جستجوی استاد راهنمای دانشگاهی

Academic Profile: Damir Filipovic is a Full Professor and the Swissquote Chair in Quantitative Finance at the College of Management of Technology (CDM) of École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He previously held academic positions at the University of Vienna, University of Munich, and Princeton University, and served as Head of the Vienna Institute of Finance. Research Focus: Quantitative finance, risk management, stochastic processes, term structure modeling, volatility risk, and machine learning applications in financial markets. Industry Collaboration: Co-developed the Swiss Solvency Test for insurance capital requirements while consulting for the Swiss Federal Office of Private Insurance. Publications: Contributed extensively to journals like Journal of Financial Economics, Mathematical Finance, and Annals of Applied Probability, with a textbook on Term-Structure Models. Academic Service: Editorial board member of multiple journals and organizer of advanced workshops on systemic risk and financial technology. Recent Research: His work emphasizes machine learning for portfolio risk management, kernel-based yield curve estimation, and robust stochastic modeling. Keynote speaker at international conferences on finance and insurance mathematics, with over 15 recent publications in 2023-2025 addressing high-dimensional financial problems, neural control systems, and causal inference in market data. Education: Ph.D. in Mathematics from ETH Zurich (2000). Graduate of ETH Zurich and University of Vienna. Teaching & Mentorship: Supervises current and former EPFL Ph.D. students in quantitative finance, including Nicolas Camenzind, Joshua Hayes, Andrea Ruglioni, and ten others. Former students like Damien Ackerer and Lotfi Boudabsa now lead research in risk management. Labs & Programs: Directs EPFL's Finance and Technology Programme, leads the Computational Finance Group (CSF) at EPFL, and contributes to Swiss Finance Institute initiatives. Scientific Leadership: Served on EPFL Committee of Academic Evaluation and Doctoral Program Finance committee.

• Quantitative Finance
• Risk Management
• Machine Learning
• +۷ مورد دیگر

Fabio Nobile is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), Department of Mathematics (MATH), holding the CADMOS Chair in Scientific Computing and Uncertainty Quantification. He leads the CSQI (Chair of Scientific Computing and Uncertainty Quantification) group. His work focuses on numerical methods for partial differential equations (PDEs), uncertainty quantification, stochastic modeling, and computational fluid dynamics. He is involved in collaborative projects involving fluid-structure interaction, cardiac electro-mechanics, and energy systems. Professor Nobile has extensive teaching experience, including courses on advanced analysis, stochastic simulation, and numerical integration of stochastic differential equations. He supervises numerous PhD students and has contributed to over 200 peer-reviewed publications, covering topics such as low-rank approximation methods, multilevel Monte Carlo techniques, and optimal control under uncertainty. His research emphasizes interdisciplinary applications, including biomedical engineering (e.g., personalized cardiac simulations) and renewable energy (e.g., probabilistic load forecasting). He collaborates with industries and academic institutions globally, advancing computational methodologies for engineering and scientific challenges.

• Scientific Computing
• Uncertainty Quantification
• Numerical Analysis
• +۵ مورد دیگر

Patrick Gagliardini is a Full Professor of Econometrics at the University of Lugano (USI) within the Faculty of Economics and the Institute of Finance. He also serves as Pro-Rector at USI. His academic journey includes a PhD in Econometrics from USI (2003) and studies in Physics at ETH Zurich (1998). He has held roles such as Visiting Fellow at CREST Paris (2003) and Assistant Professor at the University of St. Gallen (2004–2006). His research focuses on econometric methods (nonparametric techniques, GMM, latent factor models) and financial applications such as credit risk, asset pricing, and risk management. Competence areas include Big Data, investment decisions, and systematic risk analysis. He teaches courses in econometrics, financial econometrics, and time series at the undergraduate, graduate, and PhD levels. Recent publications explore latent factor models, econometric testing (e.g., eigenvalue tests for factor detection), and financial decision-making in small data regimes. His work bridges theoretical econometrics with practical applications in finance and risk modeling. Notably, his research addresses challenges in dynamic latent factor models, hedge fund performance evaluation, and granularity theory in financial systems. He maintains an active academic profile with contributions to both theoretical and applied econometrics.

