Find Academic Supervisors

• Observational Cosmology
• Cosmic Expansion Rate
• Dark Energy and Dark Matter
• +7 more

Dr. Sherry H. Suyu is an Associate Professor at the Technical University of Munich (TUM) and a Max Planck Fellow at the Max Planck Institute for Astrophysics. Her research focuses on gravitational lensing applications for cosmology, particularly in measuring the cosmic expansion rate and studying dark energy/dark matter. She leads the HOLISMOKES program funded by an ERC Consolidator Grant. Her research group at TUM School of Natural Sciences Department of Physics investigates: Dark cosmos through gravitational lensing Supernova observations and Tidal Disruption Events Bayesian inference methods for astrophysical modeling Machine learning applications in lensed system analysis Scientific achievements include: 2021 Berkeley Prize from American Astronomical Society ERC Consolidator Grant for HOLISMOKES program Discovery of 330 high-quality lens candidates in Pan-STARRS survey She teaches courses including: Experimental Physics 1 (Winter 2024/5) Gravitational Lensing (Winter 2024/5) Introduction to Nuclear/Particle/Astrophysics (Summer 2025) The group includes 13 active members and 14 alumni across 7 institutions. Current work integrates HST and LSST imaging for next-generation lens discovery.

• Statistics
• Data Mining
• Machine Learning
• +4 more

Dr. Nicos Pavlidis is a Senior Lecturer in the Department of Management Science at the Management School LUMS, Lancaster University. His research focuses on developing novel methods for clustering and classification in high-dimensional data and time-varying environments. He teaches courses including MSCI 562: Intelligent Data Analysis and Visualisation and MSCI 331: Data Mining for Direct Marketing and Finance , supported by DataCamp. Pavlidis is affiliated with research groups such as STOR-i Centre for Doctoral Training and the Centre for Marketing Analytics & Forecasting. His research interests span statistics, data mining, and machine learning, with an emphasis on adaptive algorithms for complex data challenges. He has led projects like Efficient clustering for high-dimensional data sets (2017–2021). He actively participates in conferences like the IEEE Congress on Evolutionary Computation and serves on editorial boards for journals including IEEE Transactions on Evolutionary Computation and Computational Statistics and Data Analysis . A PhD student under his supervision is Danielle Notice. Pavlidis has contributed to peer-reviewed work on topics ranging from niche optimization methods to dynamic GARCH models. His work bridges theoretical advancements and practical applications in data-driven decision-making.

Maarten BuisView profile

Lecturer

• Social Stratification
• Educational Attainment
• Statistics
• +3 more

Maarten Buis is a Lecturer of Statistics for the Social Sciences at the Department of Sociology, University of Konstanz. Previously, he held positions such as Senior Research Fellow at the WZB Berlin Social Science Center and Research Fellow roles at Eberhard Karls Universität Tübingen and VU University Amsterdam. He earned his PhD from VU University Amsterdam in 2010 with a thesis on educational inequality in the Netherlands. Education: PhD in Social Science (2010), VU University Amsterdam Master's in Social and Institutional Economics (2003), University of Utrecht His research focuses on social stratification, educational attainment, statistical methodologies, and the application of Stata software. He has contributed significantly to statistical techniques in non-linear models and has developed several Stata packages. His publications span peer-reviewed articles in journals like European Sociological Review and The Stata Journal , as well as edited volumes. He frequently presents at international conferences and Stata Users' Group meetings, focusing on topics like agent-based models and statistical software best practices. Labs/Teams: Former member of the Research Department 'Skill Formation and Labor Markets' at WZB Berlin Grants/Advising: No formal student advising listed, but extensive collaborative research engagements

