جستجوی استاد راهنمای دانشگاهی

• Wireless Communication
• Machine Learning
• Signal Processing
• +۴ مورد دیگر

Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.

Marcel Prokopczukمشاهده پروفایل

استاد

• Commodity Markets
• Capital Market Research
• Derivatives
• +۳ مورد دیگر

Prof. Dr. Marcel Prokopczuk is a full Professor of Finance and Commodity Markets at Leibniz University Hannover, serving as Dean of the Faculty of Economics and Management since 2024. He holds a Ph.D. from the University of Mannheim and is a CFA Charterholder. His research focuses on commodity markets, capital market dynamics, derivatives, risk management, and household financial decisions. Affiliations: Institute of Finance and Commodity Markets, Leibniz Research Centre Energy 2050, Hannover Center of Finance and Insurance (HCFI). Roles: Editor of the Journal of Commodity Markets , Associate Editor of the Journal of Banking and Finance , and Board Member of CEMA. Research Interests: Commodity markets, derivatives pricing, energy finance, risk management, and the impact of historical factors on financial development. He has published extensively in top journals like Review of Economic Studies and Journal of Banking and Finance . Awards: Includes the Deutsche Bundesbank Prize (2024), FIRN Annual Conference Award (2017), and Best Teaching Award (2015). Grants & Advisory: Member of the German Business Foundation and involved in energy policy initiatives. His work bridges academic research with practical applications in financial markets and commodity pricing.

Rebecca Schaumbergمشاهده پروفایل

استادیار

• Organizational Behavior
• Psychological Theory in Business
• Emotions in Organizations
• +۶ مورد دیگر

Rebecca Schaumberg is an Assistant Professor of Organizational Behavior at ESMT Berlin, joining the faculty on July 1, 2025. She is part of a diverse international faculty of 47 members from 18 countries, contributing to ESMT's interdisciplinary and globally engaged research environment. Her research lies at the intersection of psychological theory and business practice, with a focus on self-conscious emotions such as shame, guilt, pride, and embarrassment. These emotions are studied in the context of decision-making, leadership processes, and organizational culture. Her work has been published in leading academic journals including Psychological Science , Academy of Management Journal , and Organizational Behavior and Human Decision Processes . The body of her research, as reflected in the 15 most recent conceptual articles, spans themes in organizational behavior, psychology, leadership, and ethics. Key trends include the influence of emotions on accountability, status dynamics, feedback reception, and ethical behavior within organizations. Her work integrates insights from social and cognitive psychology into management science. Scientific Awards: Teaching Excellence Award at the Wharton School Advising and Teaching: Dr. Schaumberg has been recognized for her excellence in teaching, particularly in negotiation, at the Wharton School. At ESMT, she will teach organizational behavior in the master’s programs. While no formal advisees are listed, her academic mentorship is expected to grow within her new role. Labs and Research Teams: No specific labs or research teams are mentioned in the available text.

Kirstin Drenkhahnمشاهده پروفایل

استاد

• Criminology
• Criminal Law
• Prison Studies
• +۳ مورد دیگر

Prof. Kirstin Drenkhahn is a Professor of Criminal Law and Criminology at the Free University of Berlin's Faculty of Law. Her research focuses on prison climate (MQPL+ project), rural criminology (Farm Crime study), restorative justice, juvenile justice, and judicial practices. She leads international collaborations, including projects on prison social climate in Germany and Switzerland, and rural crime against farmers. Her teaching includes courses on criminal law, criminology, and juvenile justice. She advises PhD students and organizes academic events like the North Crimea Conference and Criminology Network meetings. Her work emphasizes resocialization, legal education reform, and policy-driven research. Research Highlights: MQPL+ Project: Investigates prison climate's impact on resocialization, supported by DFG/SNSF. Rural Criminology: Examines agricultural crime and farmer victimization through international surveys. Judicial Practices: Analyzes decision-making in penal enforcement chambers and law student well-being. Teaching & Outreach: Offers courses in criminal law, criminology, and juvenile justice. Organizes the 'University in Prison' initiative for student-inmate academic exchanges. Grants & Collaborations: DFG/SNSF-funded MQPL+ project. Swiss Federal Office of Justice-funded pilot on prison climate. International partnerships with Bern, Cologne, and Cambridge universities.

