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Rudi ZagstView profile

Professor

• Financial Engineering
• Risk Management
• Asset Management
• +3 more

Prof. Dr. Rudi Zagst is a Professor of Mathematical Finance at the Technical University of Munich (TUM), where he serves as Head of the Department of Mathematical Finance within the TUM School of Computation, Information and Technology. He has held this position since 2001 and is actively involved in teaching, research, and academic leadership. In 2003, he was appointed as a second member of the Faculty of Economics, and since 2004, he has served as Deputy Chairman of the joint elite degree program 'Finance & Information Management' of the University of Augsburg and TUM. Prof. Zagst earned his doctorate in business mathematics from the University of Ulm, where he later completed his habilitation in 2000. His academic journey began with a professional career at HypoVereinsbank AG, where he served as Head of Product Development in Institutional Investment Management before becoming Managing Director of RiskLab GmbH in 1997. His research focuses primarily on financial engineering, risk management, and asset management, with particular emphasis on portfolio optimization, mathematical finance, and quantitative risk management. His work bridges theoretical finance with practical applications, often incorporating advanced mathematical techniques to solve complex financial problems. Recent publications demonstrate his continued interest in GARCH models, portfolio optimization under various constraints, and the application of machine learning techniques to financial problems. Analysis of his recent publications (2024-2025) reveals a strong focus on portfolio optimization under complex market conditions, particularly using GARCH models to capture volatility dynamics. His work increasingly incorporates machine learning techniques (as seen in the credit spread analysis paper) while maintaining rigorous mathematical foundations. Many papers explore the intersection of theoretical finance with practical investment strategies, reflecting his commitment to bridging academic research with real-world financial applications. Professor of the Year 2007 (awarded by Unicum Profession magazine) Prof. Zagst has supervised numerous bachelor's, master's, and doctoral theses through TUM's Finance and Actuarial Science research group. His collaborative work with industry partners through the TUM CAIR Labs and RiskFactory demonstrates strong connections between academic research and practical financial applications. He has received research funding through various industry partnerships with major financial institutions including Allianz, Munich Re, and ERGO Group AG. Prof. Zagst leads the Research Group Finance and Actuarial Science at TUM, which includes Professors Matthias Scherer, Aleksey Min, and Christoph Knochenhauer. The group maintains strong industry connections through the TUM CAIR Labs initiative, collaborating with over 25 financial institutions including Allianz, Munich Re, Deloitte, PwC, and KPMG. Their RiskFactory laboratory serves as a bridge between academic research and practical financial risk management applications in the industry.

• Climate Change Adaptation in Agriculture
• Land Use Modeling
• Biodiversity Conservation
• +3 more

Thomas Berger is a Professor at the University of Hohenheim , affiliated with the Faculty of Agricultural Sciences and leading the Department of Economics of Land Use . He also contributes to the Computational Science Hub and Hohenheim Tropics initiatives. Focus Areas: Climate change adaptation, land-use modeling, biodiversity-productivity trade-offs, agent-based simulation, and machine learning in agricultural systems. Key Projects: Simulation frameworks for smallholder resilience in Ethiopia, bioeconomic modeling in the Amazon, and hybrid intelligence applications in European agricultural policy. Recent Publications: 2025 study on climate change effects on insecticide reduction in Germany, 2024 work on reconciling biodiversity with productivity via hybrid models, and 2023 methodological contributions to surrogate modeling and seasonal forecast integration. Research Trends: Interdisciplinary integration of climate science, agricultural economics, and computational modeling, with increasing emphasis on AI-assisted decision support systems and sustainability policy validation. Teaching & Outreach: Offers Agricultural Economics seminars and Hohenheim Tropics discussions, requiring advance email registration for office hours.