• Econometrics
• Financial Econometrics
• Risk Management
• +۶ مورد دیگر

Andrew J. Patton is a Professor in the Department of Economics at Duke University's Trinity College of Arts & Sciences, with a secondary affiliation at Singapore Management University's School of Economics. His office is located at 213 Social Sciences Building, Box 90097, Durham, NC 27708-0204, United States, and his professional website is available at http://econ.duke.edu/~ap172/. Dr. Patton's research focuses on financial econometrics, with particular expertise in volatility modeling, high-frequency data analysis, and dependence structures in financial markets. His work spans several key areas including realized variance methods, copula modeling for financial time series, risk management techniques, and forecast evaluation methodologies. He has made significant contributions to understanding market microstructure, hedge fund risk exposures, and the dynamics of financial correlations. His publication record demonstrates consistent output in top finance and econometrics journals, with recent work exploring machine learning applications in volatility forecasting, granular risk measures, and the impact of high-frequency data on asset pricing models. The research shows a clear trajectory toward increasingly sophisticated modeling of financial dependencies and risk structures. Dr. Patton has received recognition for his scholarly contributions, ranking among SSRN's top 1,427 authors by total paper downloads and top 1,090 by total paper citations. His work is frequently cited in the field of financial econometrics, indicating substantial influence on contemporary research. He maintains active research collaborations with leading economists including Tim Bollerslev, Kevin Sheppard, Tarun Ramadorai, and Robert Engle, among others. These collaborations span multiple institutions and have produced influential work on volatility modeling, risk measurement, and financial market dynamics.

• Financial Econometrics
• Volatility Modeling
• Time Series Analysis
• +۴ مورد دیگر

Dr. Jonas Schmitt is a Lecturer at ETH Zurich's Department of Environmental Systems Science, specializing in Agricultural Economics and Policy. His office is located at SOL C 10.1, Sonneggstrasse 33, Zürich. Education PhD in Agricultural Economics, ETH Zurich (2021-2024) M.Sc. in Agricultural Economics, Georg August University of Göttingen (2016-2018) B.Sc. in Agricultural Economics, Georg August University of Göttingen (2012-2015) Research Focus Dr. Schmitt's research investigates climate risk management strategies in agriculture, focusing on: On-farm interventions like crop diversification Development of weather-indexed insurance products Economic impacts of extreme weather events Policy analysis of agricultural subsidy systems Statistical modeling of climate vulnerabilities Professional Experience Researcher, Thuenen Institut of Farm Economics (2020-2024) Policy Analyst, Federal Ministry of Agriculture, Berlin (2023) Data Analyst at Lavazza (2019-2020)

• Agricultural Economics
• Risk Management
• Crop Diversification
• +۲ مورد دیگر

Umberto Michelucci is a Professor of Scientific Machine Learning at Lucerne University of Applied Sciences and Arts (HSLU), Switzerland. He holds a PhD in Machine Learning applied to Physics and has over 20 years of industry experience. He is the Subject Head of Applied Data Intelligence in Continuing and Executive Education, Head of Certificates in Machine Learning/Data Engineering, and founder of TOELT LLC and the AI Center of Excellence at Helsana Versicherung AG. His research focuses on machine learning applications in science, astrophysics, uncertainty quantification, and sensor technology. Education PhD in Machine Learning applied to Physics (Portsmouth University) Master in Theoretical Physics (University of Florence) Postgraduate Certificate in Higher Education (Open University, UK) Research Interests Michelucci’s work bridges machine learning and scientific disciplines. Key areas include: Machine learning for astrophysics (INAF collaborations) Uncertainty analysis in high-stakes ML systems Deep learning for optical sensing (e.g., olive oil quality analysis) Foundational mathematical concepts for ML in science Awards & Recognition World’s Top 2% Scientists (Stanford List) Google Developer Expert in Machine Learning AI Global Ambassador (2022) TOP AI Influencer in Switzerland (2021) Grants & Collaborations He collaborates with institutions like INAF (Italy) and NVIDIA/Google, leading projects on AI for agrifood, medical imaging, and astrophysics. His work includes $multi-million industry partnerships and EU-funded research. Labs & Teams Director of the TOELT AI Lab and oversees HSLU’s Applied Data Intelligence programs. Active in open-source initiatives and global AI standardization efforts.

