Dr. Wenqi Wei Tenure-track Faculty, ex-IBMer Computer and Information Sciences Department Fordham University Email: wenqiwei@fordham.edu Address: 610H, 113 West 60th street, New York City, NY 10023 "Talk is cheap, show me the code." [Curriculum_vitae]

News

• Research Group Recruitment

  We have PhD openings with guaranteed support for 5 years. Feel free to contact me regarding the PhD in Computer Science program (Deadline every year around mid January) and visiting students/scholars.

• Workshop recruitment I will serve as the workshop co-chair for IEEE CIC/TPS/CogMI 2024. Workshop proposals are welcome!
• Apr 2024 Our paper "Imperio: Language-Guided Backdoor Attacks for Arbitrary Model Control" has been accepted to IJCAI24.
• Apr 2024 I am invited as a reviewer for NeurIPS24, TPS24.
• Apr 2024 I am invited to give a talk at Fordham Data Science Symposium: Empowering Society for the Greater Good.
• Mar 2024 I am selected as a Fordham-IBM Fellow for Summer 2024.
• Mar 2024 I am invited as a reviewer for ACM TOIT.
• Feb 2024 I am invited as a reviewer for IEEE TIFS.
• Jan 2024 Our paper "Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance" has been accepted to ACM Computing Surveys.
• Jan 2024 I will serve as a NSF panelist for SaTC.
• Jan 2024 One paper on efficient training of language models for Ethereum Fraud Detection has been accepted to TheWebConf 2024.
• Jan 2024 I am invited as a reviewer for ACM TIST, ACM TOIT, SIGKDD24, ECCV24.
• Dec 2023 Our paper "Demystifying Data Poisoning Attacks in Distributed Learning as a Service" has been accepted to IEEE Trans. Services Computing.
• Dec 2023 I am invited as a reviewer for IEEE TKDE and a PC for ACM SACMAT2024, IJCAI2024.
• Dec 2023 I am part of the Fordham Cybersecurity program committee.
• Nov 2023 I am invited as a reviewer for IEEE Transactions on Big Data, ACM TIST.
• Nov 2023 I am the recipient of the Excellent Serivce Award for IEEE CIC/TPS/CogMI 2023. Thank you, organizing committee!
• Nov 2023 Call for Participation: IEEE CIC/TPS/CogMI 2023 will take place in Atlanta, GA from Nov 1st to Nov 3rd. We cordially invite you to join us. We will have M. Brian Blake, President of Georgia State University, for the Welcome and Opening Remarks. We have four keynotes (Erwin Gianchandani from NSF, Prof. Ling Liu from Georgia Tech, Prof. Alessandro Acquisti from CMU, and Prof. Jiawei Han from UIUC), three panels on AI Impact, GenAI and LLMs, and Cybersecurity, and so much more (with 20+ distinguished speakers!). Please check out the website for more details.
• Nov 2023 Call for Participation: We are organizing a IEEE CIC/TPS/CogMI 2023 student mentoring workshop on Nov 1st. We have invited 5 panelists: Prof. Murat Kantarcioglu from UT-Dallas, Prof. Jaideep Vaidya from Rutgers University, Prof. Rachel Cummings from Columbia University, Prof. Mei-Ling Shyu from University of Missouri-Kansas City, and Adrienne Raglin from Army Research Lab.
• Oct 2023 Our paper "GNN-Ensemble: Towards Random Decision Graph Neural Networks" has been accepted to IEEE International Conference on Big Data 2023.
• Oct 2023 Our paper "Hierarchical Pruning of Deep Ensembles with Focal Diversity" has been accepted to ACM TIST.
• Oct 2023 I am invited as a reviewer for CVPR24.
• Oct 2023 One paper has been accepted by WACV24.
• Oct 2023 I am appointed as a review committee member on behalf of faculty for High Performance Computing Cluster research initiative at Fordham University.
• Oct 2023 I will serve as the admission committee member for MSDS program.
• Oct 2023 I am invited as a session chair for Forham-IBM Workshop 2023.
• Sep 2023 One paper on analyzing the decoupling effect of poisoning attacks has been accepted to HICSS 2024. See you in Hawaii!
• Sep 2023 Two papers on privacy analysis and adversarial robustness have been accepted to IEEE ICDM23.
• Sep 2023 I am invited as a PC member for ICLR24, SDM24 and a reviewer for ACM TWEB, ICWSM24.
• Aug 2023 I am invited as a reviewer for ML4H23 and WACV24.
• Aug 2023 I am honored to serve as the tutorial co-chair for IEEE International Conference on Big Data 2023.
• Jul 2023 I am invited as a PC member for AAAI24 and a reviewer for ACM TIST.
• Jul 2023 My second US Patent 20230206029 has been filed!
• Jun 2023 I will serve as a NSF panelist for SafeLearning.
• Jun 2023 I am invited as a reviewer for HICSS24 and IEEE TIFS.
• May 2023 Our paper "Securing Distributed SGD against Gradient Leakage Threats" has been accepted to IEEE TPDS.
• May 2023 I am invited as a reviewer for ACM TWEB and a PC member for IEEE ISI23.
• Apr 2023 I am invited as a reviewer for IEEE TIFS, SCN and a PC member for NeurIPS23, IEEE TPS23.
• Apr 2023 I am invited to give a talk at Fordham Data Science Symposium: Doing Good with Data.
• Feb 2023 Our paper "STDLens: Securing Federated Learning Against Model Hijacking Attacks" has been accepted to CVPR23.