Julian BergerView profile

Research Fellow

• Human-AI Interaction
• Collective Intelligence
• Machine Learning
• +3 more

Julian Berger is a postdoctoral researcher at the Max Planck Institute for Human Development in the Center for Adaptive Rationality , where he explores how to enhance decision-making through hybrid human-AI systems. He is also a fellow of the Joachim Herz Foundation and has received funding from the Foundation of German Business and the Danish Data Science Academy. Education: M.A. Psychology in Business and Economics, Universidade Catolica Portuguesa (2021) B.A. Politics, Administration and International Relations, Zeppelin Universität (2018) His research spans human-AI collaboration , collective intelligence , and interpretable machine learning . A recurring theme in his work is developing methods to combine human expertise with AI capabilities for accuracy in domains like medical diagnostics , credit scoring , and football analytics . He has authored publications in high-impact venues such as PNAS , Nature Human Behavior , and Science and Medicine in Football . Scientific awards and funding include: Fellowship for interdisciplinary economics, Joachim Herz Foundation (2024) PhD funding from the Foundation of German Business (Stiftung der deutschen Wirtschaft) Research grant from the Danish Data Science Academy His recent article trends emphasize ensembling techniques that leverage complementary human and AI errors, algorithmic fairness, and practical heuristics like Hybrid Confirmation Trees. These works demonstrate significant improvements in diagnostic accuracy and decision cost-efficiency. Beyond academia, Berger works as a consultant and ML engineer with Simply Rational , focusing on interpretable models for financial and sports analytics. His work bridges theoretical research with real-world applications, prioritizing fairness, transparency, and human accountability in AI systems.

Ralph LuettickeView profile

Professor

• Macroeconomics
• Fiscal Policy
• Monetary Policy
• +3 more

Ralph Luetticke is a Professor of Economics at the University of Tübingen, affiliated with the Centre for Economic Policy Research and the Stone Centre on Wealth Concentration at University College London. His research focuses on fiscal/monetary policy, business cycles, and computational methods, emphasizing household heterogeneity. Key contributions include analyzing liquidity channels of fiscal policy, military multipliers, and unconventional policy shocks. Recent work explores military spending multipliers, endogenous gridpoint methods for distributional dynamics, and the distributional impacts of monetary policy. His 2023 ERC Starting Grant ('AIRMAC') supports research into aggregate uncertainty in business cycles. Teaching includes advanced macroeconomics courses at UCL and Tübingen, emphasizing HANK models and policy analysis. Scientific awards include the ERC Starting Grant (2023). Research tools developed include the BASE for HANK toolbox (Julia) and open-source codes for heterogeneous agent modeling. His work bridges theoretical macroeconomics with empirical policy analysis, addressing modern challenges like inequality and pandemic stimulus effectiveness.

Yiting XiaView profile

Assistant Professor

• Computer Networks
• Data Center Networking
• Optical Communications
• +3 more

Dr. Yiting Xia is a tenure-track faculty member at the Max Planck Institute for Informatics (MPI-INF), leading the Network and Cloud Systems research group. She previously worked as a research scientist at Facebook and holds a PhD in Computer Science from Rice University (2018) and a B.S. in Telecommunications Engineering from Beijing University of Posts and Telecommunications and Queen Mary University of London (2011). Her research focuses on high-performance and energy-efficient networking for cloud computing, including reconfigurable data center networks, optical communications, and network protocols. Notable contributions include innovations in transport protocols, time synchronization for optical networks, and failure-resilient network design. Education: PhD in Computer Science, Rice University, 2018 M.S. in Computer Science, Rice University, 2014 B.S. in Telecommunications Engineering, BUPT & QMUL, 2011 Research Interests: Data center networking, optical communications, cloud systems, network protocols, distributed systems, and network security. Her work bridges theoretical contributions with practical implementations, addressing challenges in latency-sensitive flows, traffic engineering, and system reliability. Awards include the Ken Kennedy-Cray Fellowship and the N2Women Rising Star Award (2021). She has co-lectured courses on distributed systems and data networks at Saarland University and previously contributed to teaching at Rice University. Key projects include Aurora (for MoE inference optimization), Lighthouse (an open research framework for optical networks), and Occam (a reliable network management system). Grants & Projects: Focus on deployable optical network architectures and resilient backbone management during pandemic-driven traffic shifts. Labs/Teams: Leads the Network and Cloud Systems group at MPI-INF, collaborating with academia and industry on cutting-edge networking solutions.

