Ka-Ho Chow (周嘉豪)

Assistant Professor
Department of Computer Science
The University of Hong Kong (HKU)
Email: kachow@cs.hku.hk
Office: CB-403, Chow Yei Ching Building, Pok Fu Lam Rd, Hong Kong

News

Jul 2024 A preprint on genomic data privacy is released on bioRxiv
Jul 2024 I will chair the security and privacy session at IJCAI 2024
Jul 2024 One paper on personalized privacy protection against unauthorized face recognition accepted by ECCV 2024
Jul 2024 A preprint on robust GNN against backdoor attacks is released on arXiv
Jun 2024 Honored to receive funding from the RGC Early Career Scheme to investigate a new backdoor threat in federated learning
May 2024 Honored to receive funding from the Croucher Foundation to make distributed machine learning accountable
May 2024 One paper on privacy protection against unauthorized face recognition accepted by PETS 2024
May 2024 One demo paper on data poisoning in federated learning accepted by ICDCS 2024
Apr 2024 One paper on LLM for backdoor attacks accepted by IJCAI 2024
Apr 2024 One paper on efficient privacy attacks in federated learning accepted by CVPRW 2024
Apr 2024 A preprint on robust few-shot learning is released on arXiv
Feb 2024 A survey on privacy threats in vertical federated learning is released on arXiv
Jan 2024 One paper on LLM for blockchain security accepted by WWW 2024

[More]

Dec 2023 One paper on data poisoning attacks accepted by IEEE TSC
Oct 2023 One paper on efficient neural network inference accepted by WACV 2024
Oct 2023 One paper on robust ensemble learning accepted by ACM TIST
Sep 2023 One paper on backdoor-resilient federated learning accepted by NeurIPS 2023
Sep 2023 One paper on scalable and secure microservices accepted by EuroSys 2024
Sep 2023 Two papers on defenses against gradient leakage attacks and adversarial attacks accepted by ICDM 2023
May 2023 One paper on gradient leakage-resilient federated learning accepted by IEEE TPDS
Mar 2023 One demo paper on efficient neural network inference accepted by WWW 2023
Feb 2023 One paper on hijacking-resilient federated learning accepted by CVPR 2023
Feb 2023 One demo paper on scaling microservices with hybrid clouds accepted by SIGMOD 2023
Aug 2022 One paper on error diversity-driven ensembles for robust object detection accepted by ICDM 2022
May 2022 Awarded the IBM PhD Fellowship 2022
Apr 2022 One paper on local differential privacy accepted by IEEE TIFS
Feb 2022 Received the travel award to attend EuroSys 2022
Jan 2022 One paper on scalable and secure microservices accepted by EuroSys 2022
Oct 2021 Two papers on robust and privacy-preserving federated learning accepted by TPS-ISA 2021
Oct 2021 One paper on network memory storage accepted by BigData 2021
Sep 2021 Released Security4AI vLab and AI-Privacy vLab to demonstrate security and privacy vulnerabilities in AI
Jun 2021 One paper on ubiquitous localization accepted by IEEE TMC
May 2021 One paper on robust object detection accepted by SIGKDD 2021
Apr 2021 Received the Croucher Scholarship 2021
Mar 2021 One paper on ensemble learning accepted by CVPR 2021
Feb 2021 One demo paper on AI-assisted disaster recovery from ransomware attacks accepted by SIGMOD 2021
Oct 2020 Two papers on evasion attacks accepted by TPS-ISA 2020
Oct 2020 One paper on ensemble learning accepted by CogMI 2020
Jun 2020 Two papers on evasion attacks and gradient leakage attacks accepted by ESORICS 2020
Apr 2020 Won the best paper award at EdgeSys 2020
Mar 2020 One paper on local differential privacy for federated learning accepted by EdgeSys 2020
Feb 2020 One paper on robust machine learning accepted by ICNC 2020
Nov 2019 Received the travel award to attend BigData 2019
Oct 2019 One paper on graph mining accepted by CogMI 2019
Oct 2019 Two papers on robust machine learning and learning rate policies accepted by BigData 2019
Feb 2019 One paper on anomaly detection accepted by IEEE TMC

