Find Academic Supervisors

Bin ChangView profile

Associate Professor

• Corporate Payout Policy
• Corporate Governance
• Cash Policy
• +7 more

Dr. Bin Chang is an Associate Professor of Finance at the Faculty of Business and Information Technology, Ontario Tech University. She holds a PhD from the University of Toronto and has prior experience as a credit derivatives risk analyst at CIBC, an instructor at the University of Toronto, and a foreign exchange trader at the Bank of China. Her research focuses on corporate payout policy, corporate governance, cash policy, innovation, entrepreneurship, pension systems, and climate change. She has secured multiple SSHRC grants, including a 2021 Insight Development Grant as Principal Investigator. Her work bridges theoretical frameworks with practical applications in financial policy and supply chain resilience. Dr. Chang contributes to interdisciplinary initiatives within the faculty, emphasizing data-driven decision-making and sustainable business practices. Education: PhD in Finance, University of Toronto MA in Finance, Queen's University MA, LLB, and BA from Wuhan University, China Research Interests: Dr. Chang’s research explores corporate financial policies, governance structures, and the impact of external factors like climate change on financial decision-making. She employs computational methods to analyze supply chain resilience, risk management, and sustainable infrastructure planning (e.g., EV charging stations). Her work often addresses global challenges, such as balancing economic growth with environmental stewardship. Grants & Collaborations: As Principal Investigator, she led a 2021 SSHRC grant examining corporate governance’s role in cash policies. Co-investigations include studies on dividend policies and financial crisis impacts. Her collaborations emphasize cross-disciplinary approaches to solving real-world business and environmental challenges. Labs/Teams: While primarily affiliated with the Finance department, her research aligns with the faculty’s broader initiatives in business analytics and AI, particularly in optimizing supply chain and financial systems.

Markus PelgerView profile

Associate Professor

• Financial Risk Management
• Machine Learning in Finance
• High-Dimensional Statistical Analysis
• +5 more

Markus Pelger is an Associate Professor at Stanford University's School of Engineering , affiliated with the Department of Management Science and Engineering and the National Bureau of Economic Research . His research bridges mathematical finance, machine learning, and statistical econometrics. PhD in Economics, UC Berkeley (2015) Diplom in Mathematics and Economics, University of Bonn (2012, 2009) His work focuses on: Financial Risk Modeling (high-frequency, jump, and continuous risk factors) Machine Learning Applications in asset pricing, statistical arbitrage, and portfolio optimization High-Dimensional Data Analysis (factor models, missing data imputation, and proximate factors) He has contributed to machine learning-based yield curve estimation , deep learning asset pricing , and interpretable factor models . His research has been honored with the Dennis Aigner Award , Bates-White Prize , and Crowell Memorial First Prize . He serves as Associate Editor for Management Science , Operations Research , and Digital Finance . He is a founding organizer of the Advanced Financial Technology Laboratories and AI & Big Data in Finance Research Forum , with affiliations spanning Stanford's computational, AI, and environmental research institutes.

• Financial Mathematics
• Machine Learning
• Data Science

Dr. Huy Chau is a Lecturer in Financial Mathematics and Statistics at the Department of Mathematics, The University of Manchester. He holds a PhD from the University of Padova and Université Paris Diderot, under Professors Wolfgang J. Runggaldier and Peter Tankov. Prior to his current role, he worked as a Post-doctoral researcher at the Alfred Renyi Institute of Mathematics and as a Specially Appointed Assistant Professor at Osaka University's Center for Mathematical Modeling and Data Science. His research focuses on financial mathematics, machine learning, and data science. Key areas include stochastic processes, utility maximization, transaction costs modeling, and algorithmic trading strategies. His work often bridges theoretical advancements with practical applications in quantitative finance and statistical learning. Recent publications emphasize methodological innovations in pricing/hedging frameworks under market uncertainty, stochastic optimization techniques, and the analysis of behavioral investor dynamics in conic market models. Notable contributions address super-replication strategies under transaction costs and inversion-free numerical methods in statistical computing. Dr. Chau is actively involved in supervising PhD students and teaches advanced courses like Martingales with Applications to Finance. He collaborates internationally on projects such as the UoM-IISc Bangalore PhD initiative.

Kun ZhangView profile

Professor

• Machine Learning
• Artificial Intelligence
• Causal Discovery
• +6 more

Kun Zhang is a Professor at Carnegie Mellon University's Department of Philosophy and an affiliate faculty member in the Machine Learning Department. His research bridges causal discovery, machine learning, and philosophical foundations of AI, with applications in neuroscience, computational finance, and climate analysis. His work focuses on: Automated causal discovery from heterogeneous data Causality-based learning for transfer and deep learning Philosophical principles of causation and machine learning Recent research trends emphasize causal representation learning, nonparametric modeling, and real-world applications across biomedical, financial, and Earth sciences. He leads the Causal Learning and Reasoning (CLeaR) group at CMU and collaborates with the Center for Integrative AI (CIAI) at MBZUAI.