• Machine Learning
• Artificial Intelligence
• Computer Science
• +۳ مورد دیگر

Prof. Felix Bießmann holds a professorship in Computer Science and Media at Berlin University of Applied Sciences' Department VI. His research focuses on machine learning applications in diverse fields including healthcare, urban planning, environmental science, and robotics through his Cognitive Algorithms Lab. He teaches courses such as Machine Learning, Deep Learning, and Data Science Workflows, alongside roles at TU Berlin and Korea University. Education: PhD (Dr. rer. nat.) in Natural Sciences Research interests span machine learning theory and practical implementations across domains like computer vision, generative AI, and sensor data analysis. His work addresses challenges in automated systems, healthcare monitoring, and sustainable technologies. Recent student theses explore topics like license plate recognition, adaptive game soundtracks, and bird song detection using TinyML. Collaborations include projects with the Charité Berlin and Robert-Koch Institute. Lab: Cognitive Algorithms Lab (developing machine learning methods/applications) Contact: felix.biessmann@bht-berlin.de | Office D138, Berlin University of Applied Sciences.

• Climate Dynamics
• Atmospheric Science
• Oceanography
• +۳ مورد دیگر

Dr. Tido Semmler is a Senior Scientist in the Climate Dynamics department at the Alfred Wegener Institute (AWI). His research focuses on polar-latitude linkages in the atmosphere and ocean, particularly the response of the climate system to anthropogenic emissions. He contributes to global climate modeling efforts, including CMIP6 simulations with the AWI-CM model, and investigates Arctic amplification effects on mid-latitude weather patterns. Affiliations: AWI Climate Dynamics Department, Helmholtz Association Key Projects: APPLICATE (Arctic Predictability and Prediction of sea ice and climate), HighResMIP (High Resolution Model Intercomparison Project) His work emphasizes high-resolution modeling, data assimilation for sea ice prediction, and understanding mechanisms behind Arctic sea ice decline and its global impacts. Semmler collaborates internationally on climate assessment frameworks and contributes to advancements in coupled ocean-atmosphere modeling systems.

Ursula Bergerمشاهده پروفایل

استاد پژوهش

• Biostatistics
• Epidemiology
• Public Health
• +۵ مورد دیگر

PD Dr. Ursula Berger is a Senior Biostatistician and Epidemiologist at Ludwig-Maximilians University Munich's Department of Medical Informatics, Biostatistics and Epidemiology (IBE). She holds a Venia Legendi for Biostatistics and Epidemiology from LMU and has extensive experience in health research, including positions at the University of Glasgow, Universität Bielefeld, and as a consultant for IGES in Berlin. Her research focuses on the evaluation of health programs and interventions, health inequality research, and risk factor assessment using models for complex data structures. Dr. Berger specializes in spatial epidemiology, statistical methods development, and the application of advanced biostatistical techniques to public health problems. She has made significant contributions to understanding health disparities, environmental health impacts, and pandemic response strategies. Dr. Berger has supervised numerous doctoral and master's theses across diverse topics including spatial epidemiology, non-communicable diseases, environmental exposures, and health systems research. Her recent publications demonstrate expertise in statistical modeling of pandemic data, health inequalities, and methodological approaches to epidemiological challenges. Her scientific recognition includes the Harald Mückter Teaching Innovation Award 2025 from the LMU Medical Faculty for her commitment to teaching and promotion of statistical competence in medical education. She has developed innovative educational tools including epiLEARNER, an interactive e-learning program for epidemiology and statistics. As an educator, Dr. Berger coordinates and teaches advanced courses including Quantitative Methods, Survival Analysis, Multilevel Analysis, and Statistical Methods for Spatial Epidemiology. She has played a key role in restructuring epidemiology and biostatistics curriculum for medical students through the MeCuM Science program and has coordinated the development of a national catalogue of learning objectives in biostatistics.

