جست‌وجوی استاد و پژوهشگر

Richard Saldanhaمشاهده پروفایل

عضو هیئت علمی

• Statistical Machine Learning
• Quantitative Finance
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Richard Saldanha is a Visiting Lecturer at the School of Economics and Finance, Queen Mary University of London. His expertise spans statistical machine learning, quantitative finance, and AI applications. He holds a DPhil in graph theory and multivariate statistics from the University of Oxford. Alongside academia, he manages Oxquant, an Oxford-based quantitative finance consultancy, and contributes to The Alan Turing Institute’s AI for Control Problems Project. Dr. Saldanha’s career includes over 20 years in City of London financial institutions, specializing in risk management, trading, and fund management. He is a Chartered Statistician, Chartered Scientist, and a Fellow in Artificial Intelligence with the Institute of Science and Technology. His research emphasizes bridging theoretical AI advancements with practical financial systems and regulatory challenges. Publications highlight themes like large language models, artificial general intelligence, and ethical AI. His work frequently intersects technical innovation with industry application, particularly in finance and regulatory technology.

• Finance
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Alexis Marchal is an Assistant Professor in the Department of Finance at the École Polytechnique Fédérale de Lausanne (EPFL), specifically affiliated with the School of Management (HEC Lausanne). Based at Odyssea 1.04, Station 5 in Lausanne, Switzerland, Marchal maintains an active research profile in quantitative finance and machine learning applications to financial markets. Marchal's research interests focus on the intersection of finance and artificial intelligence, with particular emphasis on deep learning applications for financial modeling. Their work spans volatility modeling, asset bubble detection, and innovative applications of computer vision to financial data analysis. The research demonstrates a strong methodological approach combining traditional financial economics with cutting-edge machine learning techniques. Analysis of Marchal's publications reveals a consistent focus on applying advanced computational methods to solve complex problems in asset pricing and financial market analysis. The research outputs show increasing sophistication in methodology, moving from traditional statistical approaches to more complex deep learning architectures, particularly Long Short-Term Memory (LSTM) networks. This trajectory indicates a researcher at the forefront of computational finance. Nonstandard Errors (2024) - Published in the Journal of Finance Deep Learning, Jumps, and Volatility Bursts (2020) Deep Learning for Asset Bubbles Detection (2020) Risk & Returns around Fomc Press Conferences (2021) While specific grant information isn't detailed in the available materials, Marchal's affiliation with the Swiss Finance Institute suggests participation in collaborative research initiatives. The work on the multi-author 'Nonstandard Errors' paper indicates engagement with broad research communities in finance methodology. Marchal's research has gained significant attention, with the 'Nonstandard Errors' paper accumulating over 17,000 downloads.

• Commodity Markets
• Asset Pricing
• Volatility Modeling
• +۳ مورد دیگر

Marcel Prokopczuk is a Professor at Leibniz Universität Hannover's Faculty of Economics and Management and the University of Reading's ICMA Centre. His research spans commodity markets, asset pricing, volatility modeling, and financial derivatives with significant contributions to understanding market dynamics and risk factors. His research focuses on commodity price dynamics, volatility term structures, and systematic risk. Key areas include Commodity market microstructure and quality Volatility forecasting and risk premia Factor models across asset classes Market anomalies and behavioral finance Derivatives pricing and hedging strategies His publication portfolio reveals strong trends in commodity market analysis, particularly examining volatility patterns, jump risks, and the economic drivers of commodity prices. Recent work integrates machine learning techniques for beta estimation and equity return prediction while maintaining a strong foundation in traditional financial econometrics. Prokopczuk has co-authored numerous high-impact papers in top journals including the Journal of Finance, Journal of Financial and Quantitative Analysis, and Journal of Banking and Finance. His collaborative work spans multiple international institutions with frequent co-authorship patterns indicating strong research teams. His advisory activities include supervision of PhD candidates and research collaboration across European institutions. Current projects focus on market quality metrics, responsible investing frameworks, and cross-asset factor modeling. He maintains active research groups examining commodity markets and volatility dynamics.

