جست‌وجوی استاد و پژوهشگر

• Fintech
• Sustainable Finance
• Behavioural Finance
• +۴ مورد دیگر

Professor Qingwei Wang holds a Personal Chair of Finance at Cardiff Business School, Cardiff University, where he serves as Head of the Accounting and Finance section. He is the co-founder and co-director of the Cardiff Fintech Research Group and has previously directed the Cardiff Empirical Finance Group and Centre for China Business Research. Before joining Cardiff in 2014, Professor Wang was a Lecturer in Finance at Bangor Business School and a researcher at the Centre for European Economic Research in Mannheim, Germany. Professor Wang earned his Ph.D. in Finance from the University of Mannheim, M.A. in Economics and Management Science from Humboldt University at Berlin, LL.B. in International Economic Law from Fudan University, and BA in Economics from Anhui Normal University. He also holds a PGCert in Higher Education (2014). His research focuses on three interconnected areas: Fintech (cryptoassets, machine learning applications, big data investing), Sustainable Finance (ESG, CSR, greenwashing, climate risk), and Behavioural Finance (investor sentiment, attention, biases in financial decision-making). His work has been published in top journals including Journal of International Business Studies, Journal of Empirical Finance, European Financial Management, and International Journal of Forecasting. Professor Wang's recent publications reveal a strong focus on cryptocurrency market efficiency, behavioral aspects of investing, and the intersection of technology with traditional finance. His work spans both theoretical and empirical approaches, with increasing emphasis on sustainable finance applications in recent years. Best AFS Paper Award at the AFS 2025 Conference Best Paper Award at the 8th China Finance and Accounting Conference (2024) Best (PhD) Paper Award at the 2024 Edinburgh World-Class Workshop in Fintech and Sustainable Finance Best Paper Award at the 22nd BAFA SIG-AFEE Workshop (2022) Multiple finalist nominations for Fintech Wales Awards (2021-2023) Professor Wang has supervised numerous PhD students who have secured positions at universities including Bangor, Cardiff, Southampton, Northampton, Greenwich, and Groningen. He serves on editorial boards of British Accounting Review, Finance Research Letters, and The European Journal of Finance, and has delivered invited talks at prestigious institutions worldwide including European Central Bank, University of Glasgow, and Xiamen University.

Yili Hongمشاهده پروفایل

استاد

• Machine Learning
• Reliability Analysis
• Survival Analysis
• +۴ مورد دیگر

Yili Hong is a Professor in the Department of Statistics at Virginia Tech's College of Science. Her research focuses on statistical methodologies in reliability analysis, machine learning, survival analysis, and spatial data analysis. She holds a Ph.D. from Iowa State University (2009), with prior degrees from the same institution and the University of Science and Technology of China. Hong has been recognized with awards such as the ASA Physical and Engineering Sciences Award (2017) and the DuPont Young Professor Award (2011). She serves on editorial boards for journals like Technometrics and Journal of Quality Technology, emphasizing contributions to statistical reliability and big data applications. Her work bridges theoretical statistics with practical challenges in engineering and public health, including studies on Lyme disease emergence in Virginia through spatial modeling. Education: Ph.D. in Statistics, Iowa State University, 2009 M.S. in Statistics, Iowa State University, 2005 B.S. in Statistics, University of Science and Technology of China, 2004 Research interests span machine learning robustness, reliability of AI systems, degradation analysis, and statistical computing. Notable achievements include pioneering work on mixture experiments for AI algorithm evaluation and statistical methods for high-performance computing variability. Her recent projects address the reliability of autonomous systems and AI safety through recurrent event data analysis. Awards and recognitions highlight her contributions to statistical methodology and industrial applications. Editorial activities reflect her leadership in advancing statistical practice through peer-reviewed journals.

Kanishka Bhattacharyaمشاهده پروفایل

دانشیار مدعو

• Machine Learning
• Artificial Intelligence
• Generative AI
• +۷ مورد دیگر

Kanishka Bhattacharya is an Adjunct Associate Professor of Management Science and Operations at London Business School since 2019. He is also a Partner at Bain & Company, specializing in advising corporations on digital transformation, AI integration, and strategic innovation, particularly in Private Equity and Financial Services. His career spans academia, quantitative finance, and technology consulting. Education: BSc (India), MSc and DPhil (Oxford). Research interests focus on machine learning applications in business strategy, generative AI tools, and leveraging data science for organizational decision-making. He teaches core courses in statistics, machine learning, and leadership in digital innovation at London Business School. Professional experience includes developing high-frequency trading algorithms as a quant and creating AI-driven products for large enterprises. His cross-sector advisory work combines technical expertise with strategic business acumen.

