جست‌وجوی استاد و پژوهشگر

Hamid Arianمشاهده پروفایل

استادیار

• Financial AI
• Algorithmic Trading
• Risk Management
• +۴ مورد دیگر

Hamid Arian is an Assistant Professor in the School of Administrative Studies at York University, specializing in finance. His research focuses on quantitative methods in finance, including derivatives pricing, risk management, and machine learning applications in algorithmic trading. Education: PhD in Mathematical Finance, University of Toronto MSc and BSc in Pure Mathematics, Sharif University of Technology CFA charter from CFA Institute, USA FRM charter from GARP, USA Arian's research spans environment and sustainability management, derivatives securities, risk management, financial AI, algorithmic trading, and fintech. His recent publications demonstrate extensive work on portfolio optimization, neural networks for financial modeling, and advanced statistical methods for risk assessment. Articles predominantly explore machine learning innovations in finance, showing consistent focus on robust modeling techniques for derivatives pricing, risk quantification, and algorithmic trading systems.

• Statistics
• Machine Learning
• Environmental Data Analysis
• +۳ مورد دیگر

Nikolaus Umlauf is an Associate Professor at the Department of Statistics, University of Innsbruck. He specializes in Bayesian distributional regression, structured additive models, and spatiotemporal analysis, with applications in public health, climate science, and real estate valuation.

• Sustainable Energy Systems
• Numerical Optimization
• Deep Learning
• +۳ مورد دیگر

Dr. Manuel Dahmen is the Head of Department at the Institute of Climate and Energy Systems (ICE-1) within the Research Center Jülich. His research focuses on designing sustainable and cost-efficient energy systems through advanced techniques like numerical optimization and deep learning. Key areas include renewable energy integration, process network analysis, and machine learning-driven system design. His work addresses challenges in decarbonizing industries, optimizing energy systems, and developing tailor-made fuels for high-efficiency engines. Dahmen leads interdisciplinary projects combining computational methods with practical engineering solutions, emphasizing robust design under uncertainty. He contributes to open-source tools like COMANDO for energy systems optimization and explores innovations in graph neural networks for molecular property prediction. Research trends reflected in his publications highlight optimization algorithms (e.g., semi-infinite programming), demand response strategies for industrial processes, and physics-informed machine learning for dynamic systems. Dahmen’s contributions bridge theoretical advancements with real-world applications, aiming to accelerate the transition to sustainable energy systems.

Wang Qianمشاهده پروفایل

دانشیار

• Transportation Systems Engineering
• Urban Freight Logistics
• Traffic Accident Analysis
• +۳ مورد دیگر

Qian Wang is an Associate Professor of Teaching and Director of Undergraduate Studies in the Department of Civil, Structural, and Environmental Engineering at the University at Buffalo (SUNY). He holds multiple leadership roles including Co-Director of the Lab Expenditure Committee and Director/Principal Investigator of the 2021 National Summer Transportation Institute. His primary affiliation is with the School of Engineering and Applied Sciences. Wang's research focuses on transportation systems engineering, with emphasis on traffic accident analysis, urban freight logistics, and data-driven decision support systems. His work integrates advanced statistical techniques like entropy maximization and machine learning with transportation infrastructure challenges. Key contributions include predictive models for traffic accidents and border crossing delays, as well as innovative approaches to assessing transportation sustainability and community livability. His publication trajectory reflects sustained contributions to transportation engineering since 2003, with recent work emphasizing real-time traffic prediction, freight carrier behavior analysis, and policy evaluation for road pricing initiatives. Over 15 peer-reviewed articles demonstrate methodological rigor and practical relevance to urban transportation challenges. Wang has actively contributed to transportation education through undergraduate program leadership and has led research initiatives funded by transportation authorities including the Port Authority of New York and New Jersey. His work bridges theoretical advancements with applied solutions for smarter, safer transportation systems.

• Exploratory Data Analysis
• Dimension Reduction
• Cluster Analysis
• +۷ مورد دیگر

Michel van de Velden is an Associate Professor of Statistics at the Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam. His work bridges statistical methodology and data science, with a focus on exploratory techniques and machine learning. His research interests lie primarily in exploratory data analysis , with a focus on dimension reduction , cluster analysis , and data visualization . He also actively contributes to supervised machine learning , particularly using tree-based methods such as random forests. His methodological work often addresses challenges in categorical data and mixed-type variables . The recent articles highlight a strong trend in developing exact and unbiased statistical methods for clustering and distance computation. His work spans correspondence analysis , hierarchical clustering algorithms , and software tools for mixed data, reflecting a blend of theoretical rigor and practical implementation. He has served on the editorial boards of Computational Statistics and Statistical Papers , contributing to academic peer review and publication quality. Michel van de Velden has supervised academic work and contributed to open datasets and software, indicating active mentorship and collaboration. His research is supported by ongoing publications and editorial activities, suggesting sustained engagement in the academic community. He is involved in developing software such as manystat and datasets related to categorical data visualization, indicating leadership in reproducible and open research practices.

