Hasso-Plattner-Institut
Prof. Dr. Christoph Lippert
  
 

Stefan Konigorski, Dr. rer. nat.

Postdoctoral Researcher

Phone: +49 331 5509 4873


Room: Campus III - Building G2 - G-2.1.25


Email: stefan.konigorski(at)hpi.de


Web: Linkedin -- Researchgate -- Google Scholar -- CV

Research Interests

  • N-of-1 trials for personalizing health interventions
  • Causal inference in estimation, hypothesis testing, and prediction models
  • Statistical and machine learning models of multi-modal molecular and genetic data

Teaching

At Berlin School of Public Health, Charité Berlin

  • “Statistical data analysis using R” (2019-current)
  • “Biostatistics II” (2016-current)
  • “Molecular Epidemiology” (2016-2018)
  • “Statistical data analysis using SPSS” (2016-2018)
  • “Multilevel Modeling” (2016)
  • “SAS Programming” (2015-2018)
  • “Biostatistics I” (2015-2018)

Student thesis supervision

Ongoing

  • Alexander Zenner, App-based creation of user-centric N-of-1 trials (MSc IT Systems Engineering, HPI)
  • Nils Strelow, Distributed N-of-1 trials system (MSc IT Systems Engineering, HPI)
  • Thomas Gärtner, Statistical models to estimate causal effects of time-varying exposures on health outcomes in N-of-1 trials (MSc Digital Health, HPI)
  • Lukas Ehrig, Machine learning- and expert-based clinical screening app for fetal alcohol disorder (MSc IT Systems Engineering, HPI)
  • Dr. Luca Meoli, Target N-of-1 trials of observational metabolomics profiles (MSc Epidemiology, Berlin School of Public Health, Charité Berlin)

2020

  • Antonia Winne, Protocol for a series of N-of-1 trials: App-based physical exercise interventions for a chronic low back pain population in Germany (MSc Digital Health, HPI)
  • Pia Rautenstrauch, seak: sequence annotations in kernel-based tests (MSc Bioinformatics, University of Tübingen)
  • Dr. Jana Sticht, Genome wide association study (GWAS) of genetic variants in human leukocyte antigen (HLA) genes with autoimmune diseases in the UK Biobank (MSc Epidemiology, Berlin School of Public Health, Charité Berlin)
  • Theresa Hellwig, Identifikation von Faktoren, die zur Verbesserung der Langzeitprognose bei Patienten mit Lungenkrebs führen [Identification of factors to improve long-term prognosis of lung cancer patients] (MSc Epidemiology, Berlin School of Public Health, Charité Berlin)

2019

  • Julia Fiebig, Age-period-cohort analysis of cancer risk and mortality (MSc Epidemiology, Berlin School of Public Health, Charité Berlin)
  • Stephanie Pape, Comparison of the validity of biomarker tests for the differential diagnosis of Creutzfeldt-Jakob disease - a network meta-analysis (MSc Epidemiology, Berlin School of Public Health, Charité Berlin)
  • Dr. Iris Meier, Infektionsrisiko der latenten Tuberkulose nach Auslandseinsatz bei medizinischem Personal [Risk of tuberculosis infection of health workers after field assignments overseas] (MSc Epidemiology, Berlin School of Public Health, Charité Berlin)

2018

  • Arina Levchaeva, Aktuelle TB Situation in der Ukraine im Zeitraum 2007-2017: deskriptive Trendanalyse anhand der öffentlich zugänglichen Sekundärdaten [Current tuberculosis situation in the Ukraine between 2007-2017: descriptive trend analysis based on publicly available secondary data] (MPH Public Health, Berlin School of Public Health, Charité Berlin)
  • Nina Sodogé, Funktionelle Unabhängigkeit als Prädiktor für den Rehabilitationsverlauf in neurologischen Rehabilitationkliniken in der Schweiz [Functional independence as predictor for the rehabilitation progress in neurological rehabilitation centers in Switzerland] (MPH Public Health, Berlin School of Public Health, Charité Berlin)

2017

  • Eugenia Romo Ventura, Dietary intake of milk and dairy products and blood concentrations of insulin-like growth factor 1 (IGF-I) (MSc Epidemiology, Berlin School of Public Health, Charité Berlin)