• CGE Modelling
• Climate Change Economics
• Financial Inclusion
• +۸ مورد دیگر

Athula Naranpanawa is an Associate Professor at Griffith University's Department of Accounting, Finance and Economics (part of the Griffith Business School). He specializes in computable general equilibrium (CGE) modelling, focusing on climate change adaptation, financial inclusion, and development economics. His research addresses poverty, inequality, and economic policy in regions like Sri Lanka, Bangladesh, and Africa. Education: B.Sc. in Agricultural Science (University of Peradeniya, Sri Lanka), PhD in Economics (Griffith University). Academic roles include Associate Professor (2025–present), Senior Lecturer (2015–2024), and Lecturer (2010–2014). He is a member of the Griffith Institute for Tourism and Griffith Asia Institute. Research interests span CGE modelling applications in tourism, energy economics, and regional development. Notable projects include analyzing climate change impacts on agriculture, financial inclusion's role in economic growth, and post-disaster economic recovery (e.g., Sri Lanka's tourism during the pandemic). He has supervised over 15 PhD students, focusing on topics like climate change vulnerability, financial inclusion effects, and tourism resilience. Awards include the 2011 Group Learning and Teaching Citation. Professional roles include editorial board memberships (e.g., Forecasting ) and thesis examinations. Funded projects include FAO dairy sector analysis, Queensland fuel price policy evaluation, and economic impacts of the Commonwealth Games. His work aligns with UN Sustainable Development Goals (No Poverty, Climate Action, Sustainable Consumption).

• Macroeconomics
• Economic Growth
• International Trade
• +۳ مورد دیگر

Tarlok Singh is an Associate Professor in the Department of Accounting, Finance and Economics at Griffith Business School, Griffith University, Australia. He holds a PhD from the University of NSW and has been affiliated with the Griffith Asia Institute since 2003. His research focuses on macroeconomics, economic growth, international trade, and poverty reduction, with notable publications including the book Trade Liberalisation and Poverty: Vietnam Now and Beyond (2016). He has supervised 16 successful PhD students and actively engages in research supervision across macroeconomic history, financial markets, and development economics. Dr. Singh’s teaching interests span macroeconomic analysis and open economy macroeconomics. His recent research explores global economic uncertainty, stock market return predictability, and cross-asset momentum strategies. He has received awards for teaching and research supervision, including the 2024 Highly Commended Excellence in Research Supervision Award. His funded research includes projects on Pacific digital economies, Vietnam’s development, and international capital mobility. Key contributions include analyses of China’s outward FDI drivers, India’s economic policy impacts, and the sustainability of BRICS current accounts. He collaborates internationally and contributes to policy-oriented research through grants from institutions like the United Nations Development Program and the Australian Department of Foreign Affairs and Trade.

Soon Hyeok Choiمشاهده پروفایل

استادیار

• Real Estate Finance
• AI/ML (Neural Networks, NLP)
• Asset Pricing (Bubbles)
• +۴ مورد دیگر

Soon Hyeok Choi is an Assistant Professor of Real Estate Finance at the Saunders College of Business, Rochester Institute of Technology. He holds a Ph.D., MA, and MPS from Cornell University, and a BA from Bowdoin College. His research focuses on real estate finance, AI/ML applications, asset pricing dynamics, housing affordability, credit risk, and climate risk modeling. Current projects include neural network frameworks for detecting asset price bubbles, winner's curse effects in housing markets, and pricing co-tenancy clauses in retail contracts. He has received awards such as the Best Practitioner Research Award (2023) and the Innovative Thinking Award (2024). Professor Choi teaches courses in financial economics, personal finance, and hospitality asset management. His work bridges theoretical finance with practical real estate challenges, including climate risk's impact on real estate valuations and mortgage lending disparities. Collaborations with scholars like Robert Jarrow and Patrick Smith highlight his interdisciplinary approach to financial markets and real estate dynamics. Award-winning research includes analyzing federal lease terminations' impact on CMBS bonds and modeling network effects on real estate agent career trajectories. His lab integrates machine learning tools to uncover patterns in housing markets and financial systems, contributing to both academic and practitioner understanding of emerging risks and opportunities.

