Find Academic Supervisors

Xin Guo is Professor and Department Chair of Industrial Engineering and Operations Research (IEOR) at UC Berkeley's College of Engineering, holding the Coleman Fung Chair in Financial Modeling. Her research bridges mathematical finance, stochastic control, and machine learning with applications in risk analytics and quantitative trading. Education: Ph.D. in Mathematics, Rutgers University (1999) Research Interests: Professor Guo's work centers on mathematical finance , stochastic games , and reinforcement learning . She develops theoretical frameworks for α-potential games and mean-field systems while applying signature methods and GANs to financial data. Her research addresses critical problems in portfolio optimization, fraud detection (e.g., Medicare analytics), and market forecasting, emphasizing the intersection of stochastic control with machine learning for real-world decision-making under uncertainty. Publication Trends: Recent work (2023-2025) shows increasing focus on multi-agent reinforcement learning through mean-field game theory, with applications spanning finance (corporate bonds, trading), healthcare (fraud detection), and transportation (rate forecasting). Key innovations include BSDE approaches for stochastic games, signature-based time series analysis, and theoretical guarantees for GAN training dynamics. Scientific Awards: Holds the prestigious Coleman Fung Chair in Financial Modeling, reflecting significant contributions to quantitative finance research. Advising and Grants: As IEOR Department Chair, Professor Guo mentors graduate students in stochastic modeling and financial engineering. Her research is supported by the Coleman Fung Endowment Fund, with collaborations spanning finance, healthcare, and transportation sectors through industry partnerships. Labs and Teams: Leads the Risk Analytics & Data Analysis Research (RADAResearch) Lab ( https://risklab.ieor.berkeley.edu/ ), which develops cutting-edge methodologies for risk assessment, data-driven decision-making, and game-theoretic solutions to complex systems. The lab fosters interdisciplinary work connecting mathematical theory with practical applications in FinTech and beyond.

• Mathematical Finance
• Financial Engineering
• FinTech
• +7 more

Huiyan Sang is a Professor and Director of the Undergraduate Program in the Department of Statistics at Texas A&M University (College of Arts & Sciences). She earned her Ph.D. in Statistics from Duke University and a B.Sc. in Mathematics and Applied Mathematics from Peking University. Her research focuses on spatial statistics, Bayesian nonparametric methods, machine learning, computational statistics, and applications in environmental sciences, geosciences, urban planning, and biomedical research. Her interdisciplinary work integrates statistical methodologies with real-world challenges, such as analyzing extreme environmental events, optimizing urban infrastructure, and modeling complex systems like human mobility during pandemics. She has contributed to advancing spatio-temporal modeling, Gaussian processes, and Bayesian hierarchical frameworks for large datasets. Recent publications highlight innovations in nonparametric regression, spatial functional data analysis, and stochastic frontier analysis, often leveraging computational efficiency and scalability. Her work addresses critical societal issues, including the impact of community design on public health and environmental monitoring through remote-sensing data. No scientific awards are explicitly listed in the provided texts. She advises no students or grants in the current dataset but collaborates widely on interdisciplinary projects. Her research lab focuses on developing cutting-edge statistical tools with applications in engineering, public health, and environmental science.