Ka-Ho Chow (周嘉豪)

The University of Hong Kong

I am an Assistant Professor in the Department of Computer Science at the University of Hong Kong (HKU). I was named an IBM PhD Fellow in 2022 and a Croucher Scholar in 2021. Before joining HKU, I was a research scientist at IBM Research and received my Ph.D. in Computer Science from the Georgia Institute of Technology (Georgia Tech), advised by Prof. Ling Liu.

My research interests are at the intersection of machine learning, cybersecurity, and systems. The overarching goal is to amplify the real-world impact of artificial intelligence by building trustworthy technologies through an adversarial lens. To this end, my recent work focuses on (i) understanding new security and privacy threats to AI systems and (ii) developing attack-resilient solutions through algorithmic and infrastructure optimization. These efforts span various learning approaches, including centralized and federated learning, and cover a range of applications across, e.g., large language models and visual recognition.

Recruitment: I have several openings for Ph.D. students, postdoctoral researchers, research assistants, and interns. If you are interested in machine learning, cybersecurity, and systems, please reach out to me via email: kachow@cs.hku.hk

Research Interests

Trustworthy AI Systems; Cybersecurity; ML for Systems & Systems for ML

Selected Publications

[Google Scholar]

ECCV 2024 Ka-Ho Chow, Sihao Hu, Tiansheng Huang, and Ling Liu, "Personalized Privacy Protection Mask Against Unauthorized Facial Recognition," European Conference on Computer Vision (ECCV), Milan, Italy, Sep. 29-Oct. 4, 2024. [PDF]
IJCAI 2024 Ka-Ho Chow, Wenqi Wei, and Lei Yu, "Imperio: Language-Guided Backdoor Attacks for Arbitrary Model Control," International Joint Conference on Artificial Intelligence (IJCAI), Jeju, South Korea, Aug. 3-9, 2024. [PDF] [CODE]
CVPRW 2024 Nawrin Tabassum, Ka-Ho Chow, Xuyu Wang, Wenbin Zhang, and Yanzhao Wu, "On the Efficiency of Privacy Attacks in Federated Learning," IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops - FedVision (CVPRW), Seattle, WA, USA, Jun. 17-21, 2024. [PDF] [CODE]
WWW 2024 Sihao Hu, Tiansheng Huang, Ka-Ho Chow, Wenqi Wei, Yanzhao Wu, and Ling Liu, "ZipZap: Efficient Training of Language Models for Ethereum Fraud Detection," The Web Conference (WWW), Singapore, May 13-17, 2024. [PDF] [CODE]
EuroSys 2024 Ka-Ho Chow, Umesh Deshpande, Veera Deenadhayalan, Sangeetha Seshadri, and Ling Liu, "Atlas: Hybrid Cloud Migration Advisor for Interactive Microservices," ACM European Conference on Computer Systems (EuroSys), Athens, Greece, Apr. 22-25, 2024. [PDF] [CODE]
WACV 2024 Fatih Ilhan, Ka-Ho Chow, Sihao Hu, Tiansheng Huang, Selim Furkan Tekin, Wenqi Wei, Yanzhao Wu, Myungjin Lee, Ramana Kompella, Hugo Latapie, Gaowen Liu, and Ling Liu, "Adaptive Deep Neural Network Inference Optimization with EENet," IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, Jan 4-8, 2024. [PDF] [CODE]
NeurIPS 2023 Tiansheng Huang, Sihao Hu, Ka-Ho Chow, Fatih Ilhan, Selim Furkan Tekin, and Ling Liu, "Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training," Neural Information Processing Systems (NeurIPS), New Orleans, LA, USA, Dec 10-16, 2023. [PDF] [CODE]
ICDM 2023 Wenqi Wei, Ka-Ho Chow, Fatih Ilhan, Yanzhao Wu, and Ling Liu, "Model Cloaking against Gradient Leakage," IEEE International Conference on Data Mining (ICDM), Shanghai, China, Dec 1-4, 2023. [PDF] [CODE]
ICDM 2023 Yanzhao Wu, Ka-Ho Chow, Wenqi Wei, and Ling Liu, "Exploring Model Learning Heterogeneity for Boosting Ensemble Robustness," IEEE International Conference on Data Mining (ICDM), Shanghai, China, Dec 1-4, 2023. [PDF] [CODE]
