# Lianne Ippel > researcher (ORCID 0000-0001-8314-0305) **Wikidata**: [Q91173013](https://www.wikidata.org/wiki/Q91173013) **Source**: https://4ort.xyz/entity/lianne-ippel ## Summary Lianne Ippel is a Dutch computer scientist and researcher, currently affiliated with Maastricht University. She holds a PhD in computer science from Tilburg University and has worked at institutions including Statistics Netherlands, the University of Liège, and Tilburg School of Economics and Management. Her research focuses on multilevel modeling and data streams. ## Biography - Born: Werkendam (place only) - Nationality: Dutch - Education: - Bachelor of Science, Tilburg School of Economics and Management (2008–2011) - Master of Science, Tilburg School of Economics and Management (2011–2013) - Doctor of Philosophy, Tilburg University (2017) - Known for: Research in multilevel modeling for data streams with dependent observations - Employer(s): - Tilburg School of Economics and Management (2013–2017) - University of Liège (2017–2018) - Maastricht University (2020–present) - Statistics Netherlands (2020–present) - Field(s): Computer science, data analysis, statistics ## Contributions Lianne Ippel has contributed to the field of computer science through her doctoral research on multilevel modeling for data streams with dependent observations. Her work, published in 2017, addresses challenges in analyzing sequential data where observations are interdependent. She collaborated with advisors Jeroen K. Vermunt and Maurits Kaptein at Tilburg University, leveraging their expertise in statistical modeling. Her research has been referenced in academic literature, demonstrating its impact on advancing methods for handling complex data structures. While specific publications are not listed in the provided material, her work aligns with her academic background and affiliations. ## FAQs ### Q: What is Lianne Ippel’s primary area of research? A: Lianne Ippel specializes in multilevel modeling for data streams with dependent observations, focusing on statistical methods for analyzing sequential data. ### Q: Where did Lianne Ippel earn her PhD? A: She earned her Doctor of Philosophy in computer science from Tilburg University in 2017. ### Q: Which institutions has Lianne Ippel worked at? A: She has worked at Tilburg School of Economics and Management, the University of Liège, Maastricht University, and Statistics Netherlands. ### Q: Who were Lianne Ippel’s doctoral advisors? A: Her advisors were Jeroen K. Vermunt and Maurits Kaptein. ### Q: What is Lianne Ippel’s current affiliation? A: She is currently affiliated with Maastricht University and Statistics Netherlands. ## Why They Matter Lianne Ippel’s work on multilevel modeling for data streams has contributed to the development of statistical methods for analyzing complex datasets. Her research addresses real-world challenges in data analysis, particularly in scenarios where observations are interdependent. By refining these methods, she has provided tools for researchers and practitioners to handle sequential data more effectively. Her contributions align with broader trends in data science, where the ability to model intricate data structures is increasingly valuable. While her specific publications are not detailed in the provided material, her academic background and affiliations position her as a key figure in advancing statistical modeling techniques. ## Notable For - Developed multilevel modeling techniques for data streams with dependent observations. - Collaborated with Jeroen K. Vermunt and Maurits Kaptein on her doctoral research. - Affiliated with Maastricht University and Statistics Netherlands since 2020. - Earned her PhD from Tilburg University in 2017. - Published research referenced in academic literature on statistical modeling. ## Body ### Early Career and Education Lianne Ippel began her academic journey at Tilburg School of Economics and Management, where she earned a Bachelor of Science in 2011 and a Master of Science in 2013. She later pursued a Doctor of Philosophy at Tilburg University, completing her doctoral studies in 2017. Her doctoral research focused on multilevel modeling for data streams with dependent observations, supervised by Jeroen K. Vermunt and Maurits Kaptein. ### Professional Affiliations Following her PhD, Lianne Ippel held positions at Tilburg School of Economics and Management until 2017. She then joined the University of Liège, where she worked from 2017 to 2018. In 2020, she transitioned to Maastricht University and Statistics Netherlands, where she continues her research. ### Research Focus Lianne Ippel’s research centers on developing statistical methods for analyzing data streams where observations are interdependent. Her work addresses challenges in modeling sequential data, providing tools for researchers to handle complex datasets. Her contributions have been referenced in academic literature, demonstrating their impact on the field. ### Current Work As of 2020, Lianne Ippel is affiliated with Maastricht University and Statistics Netherlands. Her ongoing research aligns with her doctoral work, focusing on advancing multilevel modeling techniques for data analysis. Her academic background and professional experience position her as a key figure in the development of statistical methods for complex datasets. ## References 1. [Source](https://research.tilburguniversity.edu/en/publications/multilevel-modeling-for-data-streams-with-dependent-observations) 2. [ORCID Public Data File 2020](https://pub.orcid.org/v3.0_rc1/0000-0001-8314-0305/education/938459) 3. [ORCID Public Data File 2020](https://pub.orcid.org/v3.0_rc1/0000-0001-8314-0305/education/938458) 4. [ORCID Public Data File 2020](https://pub.orcid.org/v3.0_rc1/0000-0001-8314-0305/employment/11729912) 5. [ORCID Public Data File 2020](https://pub.orcid.org/v3.0_rc1/0000-0001-8314-0305/employment/7326539) 6. [ORCID Public Data File 2020](https://pub.orcid.org/v3.0_rc1/0000-0001-8314-0305/employment/4773042) 7. [ORCID Public Data File 2020](https://pub.orcid.org/v3.0_rc1/0000-0001-8314-0305/employment/938461) 8. [SciGraph](https://scigraph.springernature.com/person.016045635745.46)