Kenneth T. Co, PhD | Asian Institute of Management

Expertise

Machine Learning, Artificial Intelligence, Cybersecurity, Computer Vision, Data Science

Research Interests

Adversarial Machine Learning, Federated Learning, Anomaly Detection, Medical Imaging, Safety of Artificial Intelligence

Back to Faculty

Kenneth T. Co, PhD

Assistant Professor
Academic Program Director, Master of Science in Data Science

About
Selected Publications

Academic Background

PhD in Computing, Imperial College London
Master of Science in Computing (Machine Learning), Imperial College London
Master of Arts in Mathematics, Johns Hopkins University
Bachelor of Arts in Mathematics, Johns Hopkins University

Professional and Academic Experience

Machine Learning Consultant, Thinking Machines
Research Scientist, DataSpartan UK

Affiliations, Awards, and Honors

2023 Endorsement for Global Talent Visa, Royal Academy of Engineering, United Kingdom
2020 3rd Place, CSAW’20 Cybersecurity Applied Research Competition, Europe, hosted by New York University (NYU) and Grenoble INP-ESISAR
2018-2022 PhD Programme Funding, Department of Computing, Imperial College London
2016 J.J. Sylvester Undergraduate Award, Department of Mathematics, Johns Hopkins University
2016 Applied Mathematics and Statistics Achievement Award, Department of Applied Mathematics and Statistics, Johns Hopkins University
2015 Naddor Prize, Department of Applied Mathematics and Statistics, Johns Hopkins University
2012-2016 Woodrow Wilson Research Fellow, Johns Hopkins University
2012 Bronze Medalist, International Mathematical Olympiad (IMO), Mar del Plata, Argentina

PEER-REVIEWED CONFERENCE PROCEEDINGS

Castiglione, L. M., Hau, Z., Co, K. T., Muñoz-González, L., Teng, F., & Lupu, E. (2022). HA-Grid: Security Aware Hazard Analysis for Smart Grids. In Proceedings of the 2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 446-452. https://doi.org/10.1109/SmartGridComm52983.2022.9961003
Co, K. T., Muñoz-González, L., Kanthan, L., Glocker, B., & Lupu, E. C. (2021). Universal adversarial robustness of texture and shape-biased models. In Proceedings of the 2021 IEEE International Conference on Image Processing (ICIP), 799-803. https://doi.org/10.1109/ICIP42928.2021.9506325
Co, K. T., Muñoz-González, L., de Maupeou, S., & Lupu, E. C. (2019). Procedural noise adversarial examples for black-box attacks on deep convolutional networks. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (CCS '19), 275–289. https://doi.org/10.1145/3319535.3345660

BOOK CHAPTER

Muñoz-González, L., Carnerero-Cano, J., Co, K. T., & Lupu, E. C. (2019). Challenges and Advances in Adversarial Machine Learning. Resilience and Hybrid Threats, 102-120.