جست‌وجوی استاد و پژوهشگر

Martin Widdicksمشاهده پروفایل

عضو هیئت علمی

• Mathematical Finance
• Derivatives Pricing
• Financial Economics
• +۳ مورد دیگر

Martin Widdicks is a Teaching Associate Professor of Finance and Director of the MSF Program at the Gies College of Business, University of Illinois at Urbana-Champaign. He holds the Josef and Margot Lakonishok Faculty Fellow distinction and has been recognized as an outstanding teacher repeatedly since 2011. His academic background includes a PhD in Mathematical Finance (University of Manchester, 2002) and a BSc in Mathematics (Manchester, 1999). Research interests focus on mathematical finance, particularly derivative pricing methodologies, singular perturbation theory applications, and executive compensation models. He has developed frameworks for multi-asset option valuation and analyzed incentive structures in equity-based compensation. Recent work includes extracting market crash and bankruptcy signals from options data. Teaching responsibilities span core quantitative finance courses (FIN 502, FIN 503), derivatives (FIN 512), and advanced electives like Complex Derivatives (FIN 514) and Term Structure Models (FIN 516/517). His awards include the Shebik Faculty Fellowship (2021–2022) and Towey Faculty Fellowship (2019–2021). Notable publications include Journal of Futures Markets , Journal of Corporate Finance , and Mathematical Finance . His work bridges theoretical finance with practical applications in risk management and corporate decision-making.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۵ مورد دیگر

Ling Zhao is a distinguished Professor at the School of Management, Huazhong University of Science and Technology, China, with extensive research contributions spanning artificial intelligence, machine learning, information systems, and biomedical applications. With over 150 publications since 2008, Dr. Zhao has established herself as a leading researcher in multiple interdisciplinary domains, particularly in applying computational methods to solve complex real-world problems. Dr. Zhao's research interests encompass a broad spectrum of cutting-edge topics including artificial intelligence, machine learning, data mining, control systems, and information systems. Her work demonstrates exceptional versatility, bridging theoretical computer science with practical applications in healthcare, transportation, cybersecurity, and business management. Notably, she has made significant contributions to sentiment analysis, medical image processing, algorithmic management, and privacy-preserving data analysis. Her research methodology often combines deep learning approaches with domain-specific knowledge to develop innovative solutions. Analysis of Dr. Zhao's recent publications (2023-2025) reveals a strong focus on interdisciplinary applications of AI, with particular emphasis on healthcare informatics (medical image analysis, disease diagnosis), human-computer interaction (algorithmic management effects), and advanced machine learning techniques (graph neural networks, multimodal learning). Her work shows a consistent trend toward increasingly complex and integrated systems that address real-world challenges across multiple domains. Dr. Zhao has made substantial contributions to academic advising and research mentorship, though specific student names aren't detailed in the available publications. Her research has been supported by various grants enabling work in AI applications, biomedical engineering, and information systems. Dr. Zhao maintains active collaborations with researchers across China and internationally, as evidenced by her co-authorship patterns. While specific laboratory information isn't explicitly mentioned in the publication records, Dr. Zhao appears to lead or be significantly involved in research groups focusing on AI applications in management and healthcare. Her work on medical imaging, sentiment analysis, and control systems suggests involvement in multiple specialized research teams addressing different application domains through computational approaches.

Philippe Moireauمشاهده پروفایل

استاد

• Inverse Problems
• Data Assimilation
• Partial Differential Equations
• +۴ مورد دیگر

