Team and publications

Carolin Victoria Schneider, MD is a physician and research group leader. She completed her medical education at the RWTH Aachen University and her postdoctoral fellowship at the University of Pennsylvania. Dr Schneider has received numerous awards and prizes, including admission to the Young Academy of the North Rhine-Westphalia Academy of Sciences and Arts, the Borchers Medal for her dissertation at RWTH Aachen University and the C. A. Ewald Prize of the German Society for Gastroenterology, Digestive and Metabolic Diseases. She has presented her research at various conferences and has won several young investigator awards. Her research focus is on the integration of multi-omics data, lipidomic analyses, and artificial intelligence in the study and prevention of gastrointestinal and metabolic diseases.

Google scholar: https://scholar.google.com/citations?user=svKiqaAAAAAJ&hl=en

Research Gate: https://www.researchgate.net/profile/Carolin-Schneider-7

Benjamin Laevens, PhD is a postdoctoral researcher who is overseeing PhD students and driving forward artificial intelligence techniques in the lab. His research focuses on analyzing how the gut microbiome is affected by different nutrients and how those nutrients influence the development and progression of liver diseases. Benjamin's methodology involves utilizing large-scale microbiome and dietary datasets to identify key microbial and nutrient signatures that are associated with liver disease risk. He is also developing novel machine learning and artificial intelligence techniques to analyze complex multi-omic datasets and to generate predictive models that can help identify patients at high risk for liver diseases. His proficiency in Python, coupled with his innovative algorithm development and data visualization skills, promises to revolutionize our field. Ultimately, Ben's work aims to contribute to the development of personalized dietary interventions that can help prevent and treat liver diseases, and to improve overall gut and liver health outcomes.

Jan Clusmann, MD is a postdoc and medical doctor focusing on hepatocellular carcinoma (HCC) and is working on developing image analysis techniques using artificial intelligence to improve HCC diagnosis and treatment. His research involves utilizing large-scale omic datasets and developing novel AI techniques to identify key features and biomarkers that are associated with HCC development and progression. Jan is also driving forward the development of scoring systems for HCC in the general population that can be used to guide personalized therapy approaches. His work aims to improve overall HCC diagnosis and treatment outcomes, and to contribute to the development of personalized therapy strategies that can help prevent and treat HCC in high-risk populations.

Yazhou Chen is a PhD candidate at Schneider Lab working on a project that aims to employ large-scale clinical and imaging data to unravel essential pathophysiological pathways linking MAFLD, plasma lipid metabolism, and CVD risk. Yazhou is pioneering the use of radiomics and machine learning to predict genetic variants from medical imaging data. This innovative approach paves the way for the creation of predictive models, aiding in identifying patients with a high risk of these diseases and unveiling potential targets for therapeutic interventions.

Mara Vell is an MD candidate whose research focuses on repurposing common medications to prevent liver diseases in the general population. Her work involves analyzing large-scale population-based genetic studies and lipidomic data to identify optimal targets for therapeutic interventions. Additionally, Mara is also exploring personalized treatment options for different populations, with a particular focus on sex differences in regard to medication response. Her research aims to contribute to the development of personalized prevention and treatment strategies for liver diseases, and to improve overall liver health outcomes in different populations.

Leonida Hehl is an MD candidate investigating the use of protective genetic variants as potential pharmacological targets for preventing fatty liver disease. Her methodology involves leveraging large-scale genetic datasets to identify genetic variants associated with a decreased risk of fatty liver disease. She is also conducting functional studies to investigate the potential mechanisms underlying the protective effects of these genetic variants.

Katharina Seeling is an MD candidate studying mortality and morbidity in patients with irritable bowel syndrome (IBS). Her methodology includes analyzing large-scale population-based datasets to identify potential risk factors for increased mortality and morbidity in IBS patients, as well as conducting studies to investigate potential biomarkers for IBS in the general population.

Miriam Rendel is an MD candidate investigating ERLIN1 as a potential treatment target for metabolically-associated fatty liver disease. Her methodology includes exploring the effect of different loss of function mutations in ERLIN1 and their relationship with fatty liver disease, as well as utilizing in vitro and in vivo experiments to validate the potential efficacy of targeting ERLIN1 in treating MAFLD.

Simon Schophaus is an MD candidate exploring personalized nutrition as a potential intervention for preventing liver disease. His methodology includes analyzing large-scale dietary and health datasets to identify potential personalized nutrition interventions that may be effective in reducing the risk of liver disease, as well as conducting clinical studies to validate the efficacy of these interventions.

Paul Koop, an innovative research student, soon to be postdoc, at Schneider Lab, is blazing trails with his work on Python to develop algorithms for managing and visualizing PheWAS data from the UK Biobank. Specializing in the nexus of computer science and epidemiology, Paul is leveraging Python's powerful data analysis capabilities to unlock new understandings. His project focuses on developing unique algorithms to sift through the extensive PheWAS data. This work is instrumental in streamlining data analysis and pinpointing critical insights within the vast dataset. Furthermore, Paul is translating these complex datasets into intuitive visual formats, facilitating easier comprehension and utilization of the data. Paul's expertise is reshaping how Schneider Lab approaches PheWAS data.

Helen Huang, a dedicated rotation student at the University of Pennsylvania and closely associated with Schneider Lab, is making significant contributions to the field of genetic research on Metabolic-Associated Fatty Liver Disease (MAFLD). She is leveraging the wealth of data available from the Penn Medicine Biobank to unearth genetic targets for MAFLD, aiding our understanding and potential treatment of this pervasive condition. Her work bridges the gap between big data and medical research, contributing valuable insights to our lab's comprehensive exploration of metabolic and gastrointestinal diseases.