Education

Experience

  • since January 2019: Postdoc in Digital Health & Machine Learning Group, Hasso-Plattner-Institut
  • 2018: Postdoc in Statistical Genomics Research, Max Delbrück Center for Molecular Medicine, Berlin
  • 2013-2018: Research Associate in Molecular Epidemiology Research Group, Max Delbrück Center for Molecular Medicine, Berlin
  • 2016 (3 months): Visiting Researcher in Department of Mathematics & Statistics, Memorial University of Newfoundland, St. John's, Canada
  • 2012-2013: Research Associate at Samuel Lunenfeld Research Institute at Mount Sinai Hospital, Toronto, Canada
  • 2011-2013: Research Associate in Statistical Genomics Laboratory, University of Toronto, Canada

Publications

Peer-reviewed journal articles

  • Wilkinson J, Arnold K, Murray EJ, van Smeden M, Carr K, Sippy R, de Kamps M, Beam A, Konigorski S, Lippert C, Gilthorpe M, Tennant P (2020). It is time to reality check the promises of machine learning-powered precision medicine. The Lancet Digital Health. https://doi.org/10.1016/S2589-7500(20)30200-4.
  • Piccininni M, Konigorski S, Rohmann JL, Kurth T (2020). Directed Acyclic Graphs and causal thinking in clinical risk prediction modeling. BMC Medical Research Methodology 20: 179. https://doi.org/10.1186/s12874-020-01058-z.
  • Konigorski S, Yilmaz YE, Janke J, Bergmann MM, Boeing H, Pischon T (2020). Powerful rare variant association testing in a copula-based joint analysis of multiple traits. Genetic Epidemiology 44: 26–40. https://doi.org/10.1002/gepi.22265.
  • Meier I, Schablon A, Nienhaus A, Konigorski S (2020). Latente Tuberkulose bei medizinischem Personal in Deutschland nach Auslandseinsatz [Latent tuberculosis infection among healthcare staff in Germany after assignments abroad]. Pneumologie 74: 1–7. https://doi.org/10.1055/a-1127-9537.
  • Konigorski S, Janke J, Drogan D, Bergmann MM, Hierholzer J, Kaaks R, Boeing H, Pischon T (2019). Prediction of circulating adipokine levels based on body fat compartments and adipose tissue gene expression. Obesity Facts 12: 590–605. https://doi.org/10.1159/000502117.
  • Romo Ventura E, Konigorski S, Rohrmann S, Schneider H, Stalla GK, Pischon T, Linseisen J, Nimptsch K (2019). Dietary intake of milk and dairy products and blood concentrations of insulin-like growth factor 1 (IGF-I). European Journal of Nutrition 59: 1413–1420. https://doi.org/10.1007/s00394-019-01994-7.
  • Nimptsch K, Konigorski S, Pischon T (2019). Diagnosis of obesity and use of obesity biomarkers in science and clinical medicine. Metabolism: Clinical and Experimental 92: 61-70. https://doi.org/10.1016/j.metabol.2018.12.006.
  • Jaeschke L, Steinbrecher A, Jeran S, Konigorski S, Pischon T (2018). Variability and reliability study of overall physical activity and activity intensity levels using 24h-accelerometry-assessed data. BMC Public Health 18(1): 530.https://doi.org/10.1186/s12889-018-5415-8.
  • Konigorski S, Wang Y, Cigsar C, Yilmaz YE (2018). Estimating and testing direct genetic effects in directed acyclic graphs using estimating equations. Genetic Epidemiology42: 174–186. https://doi.org/10.1002/gepi.22107.
  • Konigorski S, Yilmaz YE, Pischon T (2017). Comparison of single-marker and multi-marker tests in rare variant association studies of quantitative traits. PLoS One 12(5): e0178504. https://doi.org/10.1371/journal.pone.0178504.
  • Schillert A*, Konigorski S* (2016). Joint analysis of multiple phenotypes - summary of results and discussions from the Genetic Analysis Workshop 19. BMC Genetics 17(Suppl 2): 7. (*contributed equally). https://doi.org/10.1186/s12863-015-0317-6.
  • Koch SC, Konigorski S, Sieverding M (2014). Sexist behavior undermines women’s performance in a job application situation. Sex Roles 70(3-4): 79-87. https://doi.org/10.1007/s11199-014-0342-3.