Philippe Moireau is a Full Professor in the Department of Applied Mathematics at École Polytechnique, where he is also affiliated with the Center for Applied Mathematics (CMAP). He serves as the head of the Inria Project-Team MΞDISIM (Mathematical and Mechanical Modeling with Data Interaction for Simulation in Medicine) and holds the distinguished position of Ingénieur Général of The Corps des Mines. His primary research focuses on inverse problems and data assimilation for partial differential equation models, with particular emphasis on: Observer-based methods from optimal control perspectives Stabilization approaches for evolution equations Numerical analysis of time-dependent control problems Digital twin applications in cardiovascular medicine Professor Moireau's publication portfolio demonstrates consistent focus on mathematical methods for physical systems, with recurring themes in: Data assimilation techniques for PDE-based models Numerical stabilization and discretization methods Cardiovascular biomechanics and hemodynamics Stochastic modeling of biological systems Epidemiological forecasting and control He leads the ANANKΞ project-team at Inria focused on Analysis And Numerics of physical-Knowledge-based Estimation. His educational contributions include lectures on data assimilation theory at CEMRACS and courses on mathematical modeling in cardiac biomechanics at Institut Polytechnique de Paris.

Claudia Ceciمشاهده پروفایل

استاد

• Stochastic models
• Economics
• Finance
• +۷ مورد دیگر

Claudia Ceci is a Full Professor at the Department of Methods and Models for Economy, Territory, and Finance (MEMOTEF) at Sapienza University of Rome. Her academic work focuses on stochastic models in economics, finance, and insurance, with a particular emphasis on optimal stochastic control, filtering, asset pricing, credit risk, and self-protection strategies. Coordinates internationalization initiatives within MEMOTEF Leads the Rome Sapienza unit in the 2022 PRIN project "Stochastic control and games and the role of information" Manages the 2023 Grande Sapienza project "Stochastic Optimization Problems in Insurance, Finance and Economics" Member of the UMI-PRISMA group (Probability in Statistics, Mathematics, and Applications) Featured in the "100 Esperte STEM" initiative (Mathematics category) Her research explores risk management, counterparty credit risk, and reinsurance optimization under partial observation. She has contributed extensively to journals in quantitative finance, insurance mathematics, and stochastic control. Recent projects analyze climate-related financial risks and reinsurance strategies under contagion models. Claudia teaches foundational mathematics and risk management courses across multiple campuses. Exam procedures for her mathematics course involve computerized written tests with mandatory oral components under specific conditions, reflecting her analytical approach to assessment.

• Sports Biomechanics
• Exercise Science
• Sports Engineering
• +۵ مورد دیگر

Iain Spears holds dual academic appointments as Senior Lecturer in Sport & Exercise Science at Newcastle University's Faculty of Medical Sciences (Department of Biomedical Sciences) since May 2020, and as Lecturer in Biomedical/Sports Engineering at Nottingham Trent University's Department of Engineering since August 2019. His interdisciplinary position bridges sports science, biomechanics, and engineering applications in athletic performance. Dr. Spears' research focuses on several interconnected areas: Sports biomechanics and movement analysis Training load monitoring and performance assessment methodologies Injury prevention and rehabilitation strategies Technological applications in sports performance Physiological responses to exercise His publication record demonstrates methodological innovation, with recent work employing point-cloud processing for motion analysis, low-cost depth-sensing camera systems, and exergaming solutions for high-intensity training. Key research themes include differential ratings of perceived exertion, environmental effects on athletic performance, and cooling methodologies for endurance exercise. Dr. Spears' collaborative research network includes frequent co-authors Matthew Weston, Thomas Macpherson, and Sean McLaren, with publications appearing in high-impact journals such as Journal of Biomechanics, Sports Medicine, and Medicine and Science in Sports and Exercise. His work has practical applications across team sports, military training contexts, and rehabilitation programming.

Caterina Zeppieriمشاهده پروفایل

استاد

• Calculus of Variations
• Homogenization Theory
• Stochastic Analysis
• +۵ مورد دیگر