Jacob Abernethyمشاهده پروفایل

دانشیار

• Machine Learning
• Optimization
• Statistics
• +۳ مورد دیگر

Jacob Abernethy is an Associate Professor in the School of Computer Science at the Georgia Institute of Technology, part of the College of Computing. His research focuses on Machine Learning, Optimization, and their intersections with Game Theory, Statistics, and Economics. He completed his PhD at UC Berkeley in 2011 and held a Simons postdoctoral fellowship at the University of Pennsylvania. Education: PhD in Computer Science (UC Berkeley, 2011), Postdoctoral work at UPenn (2011-2013). Research Highlights: Contributions to online learning, optimization, and applications to real-world problems like the Flint water crisis. Notable work includes developing algorithms for min-max optimization and market-making mechanisms. His research group addresses topics such as lead contamination detection, GAN stability, and adversarial learning. He has advised multiple PhD students and postdocs, including Matus Telgarsky (now at UIUC) and Sindhu Kutty (now at Swarthmore College). Awards: KDD 2018 Best Student Paper Award (Research Track) COLT 2008 Best Student Paper Award Pat Goldberg Memorial Best Paper Award from IBM (2008) He has held grants such as the NSF IIS Award 1910077 and collaborated with organizations like Google Research. His teaching spans machine learning theory, advanced algorithms, and discrete mathematics.

Yongxin Yangمشاهده پروفایل

مدرس

• Machine Learning
• Computational Finance
• Medical Genetics

Dr. Yongxin Yang is a Lecturer in Financial Technology at Queen Mary University of London (QMUL), affiliated with the School of Electronic Engineering and Computer Science and the Centre for Multimodal AI. He holds a PhD from QMUL (2017), supervised by Prof. Timothy Hospedales. His research focuses on machine learning (transfer learning, domain adaptation) and its applications in finance (derivatives pricing, portfolio optimization) and medical genetics. Prior to his current role, he was a Lecturer in Machine Learning at the University of Surrey. He is an ACCA-certified accountant and professional web designer, with industry experience through co-founded ventures and consulting roles. Research interests include: Machine Learning, Computational Finance, and Medical Genetics. Notable work spans medical imaging fairness (e.g., MEDFAIR), option pricing via neural networks, and domain generalization techniques like MixStyle. His contributions bridge theoretical machine learning with practical applications in finance and healthcare. He has authored numerous publications in top venues (NeurIPS, ICML, CVPR, ICLR) and collaborates with industry partners. His work emphasizes interdisciplinary approaches, combining finance, genetics, and AI to solve real-world challenges.

Jian Yangمشاهده پروفایل

استاد

• Graph Mining
• Fake News Detection
• Fraud Detection
• +۴ مورد دیگر

Prof. Jian Yang is a Professor at Macquarie University's School of Computing and holds leadership roles in the Data Horizons Research Centre and Applied Artificial Intelligence Research Centre. She earned her PhD in Data Integration from The Australian National University (1995). Prior to Macquarie, she held roles at Tilburg University (Netherlands), CSIRO, and the University of New South Wales. Recognized as a Top 2% Global Scientist (2019–2024), her research focuses on graph mining, fraud detection, and smart city technologies. She has led over 33 projects funded by ARC, CRCs, and EU frameworks. Notable awards include Best Student Paper Awards (2021, 2024) and Excellence in HDR Supervision (2019). Education: PhD in Data Integration, ANU (1995) External Positions: Director of Industry PhD Program at Digital Finance CRC Research Interests: Prof. Yang’s work spans graph mining, trust and spam detection in social media, fraud detection in finance, data fusion for medical data, and smart city applications. Recent projects include detecting thyroid cancer via data fusion and enhancing trust in web-based systems. Grants & Projects: Active projects include MQRC Consilience Data Research Centre (2023–2028), Data-to-Data Products for Trusted Economies (2023–2027), and Graph Mining for Social Events (2023–2025). Past work includes EU Six Framework and CSIRO collaborations. Labs/Teams: Leads the Data Horizons Research Centre and collaborates with global teams on service-oriented computing and AI ethics. She chairs international conferences like ICSOC and BPM.

