جست‌وجوی استاد و پژوهشگر

Patrick Beullensمشاهده پروفایل

دانشیار

• Operational Research
• Supply Chain Management
• Logistics and Reverse Logistics
• +۴ مورد دیگر

Dr. Patrick Beullens is an Associate Professor in Operational Research and Management Science at the University of Southampton's Southampton Business School. He specializes in applied research across ocean shipping, retail supply chains, logistics, and inventory control. His work integrates mathematical techniques such as stochastic processes, optimization algorithms, and game theory to address real-world challenges like environmental performance in shipping and food waste reduction. Key roles include Principal Investigator on EC-funded projects (e.g., SEABILLA, LOGMAN) and supervision of PhD students like Fangsheng Ge. Current projects focus on maritime emission abatement and economic ship speed models. He teaches Supply Chain Management, Risk Management, and Optimization courses. Research Groups: CORMSIS, Southampton Marine and Maritime Institute, Supply Chain Excellence Centre. Grants: Over £200k from Shell and SMMI for PhD scholarships, MoD-funded inventory projects, and EU initiatives. His research spans maritime economics, reverse logistics, and decision-making under risk. Collaborations include BAE Systems, EDF Energy, and international institutions like the Joint Research Centre.

David Saundersمشاهده پروفایل

دانشیار

• Quantitative Risk Management
• Mathematical Finance
• Stochastic Optimization
• +۳ مورد دیگر

David Saunders is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, affiliated with the David R. Cheriton School of Computer Science. His research focuses on quantitative risk management, mathematical finance, and stochastic optimization with applications to credit risk, portfolio optimization, and derivatives pricing. Professor Saunders has held academic positions at the University of Pittsburgh and Cyprus International Institute of Management, and has consulted for institutions like RiskMetrics, TD Bank, and Central Bank of Cyprus. Education: PhD studies at the University of Toronto with industry collaboration at Algorithmics Inc., followed by roles at Cyprus International Institute of Management and University of Cyprus's HERMES Center. His work bridges theoretical stochastic analysis and practical financial risk management challenges. Research interests prominently feature credit risk management, optimal stopping problems, and analytical techniques in finance. Recent work addresses inverse default boundary problems, wrong-way risk in derivatives, and efficient algorithms for portfolio optimization under complex return distributions. Collaborations with financial institutions drive applied research in capital allocation, operational risk modeling, and regulatory compliance. Advising and grants include directing the Professional Science Master's in Mathematical Finance at Pitt, supervising student-industry projects on credit risk and CDO pricing, and securing funding for computational finance initiatives. Active in RiskLab Cyprus and HERMES European Center, his research extends to equity risk management and market risk frameworks.

Yulia Gelمشاهده پروفایل

استاد

• Uncertainty Quantification
• Artificial Intelligence
• Topological Data Analysis
• +۶ مورد دیگر

Yulia Gel is a Professor in the Department of Statistics at Virginia Tech and serves as a Part-Time Program Director-Expert at the National Science Foundation (NSF). She holds a MSc (summa cum laude) and PhD in Mathematics from Saint Petersburg State University (Russia) and completed a postdoc in Statistics at the University of Washington. Her research focuses on uncertainty quantification in AI, statistical foundations of data science, spatio-temporal processes, and applications in climate science, healthcare, and blockchain analytics. She has received prestigious awards including the NSF Director’s Award (2023), ASA Distinguished Achievement Medal (2018), and TIES Abdel El-Shaarawi Award (2014). Gel has led grants on wildfire prediction, climate informatics, and blockchain data science. She serves on editorial boards of Statistica Sinica, Electronic Journal of Statistics, and Technometrics, and organizes workshops on AI for climate sustainability and fragile Earth systems. Her research group develops topological and geometric methods for graph neural networks, with applications to digital twins, environmental justice, and public health. Education: MSc (1997), PhD (2000) in Mathematics from Saint Petersburg State University; Postdoc in Statistics at University of Washington (2001–2003). Past roles include Professor at University of Texas at Dallas (2015–2024) and Associate Professor at University of Waterloo (2004–2014). Selected visiting positions include NASA Jet Propulsion Lab (2016–2017) and Isaac Newton Institute (2016–2017). She has pioneered statistical software packages like snowboot and funtimes for network inference and time-series analysis. Awards highlight her contributions to environmetrics and statistical methodologies. Current projects include NSF-funded research on AI-driven wildfire prediction and blockchain analytics for climate resilience. Her lab’s recent work emphasizes topological methods (e.g., zigzag persistence) for graph-based forecasting and adversarial robustness.

