Peyman Rasouli
I am a PhD Research Fellow in Data Science at the University of Oslo (UiO), Norway, advised by Dr. Ingrid Chieh Yu (Associate Professor, UiO) and Dr. Aida Omerovic (Research Director, Norsk Regnesentral). As a member of the Center for Scalable Data Access (SIRIUS), I do research on Explainable AI (XAI). My research involves interpreting, debugging, and validating black-box machine learning models. I am deeply excited about making AI decision systems explainable, transparent, and accountable. My research interests include Machine Learning, Explainable AI, Knowledge Discovery, and Computational Intelligence. More information is available on my personal home page.
Publikasjoner
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Rasouli, Peyman & Yu, Ingrid Chieh (2021). Analyzing and Improving the Robustness of Tabular Classifiers using Counterfactual Explanations, 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE conference proceedings. ISSN 978-1-6654-4337-1. s. 1286–1293. doi: 10.1109/ICMLA52953.2021.00209. Fulltekst i vitenarkiv
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Rasouli, Peyman & Yu, Ingrid Chieh (2021). Explainable Debugger for Black-box Machine Learning Models. Proceedings of the International Joint Conference on Neural Networks. ISSN 2161-4393. doi: 10.1109/IJCNN52387.2021.9533944. Fulltekst i vitenarkiv
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Rasouli, Peyman & Yu, Ingrid Chieh (2020). EXPLAN: Explaining Black-box Classifiers using Adaptive Neighborhood Generation. Proceedings of the International Joint Conference on Neural Networks. ISSN 2161-4393. doi: 10.1109/IJCNN48605.2020.9206710. Fulltekst i vitenarkiv
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Rasouli, Peyman & Yu, Ingrid Chieh (2019). Meaningful Data Sampling for a Faithful Local Explanation Method. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. s. 28–38. doi: 10.1007/978-3-030-33607-3_4. Fulltekst i vitenarkiv
Publisert 17. sep. 2018 15:34
- Sist endret 5. mars 2021 14:11