CVPR 2023 Ka-Ho Chow, Ling Liu, Wenqi Wei, Fatih Ilhan, and Yanzhao Wu, "STDLens: Model Hijacking-Resilient Federated Learning for Object Detection," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, Jun. 18-22, 2023. [PDF]
ICDM 2022 Ka-Ho Chow and Ling Liu, "Boosting Object Detection Ensembles with Error Diversity," IEEE International Conference on Data Mining (ICDM), Orlando, FL, USA, Nov. 28 - Dec. 1, 2022. [PDF] [CODE]
EuroSys 2022 Ka-Ho Chow, Umesh Deshpande, Sangeetha Seshadri, and Ling Liu, "DeepRest: Deep Resource Estimation for Interactive Microservices," ACM European Conference on Computer Systems (EuroSys), Rennes, France, Apr. 5-8, 2022. [PDF] [CODE]
SIGKDD 2021 Ka-Ho Chow and Ling Liu, "Robust Object Detection Fusion Against Deception," ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), Singapore, Aug. 14-18, 2021. [PDF] [CODE]
SIGMOD 2021 Ka-Ho Chow, Umesh Deshpande, Sangeetha Seshadri, and Ling Liu, "SRA: Smart Recovery Advisor for Cyber Attacks," ACM SIGMOD International Conference on Management of Data (SIGMOD), Xi'an, Shaanxi, China, Jun. 20-25, 2021. [PDF] 🎮 Demo
CVPR 2021 Yanzhao Wu, Ling Liu, Zhongwei Xie, Ka-Ho Chow, and Wenqi Wei, "Boosting Ensemble Accuracy by Revisiting Ensemble Diversity Metrics," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, Jun. 19-25, 2021. [PDF] [CODE]
TPS-ISA 2020 Ka-Ho Chow, Ling Liu, Margaret Loper, Juhyun Bae, 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-ISA), Atlanta, GA, USA, Dec. 1-3, 2020. [PDF] [CODE] 🎮 Demo
ESORICS 2020 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, United Kingdom, Sep. 14-18, 2020. [PDF] [CODE]
ESORICS 2020 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, United Kingdom, Sep. 14-18, 2020. [PDF] [CODE] 🎮 Demo
EdgeSys 2020 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, Apr. 27, 2020. [PDF] [CODE] 🏆 Best Paper Award
BigData 2019 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 (BigData), Los Angeles, CA, USA, Dec. 9-12, 2019. [PDF]
TSC Wenqi Wei, Ka-Ho Chow, Yanzhao Wu, and Ling Liu, "Demystifying Data Poisoning Attacks in Distributed Learning as a Service," IEEE Transactions on Services Computing (TSC), Vol. 17, No. 1, pp. 237-250, February 2024.
TIST Yanzhao Wu, Ka-Ho Chow, Wenqi Wei, and Ling Liu, "Hierarchical Pruning of Deep Ensembles with Focal Diversity," ACM Transactions on Intelligent Systems and Technology (TIST), Vol. 15, No. 15, pp. 1-24, January 2024. [PDF] [CODE]
TPDS Wenqi Wei, Ling Liu, Jingya Zhou, Ka-Ho Chow, and Yanzhao Wu, "Securing Distributed SGD against Gradient Leakage Threats," IEEE Transactions on Parallel and Distributed Systems (TPDS), Vol. 34, No. 7, pp. 2040-2054, July 2023. [PDF] [CODE]
TIFS 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," IEEE Transactions on Information Forensics and Security (TIFS), Vol. 17, pp. 1785-1799, May 2022. [PDF]
PREPRINT Jingcheng Zhang, Yingxuan Ren, Man Ho Au, Ka-Ho Chow, Yekai Zhou, Lei Chen, Yanmin Zhao, Junhao Su, and Ruibang Luo, "Towards a new standard in genomic data privacy: a realization of owner-governance," bioRxiv 2024.07.23.604393, Jul 24, 2024. [PDF] [CODE]
PREPRINT Yuxuan Zhu, Michael Mandulak, Kerui Wu, George Slota, Yuseok Jeon, Ka-Ho Chow, and Lei Yu, "On the Robustness of Graph Reduction Against GNN Backdoor," arXiv preprint arXiv:2407.02431, Jul 2, 2024. [PDF]
PREPRINT Lei Yu, Meng Han, Yiming Li, Changting Lin, Yao Zhang, Mingyang Zhang, Yan Liu, Haiqin Weng, Yuseok Jeon, Ka-Ho Chow, and Stacy Patterson, "A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective," arXiv preprint arXiv:2402.03688, Feb 6, 2024. [PDF]