Professor Caterina Zeppieri is a faculty member at the Institute for Analysis and Numerics within the Faculty of Mathematics and Computer Science at the University of Münster, Germany. She serves as an investigator in Mathematics Münster and specializes in optimization and calculus of variations. Her research spans multiple collaborative projects within the Excellence Cluster EXC 2044. Her primary research interests focus on Calculus of Variations , Homogenization Theory , and Free-Discontinuity Problems , with significant contributions to the mathematical understanding of material science phenomena. Her work bridges theoretical mathematics with practical applications in material modeling, fracture mechanics, and multi-scale analysis. She has developed sophisticated mathematical frameworks for understanding stochastic homogenization, phase-field approximations, and gradient damage models in heterogeneous materials. Analysis of her recent publications reveals a consistent trajectory toward increasingly complex multi-scale problems involving randomness and discontinuities. Her work demonstrates deep connections between Γ-convergence theory, stochastic processes, and applications to material science, particularly in modeling fracture phenomena and composite materials. A notable trend is her development of global methods that unify deterministic and stochastic approaches to homogenization problems. Zeppieri actively contributes to teaching at the University of Münster, regularly offering courses on Partial Differential Equations, Calculus of Variations, and Advanced Topics in Mathematical Modeling. Her teaching spans both undergraduate and graduate levels, including specialized seminars on cutting-edge research topics in her field. She is deeply involved in the EXC 2044 collaborative research center, particularly in project units C1 (Evolution and asymptotics), C2 (Multi-scale phenomena and macroscopic structures), and C3 (Interacting particle systems and phase transitions), where she contributes her expertise in variational methods and homogenization to multi-disciplinary research efforts.

Asaf Cohenمشاهده پروفایل

دانشیار

• Applied Probability
• Stochastic Processes
• Control Theory
• +۱۲ مورد دیگر

Asaf Cohen is an Associate Professor in the Department of Mathematics at the University of Michigan, Ann Arbor, affiliated with the College of Literature, Science, and the Arts. He holds a B.Sc., M.Sc., and Ph.D. from Tel-Aviv University (2005–2013). His research focuses on applied probability, stochastic processes, and control theory, with emphasis on mean-field games, mathematical finance, actuarial science, diffusion and large deviation analysis, machine learning, and risk-sensitive control. His work also addresses applications in stochastic networks, energy markets, epidemiology, and economics. Key research areas include diffusion approximations, large deviations, queueing theory, and partial differential equations. Dr. Cohen has contributed to the analysis of multiclass queueing systems, optimal dividend strategies, and strategic server behavior in heavy traffic regimes. His methods often involve advanced stochastic control techniques and game-theoretic models. He has published extensively on topics such as mean-field games, SIR models for epidemics, and Bayesian sequential testing. His academic contributions span theoretical advancements and practical applications in finance, insurance, and operations research.

Tim Leungمشاهده پروفایل

استاد

• Financial Mathematics
• Optimal Stochastic Control
• Algorithmic Trading
• +۵ مورد دیگر

Tim Leung is the Boeing Endowed Chair Professor of Applied Mathematics and Director of the Computational Finance & Risk Management (CFRM) Program at the University of Washington. He also serves as core faculty in the Quantitative Ecology and Resource Management (QERM) Program and is affiliated with the Center for Quantitative Science. His research focuses on Financial Mathematics, Optimal Stochastic Control, and Algorithmic Trading, with over 70 publications and multiple books. He has held professorships at Johns Hopkins University (2008-2011) and Columbia University (2011-2016). Education: Ph.D. in Operations Research & Financial Engineering from Princeton University (2008), B.S. in Operations Research & Industrial Engineering from Cornell University (2003). His work has been funded by the National Science Foundation and recognized by the Emerald Literati Network Award (2016). He leads the CFRM program, which trains professionals in computational finance, and co-edits the Modern Trends in Financial Engineering book series. He serves on editorial boards of journals like Applied Mathematical Finance and SIAM Journal on Financial Mathematics . Research interests include derivatives pricing, ETF dynamics, and risk management strategies. He has pioneered frameworks for mean reversion trading, futures portfolio optimization, and statistical arbitrage. His recent work explores regime-switching models, multiscale volatility analysis, and applications of machine learning in financial signal processing. Awards: Emerald Literati Network Award (2016) Leadership Roles: Chair of INFORMS Finance Section, Vice Chair of SIAM Activity Group on Financial Mathematics & Engineering Advisory Roles: AI for Finance Institute His research spans theoretical advancements and practical applications, with a focus on bridging stochastic control theory and real-world financial markets.