• Financial Risk Management
• Corporate Finance
• Econometrics
• +۵ مورد دیگر

Marek Káčer is an Assistant Professor at the Department of Finance within the Faculty of Economics and Finance at Bratislava University of Economics and Business. His research focuses on financial risk management, corporate finance, econometrics, and sovereign credit risk modeling. His notable research interests include SME failure prediction using machine learning, audit fee dynamics of Big 4 auditors, and sovereign credit rating analysis for emerging economies. He has collaborated with institutions like the SecureDataLab and contributed to projects on creative capacity measurement and regional development. Key contributions include studies on Slovak SMEs' financial distress prediction, gender diversity in private firms, and the equity gap in knowledge-based enterprises. His work blends quantitative methods with real-world applications in transition economies and innovation financing. Teaching responsibilities include courses like Foundations of Financial Econometrics and Risk Analysis in Finance. He maintains active international collaborations and publishes in journals such as Journal of Business Finance and Accounting and Journal of Corporate Finance .

Heiner Beckmeyerمشاهده پروفایل

استادیار

• Asset Pricing
• Machine Learning in Finance
• Financial Engineering
• +۵ مورد دیگر

Dr. Heiner Beckmeyer is an Assistant Professor at the Chair of Derivatives and Financial Engineering at the University of Münster. He holds a PhD (2022) and the CFA charter (2024). His research focuses on asset pricing, machine learning applications in finance, and derivatives markets. He has published in top journals like the Review of Financial Studies and received awards such as the Jack Treynor Prize and multiple best paper recognitions. He teaches courses in Asset Pricing and Derivatives at the Master's level, and his work frequently addresses topics like option return predictability, market efficiency, and retail trading behavior. Education: Master of Science in Business Administration (2016-2018), Bachelor of Science in Business Administration (2013-2016). Research Interests: Machine learning in finance, derivatives pricing, market microstructure, and behavioral finance. Awards: Jack Treynor Prize (2023) Best Paper Award at INQUIRE Europe/UK (2024) Finalist for Crowell Prize (2025) Recent Work: Explores intraday option reversals, unusual financial communication via AI, and retail trader behavior in 0DTE options. Active in presenting at conferences like EFA, FMA, and SFS Cavalcade.

Catherine Forbesمشاهده پروفایل

استاد

• Bayesian Statistics
• Econometrics
• Financial Econometrics
• +۵ مورد دیگر

Catherine Forbes is a Professor and Deputy Head of the Department of Econometrics and Business Statistics (EBS) at Monash University, where she also serves as Director of Education. She holds an adjunct professorship at Sunway University, Malaysia, and is an Associate Editor of Applied Stochastic Models in Business and Industry . Her research focuses on Bayesian statistics, econometrics, and computational methods for analyzing financial risks and complex time series. She has led multiple research grants, including ARC and NHMRC-funded projects, and contributed to policy reforms, such as the Victorian Children, Youth and Families Act 2005. Her research interests span Bayesian modeling, financial econometrics, robust statistical methods, and nonparametric techniques. She has supervised 20 PhD students and 20 Honours projects, emphasizing mentorship in statistical methodologies. Notable collaborations include work with the Centre of Excellence in Child and Family Welfare on predictive analytics for youth outcomes. Dr. Forbes has led over 20 research projects, including grants focused on Bayesian empirical likelihood, financial risk dynamics, and health care analysis. Her recent work includes advancements in cross-validation methods and familial inference techniques, with applications in housing affordability and mental health linkages.

