Academic Profile : Faculty
Assoc Prof Melanie Herschel
Associate Professor, College of Computing & Data Science
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Melanie Herschel's academic background is in Computer Science, more specifically in data management. Before joining the College of Computing and Data Science at Nanyang Technological University in 2024, she was a Professor for Data Engineering at the University of Stuttgart (2014 - 2024) and an Associate Professor and INRIA team member at the University Paris Saclay, France (2011-2014). Further stages that shaped her career path include a Visiting Research Professorship at the National University of Singapore (2019 - 2020) and work at IBM Research Almaden, USA (2008 - 2009). She obtained her doctorate from Humboldt-University Berlin, Germany in 2008.
Melanie's scientific publications regularly appear in the top-tier peer-reviewed venues of her field (ACM SIGMOD, VLDB, IEEE ICDE, EDBT), showing that her contributions in the data management areas of data integration, data quality, data provenance, and data analytics are at the forefront of research that aspires to get data in shape for a large variety of applications in an effective, resource efficient, user-friendly, and responsible way. Due to the omnipresence of data in various fields and industries, Melanie also pursues interdisciplinary research that offers an ideal ground to identify and tackle novel data management challenges benefitting different sectors or industries (e.g., Heath, Engineering, Construction).
Her expertise and leadership in crucial data engineering steps enabled her to play a key role as Principal Investigator in several prestigious research grants (including a Collaborative Research Center and two Clusters of Excellence during her time in Germany) and to collaborate with industrial partners.
Melanie is an active and recognized member of the international research community, being regularly involved in the organization of conferences (e.g., IEEE ICDE 2023 PhD Symposium co-chair, EDBT 2019 PC chair, ACM SIGMOD 2016 publicity chair), serving as associate editor (e.g., VLDB Journal), or serving as PC member in top-tier venues (VLDB, SIGMOD, ...). She was further appointment as member of the Expert Committee of the National Research Data Infrastructure initiative by the German National Research Foundation.
Melanie's scientific publications regularly appear in the top-tier peer-reviewed venues of her field (ACM SIGMOD, VLDB, IEEE ICDE, EDBT), showing that her contributions in the data management areas of data integration, data quality, data provenance, and data analytics are at the forefront of research that aspires to get data in shape for a large variety of applications in an effective, resource efficient, user-friendly, and responsible way. Due to the omnipresence of data in various fields and industries, Melanie also pursues interdisciplinary research that offers an ideal ground to identify and tackle novel data management challenges benefitting different sectors or industries (e.g., Heath, Engineering, Construction).
Her expertise and leadership in crucial data engineering steps enabled her to play a key role as Principal Investigator in several prestigious research grants (including a Collaborative Research Center and two Clusters of Excellence during her time in Germany) and to collaborate with industrial partners.
Melanie is an active and recognized member of the international research community, being regularly involved in the organization of conferences (e.g., IEEE ICDE 2023 PhD Symposium co-chair, EDBT 2019 PC chair, ACM SIGMOD 2016 publicity chair), serving as associate editor (e.g., VLDB Journal), or serving as PC member in top-tier venues (VLDB, SIGMOD, ...). She was further appointment as member of the Expert Committee of the National Research Data Infrastructure initiative by the German National Research Foundation.
- data integration
- data quality and data cleaning
- data provenance
- data transparency
- responsible data management
- data engineering
- data management
Courses Taught
I have conceived and conducted multiple courses related to data management. These include, among others, graduate courses on Information Integration, Data Engineering, Implementation of Database Management Systems and undergraduate courses on Introduction to Data Management and Data Structures and Algorithms.