Full Publication List

[Show]

Jingcheng Zhang, Yingxuan Ren, Man Ho Au, Ka-Ho Chow, Yekai Zhou, Lei Chen, Yanmin Zhao, Junhao Su, and Ruibang Luo, "Towards a new standard in genomic data privacy: a realization of owner-governance," bioRxiv 2024.07.23.604393, Jul 24, 2024. [PDF] [CODE]
Ka-Ho Chow, Sihao Hu, Tiansheng Huang, and Ling Liu, "Personalized Privacy Protection Mask Against Unauthorized Facial Recognition," European Conference on Computer Vision (ECCV), Milan, Italy, Sep. 29-Oct. 4, 2024. [PDF]
Ka-Ho Chow, Wenqi Wei, and Lei Yu, "Imperio: Language-Guided Backdoor Attacks for Arbitrary Model Control," International Joint Conference on Artificial Intelligence (IJCAI), Jeju, South Korea, Aug. 3-9, 2024. [PDF] [CODE]
Xueqing Zhang, Junkai Zhang, Ka-Ho Chow, Juntao Chen, Ying Mao, Mohamed Rahouti, Xiang Li, Yuchen Liu, and Wenqi Wei, "Visualizing the Shadows: Unveiling Data Poisoning Behaviors in Federated Learning," IEEE International Conference on Distributed Computing Systems (ICDCS), Jersey City, NJ, USA, Jul. 23-26, 2024.
Ka-Ho Chow, Sihao Hu, Tiansheng Huang, Fatih Ilhan, Wenqi Wei, and Ling Liu, "Diversity-driven Privacy Protection Masks Against Unauthorized Face Recognition," Privacy Enhancing Technologies Symposium (PETS), Bristol, UK, Jul. 15-20, 2024.
Yuxuan Zhu, Michael Mandulak, Kerui Wu, George Slota, Yuseok Jeon, Ka-Ho Chow, and Lei Yu, "On the Robustness of Graph Reduction Against GNN Backdoor," arXiv preprint arXiv:2407.02431, Jul 2, 2024. [PDF]
Nawrin Tabassum, Ka-Ho Chow, Xuyu Wang, Wenbin Zhang, and Yanzhao Wu, "On the Efficiency of Privacy Attacks in Federated Learning," IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops - FedVision (CVPRW), Seattle, WA, USA, Jun. 17-21, 2024. [PDF] [CODE]
Selim Furkan Tekin, Fatih Ilhan, Tiansheng Huang, Sihao Hu, Ka-Ho Chow, Margaret Loper, and Ling Liu, "Robust Few-Shot Ensemble Learning with Focal Diversity-Based Pruning," arXiv preprint arXiv:2404.04434, Apr 5, 2024. [PDF]
Lei Yu, Meng Han, Yiming Li, Changting Lin, Yao Zhang, Mingyang Zhang, Yan Liu, Haiqin Weng, Yuseok Jeon, Ka-Ho Chow, and Stacy Patterson, "A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective," arXiv preprint arXiv:2402.03688, Feb 6, 2024. [PDF]
Sihao Hu, Tiansheng Huang, Ka-Ho Chow, Wenqi Wei, Yanzhao Wu, and Ling Liu, "ZipZap: Efficient Training of Language Models for Ethereum Fraud Detection," The Web Conference (WWW), Singapore, May 13-17, 2024.
Ka-Ho Chow, Umesh Deshpande, Veera Deenadhayalan, Sangeetha Seshadri, and Ling Liu, "Atlas: Hybrid Cloud Migration Advisor for Interactive Microservices," ACM European Conference on Computer Systems (EuroSys), Athens, Greece, Apr. 22-25, 2024. [PDF] [CODE]
Fatih Ilhan, Ka-Ho Chow, Sihao Hu, Tiansheng Huang, Selim Furkan Tekin, Wenqi Wei, Yanzhao Wu, Myungjin Lee, Ramana Kompella, Hugo Latapie, Gaowen Liu, and Ling Liu, "Adaptive Deep Neural Network Inference Optimization with EENet," IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, Jan 4-8, 2024. [PDF] [CODE]
Tiansheng Huang, Sihao Hu, Ka-Ho Chow, Fatih Ilhan, Selim Furkan Tekin, and Ling Liu, "Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training," Neural Information Processing Systems (NeurIPS), New Orleans, LA, USA, Dec 10-16, 2023. [PDF] [CODE]
