Interested in contributing to our work on personalizing health interventions? New PhD, master thesis and HiWi positions are available, see here for more details.

• N-of-1 trials, adaptive trials, and micro-randomized trials for personalizing health interventions
• Reinforcement learning for sequential decision making in adaptive trials
• Causal inference in estimation, hypothesis testing, and prediction models
• Statistical and machine learning models of multi-modal health outcomes

• since July 2021: Adjunct Assistant Professor, Icahn School of Medicine at Mount Sinai, New York, USA
• since April 2021: Senior Researcher, heading the Health Intervention Analytics Lab in the Digital Health & Machine Learning chair, Hasso Plattner Institute
• July 2022 - February 2023: Visiting Faculty, Statistical Reinforcement Learning Lab, Department of Statistics, Harvard University, Cambridge, USA
• 2022 (3 months): Visiting Faculty, Department of Public Health Sciences, Seoul National University, Seoul, South Korea
• 2019-2021: Postdoc in Digital Health & Machine Learning Group, Hasso Plattner Institute
• 2018: Postdoc in Statistical Genomics Research Group, 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

• Dr. rer. nat. Computer Science, Humboldt University of Berlin (2018)
  • Thesis: “Development and application of new statistical methods for the analysis of multiple phenotypes to investigate genetic associations with cardiometabolic traits”
• M.Sc. Biostatistics, University of Toronto (2013)
• Dipl.-Math. Mathematics, University of Heidelberg (2011)
  • Thesis: “A Study of Dimension Reduction Techniques for Empirical Data Exploration” 

• Design and develop a platform and apps to conduct N-of-1 trials for personalizing digital health interventions
• Develop novel statistical models to analyze N-of-1 trials
• N-of-1 trials to investigate effectiveness of interventions for low back pain
• N-of-1 trials of sensor-based blood pressure measures and of omics biomarkers
• Development of clinically-motivated apps for screening and health interventions:
  • Animated assessment of alcohol consumption
  • AI- and expert-driven screening tool for fetal alcohol spectrum disorder
• Causal inference in risk prediction models
• Genetic association studies incorporating deep-learning derived intermediate molecular as well as complex imaging phenotypes
• Genetic and molecular association studies of cardiometabolic traits

• Master project “Adaptive personalized trials integrating EHR data” (winter 2023-2024)
• “Biostatistics & Epidemiological Data Analysis using R” (winter 2023-2024)
• Master project "Multimodal N-of-1 trials – designing the future of personalized healthcare" (summer 2023)
• “Digital N-of-1 trials and their application” (summer 2023)
• “Biostatistics & Epidemiological Data Analysis using R” (winter 2022-2023)
• Master project “Adaptive digital N-of-1 trials for personalizing digital health interventions” (summer 2022)
• “Biostatistics & Epidemiological Data Analysis using R” (winter 2021-2022)
• “N-of-1 trials and other modern study designs” (winter 2021-2022)
• Master project “Digitizing and analyzing historic patient files using Deep Learning” (winter 2020-21)
• Master project “Personalized decision support interventions to promote behavior change for people with low back pain” (winter 2020-21)
• “Biostatistics & Epidemiological Data Analysis using R” (winter 2020-2021)
• Master project “App-based N-of-1 trials in a clinically-relevant context for personalizing digital health interventions” (summer 2020)
• “Theory and applications of causal inference and causal learning” (summer 2020)
• “Personalized Medicine” (summer 2020)
• “Data Analysis using R – Statistical Epidemiology” (winter 2019-2020)

• “Biostatistics II” (2016-current)
• “Statistical data analysis using R” (2019-current)
• “Molecular Epidemiology” (2016-2021)
• “Statistical data analysis using SPSS” (2016-2018)
• “Multilevel Modeling” (2016)
• “SAS Programming” (2015-2018)
• “Biostatistics I” (2015-2018)