Linda Sandris Larsenمشاهده پروفایل

دانشیار

• Dynamic Asset Allocation
• Household Finance
• Mortgage Choice
• +۳ مورد دیگر

Linda Sandris Larsen is an Associate Professor of Finance at the Copenhagen Business School's Department of Finance. She previously held academic positions at the University of Southern Denmark (Assistant Professor 2009–2012 and Associate Professor 2012–2016). Her primary affiliations include the Pension Research Center (PerCent) at CBS and board membership at Sydinvest (Danish mutual fund). Education: PhD in Finance (2009), MSc in Mathematics-Economics (2003), both from University of Southern Denmark Her research focuses on dynamic asset allocation, household financial decision-making (particularly mortgage choices), and the impact of model/parameter uncertainty on investment strategies. Current projects explore: Life-cycle consumption-saving interactions with interest-only mortgages Design efficacy of mandatory pension plans Gender disparities in pension product selection Recent publications emphasize practical policy implications, such as recession hedging strategies and pension system welfare analysis. She actively contributes to public policy through roles like the Ministry of Business and Growth's working group on banking/mortgage competition. Her work bridges theoretical finance with real-world applications, often combining stochastic models with empirical analysis of household financial behaviors.

• Macroeconomics (incl. monetary and fiscal theory)
• Applied Economics
• International Economics

Morten O. Ravn is a Professor of Economics and Chair of Macroeconomics at University College London (UCL). He holds a PhD from the University of Southampton (1993) and a MSc from Aarhus University (1990). Previously, he held positions at the University of Southampton, European University Institute, London Business School, and Universitat Pompeu Fabra. His research focuses on macroeconomics, monetary and fiscal theory, applied economics, and international economics. He is a Fellow of the Econometric Society, European Economic Association, and an Advanced ERC Grantee. He has supervised over 40 PhD students and serves as Co-Editor of Quantitative Economics and Chairman of the Royal Economic Society Publications Committee. Ravn's academic contributions span theoretical and empirical macroeconomics, including work on business cycles, tax policy effects, and financial frictions. His research has been published in top journals like the American Economic Review and Econometrica . He actively participates in policy discussions, emphasizing evidence-based approaches to economic challenges. His recent work explores the interplay between economic uncertainty, labor markets, and policy responses. Education: PhD in Economics, University of Southampton (1993) MSc in Economics (cand.oecon), Aarhus University (1990) Awards & Roles: Fellow of the Econometric Society Fellow of the European Economic Association Advanced ERC Grant Recipient Co-Editor, Quantitative Economics Grants & Activities: Recipient of European Research Council (ERC) Advanced Grant Member of the Selection Board for Riksbankens Jubileumsfond Ravn’s research lab and collaborative networks focus on macroeconomic modeling, fiscal policy analysis, and international economic dynamics. His work frequently integrates empirical evidence with theoretical frameworks to address contemporary economic issues.

Ehsan Pashajavidمشاهده پروفایل

مدرس ارشد

• Stochastic modeling/optimisation
• Microgrids/Smartgrid
• Renewable Energy Resources
• +۳ مورد دیگر

Dr. Ehsan Pashajavid is a Senior Lecturer at Curtin University's School of Electrical Engineering, Computing and Mathematical Sciences, within the Faculty of Science and Engineering. His research focuses on stochastic optimization, renewable energy integration, microgrid control, and electric vehicle systems. Research interests include: Microgrid and smart grid control algorithms Renewable energy resource management Power system stability and operation Energy storage optimization Electric vehicle-grid integration His publications demonstrate significant contributions to power system resilience, with recent work emphasizing battery storage economics, fault-tolerant converters, and model predictive control for grid stability. Article trends show strong focus on renewable integration challenges and optimization techniques for modern energy systems. Awards include Senior Member status in IEEE and its Power & Energy, Industrial Applications, and Power Electronics societies. Teaching responsibilities encompass graduate courses in Renewable Power Generation Systems, Smart Grid Control, and Renewable Energy Principles.

Mawuli Kouami Segnonمشاهده پروفایل

پژوهشگر

• Econometrics
• Time Series Analysis
• Financial Forecasting
• +۵ مورد دیگر

Dr. Mawuli Kouami Segnon is a researcher at the Chair of Empirical Economics, School of Business and Economics, University of Münster. His work focuses on econometric modeling, financial time series analysis, and volatility forecasting across various domains including cryptocurrencies, energy markets, and macroeconomic indicators. Research interests include: Development of advanced volatility models (GARCH, multifractal, regime-switching) Applications to financial markets, energy economics, and macroeconomic policy High-frequency data analysis and mixed-frequency forecasting Count data modeling with conditional heteroscedasticity Portfolio risk management using copula and multifractal approaches Recent publications demonstrate expertise in: Geopolitical risk impacts on stock volatility Comparative analysis of realized variance measures Inflation uncertainty modeling in G7 countries Electricity price volatility in Australian markets Bitcoin market forecasting Historical economic data analysis Current projects (since 2020) involve: Innovative economic/financial time series forecasting Financial market volatility modeling Applications of multifractal structures in econometrics

