جست‌وجوی استاد و پژوهشگر

Gökhan Iderمشاهده پروفایل

پژوهشگر

• Monetary Policy
• Financial Markets
• International Macroeconomics
• +۳ مورد دیگر

Gökhan Ider is a doctoral student at the Berlin School of Economics and a Researcher at the Department of Macroeconomics, German Institute for Economic Research (DIW Berlin). He holds prior degrees in Civil Engineering and Economics, with research focusing on monetary policy, international macroeconomics, and financial markets. Bachelor's & Master's in Civil Engineering, University College London MSc in Economics, University of Konstanz His research explores the interplay between energy prices, monetary policy, macroeconomic fluctuations, and US monetary spillovers. Key projects include analyzing ECB interventions during energy crises and policy insulation mechanisms. Recent publications investigate energy price dynamics under monetary policy, Bayesian VAR applications, and inflation containment strategies. He received the DAAD International Student of the Year Award 2019 and academic excellence scholarships. He will join the International Monetary Fund in summer 2025 via the Fund Internship Program (FIP). His work has been presented at seminars and conferences including the Schumpeter Berlin School of Economics Macro Seminar and the Verein für Socialpolitik annual meetings.

Ben Schumannمشاهده پروفایل

پژوهشگر

• Monetary Macroeconomics
• International Macroeconomics
• Structural Identification
• +۳ مورد دیگر

Ben Schumann is a Research Associate in the Macroeconomics Department and the Forecasting & Economic Policy unit at DIW Berlin. He holds a PhD from the Free University of Berlin (2025), completed as part of the DIW Graduate Center Program. His research focuses on monetary and international macroeconomics, employing advanced empirical and theoretical models to assess policy impacts. Key areas include structural identification, Bayesian estimation, and the interplay between global risk, currency dynamics, and economic policy. Education: PhD in Economics (2025), Free University of Berlin Professional Experience: Postdoctoral researcher at DIW Berlin; prior roles at European Central Bank, European Parliament, and German Minimum Wage Commission Research Interests: Monetary policy spillovers, global financial cycles, dominant-currency pricing, and climate change economics . His work bridges theoretical models with real-world policy analysis, particularly in understanding how shocks propagate through global systems. Recent publications analyze the ECB’s response to energy crises, dollar appreciation effects, and spillback mechanisms from US monetary policy. He frequently presents at academic workshops and contributes to DIW’s forecasting projects. Labs/Teams: Active in DIW’s Macroeconomics and Forecasting groups, collaborating on projects like the 'Gemeinschaftsdiagnose' economic forecast initiative.

Andres Albanمشاهده پروفایل

استادیار

• Healthcare Operations Management
• Stochastic Simulation
• Clinical Trial Design
• +۳ مورد دیگر

Andres Alban is an Assistant Professor of Operations Management in the Technology & Operations Division at the Frankfurt School of Finance & Management. He holds a PhD in Operations Management from INSEAD (2021) and completed a postdoctoral fellowship at Harvard Medical School's MGH Institute for Technology (ITA). His research focuses on applying stochastic simulation and optimization to healthcare decision-making, particularly in clinical trial design, ICU patient flow management, and mobile healthcare systems. He has collaborated with institutions like Amsterdam University Medical Center and the European Sepsis Academy (EU-funded). Key research contributions include developing ICU capacity simulation tools during the COVID-19 pandemic and advancing methodologies for cost-effective clinical trials. His work integrates operations research techniques with healthcare challenges, such as personalized treatment strategies and Bayesian sequential models. Alban's research has been recognized with a finalist award in the POMS College of Healthcare Operations Management Best Paper Competition. He received his undergraduate degree in applied mathematics and physics from the New Jersey Institute of Technology. His current research interests include precision medicine, adaptive clinical trials, and healthcare operations optimization. Collaborations span academic and medical institutions globally, reflecting his interdisciplinary approach to solving complex healthcare challenges.