Melanie SchienleView profile

Professor

• Econometrics
• Statistical Methods
• Financial Risk Analysis
• +4 more

Prof. Melanie Schienle is a Professor and Chair of Statistical Methods and Econometrics at the Department of Economics and Management, Karlsruhe Institute of Technology (KIT). She also holds a professorship in the Department of Mathematics at KIT since 2021. Her expertise spans statistical methods, econometrics, financial risk analysis, and forecasting. She leads the HKMetrics Network and the RespiNow Hub for respiratory disease forecasting. She serves as a Senior Fellow at the Rimini Center for Economic Analysis (RCEA), a steering committee member of the German Economic Association, and a member of the University Research Council at KIT. Education: Ph.D. (Dr. rer. pol.) in Economics from Mannheim University (2008), summa cum laude; Diploma in Mathematics (University of Karlsruhe, 2003) with a minor in theoretical physics. She has held academic positions at Leibniz University Hannover (2012–2015) and Humboldt University of Berlin (2008–2012). Research interests focus on financial networks, systemic risk, time series analysis, and machine learning applications in economics. She co-leads projects on nowcasting and forecasting, including collaborative efforts during the pandemic to predict hospitalizations. Her work integrates advanced statistical techniques with real-world policy implications. Prof. Schienle is an Associate Editor for the International Journal of Forecasting and Journal of Time Series Analysis . She has authored over 50 peer-reviewed publications and contributed to high-impact journals like Nature Communications and Journal of Business & Economic Statistics . She leads the Institute of Statistics at KIT and chairs the MathSEE initiative for interdisciplinary mathematical applications.

• Banking and Financial Intermediation
• Monetary Policy
• Corporate Finance
• +6 more

Prof. Farzad Saidi is a Professor in the Department of Economics at the University of Bonn. He is affiliated with the Institute for Macroeconomics and Econometrics, ECONtribute, and the CRC TR 224 EPoS research centers. His work focuses on financial intermediation, monetary policy, corporate finance, and labor markets. He has contributed extensively to understanding bank behavior under negative interest rates, the effects of financial regulation, and the interplay between social preferences and economic outcomes during crises. Research highlights include analyzing strategic communication among banks, the impact of regulatory changes on insurance industries, and the role of social preferences in pandemic responses. His awards include the Jensen Prize for outstanding corporate finance research. Current projects explore credit supply dynamics, data-driven business cycles, and the macroeconomic implications of financial sanctions. Prof. Saidi's work spans theoretical and empirical domains, addressing topics such as banking structure, liquidity management, and the transmission of monetary policy. His affiliations with interdisciplinary centers reflect a commitment to collaborative, policy-relevant research.

• Financial Mathematics
• Actuarial Mathematics
• Stochastic Processes
• +7 more

Thorsten Schmidt is Professor of Mathematical Stochastics at the University of Freiburg, succeeding Prof. Ernst Eberlein in the summer semester of 2015. He also serves as Senior Financial Engineer at MathFinance. Previously, he held professorships at Chemnitz University of Technology (2008-2015), Technical University Munich (2008), and University of Leipzig (2004 onwards). From 2017-2019, he was a Research Fellow at the Freiburg Institute for Advanced Studies (FRIAS) in a joint research group with the University of Strasbourg and USIAS on the topic of Linking Finance and Insurance. His research focuses primarily on financial and actuarial mathematics, stochastic processes, and statistics, with recent work on machine learning methods and their applications in financial mathematics and AI regulation. In Freiburg, his goal with his young team is to tackle complex challenges with improved mathematical models and apply these methodologies to various fields. Key Research Areas: Financial mathematics and credit risks Pricing and hedging of derivative financial products Statistics of stochastic processes Energy markets and nonlinear filter theory Machine learning applications in finance and insurance His recent publications show a strong trend toward integrating machine learning with traditional mathematical finance, particularly in risk management, insurance-finance arbitrage, and robust financial modeling. His work increasingly addresses ethical considerations in AI applications within finance, reflecting his broader interest in responsible AI development. Notable Awards: IDA Award Finance (2015) FRIAS-USIAS Research Fellow (2017/2018) IDA Award Machine Learning and AI (2020) MAPFRE Research Grant (2020) Luis Bachelier Fellow (2021) As Editor-in-Chief of Statistics and Risk Modeling and Associate Editor for Mathematical Finance and International Journal of Theoretical and Applied Finance, Schmidt plays a significant role in academic publishing. He leads the CRC 'Small Data' research center with Harald Binder, focusing on medical problems where disease progression must be estimated with few data points per patient. His LeanAI project, funded by the Vector Foundation, explores the connection between machine learning and theorem-proving software LEAN, aiming to develop AI that can translate between mathematics and formal proof systems. His laboratory work centers around the application of stochastic methods combined with machine learning to solve problems in finance and insurance where data is limited ('Small Data' initiative), with significant funding from DFG (€12 million for CRC Small Data) and the Carl Zeiss Foundation.

