Michael Fauss is a research scientist at the ETS Research Institute. His work focuses on applying artificial intelligence to education and testing, with a particular emphasis on fairness and test security. This focus includes developing and evaluating novel methods to promote fairness in human-in-the-loop AI systems, improving the accuracy and fairness properties of plagiarism detection in language and writing assessments, and exploring techniques for classifying spoken and written communication in collaborative problem-solving tasks. Additionally, he researches the use of statistically robust methods within item response theory models to improve fairness and reliability by addressing overly optimistic assumptions.
Michael earned a Ph.D. in electrical engineering from the Technical University of Darmstadt in 2016. In 2017, he received the Dissertation Award from the German Information Technology Society for his Ph.D. dissertation on robust sequential detection. From 2019 to 2022, he was a postdoctoral researcher in Prof. H. Vincent Poor’s group at Princeton University, where he worked on statistical robustness, sequential detection and estimation, and the role of similarity measures in statistical inference. As a principal investigator (PI) or co-PI, he has secured multiple grants from the National Science Foundation and the German Research Foundation. He has published over 60 peer-reviewed papers in leading journals and conferences, including the Annals of Statistics and the ISI World Statistics Congress.
Michael Fauss | LinkedIn
Last updated: 3/10/2025