HUANG Dashanمشاهده پروفایل

دانشیار

• Asset Pricing
• Behavioral Finance
• Big Data
• +۳ مورد دیگر

HUANG Dashan is an Associate Professor of Finance and PGR Coordinator at the Lee Kong Chian School of Business, Singapore Management University (SMU). He holds a Ph.D. in Finance from Washington University in St. Louis (2013), a Ph.D. in Engineering from Kyoto University (2007), and a B.S. in Mathematics from Lanzhou University (2002). His research focuses on Asset Pricing, Behavioral Finance, Big Data, and Machine Learning, with notable contributions to portfolio optimization, factor models, and market sentiment analysis. He has received prestigious awards such as the CIRF/CFRI Research Award (2020) and the WRDS Best Paper Award (2018). His work integrates econometric methods, machine learning, and behavioral theories to analyze financial markets. Recent studies include predicting bond returns using real-time macro data, exploring presidential approval ratings' impact on stock returns, and developing dimension-reduction techniques for factor models. Huang collaborates widely, publishing in top journals like Journal of Financial Economics and Management Science . Education : Ph.D. in Finance, Washington University in St. Louis (2013) Ph.D. in Engineering, Kyoto University (2007) M.A. in Management Science, Chinese Academy of Sciences (2004) B.S. in Mathematics, Lanzhou University (2002) Awards and Recognition include multiple conference best-paper awards, emphasizing his innovative contributions to empirical asset pricing and quantitative finance. His research has been supported by grants, and he advises students on topics spanning behavioral finance and computational methods. Huang is actively involved in interdisciplinary initiatives, bridging finance with computing and social sciences. His lab develops tools like the PLS Sentiment Index and PLS Disagreement Index, widely used in academic and applied research.

Xueqin Amy Liuمشاهده پروفایل

مدرس ارشد

• Smart Grid Data Analytics
• Renewable Energy Integration
• Energy Storage Systems
• +۳ مورد دیگر

Dr. Xueqin Amy Liu is a Senior Lecturer at Queen’s University Belfast (QUB) in the School of Electronics, Electrical Engineering and Computer Science. She holds a Ph.D. from QUB (2009) through a joint program with Zhejiang University. Her research bridges power systems and data analytics, focusing on smart grid challenges, energy storage, and renewable integration. She leads industry-driven projects like the €6.7M INTERREG SPIRE2 initiative, advancing energy storage solutions and grid stability. Dr. Liu is a Senior Member of IEEE and serves as an Associate Editor for IET Energy Conversion & Economics. She has supervised 13 PhD students and 2 postdocs, many employed in energy sectors. Notable achievements include the Best Graduate Student Poster Award (2021) and the Outstanding Associate Editor Award (2024). Education: Ph.D., Electrical and Electronic Engineering, Queen’s University Belfast (2009), joint with Zhejiang University Research Interests: Smart Grid data analytics, machine learning for grid stability, energy storage optimization, and renewable integration. Her work addresses real-world challenges with industry partners like EirGrid and SONI, developing tools for grid oscillation detection and energy market strategies. Recent Articles: Focus on oscillation mode identification, dynamic mode decomposition for grid analysis, and machine learning for electricity price forecasting. These contributions enhance grid stability and renewable integration. Awards: EPSRC Impact Acceleration Award (2022) Outstanding Associate Editor Award (IET, 2024) Best Graduate Student Poster Award (IEEE PES, 2021) Advising & Grants: Supervised 13 PhD students (e.g., Javier Lopez Lorente, Mark Rafferty) and led projects totaling millions in funding. Active in the SPIRE2 project and EU INTERREG programs. Labs/Teams: Her team develops diagnostic tools for grid oscillations and behind-the-meter analytics, collaborating with transmission system operators to enhance grid visibility and flexibility.

Minh-Quan Tranمشاهده پروفایل

پژوهشگر

• Distribution System Engineering
• Distributed Energy Resource Engineering
• State Estimation
• +۵ مورد دیگر

Minh-Quan Tran is a University Researcher at Eindhoven University of Technology (TU/e) within the Department of Electrical Energy Systems, Faculty of Electrical Engineering. His work focuses on modern power distribution systems, renewable energy integration, and grid stability solutions, contributing to UN Sustainable Development Goals in sustainable energy. He maintains an ORCID profile (0000-0001-6430-7333) and has accumulated 99 Scopus citations for his 20 research outputs, with publication activity increasing from 2 works in 2019 to 7 in 2023. His key research interests include: Distribution System Engineering (100% fingerprint strength) Distributed Energy Resource Engineering (71%) State Estimation (64%) Voltage Stability (51%) Microgrid Control (40%) System State Monitoring (47%) Recent publications (2022-2024) reveal strong trends in AI-driven grid management, particularly Model Predictive Control and Graph Neural Networks for voltage stability and state estimation. Real-world case studies feature campus networks (e.g., Chalmers University), emphasizing practical validation of control strategies for low-voltage systems with high renewable penetration. Scientific Awards: No awards or honors are documented in the provided materials Advising and Grants: Tran lists no formal PhD/Master's students in the repository. His research involves IoT platforms, digital twins, and real-life demonstrations for flexibility optimization but lacks explicit grant details or funding acknowledgments in the extracted text. Labs and Teams: He operates within TU/e's Electrical Energy Systems research group, specializing in active distribution network monitoring, microgrid stability, and distributed resource control. Collaborations span international institutions as visualized in the repository's network map, with recent joint work involving Vietnamese and European universities.