• Applied Analytics
• Statistical Inference
• Time Series Analysis
• +۷ مورد دیگر

Dr. Laleh Tafakori is a Senior Lecturer in the Department of Statistics and Analytics at RMIT University's School of Science. Her research focuses on the intersection of statistical modeling, machine learning, and their applications in healthcare, finance, and environmental science. She is affiliated with the university's City Campus and can be contacted at laleh.tafakori@rmit.edu.au . Her teaching interests include Applied Analytics , Statistical Inference , Time Series Analysis , Mathematical Statistics , Stochastic Processes , and Probability Theory . She actively supervises research projects in areas such as: Healthcare modeling (e.g., diabetes onset prediction, maternal risk assessment) Environmental data analysis (e.g., extreme precipitation estimation via satellite data) Financial risk analysis (e.g., Value-at-Risk forecasting, credit portfolio management) Machine learning applications in complex networks and smart grids Her recent research trends emphasize predictive modeling for public health challenges, leveraging advanced statistical techniques (copula models, functional volatility) and machine learning (CNNs, random forests). She collaborates with institutions worldwide, addressing issues like Saudi Arabia's healthcare indicators and European financial systemic risk. While no formal awards are listed, her work demonstrates impactful contributions to interdisciplinary data science. Dr. Tafakori has advised over 16 students on topics ranging from diabetes epidemiology to edge computing optimization. Her research outputs include 28+ peer-reviewed articles, with a focus on methodological innovation and real-world problem-solving. She maintains active engagement in collaborative projects without explicitly listed grants.

Jun Daiمشاهده پروفایل

استادیار

• Artificial Intelligence in Accounting
• Blockchain Applications
• ESG Assurance
• +۴ مورد دیگر

Jun Dai is an Assistant Professor of Accounting Information Systems at Michigan Technological University's College of Business and a Richard and Joyce Ten Haken Faculty Fellow. Her research focuses on applying blockchain, AI, and Industry 4.0 technologies to enhance accounting efficiency and transparency. She holds a PhD from Rutgers University and a BS from Southwestern University of Finance and Economics in Chengdu, China. Dr. Dai serves as an associate editor of the Journal of Emerging Technologies in Accounting and contributes to editorial boards of Journal of Information Systems and International Journal of Accounting Information Systems . She chairs the Strategic and Emerging Technologies Research Workshop at the American Accounting Association Annual Meetings. Her work has been published in top journals such as Accounting and Finance , Accounting Horizons , and CPA Journal . Her research interests include ESG assurance, audit analytics, and blockchain-based accounting systems. Notable achievements include the 2023 Outstanding Researcher Award from the American Accounting Association, the 2021 Notable Contributions Award, and the 2017 Bright Idea Award for innovative research. She has also explored topics like AI ethics in managerial accounting, smart contracts in P2P lending, and satellite-based GHG emissions auditing. Teaching focuses on accounting systems, analytical methods, and database management. Her interdisciplinary approach bridges technology and accounting, addressing modern challenges in audit processes, fraud prevention, and sustainable reporting.

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

Alexandra Chronopoulou is a Clinical Associate Professor in the Department of Statistics at the University of Illinois. She is affiliated with the Coordinated Science Lab and the Center for Social & Behavioral Science, contributing to interdisciplinary research in both mathematical theory and healthcare applications. Her roles emphasize bridging statistical methodologies with practical challenges in diverse fields. Her research interests span stochastic processes, including fractional Brownian motion and volatility modeling, alongside healthcare systems analysis and medical diagnostics. She applies statistical techniques to study burnout among healthcare professionals, particularly in pandemic testing environments, and explores biomechanical factors in gastrointestinal disorders. Her work also intersects with finance, focusing on risk management and option pricing through advanced stochastic frameworks. In recent years, her articles highlight trends in volatility estimation, fractional processes simulation, and healthcare ergonomics. Earlier contributions include methodological advances in sequential Monte Carlo, community detection algorithms, and multiscale diffusions. No scientific awards were explicitly mentioned in the provided texts. Though no advising or grant information was listed, her research collaborations extend to international partnerships. She contributes to labs and centers that foster interdisciplinary innovation, including work at the Coordinated Science Lab and the Center for Social & Behavioral Science.