• Robust Statistics
• Machine Learning
• Biostatistics
• +3 more

Matias Salibian-Barrera is a full Professor in the Department of Statistics at the University of British Columbia's Faculty of Science, where he leads research in robust statistical methodologies with applications across machine learning, biostatistics, and environmental sciences. His research focuses on developing computationally efficient robust techniques resistant to outliers and model misspecification. Key areas include: Robust regression and inference for high-dimensional data Robust causal inference with complex observational data Robust functional and spatial data analysis Integration of robust methods with deep learning architectures Applications in genomics, climate science, and finance His recent publications demonstrate a strong trend toward unifying robust statistics with modern machine learning, particularly in adversarial settings and high-dimensional inference, while maintaining rigorous theoretical foundations. Notable honors include: CRM–SSC Prize in Statistics Fellow of the American Statistical Association Killam Research Prize Coxeter-James Prize He actively supervises doctoral students and secures major grants from NSERC and CIHR, focusing on robust methods for biomedical and environmental data. His work involves collaborations with UBC's Machine Learning Group and the Pacific Institute for the Mathematical Sciences, where he develops open-source software for robust statistical computing.

Anthony CoacheView profile

Research Fellow

• Reinforcement Learning
• Stochastic Modeling
• Risk Management
• +2 more

Dr. Anthony Coache is a Research Fellow in Mathematical Finance at Imperial College London's Department of Mathematics. He holds a PhD in Statistics from the University of Toronto and specializes in reinforcement learning, stochastic modeling, and risk-sensitive decision-making in financial contexts. Research Focus: Coache develops algorithms for risk-sensitive control problems using dynamic risk measures, with applications in quantitative finance, algorithmic trading, and optimization under uncertainty. Recent Publications: Work includes robust RL with distortion risk measures, dynamic convex risk frameworks, and inverse RL for risk aversion modeling. Publications appear in SIAM Journal on Financial Mathematics and Mathematical Finance. Awards: Mathematical Finance Top Cited Article (2023) G-Research Grant (2025) NSERC Postdoctoral Fellowship (2024–26) SIAG/FME Conference Paper Prize (2023) Supervision: Mentored M.Sc. students from Morgan Stanley, Deutsche Bank, and Qube RT on projects spanning financial factor analysis, default correlation models, and liquidity prediction.

Lukas GononView profile

Assistant Professor

• Artificial Intelligence
• Quantum Machine Learning
• Reservoir Computing
• +2 more

Lukas Gonon is Assistant Professor at the University of St. Gallen's School of Computer Science, with affiliation to the Center for Financial Services Innovation. He maintains an honorary position as Senior Lecturer at Imperial College London. Gonon's research develops foundations and applications of artificial intelligence in finance, specializing in quantum machine learning, neural networks, and operator learning methods. His current projects include quantum ML for financial data streams (Innovate UK) and robustness in deep hedging (J.P. Morgan). Recent publications focus on quantum neural network approximation theorems, operator-based volatility modeling, efficient trading algorithms, and graph neural networks for systemic risk. His work consistently bridges theoretical foundations with financial applications.

Mykel KochenderferView profile

Associate Professor

• Artificial Intelligence
• Aerospace Engineering
• Decision-Making Systems
• +6 more

Mykel Kochenderfer is an Associate Professor at Stanford University's Department of Aeronautics and Astronautics and holds a courtesy appointment in Computer Science. He is a Senior Fellow at the Stanford Institute for Human-Centered AI (HAI) and directs the Stanford Intelligent Systems Laboratory (SISL), focusing on advanced algorithms for robust decision-making systems in aerospace and autonomous systems. His research emphasizes safety and efficiency in uncertain environments, with applications in air traffic control, unmanned aircraft, and automated driving. Education: PhD from the University of Edinburgh (2006), M.S. and B.S. in Computer Science from Stanford (2003). Prior to Stanford, he worked at MIT Lincoln Laboratory on airspace modeling and collision avoidance, leading to the ACAS X program. He co-directs the Center for AI Safety, and is affiliated with SAIL, HAI, the Symbolic Systems Program, Bio-X, and the Wu Tsai Neurosciences Institute. Research interests span decision-making under uncertainty, optimization algorithms, AI safety, and robotics. He authored influential textbooks including Decision Making under Uncertainty (2015), Algorithms for Optimization (2019), and Algorithms for Decision Making (2022). Recognitions include the 2017 DARPA Young Faculty Award. His work bridges theoretical AI with real-world applications in aviation, healthcare, and environmental systems. Labs and collaborations include SISL, the Center for AI Safety, and interdisciplinary teams at Stanford. He advises students from multiple departments and actively participates in policy discussions on AI governance and ethics.

