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Huaxiong HuangView profile

Professor

• Applied Mathematics
• Fluid Mechanics
• Scientific Computing
• +5 more

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
• +3 more

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 LiView profile

Professor

• Financial Market Efficiency
• Asset Pricing
• Corporate Finance
• +7 more

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 FarrellView profile

Associate Professor

• Machine Learning
• Econometrics
• High-Dimensional Data Analysis
• +5 more

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
• +5 more

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 TappView profile

Professor

• Artificial Intelligence
• Theoretical Computer Science
• Natural Language Processing
• +3 more

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.

Chengchun ShiView profile

Associate Professor

• Statistical Learning
• Reinforcement Learning
• Precision Medicine
• +3 more

Dr. Chengchun Shi is an Associate Professor in the Department of Statistics at the London School of Economics and Political Science (LSE). His research focuses on statistical learning methods for individualized decision-making and analyzing complex data, with applications in precision medicine, finance, and ride-sharing. He joined LSE in 2019 after completing his PhD in Statistics at North Carolina State University, supervised by Dr. Wenbin Lu and Dr. Rui Song. His work emphasizes developing computationally efficient algorithms with statistical guarantees for real-world challenges such as personalized treatment regimes, optimal investment strategies, and dynamic resource allocation. He has contributed to reinforcement learning, causal inference, and off-policy evaluation methodologies, addressing issues like interference in spatial experiments and handling high-dimensional, heterogeneous data. Key research areas include reinforcement learning algorithms, causal mediation analysis, and robust policy evaluation. His publications span topics like off-policy evaluation in confounded environments, spatial experimental designs, and deep learning approaches for distributional modeling. Dr. Shi’s research bridges theoretical statistics and practical applications, aiming to improve decision-making across diverse domains. He holds a strong academic background with a focus on high-dimensional statistical models and their applications in dynamic treatment regimes. His work has been recognized for its innovative approaches to tackling complex data challenges through interdisciplinary methods combining machine learning and rigorous statistical theory.

• Machine Learning
• Healthcare Technology
• Sensor Networks
• +3 more

Michel Deriaz is a Senior Lecturer at the University of Geneva's Research Institute for Statistics and Information Science. He holds a Ph.D. from the same institution. His research focuses on machine learning applications in healthcare technology, sensor systems, and recommendation systems, with a strong emphasis on interdisciplinary projects involving wearable devices, data analysis, and human-centric technologies. Key research interests include: Machine learning for gait analysis and early disease detection Indoor/outdoor localization using IMU and GPS fusion AI-driven recommendation systems for seniors and startups Health monitoring through biometric sensors (e.g., grip strength analysis) Optimization of mobile positioning systems His recent work demonstrates expertise in: Convolutional neural networks for equine lameness detection Transformer-based models for localization during GPS outages Predictive analytics for foreign exchange markets Validation of medical devices for clinical assessment No scientific awards or current grants are listed, though his extensive publication record indicates active collaboration with healthcare providers and tech innovators. His work often bridges theoretical computer science with practical applications in geriatric care, veterinary science, and disaster management systems.

Thomas RobinsonView profile

Assistant Professor

• Voter behavior
• Money in politics
• Machine learning
• +3 more

Thomas Robinson is an Assistant Professor in the Department of Methodology at the London School of Economics (LSE). Previously, he held positions at Durham University's Centre for Institutions and Political Behavior and collaborated with Meta's Core Data Science team. He earned his DPhil in Politics from the University of Oxford in 2020. His research focuses on machine learning applications in political methodology, including neural network modeling, causal inference, and experimental design. Key themes include voter behavior, money in politics, and pandemic-related policy preferences. Collaborations span education policy and tech sectors, with ongoing projects on synthetic data and missing-data imputation using deep learning. Notable works include analyzing voter responses to campaign finance disclosures and studying pandemic impacts on global economic preferences. He develops open-source tools like MIDASpy and rMIDAS for data imputation. Affiliations include LSE's Data Science Institute.

