About

I'm a PhD candidate at the University of Amsterdam, supervised by Maarten de Rijke and Hinda Haned.

My research interests include explainable machine learning, geometric deep learning, and AI for social impact.

Previously, I was a Research Fellow at the Partnership on AI. I completed my BSc and MSc, both in mathematics, at McMaster University in Hamilton, Ontario. I grew up in Canada but was born in the former Yugoslavia.

News

    • June 2022: Our paper on saliency map explanations for electrocardiograms was accepted to the ICML 2022 Workshop on Interpretable ML in Healthcare.
    • June 2022: Our paper on XAI Toolsheets was accepted to the IJCAI 2022 Workshop on XAI.
    • April 2022: Our FACT-AI course had 21 student papers accepted to the ML Reproducibility Challenge, including the Best Paper Award and 2 Outstanding Paper Awards. This work will also be published in ReScience.
    • April 2022: Our tutorial on reproducibility in information retrieval was accepted to SIGIR 2022. Website coming soon!
    • January 2022: Our paper on counterfactual explanations for GNNs was accepted to AISTATS 2022. Code is available here.
    • December 2021: Our tutorial on reproducibility in NLP was accepted to ACL 2022.
    • November 2021: Our paper on counterfactual explanations for tree ensembles was accepted to AAAI 2022. Code is available here.
    • November 2021: Our paper about the FACT-AI course at the University of Amsterdam has been accepted to the AAAI 2022 Symposium on Educational Advances in AI.
    • July 2021: Our paper on counterfactual explanations for tree ensembles was accepted to the ICML 2021 Workshop on Socially Responsible Machine Learning. Code is available here.
    • July 2021: Our paper on counterfactual explanations for GNNs was accepted to two workshops at ICML 2021: Human in the Loop Learning (HILL) and the Workshop on Algorithmic Recourse.
    • July 2021: Our paper on trust scores for regression predictions was accepted to the ICML 2021 Workshop on Human in the Loop Learning (HILL).
    • June 2021: Our paper on counterfactual explanations for GNNs was accepted to the KDD 2021 Workshop on Deep Learning on Graphs. Code is available here.
    • June 2021: Our paper on comparing correlations between XAI methods and attention-based mechanisms was accepted to the ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trends of XAI. This work is an extension of a student project from the FACT-AI course.
    • May 2021: I wrote a blog post for Papers with Code detailing how we incorporated the ML Reproducibility Challenge in our FACT-AI course.
    • April 2021: Our FACT-AI course had 9 student papers accepted to the ML Reproducibility Challenge. This work will also be published in ReScience.
    • March 2021: Our paper on multistakeholder evaluation of AI transparency mechanisms was accepted to the CHI 2021 Workshop on Human-Centered XAI.
    • January 2021: I joined the Partnership on AI as a research fellow, focusing on explainable machine learning.
    • April 2020: I gave a talk at Ranstad NL about fairness in machine learning.
    • March 2020: We have released a repository with implementations and analyses of existing FACT-AI algorithms from top AI venues, done by students during the FACT-AI course at the University of Amsterdam.
    • January 2020: Our paper on contrastive explanations for retail forecasting won the Best (CS) Student Paper Award at FAccT 2020! Slides and code are available.
    • January 2020: I helped create and teach a new course for graduate students on Fairness, Accountability, Confidentiality and Transparency (FACT) in AI at the University of Amsterdam. We wrote a blog post about the course, and my slides from my lecture on transparency are available.
    • August 2019: Our paper on contrastive explanations for retail forecasting was accepted to the IJCAI 2019 Workshop on XAI.
    • July 2019: Our paper on explaining predictions from tree-based boosting ensembles was accepted to the SIGIR 2019 Workshop on FACTS-IR.

Publications