• Machine Learning
• Quantum Computing
• Robotics
• +3 more

Michael Osborne is a Professor of Engineering Science at the University of Oxford, affiliated with Exeter College as an Official Fellow. His research focuses on intelligent algorithms for big data analysis, with applications in astrostatistics, autonomous robotics, and societal impacts of AI. He holds a DPhil and is a Faculty Member at the Oxford-Man Institute of Quantitative Finance. His work bridges machine learning, quantum systems, and ethical AI. Education : BSc (Honours) BEng (Honours) DPhil (Doctorate) Research Interests : Osborne’s research spans Bayesian optimization, reinforcement learning, quantum computing, and the ethical implications of AI. He has pioneered probabilistic algorithms for exoplanet detection and developed methods to assess AI’s impact on employment. His work on meta-learning and quantum device tuning demonstrates interdisciplinary innovation. Key Contributions : His recent articles address topics like carbon-efficient ML models, privacy-preserving AI systems, and robust reinforcement learning techniques. He emphasizes scalable solutions for real-world challenges, such as healthcare monitoring via smartphone data and secure data portals. Labs & Collaborations : Based at the Oxford-Man Institute, he collaborates across disciplines, advancing AI’s role in finance, engineering, and science while advocating for ethical practices in automation and data privacy.

• Machine Learning
• Finance
• Artificial Intelligence
• +7 more

Stefan Zohren is a Professor of Engineering Science at the University of Oxford, holding roles as an Associate Professor, Research Associate at the Oxford Internet Institute, and Stipendiary Lecturer at Worcester College. He leads research in applied machine learning across finance, economics, and natural sciences, with a focus on deep learning, reinforcement learning, network analysis, and quantum computing applications. His work bridges academia and industry through collaborations with NVIDIA, Graphcore, Nokia, and Lockheed Martin. Education: DPhil in Mathematical Physics from Imperial College London. He teaches machine learning modules in the MSc in Social Data Science program at the Oxford Internet Institute, including intensive courses on Applied Machine Learning. Research interests emphasize financial time series analysis, generative AI for trading strategies, and quantum computing. Recent publications explore stock market instability via graph auto-encoders, generative AI benchmarking in finance, and embedding compression techniques for noisy regression tasks. Scientific contributions include developing LOB-Bench for limit order book analysis and Position, a multimodal LLM for time series reasoning. His work addresses challenges in portfolio optimization, credit rating forecasting, and market shock causality. Collaborations with Man Group as Principal Quant and involvement in the Creative Destruction Lab’s FinTech stream highlight his industry engagement. He advises on projects ranging from SMEs to global firms, applying ML to finance and healthcare domains.

Geoffrey NichollsView profile

Associate Professor

• Bayesian Inference
• Statistical Modeling
• Monte Carlo Methods
• +7 more

Geoffrey Nicholls is an Associate Professor of Statistics at the University of Oxford and a Tutorial Fellow at St Peter's College. He joined Oxford in 2005 from the Mathematics Department at Auckland University in New Zealand and served as Head of the Statistics Department from 2012-2015. His research focuses on developing new methodology and algorithms for statistical inference, with particular expertise in Bayesian methods. Prof. Nicholls' research interests span Bayesian inference, approximation methods and calibration, misspecified models and Semi-Modular Inference (SMI), ranking and partial orders, and Monte Carlo methods. His work demonstrates a strong interdisciplinary approach, with ongoing collaborations in archaeology, business inventory management, environmetrics, finance, genetics, history and philology, satellite imaging data, geothermal reservoir imaging, ecology, social network analysis, and historical linguistics. He has developed new methodology in Statistical Machine Learning and Computational Statistics since 2015. His recent publications reveal a continuing emphasis on advancing Bayesian methodology, particularly in areas of partial orders, semi-modular inference, and diachronic sense change modeling. His work shows strong connections between theoretical statistical development and practical applications across diverse fields, with a notable focus on historical and linguistic applications alongside more traditional statistical domains. Prof. Nicholls has supervised numerous doctoral and master's students, with recent graduates working on topics including statistical inference for partial orders, Bayesian methods for misspecified models, and scalable statistical inference. His teaching includes Bayesian methods courses at both undergraduate and graduate levels, reflecting his deep expertise in this area. As an active researcher with publications extending to 2025, Prof. Nicholls continues to make significant contributions to statistical methodology while maintaining strong connections to real-world applications across multiple disciplines. His work bridges theoretical statistical development with practical implementation across diverse scientific domains.

