News

  • I will serve as associate editor for the scalable data science track of PVLDB Volume 15.

Recent Publications

All Publications

(2021). Letter from the Special Issue Editor. Special issue on “Data validation for machine learning models and applications” of the IEEE Data Engineering Bulletin (Vol 44, Issue 1).

PDF

(2021). HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning. ACM SIGMOD.

PDF

(2021). Learnings from a Retail Recommendation System on Billions of Interactions at bol.com. International Conference on Data Engineering (ICDE).

PDF

(2021). mlinspect: a Data Distribution Debugger for Machine Learning Pipelines. ACM SIGMOD (demo).

PDF

(2021). Automating Data Quality Validation for Dynamic Data Ingestion. International Conference on Extending Database Technology (EDBT).

PDF

(2020). Jenga - A Framework to Study the Impact of Data Errors on the Predictions of Machine Learning Models. International Conference on Extending Database Technology (EDBT).

PDF

(2020). Taming Technical Bias in Machine Learning Pipelines. IEEE Data Engineering Bulletin (Special Issue on Interdisciplinary Perspectives on Fairness and Artificial Intelligence Systems).

PDF

(2020). Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines. Conference on Innovative Data Systems Research (CIDR).

PDF

Team

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
Yoran Sturkenboom

Projects

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:

Publications:


Serenade

Serenade is a session-based recommender system for ecommerce platforms which handles thousands of requests per second with low latency. Serenade is implemented in Rust, deployable with Kubernetes, and features a custom index for efficient session similarity computation.

Code:

  • [Not released yet]

Publications:


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:

Collaborations

CV

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. I have been involved in the Apache Mahout, Apache Flink, Apache Giraph and the incubation of the Apache MXNet and Apache TVM projects. Furthermore, I am the creator of deequ, a library for ‘unit-testing’ large datasets with Apache Spark and recoreco, a fast item-to-item recommender written in Rust.

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, and as Associate Editor for the Scalable Data Science Track 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 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.

Contact

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.