Eric Nimako Aidooمشاهده پروفایل

استادیار

• Statistical Modelling
• Spatial Statistics
• Categorical Data Analysis
• +۵ مورد دیگر

Eric Nimako Aidoo is an Assistant Professor in Statistics at Heriot-Watt University's Dubai Campus, affiliated with the School of Mathematical & Computer Sciences. Previously, he held roles as Senior Lecturer at Kwame Nkrumah University of Science and Technology and Research Scientist at the Council for Scientific and Industrial Research (CSIR-BRRI) in Ghana. He earned his PhD in Statistics from Edith Cowan University (Australia) and MSc from Uppsala University (Sweden). His research focuses on statistical modelling applications in road traffic safety, disease risk, and spatial analysis. Key interests include categorical data analysis, simulation-based inference, and geospatial techniques. He contributes to UN SDGs related to health equity and safety. Teaching responsibilities include leading courses like Bayesian Inference, Time Series Analysis, and Probability & Statistics. Administrative roles include Deputy Programme Director for BSc Data Sciences and coordination of student disability support. He has advised numerous students and reviewed for top journals, emphasizing interdisciplinary research. Notable collaborations include studies on malaria risk, socioeconomic disparities in health, and urban traffic safety. His datasets explore topics like red-light violations and child passenger safety in Ghana.

• Self-Organizing AI
• Machine Learning
• Text Analytics
• +۳ مورد دیگر

Damminda Alahakoon is Professor of Business Analytics and AI at La Trobe University, where he founded and directs the Research Centre for Data Analytics and Cognition (CDAC). With dual appointments at Luleå University (Sweden) and University of Moratuwa (Sri Lanka), he bridges international AI research. His 15+ year academic career follows 10 years in industry, including roles in finance and IT across Australia, Sri Lanka, and the Netherlands. Research Impact: Developed the Growing Self-Organizing Map (GSOM) algorithm for adaptive AI systems applied in healthcare, utilities, and smart cities. Current projects include generative AI for healthcare chatbots and quantum optimization for energy systems. Industry Collaboration: Secured $6M+ in research funding for projects like AI-driven stroke rehabilitation monitoring and mining workforce analytics. Recent industry partnerships focus on operationalizing ethical AI frameworks in Australia-India tech corridors. Teaching: Leads the Master of Business Analytics program, specializing in predictive analytics curriculum development.

Christian Møller Dahlمشاهده پروفایل

استاد

• Theoretical Statistics
• Time Series Analysis
• Nonparametric Estimation
• +۷ مورد دیگر

Christian Møller Dahl is a Professor of Economics at the University of Southern Denmark (SDU), affiliated with the Department of Economics and the Interdisciplinary Centre on Population Dynamics (CPop). His research focuses on theoretical statistics, time series analysis in finance, nonparametric estimation, machine learning, and causal inference. He leads the Big Data Analytics and Digitization (BDAD) research unit, specializing in automated digitization of historical documents like census records and death certificates. His work bridges economics, demography, and computer science, with applications in understanding long-term mortality patterns and economic cycles. Education details are not explicitly stated, but CMD has extensive experience in interdisciplinary projects involving national archives and health institutions. His research agenda includes developing deep learning techniques for transcribing historical data, such as 20 million Danish death certificates, to aid demographers and epidemiologists. He has secured funding from Danish Research Foundations for projects like the Copenhagen Infant Health Nurse Records (CIHNR) cohort, studying early-life health policies and long-term outcomes. Key collaborations include work with national archives on citizen science strategies for digitizing health data and interdisciplinary projects on occupational standardization using machine learning (e.g., OccCANINE). CMD’s methodological contributions include advancements in random fields, neural networks, and GARCH models for financial time series analysis. Grants: Multiple grants from Danish Research Foundations for digitization and health studies. Labs/Teams: BDAD unit (Big Data Analytics and Digitization) and CPop (Interdisciplinary Centre on Population Dynamics). Future Work: Expanding digitization of historical health records and exploring multi-generational health impacts.