• Spatial Statistics
• Bayesian Nonparametrics
• Machine Learning
• +9 more

Michael U. Gutmann is a Senior Lecturer in Machine Learning at the School of Informatics, University of Edinburgh, and a member of the Institute for Adaptive and Neural Computation. His research lies at the intersection of machine learning, statistics, and scientific applications, with a focus on developing inference methods for complex and implicit models. Education: PhD in Computational Neuroscience, University of Tokyo MSc in Engineering and Applied Mathematics, Swiss Federal Institute of Technology (ETH) Zurich MSc, Ecole Centrale Paris His primary research interests include Bayesian inference, likelihood-free inference, optimal experimental design, unsupervised learning, and applications in computational biology and neuroscience. He is best known for introducing Noise-Contrastive Estimation (NCE), a foundational technique for training unnormalized statistical models. His recent work spans variational inference, density ratio estimation, flow models for missing data, and AI-driven experimental design in behavioral and biological sciences. His publications, including in NeurIPS , ICML , JMLR , and eLife , demonstrate a strong emphasis on methodological innovation for scientific discovery. He has contributed to open-source tools such as ELFI (Engine for Likelihood-Free Inference) and developed practical implementations of robust inference algorithms. Scientific Awards: No specific awards listed in the provided texts. Michael Gutmann actively supervises students and collaborates with leading researchers in machine learning and computational biology. He has secured research funding from EPSRC and BBSRC for projects in generative modeling and infectious disease epidemiology. He teaches advanced courses such as Probabilistic Modelling and Reasoning and Data Mining, reflecting his deep engagement with both theoretical and applied aspects of machine learning. Labs and Research Groups: Institute for Adaptive and Neural Computation (ANC), University of Edinburgh Former affiliations with Department of Mathematics and Statistics and Department of Computer Science at the University of Helsinki and Aalto University

• Machine Learning
• Bayesian Inference
• Likelihood-Free Inference
• +9 more

Andrew Li is an Associate Professor of Operations Research at Carnegie Mellon University's Tepper School of Business since 2024, previously serving as Assistant Professor since 2018. His research bridges statistics, optimization, and machine learning with applications to healthcare operations and retail management. Current teaching: Optimization, Business Analytics Capstone, and Topics in Optimization and Statistics PhD from MIT's Operations Research Center (2018), BS in Operations Research/Applied Mathematics from Columbia University (2012) Research Focus: Dr. Li develops data-driven decision frameworks for complex systems. Key areas include: Experience-based learning models with fairness constraints (organ allocation) Anomaly detection in low-rank matrices (retail inventory accuracy) Nanoparticle-based diagnostic systems for CAD and Alzheimer's Nonstationary demand forecasting in supply chains Publication Trends: Recent work combines bandit algorithms with healthcare applications (split liver transplants, CAD detection) and retail operations (inventory accuracy). Theoretical contributions include regret-optimal policies and entrywise anomaly detection guarantees. Scientific Honors: INFORMS Nicholson Award (2018) INFORMS Pierskalla Award (2021) NSF CAREER Award (2023) Professional Leadership: Active in INFORMS and CMU committees including MBA Analytics Curriculum, Thompson Award, and ENAiBLE AI-driven retail collaborative co-founder since 2021.

• Statistics
• Optimization
• Machine Learning
• +3 more

Lu Yi is a Professor at the Department of Economics, Tsinghua University School of Economics and Management , holding a CITIC Chair Professor since 2022. He is also Director of multiple research centers , including the Tsinghua University China Economic and Social Data Research Center and the Center for International Economics. A doctoral supervisor , he has guided students across institutions and academic programs. Lu Yi's education includes a BA from Fudan University's School of Life Sciences (1999) , an MA from Fudan University's School of Management (2003) , and a PhD from the University of Hong Kong's Faculty of Business and Economics (2007) . His research focuses on Chinese economy, public finance, and global economic governance , with particular emphasis on trade liberalization, institutional quality, and environmental policy. Key research areas : Trade policy impacts, productivity gains from trade, environmental regulation effects, and labor market responses to technological change Leadership roles : Editorial board member of American Economic Journal: Economic Policy , Co-Editor of China & World Economy , former Deputy Editor-in-Chief of Economic Journal Policy advisory : Expert for National Healthcare Security Administration's DIP payment system, National Council Member for Social and Economic Council His scientific awards span national and provincial levels , including the First Sun Shangqing Young Scholar Award , Pushan World Economics Outstanding Paper Youth Award , and multiple provincial social science achievement awards in Shandong, Liaoning, Zhejiang, Fujian, and Sichuan. His publications in top journals like the Journal of Public Economics , Review of Economics and Statistics , and Nature Climate Change demonstrate his influence in applied economics and policy research.