• Machine Learning
• Artificial Intelligence
• Astrophysics
• +۵ مورد دیگر

Prof. Henriette Elise Breymann is a Professor in the Department of Finance, Risk Management and Econometrics at the ZHAW School of Engineering, Zurich University of Applied Sciences (ZHAW). Her research focuses on financial risk modeling, algorithmic contract standards (ACTUS), big data analytics, and regulatory technology (RegTech). She has led numerous projects, including Data Driven Financial Risk and Regulatory Reporting, Strengthening Swiss Financial SMEs through Reinforcement Learning, and Explainable AI in Credit Risk Management. Her work intersects finance, physics, and computational methods, with contributions to energy economics (e.g., renewable energy systems), high-frequency financial data analysis, and quantum chaos applications. She co-authored Unified Financial Analysis: The Missing Links of Finance (2009) and pioneered the ACTUS framework for standardizing financial contracts. Projects: 12+ leadership roles in applied finance, risk modeling, and energy systems. Key Expertise: Regulatory reporting, stress testing, and data-driven financial systems. ACTUS Standard: Global initiative for granular financial contract representation. Her research spans interdisciplinary collaborations, including the Risk- and Finance-Lab, and has been presented at major conferences like the Society for Economic Measurement. She advises on regulatory innovations and financial resilience strategies for SMEs.

• Financial Risk Management
• Econometrics
• Algorithmic Contract Standards
• +۳ مورد دیگر

Dino Collalti is a postdoctoral researcher at the University of Bern's Center for Regional Economic Development (CRED), affiliated with the Economic Institute. His research focuses on understanding how climate, weather, and geography influence local economic development, particularly in the context of natural disasters and tourism. He holds a PhD in economics from the University of Bern and has been part of the Oeschger Centre for Climate Change Research (OCCR). He completed his B.Sc. in Economics at the University of Bern with a minor in Constitutional Law and Political Theory, followed by an M.Sc. in Applied Economic Analysis. His academic journey included a research stay at the University of Copenhagen. Key research areas include economic effects of hurricanes, flood resilience, and tourism demand dynamics post-disasters. He teaches modules on Environmental Econometrics, Tourism Economics, and supervises bachelor's/master's theses at the intersection of environmental and tourism economics. His recent work examines spillover effects of hurricanes on Caribbean tourism and firm-level impacts of flash floods in Central America. He frequently collaborates with institutions like the World Trade Institute and has published in journals such as Natural Hazards and Natural Hazards and Earth System Sciences .

• Climate Economics
• Environmental Economics
• Regional Economics
• +۳ مورد دیگر

Elie Bouri is a Professor of Finance at the Lebanese American University's School of Business, Department of Finance. His extensive research portfolio demonstrates expertise in cryptocurrency markets, financial volatility analysis, and cross-market risk transmission. With over 30 scholarly publications indexed on SSRN, his work has garnered significant attention with more than 26,000 downloads and 162 citations. Professor Bouri's research interests focus on cryptocurrency market dynamics, particularly Bitcoin's properties as a hedge, safe haven, or diversifier relative to traditional assets. His work employs advanced econometric techniques including asymmetric GARCH models, quantile regression, and network analysis to examine volatility spillovers, market efficiency, and extreme dependence across financial markets. Recent research extends into climate risk, energy transition, and geopolitical influences on financial markets. His publication record shows consistent output since 2016 with significant recent activity, including multiple papers published or posted in 2024-2025. His research demonstrates methodological sophistication with applications of mixed data sampling, time-varying parameter models, and higher-order moment analysis to contemporary financial questions. Bouri frequently collaborates with an international network of researchers across Europe, Asia, and the Americas. Professor Bouri's scholarly contributions have appeared in journals such as the Journal of Finance, Finance Research Letters, Applied Economics, and Resources Policy. His work on cryptocurrency market properties during crisis periods has been particularly influential in understanding Bitcoin's role in diversified portfolios. His research program addresses critical questions about market efficiency, risk transmission, and asset pricing in both traditional and emerging digital asset markets, with practical implications for portfolio management, risk assessment, and financial regulation.

