Find Academic Supervisors

Wang QianView profile

Associate Professor

• Transportation Systems Engineering
• Urban Freight Logistics
• Traffic Accident Analysis
• +3 more

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
• +7 more

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
• +5 more

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 BeckmeyerView profile

Assistant Professor

• Asset Pricing
• Machine Learning in Finance
• Financial Engineering
• +5 more

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 ForbesView profile

Professor

• Bayesian Statistics
• Econometrics
• Financial Econometrics
• +5 more

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 DashanView profile

Associate Professor

• Asset Pricing
• Behavioral Finance
• Big Data
• +3 more

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 LiuView profile

Senior Lecturer

• Smart Grid Data Analytics
• Renewable Energy Integration
• Energy Storage Systems
• +3 more

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 TranView profile

Researcher

• Distribution System Engineering
• Distributed Energy Resource Engineering
• State Estimation
• +5 more

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 KashyapView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Finance
• +5 more

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'AvernasView profile

Associate Professor

• Asset Pricing
• Financial Intermediation
• Monetary Policy
• +3 more

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 VialleView profile

Researcher

• High-Performance Computing
• GPU Computing
• Quantum Computing
• +3 more

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 LeippoldView profile

Professor

• Climate Finance
• Natural Language Processing
• Asset Pricing
• +3 more

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.

• Traffic flow modeling
• Network traffic signal control
• Optimal control and reinforcement learning
• +2 more

Dr. Zicheng Su is a Distinguished Researcher at Tongji University's College of Transportation Engineering, specializing in intelligent transportation systems. He joined the university in July 2023 and maintains affiliations with the Department of Traffic Information and Control Engineering and the MAGIC research group. His work bridges transportation engineering and artificial intelligence to solve urban mobility challenges. Dr. Su's educational background includes: Ph.D. in Advanced Design and Systems Engineering from City University of Hong Kong (2018-2022) under Prof. Andy H.F. Chow Bachelor's degree in Transportation Engineering from Sun Yat-sen University (2014-2018) under Prof. Renxin Zhong His research centers on traffic flow modeling and adaptive control systems using reinforcement learning. Key contributions include hierarchical control frameworks for stochastic traffic networks, multi-intersection management solutions, and bus service reliability optimization through connected vehicle technology. His work integrates model-based and data-driven approaches to address real-world urban mobility problems. Analysis of Dr. Su's publications (2017-2023) reveals consistent focus on reinforcement learning applications in traffic control, with increasing emphasis on decentralized solutions and multi-agent systems. His work spans transportation journals (Transportation Research Parts B/C) and top AI conferences (AAAI), demonstrating interdisciplinary impact in both transportation engineering and machine learning domains. Dr. Su's scientific recognition includes: HKSTS Outstanding Student Paper Award (1st place, 2021) Outstanding Academic Performance Award, City University of Hong Kong (2021) Best Student Presentation Award at Zhejiang University Workshop (2020) Multiple scholarships including HKRGC Postgraduate Studentship (2018-2022) As part of the MAGIC research group at Tongji University, Dr. Su contributes to projects on traffic design, cooperative vehicle infrastructure, and smart urban mobility systems. The group has developed the MAGIC Dataset and practical demo systems for intelligent driving assistance and traffic management applications.

• Operations Research
• Stochastic Programming
• Energy Systems
• +5 more

Patrizia Beraldi is an Associate Professor of Operations Research at the Department of Mechanical, Energy and Management Engineering (DIMEG), University of Calabria. She has been with the university since 2001, holding roles from Research Fellow to her current position. With over 80 publications, her work focuses on stochastic programming, probabilistic constraints, and their applications in energy systems, healthcare, finance, and logistics. Education: PhD in System Engineering and Computer Science (2000), University of Calabria Degree in Management Engineering (cum laude, 1995), University of Calabria Her research spans solution methods for network flow problems, stochastic programming with recourse, and probabilistic constraint modeling in diverse fields like energy procurement, drone routing, and financial optimization. Recent publications emphasize stochastic bilevel models for energy tariffs and ML-enhanced optimization frameworks. Scientific Awards: Best paper award, 2015 IMA Journal of Management Mathematics Best presentation award, ICEER Conference 2017 Best poster award, ICORES 2018 She supervises numerous master's and PhD students and leads the Financial Engineering and Risk Management Laboratory at DIMEG. Her editorial roles include Associate Editor for TOP , Algorithms , and IMA Journal of Management Mathematics .

Sebastian LeviView profile

Researcher

• Climate Change Politics
• Public Attitudes toward Climate Policy
• Quantitative Methods
• +3 more

Dr. Sebastian Levi is a Postdoctoral Researcher at the Hertie School ’s Centre for Sustainability , focusing on climate change politics and public attitudes. He holds a doctorate from the Berlin Graduate School of Transnational Studies, an MPhil in International Relations from the University of Oxford, and a BA in Political Science from Freie Universität Berlin. His research examines how political developments shape public preferences on climate policy. Key areas include the influence of social and political contexts on belief in human-made climate change and the design of effective climate policies. He contributes to the ARIADNE project, a BMBF-funded initiative analyzing Germany’s climate policy response, with a focus on public attitudes and institutional capacity for emission neutrality. Dr. Levi employs quantitative and computational methods , including machine learning, to study policy effectiveness and public opinion dynamics. His work has been cited in the UN IPCC report, underscoring its impact on global climate discourse. Prior roles include a Visiting Assistant in Research at Yale Program on Climate Change Communication and a researcher at the Mercator Research Institute on Global Commons and Climate Change (MCC). His research spans topics such as carbon pricing determinants, climate policy instrument mixes, and regional disparities in climate policy acceptance. He collaborates with interdisciplinary teams to address systemic challenges in climate governance and sustainable development.