on Big Data (Big Data), Los Angeles, California, USA. December 2019.  [pdf]
[C5] Yanzhao Wu, Ling Liu, Juhyun Bae, Ka-Ho Chow, Arun Iyengar, Calton Pu, Wenqi Wei, Lei Yu, and Qi Zhang, “Demystifying Learning Rate Polices for High Accuracy Training of Deep Neural Networks”, IEEE International Conference on Big Data (Big Data), Los Angeles, California, USA. December 2019.  [pdf]
[C4] Stacey Truex, Ling Liu, Mehmet Emre Gursoy, Wenqi Wei, and Lei Yu, “Effects of Differential Privacy and Data Skewness on Membership Inference Vulnerability”, IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS), Los Angeles, California, USA. December 2019.  [pdf]
[C3] Ling Liu, Wenqi Wei, Ka-Ho Chow, Margaret Loper, Mehmet Emre Gursoy, Stacey Truex, and Yanzhao Wu, "Deep Neural Network Ensembles against Deception: Ensemble Diversity, Accuracy and Robustness", IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS), Monterey, California, USA. November 2019.  [pdf]
[C2] Mehmet Emre Gursoy, Ling Liu, Stacey Truex, Lei Yu, Wenqi Wei. "Utility-aware synthesis of differentially private and attack-resilient location traces", ACM Conference on Computer and Communications Security (CCS), Toronto, Canada. October 2018.  [pdf]
[C1] Liu, Ling, Yanzhao Wu, Wenqi Wei, Wenqi Cao, Semih Sahin, and Qi Zhang. "Benchmarking Deep Learning Frameworks: Design Considerations, Metrics and Beyond", IEEE International Conference on Distributed Computing Systems (ICDCS), Vienna, Austria. July 2018.  [pdf]
[J11] Xigang Sun, Jingya Zhou, Ling Liu, and Wenqi Wei, "Explicit Time Embedding Based Cascade Attention Network for Information Popularity Prediction", accepted by Information Processing and Management (IP&M), 2023.
[J10] Mehmet Emre Gursoy, Ling Liu, Ka-Ho Chow, Stacey Truex, and Wenqi Wei, "An Adversarial Approach to Protocol Analysis and Selection in Local Differential Privacy", accepted by IEEE Transactions on Information Forensics and Security (TIFS), 2022.  [pdf]
[J9]Wenqi Wei, and Ling Liu. "Gradient Leakage Attack Resilient Deep Learning", IEEE Transactions on Information Forensics and Security (TIFS), vol. 17, pp. 303-316, 2022.  [pdf]
[J8] Wenqi Wei, and Ling Liu, "Robust Deep Learning Ensemble against Deception", IEEE Transactions on Dependable and Secure Computing (TDSC), 18(4), 1513-1527, 2021.  [pdf]
[J7] Wenqi Wei, Qi Zhang, and Ling Liu, "Bitcoin Transaction Forecasting with Deep Network Representation Learning", IEEE Transactions on Emerging Topics in Computing, 9(3), 1359-1371, 2021.  [pdf]
[J6] Mehmet Emre Gursoy, Acar Tamersoy, Stacey Truex, Wenqi Wei, and Ling Liu, "Secure and Utility-Aware Data Collection with Condensed Local Differential Privacy", IEEE Transactions on Dependable and Secure Computing (TDSC), 18(5), 2365-2378, 2021.  [pdf]
[J5] Jingya Zhou, Ling Liu, Wenqi Wei, and Jianxi Fan, "Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding", accepted by ACM Computing Surveys (CSUR). 2021.  [pdf]
[J4] Huanhuan Xu, Jingya Zhou, Wenqi Wei, and Baolei Cheng, "Multi-user Computation Offloading for Long-term Sequential Tasks in MEC Environments", accepted by Tsinghua Science and Technology. 2021.  [pdf]
[J3] Stacey Truex, Ling Liu, Mehmet Emre Gursoy, Lei Yu, and Wenqi Wei, "Demystifying Membership Inference Attacks in Machine Learning as a Service", accepted by IEEE Transactions on Services Computing (TSC).  [pdf]
[J2] Yanzhao Wu, Ling Liu, Calton Pu, Wenqi Cao, Semih Sahin, Wenqi Wei, and Qi Zhang, "A Comparative Measurement Study of Deep Learning as a Service Framework", accepted by IEEE Transactions on Services Computing (TSC).  [pdf]
[J1] Pan Zhou*, Wenqi Wei*, Kaigui Bian, Dapeng Oliver Wu, Yuchong Hu, Qian Wang. "Private and Truthful Aggregative Game for Large-Scale Spectrum Sharing", IEEE Journal on Selected Areas in Communications (JSAC), 35(2), 463-477, 2017. (* equal contribution)  [pdf]
[patent1] Mu Qiao, Wenqi Wei, Eric Butler, and Divyesh Jadav. "Machine learning based vulnerable target identification in ransomware attack." U.S. Patent Application 17/113,464, US20220179964A1, 2022.
[poster1] Wenqi Wei, Yanzhao Wu, Ling Liu. "DeepEyes: Integrating Deep Learning and Crowd Sourcing for Localization", Southern Data Science Conference, Atlanta, Georgia 2018 (research track poster).

Presentation and talks

IEEE Big Data, Osaka, Japan, Dec. 17-20, 2022.

Computer and Information Sciences Department, Fordham University, Bronx, NY, USA, Jan, 2022.

University Seminar Series, School of Business, Stevens Institute of Technology, Hoboken, NJ, USA, Jan, 2022.

IEEE ICDCS, Washington DC, USA, Jul. 7-10, 2021.

IEEE TPS, Atlanta, GA, USA, Dec. 1-3, 2020.

ESORICS, Guildford, UK, Sep. 14-18, 2020.

IEEE MASS, Monterey, CA, USA. Nov.4-7, 2019.

Cybersecurity Summit, Institute for Information Security & Privacy, Atlanta, GA, USA, Sep. 10, 2019

Cybersecurity Summit, Institute for Information Security & Privacy, Atlanta, GA, USA, Oct. 4, 2018

Southern Data Science Conference, Atlanta, GA, USA, Apr. 13-14, 2018

Teaching

Spring 2023                          CISC4080, Computer Algorithms

Spring 2023                          CISC5835, Algorithms for Data Science

Service

Conference Program and Organizing Committee:

   Program Committee: NeurIPS-ML4H (20,21,22), ICLR-DPML21, NeurIPS-AI4Science21, ICML-AI4Science22, TPS22, SDM22, NeurIPS22, KDD(21,22,23), ECCV22, CVPR(22,23), ICWSM23, AAAI23, TheWebConf23, IJCAI23

   Senior Program Committee: AAAI23-SRAI track

   Chairing: Publicity chair CIC/TPS/CogMI22

Reviewer and Editor:

   Journal Reviewer: ACM TOIT(6), IEEE TIFS(6), IEEE TNSE(5), IEEE CL(3), Elsevier CHB(3), IEEE TNNLS(2), IEEE ToN(2), Springer ML(2), Elsevier JISA(2), IEEE TSC(1), IEEE IoTJ(1), Springer SCIS(1),

   Journal Review Board: ACM TWEB

   Sub-Reviewer: ICDE18, ICDM (20,21), TheWebConf21, MM21, Middleware21, AAAI22, IEEE TMC

Funding and Awards

Excellent Serivce Award @ CIC/TPS/CogMI22

Best Paper Award @ ACM EdgeSys20