• Cryptocurrency Markets
• Financial Econometrics
• Volatility Analysis
• +۴ مورد دیگر

Sebastian Engelke is an Associate Professor at the Research Center for Statistics at the University of Geneva. His research focuses on extreme value theory, spatial statistics, graphical models, and machine learning applications in climate science and risk assessment. He holds an SNSF Eccellenza Grant and has contributed to foundational work on extremal graphical models and causal inference in heavy-tailed systems. Affiliations: University of Geneva, Research Center for Statistics Grants: SNSF Eccellenza Grant (2020–2025), Ambizione Fellowship (2015–2018) Education: PhD in Mathematics (2013, Georg-August-University of Göttingen), studies at UC Berkeley and University of Göttingen. Research Interests Engelke’s work bridges extreme value theory with modern machine learning, addressing challenges in climate extremes, risk quantification, and graphical model structures. Key areas include: Statistical modeling of multivariate extremes Causal discovery in heavy-tailed distributions Applications in environmental science and climate informatics Key Contributions Recent work includes advancements in extremal graphical models (via matrix completions), neural network approaches for extreme quantile regression, and validation methods for deep learning weather models. His research is published in top journals like Annals of Statistics and Journal of the American Statistical Association . Awards & Recognition Lambert Award (2021) Gumbel Lecture (2023) Fields Research Fellowship (2019) Teaching & Supervision Engelke teaches courses on machine learning, probability, and advanced topics in statistics at the University of Geneva. He supervises PhD students and postdocs in machine learning and extreme value theory. His students include Edoardo Vignotto (extremal random forests) and Nicola Gnecco (causal discovery in heavy-tailed models).

• Extreme Value Theory
• Spatial Statistics
• Graphical Models
• +۵ مورد دیگر

Anthony Christopher Davison is a Professor at the Institute of Mathematics within the School of Basic Sciences at École polytechnique fédérale de Lausanne (EPFL). He maintains an active research program in statistical methodology with a particular focus on extreme value theory and its applications across various domains including climate science, environmental statistics, and insurance. His work bridges theoretical developments with practical applications, addressing challenging problems in multivariate and spatial extremes. His research interests span Extreme Value Theory , Statistical Modeling , Multivariate Statistics , Spatial Statistics , and Bayesian Inference . Professor Davison has made significant contributions to the understanding of extremal dependence structures, developing novel methodologies for modeling multivariate extremes using structural equation models, graphical representations, and flexible nonparametric approaches. His work often addresses the challenges of non-stationarity in extreme events, particularly relevant in climate change contexts. Analysis of his recent publications reveals a consistent focus on advancing methodological frameworks for extreme value analysis while maintaining strong connections to real-world applications. His work demonstrates increasing sophistication in handling high-dimensional extremal dependence structures and addressing challenges in causal inference for extreme events. The publications span theoretical developments in statistical methodology alongside applications in climate science, environmental risk assessment, and insurance modeling. Professor Davison has supervised numerous doctoral students including Mario Krali, Timmy Rong Tian Tse, and Sonia Alouini, whose theses address cutting-edge problems in extreme value theory. His research has been supported by various funding bodies including the Swiss National Science Foundation and other Swiss foundations.

• Extreme Value Theory
• Statistical Modeling
• Multivariate Statistics
• +۳ مورد دیگر

