I am an Assistant Professor with the University of Amsterdam, conducting research at the intersection of data management and machine learning. I manage the AI for Retail Lab Amsterdam, and have a joint appointment as Research Fellow at Ahold Delhaize, an international retailer based in the Netherlands.

My work covers many aspects, such as automating data quality validation, optimizing programs that combine operations from linear and relational algebra or tracking the lineage of machine learning pipelines.

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.

• We will present a paper on learnings from a real world recommender system at one of Europe’s largest e-commerce platforms at ICDE 2021, and a demo on a data distribution debugger for machine learning pipelines at SIGMOD 2021.

• Stefan Grafberger and Barrie Kersbergen joined our lab as Ph.D. students to work on data validation for ML pipelines and scalable recommender systems!

• I will serve as Associate Editor and Scalable Data Science Track Chair for PVLDB Volume 15 for the period of April 2021 through March 2022.

• We presented our work on Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines at CIDR. Checkout the video of our talk and our example notebook!

• I will be the editor for the March 2021 issue on “Data validation for machine learning models and applications” of the IEEE Data Engineering Bulletin.

• Our work on scalable data validation has been integrated into the new Amazon SageMaker Model Monitor service for concept drift detection. Here is the announcement at the AWS Reinvent conference:
  
  

I am part of the Intelligent Data Engineering Lab led by Paul Groth.

PhD Students
Mozhdeh Ariannezhad (co-supervised with Maarten de Rijke)	Olivier Sprangers (co-supervised with Maarten de Rijke)	Arezoo Sarvi (co-supervised with Maarten de Rijke)
Barrie Kersbergen (co-supervised with Maarten de Rijke)	Stefan Grafberger (co-supervised with Paul Groth)

Associated Researchers & Research Engineers
Shubha Guha	Dr. Ji Zhang

Master Students
Benjamin Wang

• 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 conducting research on data validation and data cleaning for machine learning with Prof. Felix Biessmann from Beuth University, Berlin.
  
  

• 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 Core AI 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. 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, where I currently mentor the Apache TVM project on behalf of the Apache Incubator. In the past, I have been involved in the Apache Mahout, Apache Flink, Apache Giraph and Apache MXNet projects. I am currently actively contributing to deequ, a library for ‘unit-testing’ large datasets with Apache Spark and recoreco, a fast item-to-item recommender written in Rust.

I am the founder and chair of the workshop series on Data Management for End-To-End Machine Learning (DEEM) at ACM SIGMOD, which started in 2017. I will also serve as the editor for two issues of the IEEE Data Engineering Bulletin in 2021 and 2022, and as Associate Editor and Scalable Data Science (SDS) Track Chair for PVLDB Volume 15 for the period of April 2021 trough March 2022.

I regularly review submissions to top tier data management conferences. I have been on the program committee at SIGMOD 2017, 2019-2022, VLDB 2021, ICDE 2018-2021, EDBT 2017 & 2021, CIKM’20, 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, 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 am also part of the review board of the Journal of Systems Research (JSys), and have been a reviewer for the Amazon Research Awards.

At the University of Amsterdam, I coordinate the honors program for the bachelor AI.

I’m reachable via email at s.schelter[at]uva.nl. I’m also very actively using twitter as @sscdotopen. Most of the research code that I write is available under an open source license in my github account. Last but not least, I also have a profile in google scholar.