Huaxiong Huangمشاهده پروفایل

استاد

• Applied Mathematics
• Fluid Mechanics
• Scientific Computing
• +۵ مورد دیگر

Huaxiong Huang is a Full Professor of Mathematics and Statistics at York University and Executive Director of the Joint Math Centre. He holds a BSc from Fudan University and a PhD from the University of British Columbia's Institute of Applied Mathematics. His research spans fluid mechanics, scientific computing, and their applications in biology, medicine, and finance, with over 100 publications and editorial roles at leading journals. He has held visiting positions at the University of Tokyo, University of Canterbury, and University of Paris VI. His honors include the Fields Institute Industrial Mathematics Prize and lifetime fellowship from the Fields Institute. Research interests focus on mathematical modeling of biomedical systems (e.g., optic nerve microcirculation, cellular ion transport), machine learning applications in healthcare diagnostics (e.g., PPG-to-ECG reconstruction), and advanced materials science (e.g., polymeric fluid dynamics). Recent work integrates deep learning with biomedical signal processing and electrochemical systems. His articles highlight interdisciplinary approaches combining computational methods with experimental validation. Scientific Awards: Fields Institute Industrial Mathematics Prize (Canadian Industrial and Applied Mathematics Society) Lifetime Fellow, Fields Institute for Research in the Mathematical Sciences Advising focuses on graduate research in applied mathematics and interdisciplinary projects. He has contributed to grants in health analytics, materials science, and computational modeling. His research team collaborates with the Joint Math Centre to bridge academic-industry partnerships in applied sciences. Labs/Teams: Executive Director of the Joint Math Centre, fostering collaborative research in applied mathematics across disciplines.

• Financial Mathematics
• Actuarial Science
• Stochastic Processes
• +۳ مورد دیگر

Francesca Biagini is a Professor of Mathematics at the University of Munich (LMU), specializing in Financial and Insurance Mathematics. She holds significant administrative roles, including Vice President for International Affairs and Diversity at LMU since 2019, and serves as President of the Bachelier Finance Society (2022–2023). Her research focuses on stochastic processes, financial mathematics, systemic risk, and actuarial science. She leads the Munich Risk and Insurance Center (MRIC) and contributes to initiatives like the quantLab workshop series. Her work bridges theoretical advancements with practical applications in finance and insurance, addressing topics such as asset pricing bubbles, credit risk modeling, and deep learning applications in finance. Research interests include stochastic calculus, fractional Brownian motion, risk-minimization strategies, and systemic risk modeling. Notable contributions span publications on financial derivatives, insurance valuation, and regulatory frameworks. She has held visiting positions and collaborates internationally, reflecting her leadership in academic and professional communities. Publications highlight innovative methods in financial modeling, such as neural network-based superhedging and liquidity-driven bubble analysis. Her work on xVA calculations and systemic risk transfer equilibrium underscores practical applications for financial institutions. Academic service includes roles in educational programs and professional societies, emphasizing interdisciplinary research and international collaboration.

Steven Liمشاهده پروفایل

استاد

• Financial Market Efficiency
• Asset Pricing
• Corporate Finance
• +۷ مورد دیگر

Professor Steven Li is a Professor of Finance at the College of Business and Law (COBL), RMIT University, with a PhD from Delft University of Technology, an MBA from Melbourne Business School, and a BSc from Tsinghua University. He specializes in financial market efficiency, asset pricing, corporate finance, derivatives, FinTech, and sustainable finance, with over 100 published papers. His teaching includes Financial Management, FinTech, and Sustainable Finance. He has held visiting scholar roles at institutions like the Bank of Japan and University of Wollongong. His consulting expertise spans News Corporation, Bank of Japan, and Australian government agencies. Research interests span quantitative finance, corporate finance, derivatives pricing, market efficiency, and sustainable finance. Recent articles focus on optimization algorithms, financial markets, and multi-UAV systems. He actively supervises PhD and Master’s students in finance-related topics. His work integrates mathematical modeling to address real-world business challenges.