Wenqi Wei, Ka-Ho Chow, Yanzhao Wu, and Ling Liu, "Demystifying Data Poisoning Attacks in Distributed Learning as a Service," IEEE Transactions on Services Computing (TSC), Vol. 17, No. 1, pp. 237-250, February 2024.
Yanzhao Wu, Ka-Ho Chow, Wenqi Wei, and Ling Liu, "Hierarchical Pruning of Deep Ensembles with Focal Diversity," ACM Transactions on Intelligent Systems and Technology (TIST), Vol. 15, No. 15, pp. 1-24, January 2024. [PDF] [CODE]
Wenqi Wei, Ka-Ho Chow, Fatih Ilhan, Yanzhao Wu, and Ling Liu, "Model Cloaking against Gradient Leakage," IEEE International Conference on Data Mining (ICDM), Shanghai, China, Dec 1-4, 2023. [PDF] [CODE]
Yanzhao Wu, Ka-Ho Chow, Wenqi Wei, and Ling Liu, "Exploring Model Learning Heterogeneity for Boosting Ensemble Robustness," IEEE International Conference on Data Mining (ICDM), Shanghai, China, Dec 1-4, 2023. [PDF] [CODE]
Wenqi Wei, Ling Liu, Jingya Zhou, Ka-Ho Chow, and Yanzhao Wu, "Securing Distributed SGD against Gradient Leakage Threats," IEEE Transactions on Parallel and Distributed Systems (TPDS), Vol. 34, No. 7, pp. 2040-2054, July 2023. [PDF] [CODE]
Ka-Ho Chow, Ling Liu, Wenqi Wei, Fatih Ilhan, and Yanzhao Wu, "STDLens: Model Hijacking-Resilient Federated Learning for Object Detection," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, Jun. 18-22, 2023. [PDF]
Ka-Ho Chow, Umesh Deshpande, Veera Deenadhayalan, Sangeetha Seshadri, and Ling Liu, "SCAD: Scalability Advisor for Interactive Microservices on Hybrid Clouds," ACM SIGMOD International Conference on Management of Data (SIGMOD), Seattle, WA, USA, Jun. 18-23, 2023. [PDF] 🎮 Demo
Fatih Ilhan, Selim Furkan Tekin, Sihao Hu, Tiansheng Huang, Ka-Ho Chow, and Ling Liu, "Hierarchical Deep Neural Network Inference for Device-Edge-Cloud Systems," The Web Conference (WWW), Austin, TX, USA, May 1-4, 2023. [PDF]
Jiajie Tan, Hang Wu, Ka-Ho Chow, and Shueng-Han Gary Chan, "Implicit Multimodal Crowdsourcing for Joint RF and Geomagnetic Fingerprinting," IEEE Transactions on Mobile Computing (TMC), Vol. 22, No. 2, pp. 935-950, February 2023. [PDF]
Ka-Ho Chow and Ling Liu, "Boosting Object Detection Ensembles with Error Diversity," IEEE International Conference on Data Mining (ICDM), Orlando, FL, USA, Nov. 28 - Dec. 1, 2022. [PDF] [CODE]
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," IEEE Transactions on Information Forensics and Security (TIFS), Vol. 17, pp. 1785-1799, May 2022. [PDF]
Ka-Ho Chow, Umesh Deshpande, Sangeetha Seshadri, and Ling Liu, "DeepRest: Deep Resource Estimation for Interactive Microservices," ACM European Conference on Computer Systems (EuroSys), Rennes, France, Apr. 5-8, 2022. [PDF] [CODE]
Ka-Ho Chow and Ling Liu, "Perception Poisoning Attacks in Federated Learning," IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS-ISA), Virtual Conference, Dec. 13-15, 2021. [PDF] [CODE]
Stacey Truex, Ling Liu, Mehmet Emre Gursoy, Wenqi Wei, and Ka-Ho Chow, "The TSC-PFed Architecture for Privacy-Preserving Federated Learning," IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS-ISA), Virtual Conference, Dec. 13-15, 2021. [PDF]
Juhyun Bae, Ling Liu, Ka-Ho Chow, Yanzhao Wu, Gong Su, and Arun Iyengar, "Transparent Network Memory Storage for Efficient Container Execution in Big Data Clouds," IEEE International Conference on Big Data, Orlando, FL, USA, Dec. 15-18, 2021. [PDF]