Ullrich KötheView profile

Associate Professor

• Machine Learning
• Neural Networks
• Explainable AI
• +6 more

Prof. Dr. Ullrich Köthe is an Associate Professor and group leader in the Visual Learning Lab Heidelberg at the University of Heidelberg . He focuses on Explainable Machine Learning , leveraging Invertible Neural Networks to enhance transparency and utility in image analysis and medical applications . He also maintains the widely used VIGRA image analysis library. Education: PhD in Informatics, University of Hamburg, 2000 Habilitation in Informatics, University of Hamburg, 2008 His research interests center around machine learning , image analysis , and scientific computing , particularly the development of robust algorithms for medical imaging , computer vision , and life sciences . His work on invertible neural networks and parameter-free segmentation has led to significant advancements in the field. Recent publications highlight his contributions to Bayesian inference , neural network interpretability , and stochastic modeling , with applications ranging from disease outbreak dynamics to connectomics . Key trends include generative models , parameter-free segmentation , and likelihood-free inference . Scientific Awards: DAGM 2003 Main Prize DAGM Best Paper Award 2008 He has supervised numerous Master and Bachelor theses in machine learning and image analysis , with teaching roles in Advanced Machine Learning and Explainable AI . His collaborative research grants include funding from HARMAN International (2024). He leads the Explainable Machine Learning subgroup and has contributed to open-source software projects like ilastik and VIGRA , which are critical tools in bioimage analysis .

Vincent HeuvelineView profile

Professor

• Uncertainty Quantification
• High Performance Computing
• Data Intensive Computing
• +3 more

Professor Vincent Heuveline serves as Professor and Head of the Engineering Mathematics and Computing Lab (EMCL) at Heidelberg University's Interdisciplinary Center for Scientific Computing (IWR). He concurrently holds the positions of Chief Information Officer (CIO) and Director of the Computing Centre at the university, while leading the Data Mining and Uncertainty Quantification (DMQ) group at HITS gGmbH. His academic background includes: Studies in Mathematics, Physics, and Computer Science at Caen (France) and Würzburg (Germany) PhD in Computer Science (1997) from Université de Rennes and INRIA Habilitation in Mathematics (2002) at Heidelberg University Research focuses on uncertainty quantification (UQ) , high-performance computing (HPC) , and data-intensive computing with applications in medical engineering. He is deeply engaged in IT security research and teaching, developing practical frameworks for industrial deployment of numerical simulations and secure computing infrastructures. Recent publications reveal converging trends in biomedical image segmentation (e.g., Biomedisa platform), metabolic modeling for newborn diagnostics, and cybersecurity solutions like DGA detection. Key interdisciplinary themes include machine learning for medical applications, HPC algorithm optimization, and network security frameworks addressing real-world industrial challenges. He leads the EMCL and DMQ research groups, driving innovation in computational science. As CIO, he shapes Heidelberg University's digital infrastructure strategy while maintaining active industry collaborations for translating academic research into simulation and security solutions.

• Statistics
• Probability Theory
• Mathematical Modeling
• +3 more

Ariane Hanebeck is affiliated with the Department of Mathematics at the Technical University of Munich (TUM). Her research focuses on statistical methodologies, probability theory, and their applications in risk analysis and data science. She has contributed to advancements in distribution function estimation, observability of Gaussian models, and vine copula-based modeling. Her work integrates mathematical rigor with practical applications, such as improving flight safety through model correction and addressing challenges in nonlinear system observability. Notable collaborations include projects with the TUM-ICL Mathematical Sciences Hub and participation in research groups like Data Science and Probability Theory. Publications include contributions to the Annals of the Institute of Statistical Mathematics and the International Conference on Information Fusion . Though inactive in certain platforms, her research remains impactful in areas like statistical simulation and risk assessment.

