Chow Ka Ho 周嘉豪

  • Scholarship in 2021 at Georgia Institute of Technology
I am a Ph.D. candidate in the School of Computer Science at the Georgia Institute of Technology (Georgia Tech), under the supervision of Prof. Ling Liu at the Distributed Data Intensive Systems Lab (DiSL). My research makes applied machine learning trustworthy and cloud computing systems scalable and reliable. I was named an IBM PhD Fellow in 2022 and a Croucher Scholar in 2021. Apart from conducting research at academic institutions, I collaborate closely with IBM Research to push the frontiers of machine learning on industrial systems problems. With its significance in academia and industry, my research has been published at top-tier conferences (e.g., SIGKDD, SIGMOD, CVPR, EuroSys). I am passionate about teaching and have developed online learning platforms to help nurture the next-generation computer scientists at Georgia Tech. Before joining DiSL, I obtained my Bachelor's and Master's degrees in Computer Science at the Hong Kong University of Science and Technology (HKUST), where I worked at the Multimedia Technology Research Center and was supervised by Prof. S.-H. Gary Chan. For more details, please visit

Research Interests

Cybersecurity; Data Science; Cloud Computing; Machine Learning for Systems

Selected Publications [Google Scholar] [DBLP]

  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.