Sinan Yıldırımمشاهده پروفایل

پژوهشگر

• Bayesian Statistics
• Monte Carlo Methods
• Data Privacy
• +۳ مورد دیگر

Sinan Yıldırım is a Researcher in the Faculty of Engineering and Natural Sciences at Sabancı University, Turkey. His primary research focuses on Bayesian Statistics, Monte Carlo methods, and data privacy, with interdisciplinary applications in machine learning and signal processing. He holds a BSc and MSc in Electrical and Electronics Engineering from Boğaziçi University, followed by a PhD in Mathematical Statistics from the University of Cambridge. Postdoctoral research (2013-2015) at the University of Bristol’s School of Mathematics involved the EPSRC-funded project 'Intractable Likelihood: New Challenges from Modern Applications (i-like).' His work bridges theoretical statistics with practical problems in privacy, control systems, and energy optimization. Research interests emphasize Bayesian methodologies for privacy-preserving data analysis, dynamic modeling of complex systems, and stochastic optimization algorithms. Recent publications explore differential privacy in machine learning, Monte Carlo techniques for high-dimensional inference, and applications of Bayesian methods in robotics and energy systems. Advising and grants include contributions to multi-party resource sharing frameworks and privacy-aware algorithms. His work integrates computational methods with real-world challenges in engineering and policy modeling.

• Financial Mathematics
• Insurance Mathematics
• Stochastic Processes
• +۳ مورد دیگر

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.

Ina Fichtnerمشاهده پروفایل

استاد

• Computer Science
• Sports Informatics
• Data Science
• +۵ مورد دیگر

Ina Fichtner is a Professor at the Faculty of Digital Transformation of University of Applied Sciences HTWK Leipzig since 2022. Previously, she led the MINT department at the Institute for Applied Training Science (IAT) in Leipzig for 13 years (2009–2022), focusing on integrating mathematics, informatics, and natural sciences into sports research. Her work bridges computer science , biomechanics , and sports informatics , with extensive projects on athlete movement analysis, data systems (IDA), and digital tools for elite sports. PhD in Computer Science (2007) from TU Dresden and Leipzig University Diplom in Mathematics and Computer Science (2002) from Jena, Dresden, and Sheffield Her research spans data science , sports technology , and applied informatics , particularly in ski jumping , dive analysis , and athlete biomechanics . She has co-authored numerous publications in theoretical computer science and applied sports informatics , including studies on 3D body scanning , inertial sensors , and force-velocity profiling . She served as Alumni Representative and Treasurer of the Friends' Association at HTWK Leipzig, with memberships in German Mathematical Society and German Sports Science Association .

Samuel Herrmannمشاهده پروفایل

استاد

• Stochastic Processes
• Probability Theory
• Applied Mathematics
• +۴ مورد دیگر

Samuel Herrmann is a Professor of Applied Mathematics at the University of Burgundy, France. He is a member of the Statistics, Probability, Optimization and Control team and an external member of the TOSCA project team at INRIA. His research focuses on stochastic processes, particularly asymptotic analysis of non-linear stochastic processes, large deviations, and stochastic resonance phenomena, with applications in climatology, biology, and financial modeling. Education: PhD in Mathematics (2001) - University of Burgundy Habilitation (2009) - Asymptotic analysis related to stochastic processes Research Interests: Stochastic differential equations and their numerical simulation Large deviation phenomena in stochastic processes Self-stabilizing diffusions and stochastic resonance First-passage and exit time problems for diffusions Applications in climatology, biology, and finance Scientific Contributions: Professor Herrmann has published extensively on stochastic processes, with over 50 peer-reviewed articles and a monograph on stochastic resonance. His work includes exact simulation methods for diffusion processes, studies on self-stabilizing systems, and theoretical contributions to large deviations theory. He has collaborated with leading researchers such as Peter Imkeller and David Peithmann. Awards and Recognition: Contributed to the encyclopedia of mathematical physics Co-authored the book "Stochastic Resonance: A Mathematical Approach in the Small Noise Limit" (2014) Teaching and Supervision: He teaches courses on stochastic processes and their simulation at both undergraduate and master's levels, including the Master in Turin program. He has supervised numerous PhD and master's students in stochastic processes and related fields.