Hamza Alkhatibمشاهده پروفایل

پژوهشگر

• Bayesian Modelling
• Monte Carlo Methods
• Uncertainty Modelling
• +۵ مورد دیگر

Hamza Alkhatib is a Senior Researcher at the Geodetic Institute within the Faculty of Civil Engineering and Geodetic Science at Leibniz University Hannover. His research focuses on advanced computational methods in geodetic data analysis, including Bayesian modeling, Monte Carlo simulations, uncertainty quantification, and filtering techniques for multi-sensor systems. He leads projects funded by DFG, BfG, and BMVI, addressing topics like hydrographic monitoring, cooperative sensor networks, and machine learning applications in geodesy. Alkhatib holds a PhD from the University of Bonn (2007) and a Habilitation from Leibniz University Hannover (2020). Education & Career : PhD (2007): University of Bonn, Dissertation on Monte Carlo methods for satellite gravity missions Habilitation (2020): Leibniz University Hannover, Advanced methods in geodetic data analysis Senior Research Staff since 2019 Research Interests : Bayesian inference and robust statistical methods Filtering algorithms (Kalman, Particle Filters) for dynamic systems Machine learning for geodetic point cloud analysis and damage detection Sensor fusion and multi-sensor positioning systems Hydrographic monitoring and marine infrastructure assessment Key Projects : DFG-funded adaptive robust adjustment of geodetic measurement processes Research Training Group GRK 2159 on dynamic sensor networks WaMUT project for hydrographic water change zone monitoring 3D HydroMapper for port infrastructure inspection Awards : DAAD Award for Outstanding Achievements (2000) Syrian Ministry of Academic Education Award (1998) Teaching : Teaches courses on geodetic data analysis, recursive state estimation, and machine learning applications at both bachelor and master levels.

Nils Thuereyمشاهده پروفایل

استاد

• Computer Graphics
• Fluid Dynamics
• Machine Learning
• +۳ مورد دیگر

Prof. Nils Thuerey is a Professor of Physically Based Simulation at the Technical University of Munich (TUM), within the TUM School of Computation, Information and Technology. He specializes in computer graphics, fluid dynamics, and machine learning applications in physics-based simulations. His work focuses on developing algorithms for fluid simulations, turbulence modeling, and differentiable physics solvers. He earned his doctorate in fluid simulations from the University of Erlangen-Nuremberg (2006) and held positions at ETH Zurich, ScanlineVFX, before joining TUM in 2013. Research Interests: Fluid Dynamics and Turbulence Modeling Machine Learning for Physics Simulations Deep Learning in Computer Graphics Differentiable Simulations and Inverse Problems Key Awards: Technical Oscar (2012) for contributions to fluid effects in films/games ERC Starting Grant (2014) for 'realFlow' research University of Erlangen Staedtler Graduation Award (2008) His recent work emphasizes integrating deep learning with physical models, such as differentiable solvers for fluid dynamics and probabilistic methods for weather forecasting. He leads projects like WeatherBench and develops tools like φ-ML and phiflow for scientific computing.

Elisabeth Ullmannمشاهده پروفایل

دانشیار

• Uncertainty Quantification
• Scientific Computing
• Numerical Analysis
• +۳ مورد دیگر

Prof. Elisabeth Ullmann is an Associate Professor for Scientific Computing and Uncertainty Quantification at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information, and Technology and the Department of Mathematics. She holds a PhD from TU Bergakademie Freiberg (2008) and has held academic roles including postdoctoral positions at the University of Bath, University of Hamburg, and University of Maryland. Her research focuses on developing efficient algorithms for uncertainty quantification in partial differential equations with random coefficients, Bayesian inverse problems, and rare event simulation. She is an Associate Editor for the SIAM Journal on Scientific Computing and SIAM/ASA Journal on Uncertainty Quantification , and teaches courses on numerical methods for uncertainty quantification and partial differential equations. Her work emphasizes multilevel Monte Carlo methods, stochastic Galerkin discretizations, and probabilistic numerical methods. Notable contributions include error analysis for rare event probabilities and multilevel estimators for high-dimensional problems. She collaborates internationally and maintains an active research group in Scientific Computing & Uncertainty Quantification at TUM.