• Econometrics
• Applied Microeconomics
• Quantitative Methods
• +۳ مورد دیگر

Daniel Gutknecht, Ph.D., is a Professor in the Department of Economic Policy & Quantitative Methods (EQ) at the Faculty of Economics, Goethe University Frankfurt am Main. His academic work spans econometrics, applied microeconomics, and quantitative methods, with a focus on causal inference and time series analysis. Econometrics Methodology Quantile Regression Panel Data Analysis Nonlinear Models Recent research contributions include advanced econometric techniques such as staggered adoption DiD designs, sparsity tests for high-dimensional regressions, and intercept estimation in nonlinear selection models. His publications address critical challenges in quantile forecast optimality, nowcasting monotonicity, and heaped duration data modeling, reflecting interdisciplinary applications in public health and macroeconomic policy. Current teaching includes Advanced Econometrics 1 and Fundamentals of Econometrics for the Winter semester 2025/26. Contact details: Office RuW 3.211, Theodor-W.-Adorno-Platz 4, Frankfurt am Main; email: gutknecht@wiwi.uni-frankfurt.de .

Thilo Streckمشاهده پروفایل

استاد

• Soil Physics
• Environmental Modeling
• Land-Atmosphere Exchange
• +۶ مورد دیگر

Prof. Dr. Thilo Streck is a Professor of Biogeophysics at the Institute of Soil Science and Land Evaluation, University of Hohenheim, Germany . He has served as Head of the Department of Biogeophysics since 2001 and was Head of the Institute of Soil Science and Land Evaluation (2002-2006) . Since 2023, he is the Research Director of the Computational Science Hub (CSH), University of Hohenheim . His research focuses on: Soil and environmental physics Measurement and modeling of terrestrial ecosystem processes Land surface processes (soil-plant-atmosphere exchange) Environmental fate of chemicals Regionalization and risk assessment Recent publications highlight his work in 2025 on trait-based microbial modeling , land-atmosphere feedback observatories , and climate change impacts on agricultural systems , with 2024 work on crop yield prediction , water demand modeling , and organic matter stabilization . Scientific honors include: Gips-Schüle Award 'Freedom for Research' (2016) Fritz Scheffer Award of the German Soil Science Society (1994) He has led major DFG research initiatives: Research Unit 1695 (2012-2019) : Agricultural Landscapes under Global Climate Change PAK 346 (2008-2011) : Structure and Functions of Agricultural Landscapes

• Business Cycles
• Monetary Policy
• Fiscal Policy
• +۳ مورد دیگر

Benjamin Born is Professor of Macroeconomics at Frankfurt School of Finance & Management and will join the University of Bonn as Professor of Macroeconomics in September 2025. He serves as Research Director at the ifo Institute, is a Research Fellow at CEPR and CESifo, advises the European Commission's DG ECFIN, and serves on the European Parliament's Expert Group on Monetary Policy and the CEPR–EABCN Euro Area Business Cycle Dating Committee. His educational background includes a PhD in Economics (2011) from the University of Bonn, an MSc in Econometrics and Economics (2007) from the University of York, and a BA/MA in Economics (2006) from the University of Siegen. Professor Born's research focuses on business cycles, monetary and fiscal policy, heterogeneous agents, and empirical methods. His work bridges theoretical macroeconomic models with empirical evidence, often using survey data and innovative methodologies to understand expectation formation, policy transmission mechanisms, and the effects of economic uncertainty. He has made significant contributions to understanding how fiscal and monetary policies affect the economy under different conditions. His recent publications show a strong focus on heterogeneous agent models, policy evaluation during crises (particularly the COVID-19 pandemic), and the analysis of expectation formation processes. His work frequently combines theoretical modeling with empirical validation, using both traditional economic data and innovative sources like social media. A notable trend is his increasing focus on micro-founded macroeconomic models that account for distributional effects. He serves on the editorial boards of the Journal of Monetary Economics and the European Economic Review, reflecting his significant contributions to the field. Professor Born supervises theses in macroeconomics and applied econometrics. He teaches Macroeconomics II as a first-year PhD course at BGSE and has taught Macroeconomics and Econometrics at all levels. He is actively involved in organizing academic events including the BASEforHANK Winterschool, ifo Conferences, and the Konstanz Seminar on Monetary Theory and Monetary Policy. As Research Director at the ifo Institute, he leads research initiatives in macroeconomics and contributes to the institute's policy-relevant research agenda. His work at the ifo Institute involves close collaboration with other researchers on projects related to business cycle analysis, policy evaluation, and expectation formation.