Lisa Maria KreusserView profile

Associate Professor

• Partial Differential Equations
• Machine Learning
• Data Analysis
• +4 more

Dr. Lisa Maria Kreusser is a Reader (equivalent to Associate Professor) at the University of Bath's Department of Mathematical Sciences and Fellow at the Institute for Mathematical Innovation. Her research integrates partial differential equations, data analysis, and machine learning with applications spanning biology, climate modeling, and engineering. Her research program focuses on: Mathematical foundations of machine learning Numerical methods for PDEs Multiscale modeling in climate science Applications in healthcare technology Innovations in optimization algorithms Her recent publications (2019-2025) demonstrate strong interdisciplinary focus with 33% in machine learning foundations, 27% in scientific computing, 20% in applied mathematics, and 13% in healthcare/climate applications. Core themes include diffusion models, optimization theory, and computational methods for physical systems. Significant scientific recognition includes: Reinhart Heinrich Award from ESMTB (2020) Rising Stars in Computational and Data Sciences (2020) Multiple grants including ICMS Knowledge Exchange Catalyst Fellowship (2023-24) Nevile Junior Research Fellowship at Magdalene College, Cambridge (2019-2022) She leads interdisciplinary projects on digital health interventions for chronic diseases and renewable energy auctions. As Environmental Extremes Lead at IMI, she coordinates climate-related mathematical research.

Hangjie JiView profile

Assistant Professor

• Nonlinear Partial Differential Equations
• Scientific Computing
• Data-driven Mathematical Modeling
• +3 more

Hangjie Ji is an Assistant Professor of Mathematics at North Carolina State University (NC State), located in Raleigh, NC. He previously served as an Assistant Adjunct Professor at UCLA's Department of Mathematics and completed his Ph.D. in Mathematics at Duke University under Dr. Thomas P. Witelski. His research focuses on nonlinear partial differential equations, scientific computing, and data-driven modeling with applications in physics, engineering, and medicine. Current research is supported by NSF Award DMS-2309774 and the Simons Foundation MPS-TSM grant. His expertise spans thin film dynamics, fluid-structure interactions, and mathematical models in biomedical contexts. Notable contributions include work on thermocapillary instabilities, droplet control mechanisms, and explainable AI for medical imaging analysis. As a co-organizer of NCSU's Computational and Applied Mathematics (CAM) Seminar, he fosters interdisciplinary collaboration. Recent professional activities include organizing a SIAM minisymposium on AI applications in finance/medicine and mentoring REU projects on interfacial fluid modeling. He is actively involved in outreach through programs like the SECU Rural Works! Internship and the Graduate Student Mathematical Modeling Camp (GSMMC). Education: Ph.D. Mathematics (Duke University, 2017) Awards/Grants: NSF DMS-2309774, Simons Foundation MPS-TSM Teaching: MA 501 (Spring 2025), MA 428 (Spring 2025) Labs/Teams: Computational Applied Mathematics Group at NCSU

Enrique Ter HorstView profile

Associate Professor

• Quantitative Finance
• Bayesian Statistics
• Statistical Modeling
• +12 more

Enrique Ter Horst is an Associate Professor in the Faculty of Management at Universidad de los Andes, Colombia, affiliated with the Finance department. His research spans quantitative finance, Bayesian statistics, sentiment analysis, and interdisciplinary applications in marketing, psychology, and public policy. He teaches courses such as Derivatives Markets, Multivariate Models, and Computational Methods in Finance at both undergraduate and graduate levels. PhD in Bayesian Statistics, Duke University, 2003 Maitrise de Sciences Economiques, Université Louis Pasteur, 1999 Licence d'Économétrie, Université Louis Pasteur, 1998 Ter Horst's research focuses on developing advanced statistical and machine learning models, particularly using Bayesian frameworks and maximum entropy methods. His work applies to diverse domains including financial risk modeling, art market analysis, climate change news impact, and brand ethics. He has made significant contributions to understanding sentiment-driven portfolio hedging, brand commitment during crises, and topic modeling in academic publishing. His recent publications (2020–2025) reveal a strong trend in applying Bayesian and entropy-based methods to interdisciplinary problems, especially in finance, marketing, and social media analytics. He frequently employs computational techniques such as multinomial inverse regression and deep reinforcement learning to analyze large-scale textual and financial data. Financial Risk Manager (GARP) Secretary, Economics, Finance and Business Section, ISBA (2015–2016) Ter Horst advises doctoral students such as Manuel Andrés Martínez Patiño and has previously mentored Jovelyn Ferrer Garcia. His industry experience includes quantitative research roles at Credit Suisse First Boston and Morgan Stanley, where he co-edited the 'Quant Strategist' research series. He has consulted for the European Central Bank, Eurostat, and the Inter-American Development Bank. He is a member of the Finance and Financial Economics research group at Uniandes, which supports collaborative work in asset pricing, risk management, and behavioral finance.

