I am currently a Faculty Fellow with the Center for Data Science at New York University. My research focuses on the intersection of data management and machine learning, with the interdisciplinary application to computational social science.

My work covers a wide spectrum. I enjoy tackling technical challenges in data management, such as automating data quality verification, optimizing programs that combine operations from linear and relational algebra or tracking the lineage of machine learning pipelines.

At the same time, I am convinced that modern data analysis can have a huge impact in answering pressing societal questions. In related research, I have analyzed the rise of the German far-right in social media, and cartographed the online tracking sphere from a webcrawl of several billion pages.

• SIGMOD’20: Our work on Learning to Validate the Predictions of Black Box Classifiers on Unseen Data has been accepted as a short paper, and I am co-chairing the 4th edition of the International Workshop on Data Management for End-to-End Machine Learning (DEEM) with Steven Whang and Julia Stoyanovich.

• CIDR’20: I presented our work on “Amnesia” - A Selection of Machine Learning Models That Can Forget User Data Very Fast.

• EDBT’20: We have a short paper on FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventions and a full paper on Zooming Out on an Evolving Graph accepted.

• 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:
  
  

• Our paper on Datawig: Missing Value Imputation for Tables has been accepted at the open source track of the Journal of Machine Learning Research (JMLR).

Academia

• I am hosting Ji Zhang from Huazhong University of Science and Technology (HUST) as a visiting Ph.D. student at NYU. We conduct research on machine learning for data-intensive systems.

• I am collaborating with Prof. Julia Stoyanovich from New York University on research with the regard to the impact of data preprocessing on the fairness of machine-assisted decision making.

• I am co-supervising Sergey Redyuk who is a Ph.D. student at Technische Universität Berlin. We conduct research on novel systems for reproducibility and automated documentation of data science experiments.

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

Industry

• I am consulting Amazon AI as a part-time Senior Applied Scientist, and work on open source software for large-scale data quality verification with a team from Berlin.

• I regularly discuss my research on data quality and model validation with Immuta, a company building a data management platform for data science.

Before joining New York University, I have been 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 regularly review submissions to top tier data management conferences. I have been on the program committee at SIGMOD 2017, 2019 & 2020, VLDB 2020, ICDE 2018, 2019 & 2020, EDBT 2017, the workshop on Exploiting Artificial Intelligence Techniques for Data Management at SIGMOD 2019 and the Large-Scale Recommender Systems workshop at the ACM RecSys 2013-2015. Additionally, I have reviewed submissions to journals for IEEE TKDE, ACM TIST, IEEE TPDS, IEEE TNNLS, VLDB Journal, the journal track of ECML/PKDD and the open source track of JMLR. I have also been a reviewer for the Amazon Research Awards.

I’m reachable via email at sebastian.schelter[at]nyu.edu. 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.