Reiner Braunمشاهده پروفایل

استاد

• Innovation Finance
• Venture Capital
• Private Equity
• +۳ مورد دیگر

Prof. Reiner Braun is a Professor of Entrepreneurial Finance at the TUM School of Management, Technische Universität München (TUM). His research focuses on innovation finance, venture capital, and private equity, with a particular emphasis on startup financing strategies and the application of artificial intelligence in financial decision-making. Braun holds a PhD from the University of Wuppertal (2007) and a habilitation from TUM (2012). Prior to his current position, he served at Friedrich-Alexander University Erlangen-Nuremberg from 2013 to 2015. His academic career includes research visits at Oxford, Harvard, and Manchester. Key research areas include private equity performance persistence, venture capital financing dynamics, and the role of social media sentiment (e.g., Twitter) in investment decisions. Braun’s work frequently bridges theoretical finance with empirical analysis of real-world investment behaviors and market mechanisms. His publications span high-impact journals like Journal of Financial Economics and Journal of Business Venturing , with recent trends focusing on AI-driven investment strategies, cross-border startup movements, and the impact of unfunded capital commitments on institutional investors. Braun has advised on policy reforms related to venture capital ecosystems and innovation financing in Germany. Notably absent from his profile are explicit mentions of academic awards, student advisees, or active grants. His research often collaborates with industry partners, reflecting his dual focus on academic rigor and practical financial applications.

Mahalakshmi Sabanayagamمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning Theory
• Graph-based Learning
• +۳ مورد دیگر

Mahalakshmi Sabanayagam is a Researcher at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology and the Department of Theoretical Foundations of Artificial Intelligence. Her research focuses on the theory of deep learning and graph-based learning, with notable contributions to adversarial robustness, neural network certification, and graph representation learning. She currently serves as a Teaching Assistant for the course 'Gems of Informatics 3 (IN2176): Modelling and analysis of real-world graphs.' Her work bridges theoretical foundations and practical applications, including analysis of neural tangent kernels in graph networks, robustness certification against adversarial attacks, and cross-domain machine learning techniques. Key research directions include understanding decision boundaries via Hessian analysis, tensorized autoencoder architectures, and graphon-based network clustering. Publications span topics from adversarial machine learning to interdisciplinary applications in physics and finance. While currently no awards or grants are listed, her active publication record reflects ongoing contributions to artificial intelligence theory and graph-based methodologies. She is part of Prof. Debarghya Ghoshdastidar's research group at TUM's Faculty of Computer Science. Lab affiliations include the Theoretical Foundations of Artificial Intelligence (I7) group, focusing on rigorous mathematical analysis of AI systems and their real-world graph applications. No advising relationships or student collaborations are explicitly documented in available materials.

• Systems and Control Theory
• Communication Networks
• Cyber-Physical Systems
• +۶ مورد دیگر

John S. Baras is a Professor at the University of Maryland, holding joint appointments in the Department of Electrical and Computer Engineering and the Institute for Systems Research (ISR). He is a Hans Fischer Senior Fellow at the TUM Institute for Advanced Study (TUM-IAS), hosted by Sandra Hirche, focusing on Networked Cyber-Physical Systems (NetCyPhy). His research spans systems theory, communication networks, cyber-physical systems, and quantum control, with notable contributions to trust models in networks and hybrid communication systems. Baras has pioneered interdisciplinary research, founding ISR and directing the Maryland Hybrid Networks (HyNet) Center. He has over 90 patents, including foundational work on satellite internet delivery (HughesNet®), earning him prestigious awards like the AAAS and SIAM Fellowships. His work emphasizes bridging theory and practice in complex systems, with applications in security, smart grids, and healthcare. Education & Career: Founding Director of the Institute for Systems Research (ISR) Lockheed Martin Chair in Systems Engineering Guest Professor at Royal Institute of Technology (KTH), Stockholm Over 40 years of academic leadership and industry collaboration Research Focus: Baras's work addresses multi-disciplinary challenges in networked systems, integrating control theory, information science, and computing. Recent projects include trust-aware crowdsourcing, event-triggered control, and optimal sensor scheduling. His contributions to quantum control and systems biology reflect his interest in emerging interdisciplinary frontiers. Awards & Recognition: AAAS Fellow (2014), SIAM Fellow (2014) IEEE Life Fellow (2013) Outstanding Invention Awards (2008, 1998, 1994, 1991) Grants & Labs: Leads HyNet Center, focusing on hybrid networks, and collaborates on projects like trust evaluation in ad-hoc networks. His research is funded by NSF, DoD, and industry partners.