Aaron William ThorntonView profile

Senior Lecturer

• Mathematics
• Computational and Simulation Sciences
• Chemistry
• +11 more

Dr Aaron William Thornton is a Principal Research Scientist at CSIRO and a Senior Lecturer at Southern Cross University, with a strong interdisciplinary focus bridging mathematics, computational sciences, materials engineering, and industrial applications. His work integrates fundamental research with commercial innovation, particularly in energy-efficient separations, gas adsorption, and bio-inspired materials. Education: PhD in Mathematics, University of Wollongong and CSIRO (2010) Bachelor of Mathematics and Finance (1st Class Honors), University of Wollongong (2005) His research interests lie at the intersection of mathematics, computational modeling, separation science, thermodynamics, and machine learning . He develops novel solutions for challenges in aerospace, materials, and biology, with a strong emphasis on real-world impact. His work often involves high-performance simulations and in-silico design of advanced materials. His recent publications and projects reflect a consistent trend toward energy-efficient technologies, carbon capture from air, medical gas devices, and smart membranes . These works combine computational innovation with experimental validation and industrial collaboration, particularly with Air Liquide and through ARC-funded research hubs. Scientific Awards: Performance and Innovation Award, Boeing (2019) Executive Endeavour Fellowship, Department of Education (2018) Julius Career Award, CSIRO (2013–2016) Jacques-Emile Dubois Award, Chemical Structure Association Trust (2013) Young Tall Poppy Science Award - VIC, Australian Institute of Policy & Science (2012) Dr Thornton has secured major research grants from the ARC and the Science and Industry Endowment Fund, supporting projects such as 'Breaking the Limit in Membranes' and 'Engineering Ion Channels'. He has led commercial initiatives including the 'CO2Gen' device and the 'Pocketable Oxygen Device'. He has advised or collaborated on startups and deep tech innovation through programs like the Bright Arena Accelerator. He has also mentored students and early-career researchers, though specific advisees are not listed. He is actively involved in academic service as a member of the editorial boards of Industrial & Engineering Chemistry Research and Membranes (MDPI), and previously served as President of the Membrane Society of Australasia. His international scholarly collaborations include affiliations with UC Berkeley, Chimie ParisTech, Hanyang University, University of Cambridge, and NTUA, reflecting a globally connected research profile.

• Artificial Intelligence
• Deep Learning
• Medical Imaging
• +3 more

Andreas Østvik is a Researcher at the Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), financed by the Liaison Committee between Central Norway Regional Health Authority and NTNU. His work focuses on developing AI-driven methods for analyzing medical images, particularly in cardiac ultrasound (echocardiography), to improve diagnostic tools and clinical workflows. He holds a PhD in automatic analysis in echocardiography using machine learning and an MSc in physics/mathematics with biophysics and medical technology specialization. Research Background : Østvik's research integrates deep learning with medical imaging, addressing challenges in cardiac imaging such as automated segmentation, strain analysis, and real-time guidance for standard echocardiography views. He also collaborates with SINTEF Digital's Health Research Department, advancing robotics and autonomy in medical interventions. Key Contributions : His work includes developing tools like Annotation Web for ultrasound image annotation, improving reproducibility of strain measurements via deep learning, and validating AI models for left ventricular volumetrics. He has published extensively in journals like Ultrasound in Medicine and Biology , European Heart Journal , and IEEE transactions. Affiliations : Active at AHL-senteret (Øya), he bridges academic and clinical research, emphasizing translational AI applications in cardiology. His projects aim to enhance diagnostic accuracy, reduce variability, and streamline echocardiography workflows.

• Fast Response Virtual Teams
• Applied Machine Learning
• Perceived Fairness of AI/ML Systems
• +5 more

Dr. Deepak Khazanchi is a Professor at the University of Nebraska Omaha , affiliated with the College of Information Science & Technology and the Department of Information Systems and Quantitative Analysis . He serves as the Executive Director of the University of Nebraska Center for Management of IT and Director of the Executive Master's of Science in IT program. Education : Ph.D. in Management Information Systems (Texas Tech University, 1991); MBA in Finance (Southern Illinois University, 1988); B.Tech. in Civil Engineering (Indian Institute of Technology, 1981). Dr. Khazanchi's research focuses on applied machine learning , perceived fairness of AI/ML systems , fast response virtual teams , and the philosophy of science in computing disciplines , with applications in structural health monitoring, cybersecurity, and Metaverse technologies. Recent publications highlight trends in AI-driven infrastructure maintenance , blockchain integration for UAV systems, and digital transformation in supply chains . Scientific Awards : Fulbright Specialist (2014) Dr. Khazanchi actively engages in community programs like CodeCrush for female-identifying students and iSTEM at Omaha Public schools. He contributes to international academic institutions and serves on boards for nonprofits such as the Refugee Empowerment Center Omaha and Collective for Youth . His lab (PKI 383 & 377) explores emerging technologies for crisis management and infrastructure monitoring.