Ka-Ho Chow and Ling Liu, "Robust Object Detection Fusion Against Deception," ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), Singapore, Aug. 14-18, 2021. [PDF] [CODE]
Ka-Ho Chow, Umesh Deshpande, Sangeetha Seshadri, and Ling Liu, "SRA: Smart Recovery Advisor for Cyber Attacks," ACM SIGMOD International Conference on Management of Data (SIGMOD), Xi'an, Shaanxi, China, Jun. 20-25, 2021. [PDF] 🎮 Demo
Yanzhao Wu, Ling Liu, Zhongwei Xie, Ka-Ho Chow, and Wenqi Wei, "Boosting Ensemble Accuracy by Revisiting Ensemble Diversity Metrics," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, Jun. 19-25, 2021. [PDF] [CODE]
Ka-Ho Chow, Ling Liu, Margaret Loper, Juhyun Bae, 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-ISA), Atlanta, GA, USA, Dec. 1-3, 2020. [PDF] [CODE]
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-ISA), Atlanta, GA, USA, Dec. 1-3, 2020. [PDF] [CODE]
Yanzhao Wu, Juhyun Bae, Ka-Ho Chow, Wenqi Wei, and Ling Liu, "EnsembleBench: An Evaluation Framework for Ensemble Learning," IEEE International Conference on Cognitive Machine Intelligence (CogMI), Atlanta, GA, USA, Dec. 1-3, 2020. [PDF] [CODE]
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, United Kingdom, Sep. 14-18, 2020. [PDF] [CODE] 🎮 Demo
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, United Kingdom, Sep. 14-18, 2020. [PDF] [CODE] 🎮 Demo
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, Apr. 27, 2020. [PDF] [CODE] 🏆 Best Paper Award
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," IEEE International Conference on Computing, Networking and Communications (ICNC), Big Island, Hawaii, USA, Feb. 17-20, 2020. [PDF] [CODE]
Lei Yu, Ling Liu, Calton Pu, Ka-Ho Chow, Mehmet Emre Gursoy, Wenqi Wei, Ming Hong, Arun Iyengar, Gong Su, Qi Zhang, and Donna Dillenberger, "GRAHIES: Multi-Scale Graph Representation Learning with Latent Hierarchical Structure," IEEE International Conference on Cognitive Machine Intelligence (CogMI), Los Angeles, CA, USA, Dec. 12-14, 2019. [PDF]
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, Los Angeles, CA, USA, Dec. 9-12, 2019. [PDF]
Yanzhao Wu, Ling Liu, Juhyun Bae, Ka-Ho Chow, Arun Iyengar, Calton Pu, Wenqi Wei, Lei Yu, and Qi Zhang, "Demystifying Learning Rate Policies for High Accuracy Training of Deep Neural Networks," IEEE International Conference on Big Data, Los Angeles, CA, USA, Dec. 9-12, 2019. [PDF] [CODE]
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, CA, USA, Nov. 4-7, 2019. [PDF] [CODE]
Ka-Ho Chow, Suining He, Jiajie Tan, and Shueng-Han Gary Chan, "Efficient Locality Classification for Indoor Fingerprint-based Systems," IEEE Transactions on Mobile Computing (TMC), Vol. 18, No. 2, pp. 290-304, February 2019. [PDF]
Fatih Ilhan, Ling Liu, Ka-Ho Chow, Wenqi Wei, Yanzhao Wu, Myungjin Lee, Ramana Kompella, Hugo Latapie, and Gaowen Liu, "EENet: Learning to Early Exit for Adaptive Inference," arXiv preprint arXiv:2301.07099, Jan. 15, 2023. [PDF]
Ka-Ho Chow, Anish Hiranandani, Yifeng Zhang, and Shueng-Han Gary Chan, "Representation Learning of Pedestrian Trajectories Using Actor-Critic Sequence-to-Sequence Autoencoder," arXiv preprint arXiv:1811.08069, Nov. 20, 2018. [PDF]