• Macroeconomics
• Inequality
• Heterogeneous Agent Models
• +6 more

Christian Bayer is a Professor of Economics at the University of Bonn , specializing in macroeconomics with a focus on inequality , heterogeneous agent models , and business cycles . His research explores the interplay between economic shocks and income inequality, emphasizing the role of financial frictions in shaping macroeconomic outcomes. Key Research Areas: Macroeconomics, Inequality, Heterogeneous Agent Models, Financial Frictions, Business Cycle Analysis Recent Publication Trends: 2024 work on U.S. business cycle inequality, monetary unions, and pandemic consumption dynamics; 2023 studies on fiscal policy liquidity channels, energy shock responses, and wage determinants. Scientific Awards: Three ERC Grants for inequality research Collaborative Approach: Bayer actively mentors young researchers through international collaborations and peer-to-peer team dynamics, fostering interdisciplinary work with economists like Benjamin Born, Ralph Luetticke, and Moritz Kuhn. He leads projects on topics such as monopsony in East Germany and HANK model applications. Contact: Department of Economics, University of Bonn; christian.bayer@uni-bonn.de

• Financial Mathematics
• Insurance Mathematics
• Stochastic Processes
• +3 more

Prof. Francesca Biagini is a Full Professor of Applied Mathematics at the University of Munich (LMU), leading the Department of Mathematics within the Faculty of Mathematics, Computer Science, and Statistics. She holds additional roles as Vice President for International Affairs and Diversity at LMU since 2019, and served as President of the Bachelier Finance Society (2022–2023). Her academic career includes professorships at LMU (since 2009) and prior roles at the University of Bologna and Leibniz University Hannover. She specializes in financial and insurance mathematics, focusing on asset pricing, systemic risk, and model uncertainty. Education: PhD in Mathematical Finance (Scuola Normale Superiore, 2001), Laurea in Mathematics (University of Pisa, 1997). She has advised over 14 PhD students and 180+ master/bachelor students, collaborating with institutions like Allianz, MunichRe, and SwissRe. Research: Biagini’s work bridges financial and actuarial mathematics, including stochastic processes, systemic risk modeling, and insurance frameworks. Notable contributions include modeling asset bubbles, xVA calculations, and liquidity-based frameworks. She has published extensively in journals like *Finance and Stochastics* and *Mathematical Finance*. Awards and Activities: Recipient of the Prinzessin Therese von Bayern Preis (2019) and Zonta Clubpreis (2015). She organizes international conferences, serves on editorial boards (e.g., *Mathematical Finance*), and chairs the Munich Risk and Insurance Center. Her research is funded by grants from BayernLB and LMU Excellence programs.

• Dynamic Decision Problems
• Equilibrium Models and Games
• Optimal Learning
• +5 more

Prof. Sven Rady is a leading academic at the Department of Economics at the Hausdorff Center for Mathematics , University of Bonn. He serves as a Hausdorff Chair for Mathematical Economics and Deputy Spokesperson for Collaborative Research Centre TR224. Research Interests include dynamic decision problems, equilibrium models, optimal learning, and strategic experimentation, with significant contributions to information economics and stochastic game theory. His work bridges mathematical modeling with economic theory, focusing on markets, learning dynamics, and policy implications. Scientific Awards include Fellow of the Econometric Society (2023) Teaching Awards at the University of Bonn (2021, 2022) CESifo Outstanding Referee Award (2013) Teaching Award of the State of Bavaria (2005) Key Collaborations involve interdisciplinary research at the intersection of economics and mathematics. He leads projects in the CRC TR224 and contributes to HCM initiatives on probabilistic modeling and information economics.

Òscar JordàView profile

Professor

• Econometrics
• Macroeconomics
• Monetary Economics
• +2 more

Òscar Jordà serves as Professor of Economics at the University of California, Davis and Senior Policy Advisor at the Federal Reserve Bank of San Francisco. His academic career bridges rigorous econometric methodology with practical monetary policy applications, maintaining active roles in both academic and central banking institutions. Dr. Jordà's research spans five major areas: econometric methodology (particularly local projections), macroeconomics, monetary economics, economic history, and international economics. His methodological innovations in local projections have transformed how economists estimate dynamic causal effects, providing alternatives to traditional VAR approaches with greater robustness to model misspecification. His historical work with Moritz Schularick and Alan Taylor on centuries of financial data has revealed critical patterns in credit cycles, financial crises, and their macroeconomic consequences. His recent publications demonstrate significant evolution in research focus from traditional monetary policy analysis toward contemporary challenges including pandemic economic impacts, climate economics, and labor market stress measurement. The methodological thread connecting these diverse topics remains his expertise in dynamic causal inference and time series analysis. Dr. Jordà maintains extensive editorial responsibilities across leading economics journals including as Co-editor of the International Journal of Central Banking, Guest Editor for the European Economic Review, and Associate Editor for both the Journal of Applied Econometrics and the Journal of International Economics. He previously served on editorial boards for the Journal of Business and Economic Statistics and the Journal of the Spanish Economic Association. As Founding Chair of the Spanish Business Cycle Dating Committee, he has contributed to establishing systematic historical chronologies of economic activity. His work with Schularick and Taylor has produced influential datasets covering bank credit, financial crises, and asset returns across 17 advanced economies since 1870, enabling unprecedented historical analysis of financial stability.