Matthias Daniel Aepli serves as a Lecturer at the Institute for Financial Services Zug (IFZ) within the School of Business at the Lucerne University of Applied Sciences and Arts (HSLU), a position he has held since 2012. His teaching responsibilities span financial management, risk management, and real estate management across multiple master's programs. His academic credentials include: Master of Arts in Banking and Finance (MA HSG) from the University of St. Gallen Doctorate in quantitative finance (Dr. oec. HSG) from the University of St. Gallen, focusing on portfolio risk prediction using multivariate dynamic copula models Certificate in higher education teaching from HSLU Dr. Aepli's research centers on Real Estate Management, Risk Management, Investments, and Corporate Finance, with particular emphasis on emerging asset classes like logistics, light industrial, and life science properties. His work bridges theoretical modeling with practical applications in Swiss and international markets, addressing critical issues such as digitalization impacts, housing affordability, and inflation effects on real estate portfolios. His recent publications reveal a distinct trend toward innovative real estate investment models, including tokenization and specialized asset classes, while maintaining strong foundations in quantitative risk analysis. These works demonstrate increasing interdisciplinary collaboration and practical industry relevance. Scientific Awards: No scientific awards, prizes, or fellowships were documented in the available materials. Dr. Aepli actively supervises master's theses across four degree programs (International Financial Management, Real Estate, Banking and Finance, Engineering) and leads multiple research initiatives including tokenized real estate investments, logistics property analysis, global inflation studies, and the Real Estate Industry Digitalization Barometer. His projects consistently address current market challenges through industry-academia partnerships. The Institute for Financial Services Zug (IFZ) serves as his primary research base, functioning as a dynamic hub for applied financial research with direct industry engagement through its Financial Services Management division.

• Real Estate Management
• Risk Management
• Investments
• +۳ مورد دیگر

Mario Paolone is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) , leading the Distributed Electrical Systems Laboratory (DESL) within the School of Engineering (STI). His work focuses on smart grid technologies, power system dynamics, and renewable energy integration. Chair of DESL Teaching roles in Electrical and Electronic Engineering PhD Program Committee Member, Doctoral Program Energy EPFL Academic Strategic Committee Member His research spans real-time monitoring , optimal grid operation , and advanced protection techniques for power systems. Key contributions include PMU-based situational awareness and convex optimization methods for Active Distribution Networks. Recent work explores hybrid AC/DC grids , linear induction motors for transportation, and machine learning applications in energy systems. Notable scientific awards include: IEEE Fellow (2022) Richard B. Schulz Best Paper Awards (2017, 2018, 2020) IEEE EMC Technical Achievement Award (2013) He has supervised numerous PhD theses on grid-aware control , synchrophasor technologies , and renewable energy systems , with research sponsored by the Swiss National Science Foundation , Swiss Electric Research , and the European Union .

• Power Systems
• Smart Grids
• Renewable Energy Integration
• +۵ مورد دیگر

Rainer Winkelmann is a Professor of Economics at the University of Zurich, affiliated with the Department of Economics within the Faculty of Economics. He joined the university in 2001 as a Professor for Statistics and Empirical Economic Research. His research focuses on econometrics, particularly discrete and panel data models, with applications to social policy analysis, work, family dynamics, and well-being. Winkelmann holds a Ph.D. from the University of Munich (1993) and has taught at institutions such as Dartmouth College, University of Canterbury, and served as a visiting professor at Harvard University, Syracuse University, and UCLA. His work bridges theoretical econometrics and applied issues in health economics, labor markets, and happiness research. He has developed influential econometric methods, including models for zero-inflated count data and fixed-effects ordered logit models. His recent research explores healthcare demand, policy evaluation, and the impact of unemployment on well-being. Teaching responsibilities include advanced econometrics courses for Ph.D. students.

• Econometrics
• Models for Discrete Data
• Panel Data Analysis
• +۴ مورد دیگر

Olivier Scaillet is a Research Fellow at the Swiss Finance Institute, affiliated with the University of Geneva. His work spans financial econometrics, quantitative finance, and risk management, with a focus on stochastic volatility models, copulas, and high-frequency data analysis. Research Interests : Financial econometrics and nonparametric estimation Stochastic volatility and jump-diffusion models Asset pricing and factor models in large panels Systemic risk and recovery rate density estimation Machine learning applications in finance Market microstructure and high-frequency data dynamics Scientific Contributions : Developed methodologies for nonstandard error analysis in multi-analyst studies Advanced techniques for testing stochastic dominance efficiency and latent factor models Explored copula-based goodness-of-fit tests and threshold effects in time series Innovated in American option pricing under complex market conditions

• Financial Econometrics
• Quantitative Finance
• Risk Management
• +۷ مورد دیگر