Giulia Di Nunnoمشاهده پروفایل

استاد

• Stochastic Analysis
• Financial Mathematics
• Risk Management
• +۴ مورد دیگر

Giulia Di Nunno is a Professor in the Department of Mathematics at the University of Oslo, specializing in stochastic analysis and its applications to finance and risk management. She also holds an adjunct professorship at the Norwegian School of Economics (NHH). Her research focuses on stochastic calculus, control theory, financial modeling, and energy finance, with a particular interest in dynamic risk measures. She has led major projects like the STORM initiative on time-space risk models and is involved in interdisciplinary research on sustainability and energy markets. Di Nunno has served as President of the Scientific Council of CIMPA and is an associate editor for several prestigious journals, including Finance and Stochastics and Stochastics . Her work bridges theoretical advancements with practical applications in finance and energy sectors. Education: PhD in Mathematical Statistics (University of Pavia, 2003), Degree in Mathematics (University of Milan, 1998). Research Groups: Risk and Stochastics, STORE (completed). Key Projects: SURE-AI (AI-driven risk modeling), Unruly Sustainability (interdisciplinary research), STORM (ToppForsk project). Editorial Roles: Associate Editor for Finance and Stochastics , DEAF , FMF , and others. Her publications emphasize stochastic processes, volatility modeling, and risk measurement, with recent contributions on time-changed dynamics and applications to energy finance. She actively contributes to the international academic community through research networks like AMaMeF and ModSimFIE.

Jochen Leidnerمشاهده پروفایل

استاد مهمان

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۴ مورد دیگر

Professor Jochen Leidner is a Visiting Professor in the Department of Computer Science at the University of Sheffield and a Research Professor for Explainable and Responsible AI at Coburg University of Applied Sciences, Germany. He has held leadership roles including Director of Research at Thomson Reuters and Refinitiv, and has founded companies like Polygon Analytics and KnowledgeSpaces. His academic background includes degrees from the University of Erlangen-Nuremberg, University of Cambridge, and a PhD in Informatics from the University of Edinburgh. Research focuses on AI ethics, natural language processing, information extraction, and geoinformatics. Notable contributions include work on question-answering systems (QED/ALYSSA), spatial toponym resolution algorithms, and risk-mining frameworks. Awards include the ACM SIGIR Doctoral Consortium Award and twice winning Thomson Reuters Inventor of the Year for patents. He has taught at institutions across Europe and advises EU funding bodies (FP7/Horizon). Holds multiple patents in information retrieval and mobile computing. Active in industry collaborations, blending academic research with real-world applications in finance, legal tech, and supply chain analytics.

Bram C.M. Cappersمشاهده پروفایل

مدرس

• Visual Analytics
• Cybersecurity
• Data Science
• +۳ مورد دیگر

Bram C.M. Cappers is a University Lecturer in the Department of Mathematics and Computer Science at Eindhoven University of Technology. His work focuses on visual analytics, cybersecurity, and predictive analytics applied to dynamic heterogeneous networks. He holds a Master’s (2014) and PhD (2018) in Computer Science from TU/e, with doctoral research on security visual analytics for cyber threat analysis. Education: Bachelor in Software, Algorithms, Control Systems (Eindhoven University of Technology, 2012) Master in Software, Algorithms, Control Systems (Eindhoven University of Technology, 2014) PhD in Computer Graphics (Interactive Visualization of Event Logs for Cybersecurity, 2018) Research Interests: Developing predictive models for energy networks, visual analytics tools for cybersecurity incident response, and anomaly detection in complex systems. His work extends to applying these techniques in healthcare, finance, and energy sectors through startups like CodeNext21 and TIBO Energy. Key Contributions: Eventpad: Award-winning visual analytics framework for malware analysis and network intrusion detection Predictive analytics for VoIP fraud prevention (KPN partnership) Co-founder of two ventures commercializing academic research Awards: ICT.Open Award (2018) for traffic data anomaly detection Elsevier’s 30 onder de 30 (2020) for innovation leadership European Venture Program Best Executive Summary (2018) Grants & Activities: Received €150K Demonstrator grant (2018) Contributed to SECREDAS EU project (2018-2021) Speaker at Blackhat USA 2018 and multiple invited talks on cybersecurity

Patrick Punمشاهده پروفایل

دانشیار

• Applied Mathematics
• Financial Mathematics
• Quantitative Finance
• +۷ مورد دیگر