Refik Soyerمشاهده پروفایل

استاد

• Bayesian Statistics
• Reliability Modeling
• Decision Analysis
• +۴ مورد دیگر

Refik Soyer is a Professor of Statistics at The George Washington University. His research focuses on Bayesian statistics, reliability modeling, decision analysis, and time series analysis. He has made significant contributions to the application of Bayesian methods in reliability engineering, queueing systems, and adversarial risk analysis. Education: D. Sc. in Statistics (1985), George Washington University His recent publications highlight advancements in Bayesian reliability analysis, adversarial decision frameworks, and computational methods for time series and queueing systems. Areas of emphasis include dynamic INAR processes, accelerated life testing, and software failure modeling. Soyer's work bridges theoretical statistics with practical applications in call centers, healthcare fraud detection, and risk management.

Hyejin Kuمشاهده پروفایل

استاد

• Mathematical Finance
• Machine Learning
• Risk Management
• +۴ مورد دیگر

Hyejin Ku is a Full Professor in the Department of Mathematics and Statistics at York University's Faculty of Science. Her research focuses on the intersection of Mathematical Finance and Machine Learning, addressing challenges in risk measurement, portfolio optimization, and quantitative finance. She develops advanced mathematical models to enhance decision-making through reinforcement learning and data analytics. Notable projects include novel algorithms for credit rating prediction using neural networks and sequence-based clustering for credit risk assessment. Her work integrates applied mathematics with real-world financial applications, such as systemic risk reduction in multi-layer networks and option pricing under liquidity constraints. She holds a prominent position in mathematical finance, contributing to both theoretical advancements and practical solutions for financial markets. Her research trends emphasize interdisciplinary approaches, combining machine learning techniques with financial modeling to solve complex problems in risk management and asset valuation. Her publications span over two decades, showcasing contributions to portfolio optimization, derivatives pricing, and computational finance. Dr. Ku is affiliated with York University’s Department of Mathematics and Statistics, where she contributes to academic leadership and research mentorship. Her office is located in DB 2025, and she can be reached at hku@yorku.ca.

Mohsen Pourahmadiمشاهده پروفایل

استاد

• Time Series Analysis
• Multivariate Statistics
• Covariance Estimation
• +۷ مورد دیگر

Mohsen Pourahmadi is a Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on developing methodologies for modeling covariance matrices in multivariate and time series data, with applications to financial analysis, longitudinal studies, neuroeconomics, and high-dimensional data. Key tools include graphical lasso algorithms, Cholesky decomposition, and Bayesian approaches. He emphasizes extending generalized linear models (GLM) to covariance matrix estimation, leveraging prediction theory and stochastic processes. Education details are not explicitly provided in the text. His work spans theoretical advancements in covariance estimation, such as sparse VAR models, nonstationary process analysis, and regularized multivariate regression. He has contributed to applications like detecting cyber attacks on infrastructure systems and analyzing breast cancer data through Bayesian networks. Research interests include time series graphical models, antedependence models for longitudinal data, and regularization techniques for high-dimensional covariance matrices. His recent work explores fused-lasso penalties, Bayesian correlation matrix estimation, and stationary subspace analysis. Pourahmadi has authored numerous articles on topics ranging from multivariate volatility modeling to nonparametric covariance estimation, emphasizing both computational efficiency and theoretical rigor.

Qiwei Yaoمشاهده پروفایل

استاد

• Statistical Inference
• Time Series Analysis
• High-Dimensional Statistics
• +۶ مورد دیگر

Qiwei Yao is a Professor of Statistics at the Department of Statistics, London School of Economics (LSE), where he maintains an active research program in statistical methodology and applications. His office is located in Columbia House, Room 7.16 at LSE's Houghton Street campus in London. Professor Yao's research focuses on statistical inference for complex time series, with particular expertise in high-dimensional time series, dynamic networks, spatio-temporal processes, functional time series, nonlinear time series, and high-frequency data. His work bridges theoretical statistics with practical applications, especially in financial econometrics. He has developed innovative methodologies for dimension reduction, factor modeling, and network analysis that have become influential in the field. His recent publications reveal a strong trend toward developing statistical methods for increasingly complex data structures, particularly focusing on high-dimensional and network-based time series. His work integrates machine learning techniques with traditional statistical approaches, as evidenced by papers on deep learning for Markov property testing and tensor decompositions for matrix time series. There's also a clear emphasis on privacy-preserving methods and differential privacy in network analysis. Professor Yao has secured substantial research funding through multiple EPSRC Programme Grants and Research Projects, including the EPSRC Programme Grant for 'Statistical Foundations for Detecting Anomalous Structure in Stream Settings (DASS)' and 'Network Stochastic Processes and Time Series (NeST)'. He also leads the EPSRC Research Project on 'Statistical Network Analysis: Model Selection, Differential Privacy, and Dynamic Structures' and has collaborated with industry partners like Andurand Capital Management on projects such as 'Forecasting Oil Prices Based on Quantitative Methods'. His research has significant applications across various domains, particularly in energy forecasting (electricity load prediction), financial modeling (volatility modeling, oil price forecasting), and ecological modeling (spatio-temporal population dynamics). Professor Yao maintains strong collaborative relationships with researchers across multiple institutions and disciplines.