• Gärtner T, Schneider J, Arnrich B, Konigorski S (2023). Comparison of Bayesian networks, G-estimation and linear models to estimate causal treatment effects in aggregated N-of-1 trials. Accepted in BMC Medical Research Methodology.
• Fehr J, Piccininni M, Kurth T, Konigorski S (2023). A causal framework for assessing the transportability of clinical prediction models. Accepted in BMC Medical Research Methodology.
• Ehrig L^#, Wagner A^#, Wolter H, Correll CU, Geisel O*, Konigorski S* (2023). FASDetect as a machine learning-based screening app for FASD in youth with ADHD. npj Digital Medicine6:130. https://doi.org/10.1038/s41746-023-00864-1
• Zenner AM, Böttinger E, Konigorski S (2022). StudyMe: a new mobile app for user-centric N-of-1 Trials. Trials 23:1045. https://doi.org/10.1186/s13063-022-06893-7
• Konigorski S, Janke J, Patone G, Bergmann MM, Lippert C, Hübner N, Kaaks R, Boeing H, Pischon T (2022). Identification of novel genes whose expression in adipose tissue is causally associated with obesity traits. European Journal of Human Genetics.https://doi.org/10.1038/s41431-022-01161-3  
• Hohmuth N, Khanyaree I, Lang A, Duering O, Konigorski S, Višković V, Heising T, Egender F, Remschmidt C, Leistner R. (2022) Participatory disease surveillance for a mass gathering - a prospective cohort study on COVID-19, Germany 2021. BMC Public Health22: 2274. https://doi.org/10.1186/s12889-022-14505-x
• Monti R, Rautenstrauch P, Ghanbari M, James AR, Kirchler M, Ohler U*, Konigorski S*, Lippert C* (2022). Identifying interpretable gene-biomarker associations with functionally informed kernel-based tests in 190,000 exomes. Nature Communications 13(5332). https://doi.org/10.1038/s41467-022-32864-2
• Konigorski S, Wernicke S, Slosarek T, Zenner AM, Strelow N, Ruether FD, Henschel F, Manaswini M, Pottbäcker F, Edelman JA, Owoyele B, Danieletto M, Golden E, Zweig M, Nadkarni G, Böttinger E (2022). StudyU: a platform for designing and conducting innovative digital N-of-1 trials. J Med Internet Res 24(6): e35884. https://doi.org/10.2196/35884.
• Kirchler M, Konigorski S, Norden M, Meltendorf C, Kloft M, Schurmann C, Lippert C (2022). transferGWAS: GWAS of images using deep transfer learning.Bioinformatics 38(14): 3621–3628. https://doi.org/10.1093/bioinformatics/btac369
• Rübsamen N, Pape S, Konigorski S, Zapf A, Rücker G, Karch A (2022). Diagnostic accuracy of cerebrospinal fluid and blood biomarkers for the differential diagnosis of sporadic Creutzfeldt-Jakob disease in a specialized care setting: a systematic review and (network) meta-analysis. European Journal of Neurology 29: 1366– 1376. https://doi.org/10.1111/ene.15258
• Wiemker V, Bunova A, Rastogi A, Neufeld M, Ferreira-Borges C, Konigorski S, Probst C (2022). Digital assessment tools using animation features to quantify alcohol consumption: a systematic review. J Med Internet Res 24(3): e28927. https://doi.org/10.2196/28927
• Wiemker V, Bunova A, Neufeld M, Gornyi B, Yurasova E, Konigorski S, Kalinina A, Kontsevaya A, Ferreira-Borges C, Probst C (2022). Pilot study to evaluate usability and acceptability of the ‘Animated Alcohol Assessment Tool’ (AAA-Tool) in Russian primary healthcare. Digital Health 8: 1–11.https://doi.org/10.1177/20552076211074491
• Klinger JE, Ravarani CHJ, Baukmann HA, Cope JL, Böttinger EP, Konigorski S, Schmidt MF (2021). Interaction-based feature selection algorithm outperforms polygenic risk score in predicting Parkinson’s Disease status. Frontiers in Genetics 12: 744557. https://doi.org/10.3389/fgene.2021.744557.
• Fehr J, Konigorski S, Olivier S, et al. (2021). Computer-aided interpretation of chest radiography reveals the spectrum of tuberculosis in rural South Africa. npj Digital Medicine  4: 106.https://doi.org/10.1038/s41746-021-00471-y.
• Rüdiger S, Konigorski S, Edelman J, Zernick D, Thieme A, Lippert C (2021). Predicting the SARS-CoV-2 effective reproduction number using bulk contact data from mobile phones. PNAS  118(31):e2026731118. https://doi.org/10.1073/pnas.2026731118.  
• Sticht J, Álvaro-Benito M, Konigorski  S (2021). Type 1 diabetes and the HLA-region: Genetic association besides classical HLA class II genes in UK Biobank whole exome sequencing data. Frontiers in Genetics 12: 1044. https://doi.org/10.3389/fgene.2021.683946.
• Konigorski S (2021). Causal inference in developmental medicine and neurology. Developmental Medicine & Child Neurology 63(5): 498. https://doi.org/10.1111/dmcn.14813.
• 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 2: e677–80.  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 Epidemiology  42: 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.