Patrick Pun is an Associate Professor in the Division of Mathematical Sciences at Nanyang Technological University (NTU), Singapore, serving as Assistant Chair (MSc Programs) and Director of the MSc in FinTech program within the School of Physical and Mathematical Sciences. His academic journey includes a Ph.D. and M.Phil. from the Chinese University of Hong Kong (CUHK) and a B.Sc. from Nankai University. Professor Pun’s research focuses on the intersection of applied mathematics and finance, with methodologies spanning stochastic controls, nonlinear partial differential equations (PDEs), robust optimization, and machine learning. His work addresses challenges in portfolio optimization, derivatives pricing, risk management, and financial data analysis. He has also contributed to interdisciplinary areas such as nanoparticle characterization and epidemiological modeling (e.g., during the COVID-19 pandemic). Award-winning scholar, Pun holds the 2016 Nicola Bruti Liberati Prize from the Bachelier Finance Society and the CUHK Young Scholars Thesis Award. He is an ad-hoc reviewer for leading journals including Automatica , SIAM Journal on Financial Mathematics , and Quantitative Finance . His professional roles include membership in the Academic Council of the Global Digital Economy Forum. Pun’s teaching and academic leadership include oversight of master’s programs and curriculum development. His research outputs emphasize innovative solutions to time-inconsistent problems, high-dimensional financial data challenges, and the integration of machine learning with traditional financial models. His recent work explores transformer-based generative models, quantum algorithms for financial PDEs, and reinforcement learning applications in portfolio management.

Martin Herdegenمشاهده پروفایل

دانشیار

• Mathematical Finance
• Probability Theory
• Stochastic Processes
• +۵ مورد دیگر

Martin Herdegen is a Reader in Financial Mathematics at the Department of Statistics, University of Warwick. He previously served as a postdoc at ETH Zürich under Johannes Muhle-Karbe and holds a PhD in Mathematics from ETH Zürich (2014) under Martin Schweizer. His research focuses on equilibrium theory, utility maximization, stochastic processes, and risk measures, with applications to financial bubbles and market microstructure. Herdegen’s academic career includes supervising multiple PhD students (e.g., Florian Gutekunst, Andreea Popescu) and postdocs (e.g., Nazem Khan). His research group explores topics like ρ-arbitrage, recursive utility, and liquidity provision under adverse conditions. He has contributed to foundational work on strict local martingales and their implications for financial markets. Publications span leading journals such as Finance and Stochastics , Mathematical Finance , and Annals of Applied Probability , addressing equilibrium models with transaction costs, optimal investment strategies, and risk measurement techniques. His work frequently integrates stochastic analysis and control theory to solve practical finance problems. Herdegen’s research also intersects with reinforcement learning applications in trading, as seen in the Mbt-gym framework for limit order book simulations. His contributions emphasize rigorous mathematical foundations while addressing real-world market frictions and liquidity dynamics.

Jichun Liمشاهده پروفایل

استاد

• Mathematical Modeling
• Scientific Computing
• Numerical Analysis
• +۴ مورد دیگر

Jichun Li is a Professor in the Department of Mathematical Sciences at the University of Nevada, Las Vegas. His research focuses on mathematical modeling, scientific computing, and numerical analysis with applications to electromagnetism and metamaterials. He specializes in developing advanced numerical methods like Finite Element Methods (FEM), Discontinuous Galerkin (DG), and Finite-Difference Time-Domain (FDTD) schemes for simulating wave propagation in complex media such as metamaterials and photonic crystals. Key research areas include metamaterials (e.g., hyperbolic metamaterials, cloaking devices), electromagnetic wave manipulation (e.g., surface plasmon polaritons on graphene), and computational techniques for solving Maxwell’s equations in dispersive and nonlinear media. His work addresses challenges like numerical stability, superconvergence analysis, and implementation of Perfectly Matched Layers (PML) for absorbing boundary conditions. Recent efforts involve integrating deep learning with numerical PDE methods for financial applications and advancing time-domain simulations for metamaterial cloaking and optical black holes. He has contributed to software tools like MATLAB-based edge element codes for metamaterial modeling. His research bridges theoretical analysis and practical simulation, with applications in photonics, quantum optics, and computational electromagnetics. Notable achievements include over 150 peer-reviewed articles, methodological innovations in numerical electromagnetics, and active participation in interdisciplinary collaborations at the Center for Applied Math & Statistics. His work emphasizes high-order methods and rigorous mathematical validation for engineering and scientific problems.