Rainer Schnellمشاهده پروفایل

استاد

• Survey Methodology
• Privacy-Preserving Record Linkage
• Health Data Analysis
• +۲ مورد دیگر

Prof. Dr. Rainer Schnell is a Professor at the Institute of Sociology, University of Duisburg-Essen. His research focuses on advanced survey methodologies, privacy-preserving data linkage techniques, and statistical analysis of health and social data. He leads the Chair of Empirical Social Research and contributes to interdisciplinary studies combining sociology, computer science, and public health. Research interests span: Survey Methodology : Innovations in web surveys, non-response analysis, and data quality assurance Data Privacy : Cryptographic techniques for secure record linkage and vulnerability assessments Health Analytics : Vaccination behavior studies, health data governance, and pandemic-related research Recent publications demonstrate a strong emphasis on: Privacy-enhancing technologies for sensitive data integration Methodological critiques of survey and data linkage practices COVID-19-related behavioral research using large-scale population data

Corinna Hooseمشاهده پروفایل

استاد

• Aerosol-Cloud Interactions
• Mixed-Phase Clouds
• Heterogeneous Ice Nucleation
• +۳ مورد دیگر

Prof. Dr. Corinna Hoose leads the Cloud Physics research group at the Institute of Meteorology and Climate Research - Tropospheric Research (IMKTRO) within Karlsruhe Institute of Technology (KIT) . She has held this W3 Professorship since 2013 and previously led a Helmholtz Young Investigators Group (2010-2016). Her work focuses on aerosol-cloud interactions , mixed-phase cloud processes , and numerical modeling of atmospheric systems . Professor of Theoretical Meteorology at KIT (2013-present) Former Helmholtz Group Leader (2010-2016) University of Oslo & ETH Zurich Postdoc experience Co-Editor of Atmospheric Chemistry and Physics Research Interests center on ice nucleation mechanisms , cloud dynamics , and climate sensitivity studies . Her group develops parameterizations for heterogeneous ice nucleation and investigates precipitation formation across different cloud types. Notable methodological contributions include SEVIRI satellite data analysis and ICON model modifications for microphysical-dynamic coupling. Publication Trends show consistent leadership in mixed-phase cloud modeling (2013-2025), with particular emphasis on Arctic cloud systems , heterogeneous ice formation , and aerosol impacts on precipitation . Her work bridges laboratory ice nucleation studies (e.g., dust-ash parameterizations) and regional climate modeling at various spatial resolutions. Teaching includes core courses in Theoretical Meteorology , Numerical Methods , and Cloud Physics at KIT. She has mentored multiple early-career researchers as evidenced by co-authorships with advisees like A. Oertel and L. Ickes. Collaborations span institutions including ETH Zurich, University of Oslo, and EU projects like EUCAARI. Her research group interacts with observational teams through field campaigns like Swabian MOSES and utilizes both in situ and remote sensing data for model validation.

Thomas Naglerمشاهده پروفایل

استاد

• Mathematical Statistics
• Machine Learning
• High-dimensional Dependence Models
• +۳ مورد دیگر