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 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. I received my Bachelor's degree in Electronics and Information Engineering (B.E.) with summa cum laude from Huazhong University of Science and Technology.

My research interest includes trustworthy AI systems, data privacy, responsible AI (fairness, accountability, transparency), data mining and analysis (for financial service, healthcare, 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 foundation models (large language models and latent diffusion). 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: 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: identifying privacy intrusion in AI systems and designing privacy-preserving solutions. [CPL attack] [AI-Privacy vLab]

3. AI Fairness: 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 intelligent 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 models.

Selected Publications

See [Google Scholar] for full list.

- Wenqi Wei and Ling Liu, "Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance", accepted by ACM Computing Surveys (CSUR). 2024.

- Ka-Ho Chow, Wenqi Wei, Lei Yu, "Imperio: Language-Guided Backdoor Attacks for Arbitrary Model Control", International Joint Conference on Artificial Intelligence (IJCAI), Jeju, August 2024.

- Wenqi Wei, Ka-Ho Chow, Yanzhao Wu, and Ling Liu. "Demystifying Data Poisoning Attacks in Distributed Learning as a Service", accepted by IEEE Transactions on Services Computing (TSC), 2024.  [pdf]

- Wenqi Wei, Ling Liu, Jingya Zhou, Ka-Ho Chow, and Yanzhao Wu. "Securing Distributed SGD against Gradient Leakage Threats", accepted by IEEE Transactions on Parallel and Distributed Systems (TPDS), 2023.  [pdf]

- 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]

- 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]

- Ka-Ho Chow, Ling Liu, Wenqi Wei, Fatih Ilhan, Yanzhao Wu. "STDLens: Securing Federated Learning Against Model Hijacking Attacks.", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, June 2023.

- 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]

- 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]

Seminars and talks

Fordham Data Science Symposium, Fordham University, NYC, NY, USA, Apr. 11, 2024.

Guest lecture, MIS 185N Technical Dimensions of Cybersecurity, McCombs School of Business, UT-Austin, April 3rd, 2024.

NSF SFS @ Fordham Center for CyberSecurity, NYC, NY, USA, Nov. 30, 2023.

Fordham Data Science Symposium, Fordham University, NYC, NY, USA, Apr. 11, 2023.

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

CISC6000 Deep Learning @ Fordham (instructor)                      Fall 24

CISC5325 Databases @ Fordham (instructor)                      Spring 24, Fall 24

CISC5835 Algorithms for Data Science @ Fordham (instructor)                    Spring 23, 24; Fall 23

CISC4080 Computer Algorithms @ Fordham (instructor)                      Spring 23; Fall 23, 24

CS6675 Advanced Internet Computing @ Georgia Tech (TA)                     Spring 19, 22

CS6220 Big Data Systems @ Georgia Tech (TA)                          Fall 19, 20, 21

Service

Conference Program and Organizing Committee:

   Program Committee: NeurIPS-ML4H (20,21,22), ML4H23, ICLR-DPML21, NeurIPS-AI4Science21, ICML-AI4Science22, TPS(22,23,24), SDM(22,24), NeurIPS(22,23,24), KDD(21,22,23,24), ECCV(22,24), CVPR(22,23,24), VTC23, ICWSM(23,24), AAAI(23,24), TheWebConf23, IJCAI(23,24), ICCV23, ISI23, HICSS24, WACV24, ICLR24, SACMAT24

   Senior Program Committee: AAAI23-SRAI track

   Chairing: Publicity chair @ CIC/TPS/CogMI(22,23), Mentoring Workshop Chair @ CIC/TPS/CogMI23, Session chair @ (AAAI23, CIC/TPS/CogMI23), Tutorial chair @ IEEE BigData23

Reviewer and Editor:

   Associate Editor: ACM TOIT 2024-

   Distinguished Review Board: ACM TWEB 2023-2025

   Journal Reviewer: ACM TOIT, IEEE TIFS, IEEE TNSE, IEEE CL, Elsevier CHB, IEEE TNNLS, IEEE ToN, Springer ML, Elsevier JISA, IEEE TSC, IEEE IoTJ, Springer SCIS, Elsevier IP&M, SCN, ACM TIST, IEEE TBD, IEEE TKDE,

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

   Internal Service: Master thesis committee (Reader*3), Admission Committee for MSDS, Program committee for the Fordham Cybersecurity program, Fordham HPC Research Initiative review committee (23-26), session chair @ Fordham-IBM Workshop23, FRG Reviewer24, panelist at the Fordham STEM FORUM,

   External Service: NSF Panelist, PhD thesis committee (FIU)

Funding and Awards

Excellent Serivce Award: CIC/TPS/CogMI(22,23)

Best Paper Award: ACM EdgeSys20

Internal funding: Fordham Research Grant (2023-2024), Fordham-IBM Research Fellow (2023, 2024)