I am an Assistant Professor with the University of Amsterdam, conducting research at the intersection of data management and machine learning.
My work addresses data-related problems that occur in the real world application of machine learning. Examples are scalable data quality validation, data debugging for machine learning pipelines, or enforcing the “right-to-be-forgotten” in deployed machine learning applications.
My research is accompanied by efficient and scalable open source implementations, many of which are applied in real world use cases, for example in the Amazon SageMaker Model Monitor service, AWS Glue Data Quality, the product recommendation system at bol.com and large-scale recommendation libraries in cloud environments such as Amazon Web Services and Microsoft Azure.
My research contributions have been recognized with an ACM SIGMOD Systems Award, an ACM SIGMOD Best Demo Runner Up Award, and a Best Paper Runner Up Award from the Table Representation Learning workshop at NeurIPS.
In the past, I have been a Faculty Fellow with the Center for Data Science at New York University and a Senior Applied Scientist at Amazon Research, after obtaining my Ph.D. at the database group of TU Berlin with Volker Markl. I am active in open source as an elected member of the Apache Software Foundation, and have extensive experience in building real world systems from my time at Amazon, Twitter, IBM Research, and Zalando.
PhD Students & Guests
(with Maarten de Rijke)
(with Maarten de Rijke)
(with Paul Groth)
(with Iacer Calixto)
(with Paul Groth)
I am collaborating with Prof. Julia Stoyanovich from New York University on research with regard to the impact of data preprocessing on the fairness of machine-assisted decision making.
I am working with Iacer Calixto from the Amsterdam UMC on problems at the intersection of responsible data management and natural language processing.
I am an associated researcher with BIFOLD, the Berlin Institute for the Foundations of Learning and Data.
Before joining University of Amsterdam, I have been a Faculty Fellow at the Center for Data Science at New York University, and a Senior Applied Scientist at Amazon Research in Berlin, where I worked on data management-related issues of machine learning applications, such as demand forecasting, metadata and provenance tracking of machine learning pipelines and automating data quality verification.
I received my Ph.D. with “summa cum laude” from TU Berlin in 2015, where I have been advised by Volker Markl, head of the database systems and information management group. My co-supervisors were Klaus-Robert Müller from the machine learning group at TU Berlin and Reza Zadeh from Stanford. During my studies, I have been interning with the SystemML group at IBM Research Almaden and the social recommendations team at Twitter in California.
I am engaged in open source as an elected member of the Apache Software Foundation since 2012. I have been involved in the Apache Mahout, Apache Flink, Apache Giraph and the incubation of the Apache MXNet and Apache TVM projects. Besides that I co-created Deequ, a library for ‘unit-testing’ large datasets with Apache Spark, and Serenade, a low-latency session-based recommender system deployed in production at a large Dutch retailer. Furthermore, I am a member of the Electronic Frontier Foundation since 2015.
I am the founder and have chaired the workshop series on Data Management for End-To-End Machine Learning (DEEM) at ACM SIGMOD from 2017 to 2020, and an Action Editor for the Journal of Data-centric Machine Learning Research and the ML Open Source Software track of the Journal of Machine Learning Research. I have served as Associate Editor for PVLDB Volume 15, as the editor for two special issues of the IEEE Data Engineering Bulletin in 2021 and 2022, and as co-chair of the industry and applications track of EDBT 2022.
I regularly review submissions to top tier data management conferences. I have been on the program committee at SIGMOD 2017, 2019-2024, VLDB 2021, ICDE 2018-2021 & 2023, EDBT 2017 & 2021, CIKM’20, the PhD Symposium at VLDB’21, the workshop on Data-Centric Machine Learning Research at ICML, the workshop on Exploiting Artificial Intelligence Techniques for Data Management at SIGMOD 2019, the Large-Scale Recommender Systems workshop at the ACM RecSys 2013-2015, the workshop on Applied AI for Database Systems and Applications at VLDB’20, on Table Representation Learning at NeurIPS’22-23, the DBML workshop at ICDE’21 and Provenance Week’20. Additionally, I have reviewed submissions to journals for IEEE TKDE, ACM TIST, IEEE TPDS, IEEE TNNLS, VLDB Journal, the VLDB Journal Special Issue on Data Science for Responsible Data Management, the journal track of ECML/PKDD and the open source track of JMLR. I have also been a reviewer for the Amazon Research Awards.
At the University of Amsterdam, I coordinate the Big Data Engineering track of the computer science master and the honors program for the bachelor AI, and teach a courses on data engineering with up to 200 students.