Michael Engelمشاهده پروفایل

پژوهشگر

• Bayesian Inverse Problems
• Machine Learning
• Optimization Methods
• +۳ مورد دیگر

Michael Engel is a Researcher at the Chair of Remote Sensing Technology, Technical University of Munich since March 2021. He holds a Master's in Materials Science and Engineering (2020) and a Bachelor's in Engineering Science (2018), both from TU Munich, with a focus on Uncertainty Quantification and Bayesian Inverse Problems. His research interests revolve around optimizing objective functions in inverse problems, leveraging methods like Curriculum Learning and Multi-Task Systems via Multiresolution Analysis. He combines classical numerical techniques with machine learning to address challenges in Bayesian problems and regression. Education: Bachelor's in Engineering Science, TU Munich (2018) Master's in Materials Science and Engineering, TU Munich (2020) Research interests include: - Bayesian Inverse Problems - Machine Learning Integration in Numerical Methods - Optimization of Multi-Task Systems - Uncertainty Quantification in Environmental Models Key projects include the Global Earth Monitor (GEM) , aiming to enhance Copernicus data utilization for large-scale environmental monitoring. His publications span remote sensing optimization, wavelet-based sensitivity analysis, and deforestation assessment using multi-sensor data. He contributes to the TU Munich's Engineering Risk Analysis Group and collaborates with the Chair of Numerical Mathematics and Hydrology. His work bridges physical modeling principles with computational efficiency in earth observation systems.

• Macroeconomics
• Monetary Macroeconomics
• Expectation Formation
• +۶ مورد دیگر

Krisztina Molnar is an Associate Professor at the Norwegian School of Economics (NHH), specializing in macroeconomics and monetary policy. Her research focuses on expectation formation, household consumption, saving decisions, and the integration of big microdata into economic models. She collaborates extensively with institutions like Banca d'Italia and the London School of Economics (LSE). Key research areas include identifying preference shocks through natural disasters, analyzing the perils of price stabilization under agent learning, and exploring income shocks' effects on household behavior. Her methodological innovations include textual analysis and survey data integration in econometric models. Notable contributions include work on the Handbook of Monetary Economics and the Journal of Monetary Economics. She has presented groundbreaking research such as the HANKSSON framework at the 2024 SED conference. Despite no explicit awards listed, her work has been incorporated into leading academic curricula, including Woodford's syllabus. Current projects involve interdisciplinary approaches to microdata and preference shocks, with ongoing collaborations on the effects of earthquakes on household savings and the implications of learning dynamics in monetary policy design.

Stephan C. Meylanمشاهده پروفایل

پژوهشگر

• Computational Cognitive Science
• Language Acquisition
• Machine Learning
• +۳ مورد دیگر

Stephan C. Meylan is a computational cognitive scientist and researcher at the Department of Linguistics at UC Berkeley. His work focuses on understanding how children and adults learn language through computational models such as neural networks and Bayesian probabilistic frameworks, with an emphasis on multi-agent and multi-task learning contexts. He previously worked at MIT's Computational Psycholinguistics Lab under Dr. Roger Levy and contributed to projects like the Bergelson Lab. His research addresses Moravec’s Paradox and employs experimental methods like eyetracking to study language processing. Funded by NIH, NSF, and DARPA, his work bridges machine intelligence development and human developmental processes. Meylan also has industry experience in data science and geospatial analysis.

