I am an Assistant Professor with the University of Amsterdam, conducting research at the intersection of data management and machine learning. I am affiliated with the Intelligent Data Engineering Lab, manage the AI for Retail Lab and have a joint appointment as research fellow at Ahold Delhaize.

My work addresses data-related problems that occur in the real world application of machine learning. Examples are the automation of data quality validation, the inspection of machine learning pipelines via code instrumentation, or the design of machine learning applications that can efficiently forget data.

Most of 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 and the product recommendation system at bol.com.

A selection of conference presentations, invited talks and lectures related to my research are available on this YouTube channel.

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.

• Aug 18, 2021 ‐ I have been a participant in the roundtable discussion on Systems for ML at VLDB’21.
  
  
• Aug 9, 2021 ‐ Our work on Understanding Multi-channel Customer Behavior in Retail lead by Mozhdeh Ariannezhad will be presented at CIKM’21.
  
  
• Jun 30, 2021 ‐ I have been invited to chair the industry and applications track at EDBT 2022 together with Fatma Ozcan.
  
  
• Jun 21, 2021 ‐ Congratulations to Benjamin Longxiang Wang for winning our bol.com Unwanted Product Detection Challenge 2021 with a test set accuracy of 88.6%!.
  
  
• Jun 21, 2021 ‐ Two papers on DuckDQ: Data Quality Assertions for Machine Learning Pipelines and Towards Efficient Machine Unlearning via Incremental View Maintenance accepted at the workshop on Challenges in Deploying and Monitoring ML Systems at ICML’21.
  
  

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)

Research Engineers, Associated Researchers & Master Students
Shubha Guha	Till Doehmen	Dr. Ji Zhang
Benjamin Wang

mlinspect

Machine Learning applications are increasingly used to automate impactful decisions, and at the same time, can be very brittle with respect to their input data, which leads to concerns about the correctness, reliability, and fairness of such applications. mlinspect is a library that helps data scientists to diagnose and mitigate technical bias that arises during data preprocessing in an ML pipeline. mlinspect can instrument natively written ML code in Python using libraries such as pandas or scikit-learn, and will automatically apply several inspections to the intermediate results of preprocessing operations.

Code:

• https://github.com/stefan-grafberger/mlinspect

Publications:

• Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines (CIDR’21)
• mlinspect: a Data Distribution Debugger for Machine Learning Pipelines (SIGMOD’21, demo)
• Fairness-Aware Instrumentation of Preprocessing Pipelines for Machine Learning (HILDA at SIGMOD’20)
• FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventions (EDBT’20)

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PGBM

PGBM (‘Probabilistic Gradient Boosting Machines’) is a probabilistic gradient boosting framework in Python based on PyTorch, developed by Olivier Sprangers. It is aimed at users interested in solving large-scale tabular probabilistic regression problems, such as probabilistic time series forecasting. PGBM provides several advantages over existing frameworks, e.g., probabilistic regression estimates instead of only point estimates, auto-differentiation of custom loss functions, and native GPU-acceleration.

Code:

• https://github.com/elephaint/pgbm

Publications:

• Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression (KDD’21)

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Snapcase

Snapcase is a research prototype for a recommender system that can instantly “forget” user data (e.g. in response to GDPR deletion requests) and update its recommendations accordingly. Snapcase models various recommendation algorithms via differential computation and is implemented in Rust on top of Differential Dataflow.

Code:

• [Not released yet]

Publications:

• ‘Amnesia’ - A Selection of Machine Learning Models That Can Forget User Data Very Fast (CIDR’20)
• HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning (SIGMOD’21)

• 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 Hannes Muehleisen from Centrum Wiskunde & Informatica (CWI) on leveraging DuckDB for the efficient execution of data preprocessing in ML pipelines.
  
  

• 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.
  
  

Scientific Career

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.

Open Source

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. Furthermore, I am a member of the Electronic Frontier Foundation since 2015.

Scientific Service

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 serve as the editor for two issues of the IEEE Data Engineering Bulletin in 2021 and 2022, as Associate Editor for PVLDB Volume 15, 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, 2019-2022, VLDB 2021, ICDE 2018-2021, EDBT 2017 & 2021, CIKM’20, the PhD Symposium at VLDB’21, 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.