Thomas Nagler is a Professor at the Department of Statistics, Faculty of Mathematics, Computer Science and Statistics at Ludwig Maximilian University of Munich (LMU Munich). He also serves as a principal investigator at the Munich Center for Machine Learning (MCML), where he leads research at the intersection of mathematical statistics and machine learning. Nagler received his academic training at Technical University of Munich (TU Munich), earning a BSc in Mathematics (2009-2012), followed by an MSc in Mathematical Finance (2012-2014), and ultimately a PhD in Mathematical Statistics (2014-2018). Prior to his current position at LMU Munich, he held assistant professor positions at TU Delft (2021-2022) and Leiden University (2019-2021). Professor Nagler's research focuses on developing novel statistical methods with theoretical guarantees and scalable algorithms. His work spans high-dimensional dependence modeling, particularly using vine copulas, statistical machine learning, time series and functional data analysis, and statistical computing. He emphasizes creating methods that can be practically implemented and applied to solve real-world problems across diverse domains. An analysis of Nagler's recent publications reveals a strong emphasis on vine copula methodology, uncertainty quantification in machine learning, and applications to climate science and epidemiology. His work bridges theoretical statistics with practical implementation, often resulting in open-source software tools that make advanced statistical methods accessible to practitioners. The interdisciplinary nature of his research is evident in collaborations spanning climate modeling, healthcare, and finance. While specific awards are not detailed in the available information, Nagler's research impact is evident through his significant contributions to statistical methodology and his active engagement with the research community through open-source software development. As a principal investigator at MCML and Professor at LMU Munich, Nagler leads a research group focused on advancing statistical methodology for complex data analysis. His GitHub profile indicates active collaboration with students and researchers, with several followers from LMU Munich and other institutions. His research program appears to be well-funded through the MCML and university resources, supporting both methodological development and application-focused projects. Nagler maintains strong ties with the computational statistics community through his leadership of the VineCopula and pyvinecopulib projects, which provide essential tools for dependence modeling. His work with the Munich Center for Machine Learning positions him at the forefront of interdisciplinary research combining statistical theory with practical machine learning applications.

Jan Vanderborghtمشاهده پروفایل

استاد

• Soil Physics
• Solute Transport in Soils
• Pesticide Fate Modeling
• +۴ مورد دیگر

Professor Jan Vanderborght serves as Director of the Agrosphere Institute (IBG-3) at Forschungszentrum Jülich, Germany, while maintaining a professorship in Soil Physics at KU Leuven, Belgium. His research focuses on fundamental soil physical processes with direct applications in agricultural and environmental management. His educational background includes an MSc (1993) and PhD (1997) in Bio-engineering from KU Leuven, followed by post-doctoral research at ETH Zurich (1998-1999). Vanderborght's research centers on solute transport in soils, pesticide fate modeling, hydrogeophysics, soil evaporation processes, and plant water/nutrient uptake mechanisms. His work bridges fundamental soil physics with practical agricultural applications, particularly in irrigation management and contaminant transport. His recent publications reveal strong focus on advanced soil moisture monitoring techniques, root water uptake modeling, and the application of digital twin technology (SWIM²) for precision agriculture. The research spans multiple scales from pore-level processes to field-scale hydrological modeling. As an editorial leader in the field, Vanderborght serves as Co-Editor of Vadose Zone Journal and Associate Editor for both Journal of Hydrology and SOIL, reflecting his significant contributions to soil and water science literature. His research group develops innovative approaches combining field measurements, advanced imaging techniques (like MRI and GPR), and sophisticated modeling frameworks to address critical challenges in soil-water-plant systems, particularly under changing climate conditions.

Massimiliano Pontilمشاهده پروفایل

استاد

• Machine Learning
• Dynamical Systems
• Optimal Transport
• +۵ مورد دیگر

Massimiliano Pontil is a Professor at University College London's Department of Computer Science within the Faculty of Mathematical & Physical Sciences, with additional affiliation at the Italian Institute of Technology in Genoa. He leads cutting-edge research at the intersection of machine learning and dynamical systems theory. His research focuses on developing theoretical frameworks for learning transfer operators of stochastic dynamical systems using kernel methods. Pontil's work provides rigorous spectral learning bounds and addresses representation learning challenges in modeling time-evolving phenomena. His research spans both theoretical foundations and practical applications in data-driven science and engineering. Pontil's recent publications reveal a strong emphasis on Koopman operator theory, with particular attention to spectral analysis, error bounds, and efficient algorithms for large-scale dynamical systems. His work connects kernel methods with dynamical systems theory to create mathematically grounded approaches for forecasting and understanding temporal phenomena. His scientific contributions include multiple publications in top-tier venues including NeurIPS and ICLR, with recent work on invariant representations, long-term forecasting, and randomized algorithms for operator regression. Pontil collaborates extensively with researchers including Vladimir Kostic, Karim Lounici, and Pietro Novelli, leading a productive research group in this specialized area of machine learning theory.