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 Data Mining and Modeling for Biomedicine group 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.
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.