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, the product recommendation system at bol.com and large-scale recommendation libraries in cloud environments such as Amazon Web Services and Microsoft Azure.

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.

• Dec 14, 2021 ‐ Barrie and Olivier’s paper on Serenade - Low-Latency Session-Based Recommendation in e-Commerce at Scale was accepted to SIGMOD 2022! This work details the novel recommender system that we built together with our industry partner bol.com.
  
  
• Dec 3, 2021 ‐ Stefan’s paper on Data Distribution Debugging in Machine Learning Pipelines was accepted for the VLDB Journal Special Issue on Data Science for Responsible Data Management! This work is part of our ongoing collaboration with Julia Stoyanovich from NYU.
  
  
• Nov 24, 2021 ‐ Stefan, Barrie, Olivier and Shubha have two abstracts accepted at the Dutch-Belgian Database Day. We will present our recent work on screening ML pipelines and building low-latency recommender systems.
  
  
• Nov 18, 2021 ‐ My student Stefan and me gave a talk on Data Provenance as the Foundation for Data Governance at the virtual Data Centric AI workshop organised by Stanford and ETH Zuerich.
  
  
• Nov 17, 2021 ‐ Our student Mozhdeh published a post about her research on ‘Understanding customer behavior in multi-channel retail’ on Albert Heijn’s tech blog.
  
  

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)

Researchers & Guests
Shubha Guha	Till Doehmen	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)

---

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)

---

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 working with Iacer Calixto from the University of Amsterdam on problems at the intersection of responsible data management and natural language processing.
  
  

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

• 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.