Chenhao Maمشاهده پروفایل

استادیار

• Graph Computing
• Data Mining
• Database Systems
• +۳ مورد دیگر

Dr. Chenhao Ma is an Assistant Professor at the School of Data Science , The Chinese University of Hong Kong, Shenzhen , where he works on large-scale data management and data mining. Previously, he was a Postdoctoral Fellow at the University of Hong Kong (2021–2022) and earned his PhD in Computer Science from the University of Hong Kong (2021) and B.Eng. from Shandong University (2017). Current research focuses on graph computing (dense subgraph discovery, motif analysis, graph learning), AI+DB (Text-to-SQL, vector search), and traffic data mining (trajectory analysis, outlier detection). He has published over 40 papers in top venues including SIGMOD, PVLDB, KDD and received the ACM SIGMOD Research Highlight Award 2021 and Best of SIGMOD 2020 (4/458). Scientific Awards : ACM SIGMOD Research Highlight Award 2021 Best of SIGMOD 2020 (4/458) Presidential Young Fellow at CUHK-Shenzhen (2023) Hong Kong and China Gas Scholarship (2019-2020) Reaching Out Award (2019) HKU Postgraduate Scholarship (2017-2021) ACM-ICPC Gold Medal (2015) National Scholarship (2014, 2015) Advising and Research Team : He leads a team including Postdoc Dr. Yuanyuan Zeng, PhD students Lujie Ban, Yuwei Xu, and MPhil students Yi Yang, Yuyang Liang. Former mentees like Yichen Xu (PhD at Berkeley) and Jiayang Pang (Master at UC San Diego) have achieved academic placements. Professional Service : He has served as PC member/reviewer for VLDB, KDD, ICDE, WWW, NeurIPS, TKDE , and guest editor for Applied Sciences and Frontiers in Big Data . He chairs sessions at ICDE and VLDB.

• Financial Derivatives
• Real Options Valuation
• Real Estate Finance
• +۴ مورد دیگر

Andrianos E. Tsekrekos is a Lecturer at the Department of Accounting and Finance, School of Business, Athens University of Economics and Business (AUEB). He earned his BSc in Operations Research and Marketing from AUEB, an M.Sc. in Finance from the University of Lancaster, and completed his PhD in Accounting & Finance at Lancaster in 2003. Prior to rejoining AUEB in 2006, he served as a lecturer at Lancaster University and Durham Business School in the UK.

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.

Sandy Iraniمشاهده پروفایل

استاد

• Quantum Computing
• Algorithm Design
• Computational Complexity Theory
• +۵ مورد دیگر

Sandy Irani is a Full Professor at the University of California, Irvine (UCI) in the Department of Computer Science within the Donald Bren School of Information and Computer Sciences. She received her Ph.D. from UC Berkeley in 1991 and has been at UCI since 1992. Her research focuses on algorithm design, computational complexity theory, and quantum computing, with notable contributions to online algorithms and quantum complexity theory. She currently serves as Associate Director of the Simons Institute for the Theory of Computing at UC Berkeley, a role she has held since 2022. This position allows her to collaborate with researchers across theoretical computer science and related disciplines. Irani’s teaching excellence is recognized through the UCI Distinguished Faculty Award for Teaching (2021), and she has contributed to education through her zyBook on Discrete Mathematics, used by over 94,000 students globally. Her work bridges foundational computer science with practical applications, including power management strategies and distributed computing algorithms. Notably, she has collaborated with industry leaders like Mike Luby on optimizing distributed systems. Her research in quantum computing explores computational problems inspired by condensed matter physics, aiming to understand quantum advantage over classical systems. She has also authored influential papers on topics like cache hierarchy design, scheduling algorithms, and the theoretical limits of electronic structure calculations. Awards: ACM Fellow (2022), UCI Distinguished Faculty Award for Teaching (2021). Key Roles: Associate Director, Simons Institute; Vice Chair, Computing Division at UCI. Recent Projects: Quantum algorithms for condensed matter systems, maximal independent set algorithms in distributed networks.