Peer-reviewed conference articles & contributions

  • Konigorski S, Monti R, Rautenstrauch P, Lippert C (2020). Fast kernel-based rare-variant association tests integrating variant annotations from deep learning. In: The 2020 Annual Meeting of the International Genetic Epidemiology Society. Genetic Epidemiology 44(5): 495. https://doi.org/10.1002/gepi.22298. [Abstract]
  • Konigorski S, Monti R, Lippert C (2019). Kernel-based tests integrating variant effect predictions from deep learning for genetic association tests of rare variants. https://dx.doi.org/10.3205/19gmds067. [Abstract]
  • Konigorski S, Khorasani S, Lippert C (2018). Integrating omics and MRI data with kernel-based tests and CNNs to identify rare genetic markers for Alzheimer’s disease. In: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018, arXiv:1812.00448. https://arxiv.org/abs/1812.00448.
  • Konigorski S, Lippert C (2018). Kernel-based tests for very rare variants. In: The 2018 Annual Meeting of the International Genetic Epidemiology Society. Genetic Epidemiology 42(7): 711. https://doi.org/10.1002/gepi.22163. [Abstract]
  • Konigorski S, Janke J, Drogan D, et al. (2018). Prediction of circulating adipokine levels by body fat compartments and adipose tissue gene expression. Revue d'Épidémiologie et de Santé Publique 66(5): S353. https://doi.org/10.1016/j.respe.2018.05.318. [Abstract]
  • Konigorski S, Yilmaz YE, Pischon T (2016). Genetic association analysis based on a joint model of gene expression and blood pressure. BMC Proceedings 10(Suppl 7): 289-294. https://doi.org/10.1186/s12919-016-0045-6.
  • Konigorski S, Wang Y, Cigsar C, Yilmaz YE (2016). Estimating and testing direct genetic effects in directed acyclic graphs with multiple phenotypes using estimating equations. In: The 2016 Annual Meeting of the International Genetic Epidemiology Society. Genetic Epidemiology 40: 609–674. https://doi.org/10.1002/gepi.22001. [Abstract]
  • Konigorski S*, Yilmaz YE*, Bull SB (2014). Bivariate genetic association analysis of systolic and diastolic blood pressure by copula models.BMC Proceedings 8(Suppl 1): S72. (*contributed equally). https://doi.org/10.1186/1753-6561-8-S1-S72.
  • Konigorski S, Kustra R (2012). Sparse principal component regression as a tool to detect causal regions in genetic studies. In: Abstracts from the annual meeting of the International Genetic Epidemiology Society. Genetic Epidemiology 36: 720–777. https://doi.org/10.1002/gepi.21677. [Abstract]

Preprints

  • Fehr J, Konigorski S, Olivier S, et al. (2020). Computer-aided interpretation of chest radiography to detect TB in rural South Africa.medRxiv:2020.09.04.20188045. https://doi.org/10.1101/2020.09.04.20188045.
  • Rüdiger S, Konigorski S, Edelman J, Zernick D, Lippert C, Thieme A (2020). The SARS-CoV-2 effective reproduction rate has a high correlation with a contact index derived from large-scale individual location data using GPS-enabled mobile phones in Germany. medRxiv:2020.10.02.20188136. https://doi.org/10.1101/2020.10.02.20188136.
  • Piccininni M, Konigorski S, Rohmann JL, Kurth T (2020). Directed Acyclic Graphs and causal thinking in clinical risk prediction modeling. arXiv:2002.09414. https://arxiv.org/abs/2002.09414. (now published in BMC Medical Research Methodology, see above)

Book chapters

  • Konigorski S, Glicksberg BS (2020). Using C-JAMP to investigate epistasis and pleiotropy. In: Wong KC (eds) Epistasis. Methods in Molecular Biology, vol 2212. Springer, New York, NY. https://doi.org/10.1007/978-1-0716-0947-7. In press.
  • Fehr J, Konigorski S, Lippert C (2020). Data Science für Digitale Medizin. [Data science for digital medicine]. In: Matusiewicz D, Henningsen M, Ehlers J (eds). Digitale Medizin – Kompendium für Studium und Praxis. [Digital Medicine – A textbook for students and professionals]. Medizinisch Wissenschaftliche Verlagsgesellschaft, Berlin. https://www.mwv-berlin.de/produkte/!/title/digitale-medizin/id/673.
  • Fehr J, Konigorski S, Lippert C. (2020) Bias and Fairness. In: DEL7.3: Data and artificial intelligence assessment methods (DAISAM) reference, FG-AI4H-I-035, Geneva, Switzerland.

Software