Christa Cuchieroمشاهده پروفایل

استاد

• Mathematical Finance
• Stochastic Processes
• Risk Management
• +۶ مورد دیگر

Christa Cuchiero is a Professor at the Department of Statistics and Operations Research , University of Vienna , and an elected member of the Austrian Young Academy (Junge Akademie) since 2020. Her research bridges rigorous mathematics and cutting-edge applications in finance, machine learning, and stochastic analysis. Education: Christa earned her M.Sc. in 2006 from TU Wien with a thesis on affine interest-rate models, her Ph.D. in 2011 from ETH Zürich on affine and polynomial processes, and completed her Habilitation at the University of Vienna in 2018 on high-dimensional finance beyond classical paradigms. Research Interests: Her work centers on affine and polynomial processes , stochastic portfolio theory , signature methods , and infinite-dimensional stochastic analysis . Recent projects explore signature-based neural SDEs for option calibration, measure-valued diffusions for energy markets, and universal approximation properties of signature transforms. Awards & Recognition: Among her accolades are the FWF START Award 2019 , the Bruti-Liberati Visiting Fellowship 2018 , the ETH Medal 2012 for an outstanding Ph.D. dissertation, and the Prix de l’Institut Europlace de Finance 2017 for the best paper in finance. Contact: christa.cuchiero@univie.ac.at , Kolingasse 14-16, 05.47, 1090 Wien, Austria.

Shimeng Yuمشاهده پروفایل

استاد

• Semiconductor Devices
• Non-Volatile Memories
• 3D Integration
• +۵ مورد دیگر

Shimeng Yu is a full professor at the Georgia Institute of Technology's School of Electrical and Computer Engineering, holding the Dean’s Professorship. He earned his B.S. from Peking University (2009) and M.S./Ph.D. from Stanford University (2011/2013). His research focuses on semiconductor devices, non-volatile memories, 3D integration, and AI hardware accelerators. Yu leads SRC/DARPA JUMP 2.0 centers on memory/storage and 3D integration, with over 400 publications and 30,000+ citations (H-index 82). He serves on flagship conference committees (e.g., IEDM, VLSI) and editorial boards (IEEE EDL, JETCAS). Education: B.S., Microelectronics, Peking University (2009) M.S./Ph.D., Electrical Engineering, Stanford University (2011/2013) Research Themes: Emerging non-volatile memories for AI Monolithic 3D integration Energy-efficient computing systems His work spans device fabrication, circuit design, and system-level co-optimization. Recent projects are funded by NSF, DARPA, DOE, and industry partners (TSMC, Intel, Samsung), totaling >$17M. His lab, located at the Pettit Microelectronics Research Center, develops prototypes with cleanroom access. Awards: IEEE Fellow (2024) ACM/IEEE DAC Under-40 Innovators Award (2020) NSF CAREER Award (2016) Multiple editorship roles and distinguished lecturer appointments (IEEE EDS/CASS) Grants & Funding: Lead of two SRC/DARPA JUMP 2.0 centers Total research funding exceeds $17M

Zhiyi Chiمشاهده پروفایل

استاد

• Stochastic Processes
• Probability Theory
• Statistical Inference
• +۲ مورد دیگر

Zhiyi Chi is a Professor in the Department of Statistics at the University of Connecticut (Storrs Campus). His research focuses on stochastic processes with large jumps, random sampling techniques, stochastic optimization, and large-scale hypothesis testing. He holds an office in AUST 336 and can be reached at zhiyi.chi@uconn.edu. Key contributions include works published in Journal of Theoretical Probability (2018) and Annals of Applied Probability (2016), addressing topics such as multivariate renewal theorems and eigenvalue properties of Markov matrices. His scholarship bridges theoretical foundations and applied methodologies in probability and statistics.