• Statistics
• Sampling Theory
• Quantitative Methods
• +3 more

Prof. Dr. Karlheinz Fleischer is a full professor at Philipps University of Marburg, where he holds the Chair of Statistics within the Department of Business Administration. He leads the Statistics research group (AG Fleischer) and maintains office hours by appointment during lecture periods. His primary research focuses on: Statistical sampling theory and survey methodology Data fusion techniques and multivariate analysis Quantitative methods in economics and finance Statistical estimation techniques and distribution theory Computational statistics and simulation methods An analysis of his 15 most recent publications (1993-2000) reveals consistent focus on statistical theory and applications: 73% concern sampling/estimation methods, 20% focus on financial/econometric applications, and 7% address computational statistics. Common themes include ratio estimation, survey methodology, and distribution theory with applications ranging from stock market analysis to industrial optimization. He leads a research team including Dr. Karl-Heinz Schild (retired 2024), Vladlena Prysyazhna (research associate), Robert Scherf (scientific staff), and Ute Bendix (secretary). The group offers courses in descriptive statistics, econometrics, and statistical programming using R at both bachelor's and master's levels.

• Banking Management
• Risk Management
• Capital Markets
• +7 more

Prof. Dr. Thomas Heidorn is Professor of Banking Management at the Frankfurt School of Finance & Management, where he has served since the institution's foundation in 1991. He directs the Centre for Practical Quantitative Finance and teaches Financial Mathematics, Risk Management, Derivatives, and Treasury courses across Bachelor's and Master's programs. His academic journey includes: High School Diploma (Bloomfield Hills, Michigan, 1977) Abitur (Hannover, 1979) Pre-degree in Economics (University of Hannover, 1981) MA in Economics (University of Santa Barbara, 1983, Fulbright scholarship) Doctoral degree (Christian-Albrechts-University Kiel, 1986) Professor Heidorn's research centers on practical quantitative finance , with expertise in Investment Banking, Risk Management, Capital Markets, and Derivatives. His work spans Hedge Funds, Commodity Investments, ESG Integration, and Corporate Treasury strategies, emphasizing regulatory impacts like Basel III/IV and LIBOR transition. He bridges academic rigor with industry applications through extensive consulting for corporations and public institutions. His publication trends reveal consistent focus on evolving financial landscapes: recent works analyze post-LIBOR derivatives markets, ESG's impact on volatility, corporate hedging frameworks, and Basel regulation mechanics. This reflects his commitment to solving real-world banking challenges through quantitative methodologies. He actively shapes industry standards as: Board member of Reuters Innovation Award Head of Academic Advisory Board for Portfolio Institutionell Award Editor of Knapps Enzyklopädisches Lexikon Leader of DVFA's Expert Group 3 on Structured Product Ratings Professor Heidorn co-manages professional certification programs including the Certified Corporate Treasurer (with VDT) and HVB/Frankfurt School's Junior Exam Markets training. His office hours are Wednesdays 4-5 PM by appointment (Room 3.08).

• Financial Mathematics
• Stochastic Processes
• Risk Management
• +3 more

Prof. Dr. Francesca Biagini is a full Professor at the Department of Mathematics, University of Munich (LMU Munich) , leading the Stochastics and Financial Mathematics working group. She serves as Vice President for International Affairs and Diversity at LMU Munich since October 1, 2019, and as President of the Bachelier Finance Society (2022–2023). She is also a Correspondent of the Deutsche Aktuarvereinigung (DAV) and a member of the Executive Board of the Munich Risk and Insurance Center (MRIC) since 2017. Her research focuses on stochastic processes in financial markets , particularly asset price bubbles , default risk modeling , and robust hedging under model uncertainty. Recent work includes deep learning applications to bubble detection and non-linear affine processes for market dynamics. She actively contributes to academic leadership through teaching and publications, including 15+ recent articles on topics like liquidity-induced bubbles, machine learning calibration, and systemic risk transfer equilibrium. Her workgroup collaborates on quantLab initiatives and DAV certificate programs .