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I’m excited to share that our paper, Towards Quantum-Based Detection of Adversarial Attacks in Natural Language Processing Systems, was accepted to the IEEE International Conference on Quantum Computing & Engineering (QCE) 2026! In this work, we explore a hybrid quantum-classical machine learning approach for detecting adversarial examples in natural language processing models, using projected quantum kernels with support vector machines. The paper will be presented at QCE in September 2026. See you in Toronto!

This past Friday, December 5th 2025, I successfully passed the Oral Candidacy Exam, officially becoming a PhD candidate. My dissertation proposal surrounds security in machine learning-based systems covering three major phases: model creation, storage, and usage. I would like to thank everyone who came to support me during my presentation, and my committee (Dr. Joanna Cecilia da Silva Santos, Dr. Taeho Jung, Dr. Peter Kogge, and Dr. Gail Kaiser) for their guidance and feedback. I am looking forward to continuing on with this work and enhancing security for machine learning-based systems.

Our paper, A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Tasks, was accepted for publication in ACM Computing Surveys! In this work, we systematically review how source code is represented in ML-based cybersecurity research. Data representation is a key part of the ML pipeline and understanding what each representation offers in terms of feature information allows researchers to choose or create a representation that best suits their needs.