Ravi Kashyapمشاهده پروفایل

دانشیار

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

Ravi Kashyap is an Associate Professor at the Estonian Business School (since 2021). Previously, he held academic roles including Visiting Professor at Singapore University of Social Sciences (2020–2021) and Assistant Professor at SolBridge International School of Business (2017–2020). Before academia, he worked in senior roles such as CEO and Head of Quantitative Research at institutions like Goldman Sachs, Morgan Stanley, and Citigroup. His expertise spans finance, artificial intelligence, and blockchain. Education: PhD in Finance, City University of Hong Kong (2020) MBA, Leonard N. Stern School of Business, New York University (2009) Bachelor of Technology in Computer Science and Engineering, National Institute of Technology Calicut (1999) Research Interests: Artificial Intelligence and Machine Learning applications in finance Risk management and portfolio optimization Blockchain technology and decentralized finance Economics of knowledge and uncertainty Evolutionary behavioral models and cognitive adaptation Recent Article Trends: Focus on integrating blockchain protocols with wealth management, dynamic trading cost analysis, and interdisciplinary applications of machine learning in finance and economics. Recent work addresses practical challenges in decentralized finance (DeFi) and the theoretical underpinnings of market microstructure. Grants & Industry Collaboration: Advises on AI incubation and collaborates with blockchain firms to develop methodologies for human collaboration systems. Former industry experience informs his research on financial markets and quantitative strategies.

Adrien d'Avernasمشاهده پروفایل

دانشیار

• Asset Pricing
• Financial Intermediation
• Monetary Policy
• +۳ مورد دیگر

Adrien d'Avernas is an Associate Professor of Finance at the Stockholm School of Economics (SSE), affiliated with the Swedish House of Finance. His research focuses on the intersection of financial markets and macroeconomics, with emphasis on financial intermediation, monetary policy, and asset pricing dynamics. He holds a Ph.D. in Economics from the University of California, Los Angeles (UCLA), advised by Andrew Atkeson and Lee Ohanian. Key research areas include: Liquidity and money market dislocations Treasury market disruptions and central bank balance sheets Financial risk capacity and bank capital dynamics Quantitative easing fiscal costs and policy trade-offs Shadow banking and central bank liquidity tools His work has been published in top journals like the Journal of Finance and Management Science. He co-developed the PyMacroFin Python toolbox for solving continuous-time macro-finance models. Current projects explore stablecoin stability mechanisms and the role of machine learning in option pricing. Adrien collaborates actively with institutions like the ECB and contributes to policy discussions on financial stability. His research often combines theoretical models with empirical analysis to address real-world financial market challenges.

Stephane Vialleمشاهده پروفایل

پژوهشگر

• High-Performance Computing
• GPU Computing
• Quantum Computing
• +۳ مورد دیگر

Stephane Vialle is a researcher at CentraleSupélec, leading the Interdisciplinary Laboratory of Digital Sciences. His research focuses on High-Performance Computing (HPC), GPU Computing, Quantum Computing, and Quantum Machine Learning. He has extensive experience in developing scalable fine-grained computing environments and optimizing parallel algorithms for distributed systems. His work spans financial engineering, energy management, and railway infrastructure through digital twin technology. Recent contributions include advancements in GPU cluster energy efficiency, stochastic control algorithms, and hybrid classical-quantum architectures for data clustering. Research interests emphasize optimizing parallel computing frameworks for diverse applications, including financial modeling, material science simulations, and transportation systems. His publications demonstrate expertise in distributed computing, fault-tolerant architectures, and algorithmic innovation across multiple computational paradigms. Current projects explore quantum computing integration with classical systems, GPU-based large-scale data processing, and real-world applications of parallel simulation techniques. Notable contributions include the parXXL development environment for coarse-grained platforms and the MINERVE digital twin for railway infrastructure management. His work bridges theoretical computing advancements with practical implementations in engineering and finance domains.

Markus Leippoldمشاهده پروفایل

استاد

• Climate Finance
• Natural Language Processing
• Asset Pricing
• +۳ مورد دیگر

Markus Leippold is a Professor of Financial Engineering at the University of Zurich and Director of the Master of Advanced Studies in Finance program. His research focuses on Climate Finance, Natural Language Processing, Asset Pricing, and Machine Learning applications in finance. Before academia, he worked in banking and corporate consulting, bringing practical insights to his academic work. Leippold emphasizes integrating modern technologies like AI into finance while cautioning against over-reliance on E-Learning for specialized education. His work bridges theoretical advancements with practical implications, such as developing models for climate risk assessment and corporate disclosures. Key contributions include studies on operational risk quantification, variance swaps, and the impact of climate policies on financial markets. He has published extensively in top journals and collaborates with institutions like the Swiss Finance Institute.