Zheng KeView profile

Associate Professor

• Network data analysis
• Text analysis
• Natural Language Processing
• +4 more

Zheng (Tracy) Ke is an Associate Professor of Statistics at Harvard University's Department of Statistics. Her academic journey includes roles at the University of Chicago and Harvard, with a Ph.D. from Princeton University and a B.S. from Tsinghua University. She specializes in network data analysis, text analysis/NLP, high-dimensional statistics, machine learning, and bioinformatics/statistical genetics. Research Contributions Her research emphasizes network analysis (e.g., SCORE normalization, community detection), text analysis (topic modeling, sentiment analysis), and high-dimensional statistics (covariance estimation, eigenvalue analysis). Notable works include the MADStat project on statisticians' networks and advancements in GWAS data harmonization. Awards & Recognition COPSS Emerging Leader Award (2024) Sloan Research Fellowship (2023) IMS Peter Gavin Hall Prize (2021) Professional Activities She serves as an Associate Editor for the Journal of the American Statistical Association and organizes workshops on statistical network analysis and complex data systems. Her work bridges theoretical statistics with applications in genomics, finance, and social networks.

Jose OlmoView profile

Professor

• Financial Econometrics
• Time Series Analysis
• Financial Economics
• +2 more

Jose Olmo is a part-time Professor of Financial Economics at the University of Southampton. He holds editorial roles at journals including International Journal of Finance and Economics. His research focuses on financial econometrics, time series analysis, and systemic risk modeling. He collaborates with PhD students on topics like carbon footprint impacts on firms and asset pricing uncertainties. Education: BSc in Mathematics (Universidad de Zaragoza), PhD in Economics (Universidad Carlos III de Madrid). Prior roles include Senior Lecturer at City University London and visiting professorships at IE Business School and Imperial College London. Currently serves as Senior Research Fellow at Fundación Agencia Aragonesa para la Investigación y el Desarrollo. Teaching includes applied econometrics, financial derivatives, and machine learning at undergraduate/master/graduate levels. Supervises PhD students in Economics. Active in research groups like the Econometrics group and Southampton Marine and Maritime Institute. Recent publications delve into climate change econometrics, deep reinforcement learning for portfolios, and causal environmental-finance linkages. His work bridges econometric theory with practical financial risk management challenges.

Mario BritoView profile

Professor

• Risk Analysis
• Artificial Intelligence
• Maritime Systems
• +4 more

Professor Mario Brito is a Professor of Risk Analysis and Risk Management at the University of Southampton, leading the Department as Head. His work bridges statistics, science, and engineering, with a focus on complex systems such as autonomous vehicles, maritime operations, and healthcare systems. He holds a PhD from the University of Bristol and has extensive industry experience, including roles at BAe Systems and the National Oceanography Centre (NOC). Research Interests: Dynamic Probabilistic Risk Assessment of Complex Systems Impact of Organizational Factors on Socio-technical Systems Risk Root Cause Analysis and Accident Investigations Bayesian Methods and Machine Learning in Risk Assessment Key Projects: EU Horizon2020 BRIDGES Project (€1.8M) KTP Marine Autonomous Systems Risk and Reliability (UK£161K) Autonomous Vehicle Risk Analysis in Arctic/Antarctic Environments Teaching: MANG6143 Project Risk Management MANG6549 Quantitative Methods for Risk Management Professional Roles: Editorial Board Member, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems General Chairman, 33rd European Safety and Reliability Conference (2023)

• Macroeconomics
• International Economics
• Global Imbalances
• +4 more

Ning Zhang is a Lecturer in Economics at the Adam Smith Business School, University of Glasgow. He joined in September 2016 and holds a PhD in Economics from the University of St Andrews. Prior to Glasgow, he was a Junior Fellow of the Royal Economic Society. His research focuses on Macroeconomics and International Economics, particularly examining global imbalances, asset allocation, and risk-sharing mechanisms. He is an active researcher with publications in high-impact journals like the Journal of Economic Dynamics and Control and European Economic Review . Dr Zhang teaches postgraduate courses in Monetary Policy and Macroeconomic Theory, as well as undergraduate modules on Economic Policies and Growth. He supervises PhD students in Macroeconomics and International Economics, emphasizing topics like portfolio optimization and global financial dynamics. His work also extends into federated learning applications in machine learning and remote sensing, reflecting interdisciplinary interests. Key research contributions include analyzing asset home bias in debtor/creditor countries, two-way capital flows, and country portfolio strategies under global imbalances. He has published widely since 2017, with recent work exploring Bayesian methods in federated learning systems and their applications in distributed machine learning scenarios.