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.
Our paper on An Intermediate Representation for Optimizing Machine Learning Pipelines has finally been accepted to VLDB 2019.
Our paper on Efficient Incremental Cooccurrence Analysis for Item-Based Collaborative Filtering has been accepted for publication at SSDBM 2019.
Our paper on Learning to Validate the Predictions of Black Box Machine Learning Models on Unseen Data has been accepted for publication at the workshop on Human-in-the-Loop Data Analytics (HILDA) at ACM SIGMOD 2019.
I am going to design and teach a novel course on Data Engineering for Machine Learning at NYU in fall.
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.
I am collaborating with the Social Media and Political Participation (SMaPP) Lab at NYU, conducting research on the polarization of online political debates on social media.
I am consulting Amazon Core 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.
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 the originator 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, ICDE 2018, 2019 (demo track) & 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, the journal track of ECML/PKDD and the open source track of JMLR. I have also been a reviewer for the Amazon Research Awards.