Thomas TrefzgerView profile

Professor

• Physics Education
• Digital Media in Teaching
• Particle Physics
• +3 more

Prof. Dr. Thomas Trefzger is a Professor and Chair Head of the Chair of Physics and its Didactics at the University of Würzburg's Faculty of Physics and Astronomy. His research spans particle physics, particularly in the ATLAS experiment at the LHC, focusing on computing infrastructure sustainability, Higgs boson characterization, and lepton physics. He also leads educational projects integrating digital media and augmented reality (e.g., PUMA app) to enhance physics teaching, emphasizing interactive learning tools in electricity education and teacher training. His work includes studies on environmental impacts of high-energy computing, quantum entanglement observations, and precision measurements of fundamental particles. Education: His academic background aligns with his roles in both physics and education. Research interests combine experimental particle physics with innovative pedagogical methods, leveraging digital tools for STEM education. He collaborates with the ATLAS collaboration, contributing to data analysis in LHC Run-2 and beyond. Key Projects: Leadership in the RTG 2994 (Particle Physics at Colliders in the LHC Precision Era), development of AR applications for classroom use, and sustainability assessments of computational resources in large-scale experiments. His work bridges cutting-edge physics research with modern educational practices. Labs/Teams: Active in the Chair of Physics and its Didactics and the ATLAS Collaboration, overseeing teams in both experimental physics and educational technology initiatives.

• Econometrics
• Empirical Finance
• Asset Pricing
• +9 more

Joachim Grammig is a Professor in the Department of Statistics and Econometrics at the School of Business and Economics, University of Tübingen, Germany. His research lies at the intersection of econometrics and empirical finance, with a focus on asset pricing, financial market microstructure, high-frequency data, and international financial markets. His research interests span Econometrics , Empirical Finance , Asset Pricing , Financial Market Microstructure , High Frequency Data , and International Financial Markets . He develops and applies advanced statistical models to understand price formation, risk, and information flow in financial markets. His recent publications show a strong trend toward integrating structural economic models with modern econometric techniques such as indirect inference, simulation-based estimation, and machine learning. His work frequently addresses long-standing puzzles in asset pricing, including the equity premium, rare disasters, and information asymmetry. He also contributes to methodological advances in duration modeling and specification testing. Diverging Roads: Theory-Based vs. Machine Learning-Implied Stock Risk Premia (2024) Estimating the SARS-CoV-2 Infection Fatality by Data Combination (2022) Empirical Asset Pricing with Multi-Period Disasters (2021) Creative Destruction and Asset Prices (2016) Tell-Tale Tails: A New Approach to Estimating Unique Market Information Shares (2013) He advises PhD students and junior researchers, including Jantje Sönksen and Franziska Julia Peter. His research is supported by access to high-frequency financial data and computational tools, and he emphasizes reproducibility by sharing programs and data. He has collaborated extensively with scholars across Europe and the U.S. He leads a research team in econometrics and empirical finance at Tübingen, fostering a collaborative environment for graduate students and visiting scholars.

Sonja GenslerView profile

Associate Professor

• Digital Marketing
• Consumer Behavior
• Multichannel Management
• +3 more

apl. Prof. Dr. Sonja Gensler is an Associate Professor at the Institute for Value-Based Marketing, affiliated with FB4. She specializes in bridging research and teaching to foster student development, emphasizing practical insights for their professional journeys. Her work focuses on digital marketing innovation, consumer behavior analysis, and multichannel strategies. She explores regulatory impacts on the sharing economy and employs advanced empirical methods like conjoint analysis and regression modeling. Key themes in her research include market trends, customer engagement, and the integration of technology in marketing practices. Her research interests span digital marketing strategies, consumer decision-making processes, and the application of statistical techniques to analyze complex datasets. Prof. Gensler’s contributions include studies on sharing economy governance, multichannel management, and the effects of social media on brand relationships. She has published extensively on methodologies such as finite mixture models, contingency analysis, and discriminant analysis, highlighting their practical relevance in marketing and economics. Prof. Gensler’s recent work reflects a trend toward addressing contemporary challenges like AI’s role in customer relationship management and the evolving dynamics of online-offline consumer interactions. Her empirical focus ensures her research remains grounded in real-world applications, benefiting both academic and industry stakeholders.