Tobias Alfersمشاهده پروفایل

پژوهشگر

• Item Response Theory
• Psychological Test Development
• Bayesian Statistics
• +۳ مورد دیگر

Tobias Alfers is an affiliate researcher at Freie Universität Berlin's Department of Education and Psychology, specializing in methods and evaluation. He concurrently works as a psychometrician and web developer at Test 4 U GmbH in Vienna. His academic background includes a Master's in Psychology from the University of Vienna (2012-2018) and ongoing PhD research at FU Berlin (since 2020) on test-taking disengagement modeling. Professional roles include adjunct lecturer in mathematics at MODUL University Vienna and teaching assistant in psychological research methods at the University of Vienna. Research focuses on item response theory, test development, and Bayesian statistical applications. Key contributions include presentations at international psychometric conferences and collaborations on RIASEC-based career counseling tools. He maintains active involvement in academic outreach, recently hosting events for prospective students and public lectures on astrobiology-related research. Current affiliations include the Methods and Evaluation division's quality assurance team, where he has mentored student assistants and contributed to departmental initiatives like Boys' Day university visits.

Martin Kolbمشاهده پروفایل

استاد

• Stochastic Processes
• Probability Theory
• Mathematical Physics
• +۳ مورد دیگر

Roles & Affiliations: Martin Kolb is a Professor (W3) at Paderborn University's Institute of Mathematics (Applied Mathematics and Stochastics group leader). He previously held roles including Professor (W2) at Paderborn (2014–2017), Lecturer at University of Reading (2012–2014), and Harrison Early Career Professor at Warwick University (2011–2012). Education: PhD in Mathematics from TU Kaiserslautern (2009), Diploma in Mathematics from Ludwig-Maximilians-University Munich (2005). Studied at University of Copenhagen (2002–2003). Research Interests: Focuses on stochastic processes, probability theory, and mathematical physics. Key topics include quasi-stationary distributions, Fleming-Viot models, spectral asymptotics, and applications in quantum computing (e.g., Photonic Quantum Computing project). His work bridges theoretical mathematics with computational methods and physical systems. Scientific Engagement: Organized major events like the Weierstrass-Lecture series (since 2019) featuring Fields medalists and co-organized workshops such as the Oberwolfach Workshop on Stochastic Processes (2020). Active in conference committees for topics like 'Stochastic Processes under Constraints' (2016). Labs/Teams: Leads research projects including 'PhoQC: Photonic Quantum Computing' and 'Super-Brownian Motion with single point source'.

Jan Eggerمشاهده پروفایل

مدرس ارشد

• Medical Image Analysis
• Artificial Intelligence
• Image-Guided Therapies
• +۳ مورد دیگر

Jan Egger is a Senior Lecturer at the Technical University of Graz and a research group leader at the Institute for Artificial Intelligence in Medicine (IKIM) at Essen University Hospital. With over 10 years of experience in medical image analysis and AI-guided therapies, he focuses on advancing healthcare through open science platforms like StudierFenster and MedShapeNet. His interdisciplinary background combines Computer Science (Ph.D., Habilitation) and Human Biology (Ph.D.). Ph.D. in Computer Science Ph.D. in Human Biology Habilitation in Computer Science His research spans medical imaging, augmented reality surgery, and explainable AI, with a strong emphasis on clinical translation and startup incubation (e.g., Augmentomy). He has secured over €14 million in third-party funding and supervised ~100 student theses. Recent publications analyze transfer learning, synthetic data augmentation, and multimodal AI applications in oncology and radiology. Jan leads the open science client/server-based medical platform StudierFenster and contributes to datasets like MedShapeNet and CytoNuke. His work bridges academic research with industry collaboration, particularly in AI-guided therapies and extended reality systems for healthcare.