Max Farrellمشاهده پروفایل

دانشیار

• Machine Learning
• Econometrics
• High-Dimensional Data Analysis
• +۵ مورد دیگر

Max Farrell is an Associate Professor of Economics and the Mellichamp Chair of Mind and Machine Intelligence at the University of California, Santa Barbara (UCSB) , within the Department of Economics of the College of Letters & Science . He is also an Amazon Scholar and serves as an associate editor for the Review of Economics and Statistics and Journal of Applied Econometrics , with editorial board membership at the Journal of Machine Learning Research . His research focuses on machine learning , high-dimensional data , semiparametric/nonparametric methods , and causal inference , with applications in econometrics and statistics. Farrell holds a Ph.D. in Economics and M.A. in Statistics from the University of Michigan, and S.B. degrees in Mathematics and Economics from MIT. His work emphasizes developing novel statistical methods, such as open-source software tools like rdrobust (for regression discontinuity designs), binsreg (binscatter regression), and nprobust (nonparametric kernel-based estimation). These tools have been recognized for their practical impact in applied research. His research trends span regression discontinuity designs , deep learning applications in causal inference, and nonlinear binning methods for visualization and estimation. His recent work explores individual heterogeneity in treatment effects and high-dimensional econometric models . Farrell’s software contributions have been widely adopted in empirical economics and statistics. Scientific Awards : His software packages have received awards for their innovation and utility in statistical analysis. Advising & Grants : While no students are explicitly listed here, his software development and editorial roles reflect significant contributions to the field. His work is supported by grants from Amazon and UCSB’s Mellichamp Chair. Labs/Teams : Collaborates extensively with researchers like Matias Cattaneo , Tengyuan Liang , and Sebastian Calonico on projects in econometrics and machine learning.

• Machine Learning
• Financial Mathematics
• Stochastic Control
• +۵ مورد دیگر

Ruimeng Hu is an Associate Professor at the University of California, Santa Barbara (UCSB), with a joint appointment in the Department of Mathematics and Department of Statistics and Applied Probability . They hold editorial roles at Digital Finance and Mathematical Finance , co-organizing special issues on deep learning and machine learning in finance. Their research focuses on interdisciplinary areas including machine learning, financial mathematics, stochastic control, game theory, and climate modeling. Education : Ph.D. in Statistics and Applied Probability, UCSB (2018) M.A. in Mathematical Statistics, UCSB (2014) B.S. in Pure and Applied Mathematics, Peking University (2012) Research Interests : Machine learning applications in finance and climate science Stochastic control and game theory in multi-agent systems Development of physics-informed neural networks Analysis of stochastic partial differential equations Their work bridges theoretical advancements with practical tools for financial risk management and environmental modeling. Key Contributions : Organized international conferences like the 3rd ACM International Conference on AI in Finance (ICAIF-22) Founded World Online Seminars on Machine Learning in Finance Developed frameworks for adversarial games and pandemic control using game theory and reinforcement learning Labs & Collaborations : Center for Financial Mathematics & Actuarial Research (UCSB) Active in interdisciplinary initiatives like the Financial Mathematics Mentoring Initiative

Alain Tappمشاهده پروفایل

استاد

• Artificial Intelligence
• Theoretical Computer Science
• Natural Language Processing
• +۳ مورد دیگر

Alain Tapp is a tenured Professor at the Université de Montréal's Faculty of Arts and Science, Department of Computer Science and Operations Research. He is affiliated with the RALI (Linguistics), LITQ (Theoretical and Quantum Informatics), and talents (AI Cybersecurity) laboratories. His research focuses on Artificial Intelligence, Theoretical Computer Science, and Natural Language Processing, with contributions to quantum computing and machine learning applications. He actively collaborates on projects like DEEL (Dependable & Explainable Learning) and NSERC CREATE in Machine Learning for Finance. Tapp has advised over 25 graduate students, guiding them in topics ranging from quantum protocols to reinforcement learning in robotics. He has secured grants from NSERC, CRIAQ, and others, emphasizing AI, cybersecurity, and quantum computing. His work bridges theoretical foundations with practical applications, including privacy-preserving AI models and sim-to-real robotics. Education: Multiple programs in Fundamental and Applied Sciences, specializing in Information and Communication Technologies. Research interests span quantum communication complexity, secure multiparty computation, and machine learning fairness. His recent work explores lossless text encoding and adversarial training for bias mitigation. Tapp is also involved in initiatives like the crypto-democracy model, advocating for privacy through distributed trust frameworks. His contributions to quantum information theory and AI ethics underscore his interdisciplinary impact.