Bernhard ReuterView profile

Researcher

• Statistical Machine Learning
• Bioinformatics
• Medical Informatics
• +5 more

Bernhard Reuter is a researcher at the University of Tübingen in the Department of Computer Science , focusing on machine learning methods for biomedical data . He develops tools for drug-resistance prediction in tuberculosis and HIV, and created the Generalized Perron Cluster Cluster Analysis (G-PCCA) for modeling non-equilibrium systems. Education Dr. rer. nat. in Theoretical Physics (2018), University of Kassel Dipl.-Phys. in Physics (2013), University of Kassel Research Highlights : His work bridges machine learning with biomolecular dynamics , particularly in drug resistance prediction and non-equilibrium system modeling . Publications in Nature Methods and Journal of Chemical Physics demonstrate his technical contributions. Awards GDCH travel scholarships (2014, 2016) CINSAT poster award (2013) Doctoral scholarship (2013) Studienstiftung scholarship (2006) He has contributed to open-source software projects like pyGPCCA and COmic , and collaborated with institutions including the Max Planck Institute for Biological Cybernetics .

Stephan GünnemannView profile

Professor

• Machine Learning
• Data Analysis
• Graph Neural Networks
• +4 more

Prof. Stephan Günnemann is a Professor of Data Analytics and Machine Learning at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. He leads the Munich Data Science Institute as Executive Director and directs the Konrad Zuse School of Excellence in Reliable AI. His research focuses on enhancing the reliability of machine learning systems, particularly in graph-based and temporal data analysis. Prof. Günnemann holds a PhD from RWTH Aachen University (2012) and has held postdoctoral and senior research positions at Carnegie Mellon University (USA), Simon Fraser University (Canada), and Siemens AG. He founded the Emmy Noether Research Group at TUM in 2015 and has been recognized with prestigious awards including the Heinz Maier-Leibnitz Medal (2022) and the ACM SIGKDD Best Paper Award (2018). His research interests span adversarial robustness, graph neural networks, and molecular data analysis. Recent work emphasizes certifiable AI safety, efficient data pruning, and uncertainty estimation in heterogeneous systems. He has contributed to over 150 peer-reviewed publications, with a focus on foundational ML challenges and real-world applications. Key Awards: Heinz Maier-Leibnitz Medal (2022), Google Faculty Award (2020), DFG Emmy Noether Programme (2015) Leadership Roles: Executive Director of Munich Data Science Institute, Director of Konrad Zuse School Past Roles: Postdoctoral Fellow at CMU, Researcher at Siemens His lab actively explores cutting-edge AI topics such as graph representation learning, quantum chemistry simulations, and trustworthy ML systems. Current projects include developing certifiable defense mechanisms against adversarial attacks and scalable molecular generation frameworks.

Francisco José Cuesta ValeroView profile

Researcher

• Climate Change
• Earth's Energy Imbalance
• Remote Sensing
• +3 more

Francisco José Cuesta Valero is a researcher at the Helmholtz Centre for Environmental Research - UFZ in the Department of Remote Sensing . His work focuses on climate change, Earth's energy imbalance, and subsurface thermal dynamics using remote sensing and climate models. Education: Ph.D. in Environmental Sciences, Memorial University of Newfoundland (2020) Master in Earth Sciences, St. Francis Xavier University (2016) BSc in Physics, University of Murcia (2013) Research Interests include: Earth's heat inventory quantification Satellite-based ground heat flux estimation Permafrost and climate feedbacks Borehole climatology methods Thermodynamic modeling of soil-bedrock systems Scientific Contributions reveal trends in climate modeling, heat storage metric development, and land-air temperature coupling analysis. His publications span 2016-2024 with 20+ peer-reviewed works. Awards: Alexander von Humboldt Research Fellowship (2021) He contributes to major projects like G-Flux (global ground heat flux from satellite data) and AI4GHEObs (machine learning for heat flux estimation). Collaborates with institutions including Memorial University, St. Francis Xavier University, and international climate research centers.