• Lang A, Višković V, Hohmuth N, Konigorski S, Leistner R, Remschmidt C (2023). App-based collection of real-world data on the covid-19 pandemic and vaccines in Germany 2021-2022. Population Medicine 5(Supplement):A454. https://doi.org/10.18332/popmed/164483. [Abstract]
• Hellwig T, Lüders H, Konigorski S, Zaatar M, Kurz S, Liebers U, Grohé C (2023). Verbesserung des Gesamtüberlebens von Patienten mit NSCLC zwischen 1999 und 2017 – eine retrospektive monozentrische Kohortenstudie. Pneumologie 77(S1): S84. https://dx.doi.org/10.1055/s-0043-1761070. [Abstract]
• Konigorski S (2020). Incorporating electronic health record data in N-of-1 trials. https://dx.doi.org/10.3205/20gmds332. [Abstract]
• Kirchler M, Konigorski S, Schurmann C, Norden M, Meltendorf C, Kloft M, Lippert C (2020). transferGWAS: GWAS of images using deep transfer learning. In: Machine Learning for Health (ML4H) Workshop at NeurIPS 2020 [Abstract]
• 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]

• Lang A, Hohmuth N, Višković V, Konigorski S, Scholz S, Balzer F, Remlschmidt C, Leistner R (2023). App-based collection of real-world data on the COVID-19 pandemic and vaccines in Germany (eCOV): Prospective observational cohort study. https://doi.org/10.2196/preprints.47070.
• Fu J, Liu S, Du S, Ruan S, Guo X, Pan W, Sharma A, Konigorski S (2023). Multimodal N-of-1 trials: a novel personalized healthcare design. arXiv.https://arxiv.org/abs/2302.07547.
• Laure T, RCME Engels, Remmerswaal D, Spruijt-Metz D, Konigorski S, Boffo M (2023). Optimization of a transdiagnostic mobile emotion regulation intervention for university students: protocol for a Micro Randomized Trial. PsyArXiv.http://psyarxiv.com/xgq2r. 
• Ehrig L, Wagner A, Wolter H, Correll CU, Geisel O, Konigorski S (2022). FASDetect - A machine learning-based app to screen for the risk of fetal alcohol-spectrum disorder in youth with attention-deficit/hyperactivity disorder symptoms. medRxiv, https://doi.org/10.1101/2022.09.12.22279880.
• Zhou T, Schneider J, Arnrich B, Konigorski S (2022). Analyzing population-level trials as N-of-1 trials: an application to gait. arXiv. https://arxiv.org/abs/2209.03253
• Hohmuth N, Khanyaree I, Lang A, Duering O, Konigorski S, Višković V, Heising T, Egender F, Remschmidt C, Leistner R. (2022) Participatory disease surveillance for a mass gathering - a prospective cohort study on COVID-19, Germany 2021.https://doi.org/10.21203/rs.3.rs-1908358/v1
• Gärtner T, Schneider J, Arnrich B, Konigorski S (2022). Comparison of Bayesian networks, G-estimation and linear models to estimate causal treatment effects in aggregated N-of-1 trials. medRxiv. https://doi.org/10.1101/2022.07.21.22277832.
• Fehr J, Piccininni M, Kurth T, Konigorski S (2022). A causal framework for assessing the transportability of clinical prediction models. medRxiv https://doi.org/10.1101/2022.03.01.22271617.
• Kirchler M, Konigorski S, Norden M, Meltendorf C, Kloft M, Schurmann C, Lippert C (2021). transferGWAS: GWAS of images using deep transfer learning. bioRxiv 2021.10.22.465430. https://doi.org/10.1101/2021.10.22.465430.
• Zenner AM, Böttinger E, Konigorski S (2021). StudyMe: a new mobile app for user-centric N-of-1 Trials. arXiv:2108.00320. http://arxiv.org/abs/2108.00320.