Research Grants

PI, "Arbitrary-Control Backdoor Threats in Federated Learning: Attacks and Defenses," Hong Kong RGC ECS, 2025 - 2028.
PI, "Trustworthy Federated Learning Against Model Leakage," Croucher Foundation Start-up, 2024 - 2029.

Teaching

[FITE 1010] Introduction to Financial Technologies: Fall 2024

Selected Awards

IBM PhD Fellowship, 2022
Croucher Scholarship, Croucher Foundation, Hong Kong, 2021
Best Paper Award, ACM International Workshop on Edge Systems, Analytics and Networking, 2020
Chair's Fellowship, School of Computer Science, Georgia Tech, 2019
Chan Tseng-Hsi Scholarship, Chan Tseng-Hsi Foundation, Hong Kong, 2013-2014
Sir Edward Youde Memorial Prize, Hong Kong, 2009-2010

Academic Services

Conference Reviewer: AAAI (2022, 2023, 2024, 2025), SIGKDD (2022, 2023, 2024, 2025), CVPR (2021, 2022, 2023, 2024), IJCAI (2024), ECCV (2022, 2024), TheWebConf (2024), WACV (2024), MASS (2024), SDM (2024), EuroSys (2024), CLOUD (2024), ICCV (2023), SIGSPATIAL (2023), ML4H (2020, 2021, 2022, 2023), HiPC (2023), HPDC (2022), TS4H (2022), Middleware (2021), INFOCOM (2017, 2018)
Journal Reviewer: IEEE TIFS, IEEE TMC, IEEE TCC, ACM TOIT