جست‌وجوی استاد و پژوهشگر

Moritz Herrmannمشاهده پروفایل

پژوهشگر

• Empirical Machine Learning
• Manifold Learning
• Representation Learning
• +۵ مورد دیگر

Dr. Moritz Herrmann is a postdoc researcher and Reproducibility & Open Science Transfer Coordinator at the Munich Center for Machine Learning (MCML). He is affiliated with the Biometry in Molecular Medicine working group led by Prof. Anne-Laure Boulesteix at Ludwig-Maximilians-Universität München, and contributes to initiatives like the LMU Open Science Center , Open Science Initiative in Statistics (OSIS) , and Open Science Initiative in Medicine (OSIM) . Ph.D. in Statistics from LMU (2022), M.Sc. in Statistics (2018), and B.Sc. in Mathematics/Sports Science (2014) His research focuses on Empirical Machine Learning , Manifold Learning , and Metascience , with emphasis on epistemological foundations and reliability in ML research. He advocates for open science practices and data literacy, as outlined in his ICML 2024 position paper on rethinking empirical ML research. As a member of the Empirical Machine Learning research focus group and the Statistical Learning and Data Science Chair , Herrmann bridges statistical methodology with biomedical applications. His work spans outlier detection, cluster analysis, and reproducibility frameworks, reflected in his recent publications in journals like Biometrical Journal and Data Mining and Knowledge Discovery .

Levent Neyseمشاهده پروفایل

پژوهشگر

• Behavioral Economics
• Metascience
• Replication Studies
• +۳ مورد دیگر

Levent Neyse is a behavioral economist and researcher at the German Institute for Economic Research (DIW Berlin), where he heads the SOEP Innovation Sample. He serves as Co-Principal Investigator of the metascientific laboratory Lab² at the Wissenschaftszentrum Berlin (WZB Berlin). His affiliations extend to the Institute of Labor Economics (IZA) as an affiliated researcher and the London School of Economics and Political Science as a visiting fellow in the Department of Psychological and Behavioural Science. His research centers on behavioral economics and metascience, focusing on how individuals make economic and social decisions through survey and experimental methodologies. Key interests include cognitive reflection, risk preferences across life domains, replication studies, and improving research transparency in social sciences. His work frequently examines the intersection of psychological processes and economic behavior, particularly in labor markets and decision-making under uncertainty. Neyse's publication record demonstrates strong emphasis on replication science and methodological rigor, with recent articles analyzing cognitive reflection's relationship to economic outcomes, domain-specific risk attitudes across the life course, and biomarker validity in economic preferences. His research consistently employs large-scale representative samples and pre-registered methodologies. As Associate Editor of the Journal of Behavioral and Experimental Economics, he contributes to advancing methodological standards in the field. His leadership of the SOEP Innovation Sample and Lab² metascience laboratory reflects his commitment to developing robust research infrastructure and promoting transparent scientific practices. Neyse actively disseminates his work through international seminars including presentations at Istanbul Bilgi University, LMU München, and Foreign Trade University Hanoi, focusing on metascience topics like pre-registration and laboratory development for collaborative economic science.