• Klinger JE, Ravarani CNJ, Baukmann HA, Cope JL, Böttinger EP, Konigorski S, Schmidt MF (2021). Interaction-based feature selection algorithm outperforms polygenic risk score in predicting Parkinson’s Disease status. medRxiv 2021.07.20.21260848.  https://doi.org/10.1101/2021.07.20.21260848.
• Monti R, Rautenstrauch P, Ghanbari M, James AR, Ohler U*, Konigorski S*, Lippert C* (2021). Identifying interpretable gene-biomarker associations with functionally informed kernel-based tests in 190,000 exomes. bioRxiv 2021.05.27.444972. https://doi.org/10.1101/2021.05.27.444972.  
• Rübsamen N, Pape S, Konigorski S, Zapf A, Rücker G, Karch A (2021). Diagnostic accuracy of cerebrospinal fluid and blood biomarkers for the differential diagnosis of sporadic Creutzfeldt-Jakob disease in a specialized care setting: a systematic review and (network) meta-analysis. medRxiv 2021.03.25.21254312. https://doi.org/10.1101/2021.03.25.21254312.
• Konigorski S, Wernicke S, Slosarek T, Zenner AM, Strelow N, Ruether FD, Henschel F, Manaswini M, Pottbäcker F, Edelman JA, Owoyele B, Danieletto M, Golden E, Zweig M, Nadkarni G, Böttinger E (2020). StudyU: a platform for designing and conducting innovative digital N-of-1 trials. arXiv:2012.14201. https://arxiv.org/abs/2012.14201.
• 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, Thieme A, Lippert C (2020). Forecasting the SARS-CoV-2 effective reproduction number using bulk contact data from mobile phones. 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)

• Edelman JA, Owoyele BA, Santuber J, Konigorski S (2022). Designing for Value Creation: Principles, Methods, and Case Insights from Embedding Designing-as-Performance in Digital Health Education and Research. In: Meinel, C., Leifer, L. (eds) Design Thinking Research. Understanding Innovation. Springer, Cham. https://doi.org/10.1007/978-3-031-09297-8_10.
• 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.
• 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.

• Konigorski S  et al. (2020). StudyU Health. 
  • https://studyu.health 
  • https://github.com/hpi-studyu/studyu
  • https://hpi.de/lippert/projects/studyu
  • https://play.google.com/store/apps/details?id=health.studyu.app
  • https://apps.apple.com/us/app/studyu-health/id1571991198
• Ehrig L, Wagner AC, Wolter H, Geisel O, Konigorski S (2021). FASDetect. https://fasdetect.dhc-lab.hpi.de/#/
• Zenner AM, Böttinger E, Konigorski S (2021). StudyMe Health. https://play.google.com/store/apps/details?id=health.studyu.me
• Scordialo N, Fiedler T, Wiemker V, Konigorski S, Probst C (2020). AAA: Animated alcohol assessment tool.
• Rautenstrauch P, Monti R, Konigorski S, Lippert C (2020). seak: sequence annotations in kernel-based tests. https://github.com/HealthML/seak.
• Konigorski S, Yilmaz YE. CJAMP: Copula-based joint analysis of multiple phenotypes. R package version 0.1.1. https://CRAN.R-project.org/package=CJAMP.
• Konigorski S, Yilmaz YE. CIEE: Estimating and testing direct effects in directed acyclic graphs using estimating equations. R package version 0.1.1.https://CRAN.R-project.org/package=CIEE.