Jonathan Peck

Post-doctoral researcher @ Ghent University

prof_pic.jpg

Campus Sterre

Krijgslaan 281

Gent, 9000 Belgium

I am a post-doctoral researcher at Ghent University, affiliated with the Department of Applied Mathematics, Computer Science and Statistics (TWIST) as well as the Saeys Lab at the VIB Inflammation Research Center. I am also a teaching assistant for the Artificial Intelligence course offered by Ghent University at the Faculty of Sciences, as well as the lecturer for the Mathematics course in Biomedical Sciences.

My main focus of research is the study of adversarial examples. Broadly speaking, adversarial examples are input samples deliberately crafted by a malicious adversary in order to obtain certain specific predictions from a targeted machine learning model. The intent here is usually to cause some form of harm, such as bypassing automated content filters, malware protections or biometric security systems. In my work, I try to devise countermeasures against this form of exploitation.

Aside from research into adversarial examples, I am also interested in issues of fairness in machine learning. In developing and deploying machine learning systems, researchers and practitioners alike are often ignorant of (or deliberately ignore) the disparate impact of their systems on women and minorities. Some of these tools, such as the recommender systems used by Twitter and Facebook, also facilitate the spread of hate and political extremism across the globe. We cannot afford to remain blind to these problems; the field of machine learning must take its social responsibilities seriously.

latest posts

selected publications

  1. Robust width: A lightweight and certifiable adversarial defense
    Jonathan Peck, and Bart Goossens
    arXiv preprint arXiv:2405.15971, 2024
  2. An Introduction to Adversarially Robust Deep Learning
    Jonathan Peck, Bart Goossens, and Yvan Saeys
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024
  3. CharBot: A Simple and Effective Method for Evading DGA Classifiers
    Jonathan Peck, Claire Nie, Raaghavi Sivaguru, and 5 more authors
    IEEE Access, 2019

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