Husain NajafiView profile

Researcher

• Hydrological Forecasting
• Climate Risk Assessment
• Environmental Informatics
• +3 more

Dr. Husain Najafi is a hydrological forecasting expert at the Department of Computational Hydrosystems within the Helmholtz Centre for Environmental Research (UFZ). He specializes in operational early warning systems for floods and droughts, currently leading national-scale impact-based forecasting platforms in Germany while supporting international initiatives under the WMO's Multi-Hazard Early Warning Systems (MHEWS) framework. Research Highlights Developed FFS4DE (Germany's experimental high-resolution flood forecast system) Created HS2S (Sub-seasonal Soil Moisture Forecast System for drought prediction) Contributed to UNDRR and IPCC aligned climate risk frameworks Active member of the IDMP expert network for drought management Scientific Contributions 2024 Nature Communications paper on 10-meter floodplain inundation modeling Recipient of UFZ Young Scientist Award 2022 for operational drought forecasting Key contributor to ESA 4DHydro project for hyperresolution hydrological modeling

Gregor BöhlView profile

Researcher

• Dynamic Macroeconomics
• Macroeconometrics
• Econometrics
• +12 more

Dr. Gregor Böhl is a Principal Investigator at the Institute for Macroeconomics and Econometrics within the Department of Economics at the University of Bonn. His research is fully funded by the German Research Foundation (DFG) under project 441540692, focusing on dynamic macroeconomics, econometrics, and public economics. He is also affiliated with the CRC TR 224 EPoS and contributes to the OSE initiative for teaching and research infrastructure. His research interests lie at the intersection of Dynamic Macroeconomics , Heterogeneous Agent Models (HANK) , DSGE modeling , and computational economics . He investigates how household heterogeneity shapes macroeconomic outcomes, particularly in the context of inequality , climate policy , monetary financing , and financial frictions . His methodological expertise includes nonlinear solution techniques, Bayesian estimation under occasionally binding constraints, and high-performance computational tools. The recent trend in his publications reveals a strong focus on the empirical and structural analysis of monetary policy , especially at the zero lower bound, the macroeconomic implications of climate policies , and the development of robust computational methods for solving complex economic models. His work combines theoretical modeling with advanced numerical techniques and open-source software development. 2017 Student Prize of the Society for Computational Economics Dr. Böhl has secured competitive research funding from the DFG and the OSE initiative. He actively mentors researchers and collaborates with leading economists such as Cars Hommes, Felix Strobel, and Gavin Goy. His academic contributions extend beyond publications to the development of widely used open-source software packages like pydsge , econpizza , and dime_sampler , which support estimation, simulation, and inference in macroeconomic models. He previously held a postdoctoral position at IMFS, Goethe University Frankfurt, with collaborations at the Hoover Institution, Stanford University. He leads a research group focused on computational macroeconomics and maintains a strong commitment to open science, reproducibility, and the use of free and open-source software (FOSS) in economic research. His team develops and maintains several GitHub repositories that provide tools for nonlinear filtering, Bayesian inference, and HANK model solutions.

Constance RichterView profile

Professor

• Human-Centered Design
• User Experience (UX)
• Information Design
• +12 more

Professor Dr. Constance Richter is a faculty member at Aalen University, affiliated with the Faculty of Optics and Mechatronics and the Human Centricity department. She serves as Vice Dean and Dean of Studies, demonstrating significant academic leadership. Her work spans teaching, research supervision, and applied projects in human-centered design and user experience. Her research interests lie at the intersection of human-computer interaction, information design, and digital transformation. She focuses on user experience in automotive interfaces, virtual reality for education, AI in technical communication, and organizational UX maturity. Her work emphasizes practical application, often in collaboration with industry leaders like Porsche, Bosch, and Mercedes-Benz. The supervised theses reflect a strong trend toward applied research in adaptive interfaces, usability, digital twins, and immersive technologies. Her publications, inferred from thesis titles and abstracts, span topics from VR learning environments to AI integration in product development, indicating a multidisciplinary and industry-relevant research profile. Scientific Awards: No awards mentioned in the provided text. She actively supervises numerous bachelor's and master's students, guiding research in UX, technical communication, and digital innovation. Her projects often involve prototyping, user testing, and real-world implementation, supported by industry partnerships. She is involved in educational innovation, including hybrid learning and virtual workspaces. She leads and contributes to initiatives in digital transformation, including the development of digital mindsets, lean UX practices, and the use of VR in academic settings. Her work extends to organizational change, as seen in projects on becoming UX-driven companies and adapting to post-pandemic work environments.