• Chinese Economy
• Public Finance
• Global Economic Governance
• +5 more

Braam Lowies is a Senior Lecturer in Property at UniSA Business, University of South Australia, based at the City West Campus. He is an active researcher and Research Degree Supervisor, focusing on the intersection of financial behaviour, mental health, and ageing in Australia. His work spans multiple disciplines including housing, public policy, gerontology, and behavioural economics. Institution: University of South Australia School: UniSA Business Department: Property Position: Senior Lecturer Email: Braam.Lowies@unisa.edu.au Dr. Lowies' research primarily explores the financial and psychological wellbeing of older Australians, particularly in the context of retirement, housing equity, and crises such as the COVID-19 pandemic. His studies examine how savings, debt, hope, and mental health influence financial decisions, and how the home environment affects health outcomes across different age groups in later life. He frequently employs large-scale datasets like HILDA and applies both qualitative and quantitative methods. His recent publications highlight trends in ageing-in-place, equity release, digital banking challenges, and the socio-ecological determinants of financial stress. These works appear in high-impact journals such as Stress and Health , PLoS One , and Ageing & Society , as well as in book chapters with Edward Elgar Publishing. His research has relevance for policy development in housing, retirement planning, and mental health support systems. Dr. Lowies collaborates extensively with researchers such as Kurt Lushington, Rajabrata Banerjee, and Vandana Arya. His work has been cited in academic databases and picked up by media outlets, indicating its societal impact. Understanding the effect of financial behaviour on mental health: evidence From Australia (2025) Ageing-in-place (2024) The home environment: influences on the health of young-old and old-old adults in Australia (2024) The effect of psychological factors on financial behaviour among older Australians (2023) COVID‐19: financial well‐being of older Australians in times of crisis (2022) Dr. Lowies has no listed scientific awards in the provided materials. He is actively involved in supervising research degrees but specific student names are not mentioned. There is no information about grants, laboratories, or research teams he leads, though his collaborative work suggests strong interdisciplinary engagement.

• Financial Behaviour
• Mental Health
• Ageing
• +7 more

Pierre Marion is a Researcher at INRIA Paris, working within the Sierra research team since September 2025. His work focuses on the theoretical foundations of deep learning and he is beginning to explore applications of AI in mathematics. Marion has established collaborations across multiple institutions including EPFL, Sorbonne Université, and Google DeepMind. His educational background includes: Engineering degree from École polytechnique (2015-2018) with specialization in Applied Mathematics Master's degree from Sorbonne Université (2019-2020) PhD from Sorbonne Université (2020-2023) under the supervision of Gérard Biau and Jean-Philippe Vert Postdoctoral research at EPFL (2024) supervised by Lénaïc Chizat Marion's research interests primarily focus on the theory of deep learning, where he investigates the optimization and statistical properties of various neural network architectures. His work spans from shallow networks to complex generative models, with a particular emphasis on understanding the mathematical foundations that govern deep learning performance. Recently, he has begun exploring applications of AI in mathematical research, aiming to bridge the gap between theoretical machine learning and mathematical discovery. His research often combines rigorous theoretical analysis with practical implications for training deep neural networks. Analysis of Marion's recent publications reveals several key trends in his research. He has made significant contributions to understanding the role of large learning rates in optimization dynamics, demonstrating how they can accelerate convergence in logistic regression and prevent memorization in score-based generative models. His work on attention mechanisms has provided theoretical guarantees for their effectiveness in specific tasks like single-location regression and clustering. Additionally, Marion has extensively studied the connections between residual networks and neural ordinary differential equations , establishing generalization bounds and exploring scaling properties in the large-depth regime. His earlier work included contributions to natural language processing and quasi-Monte Carlo methods, reflecting a broad mathematical foundation that informs his current deep learning research. Marion has received several notable scientific awards: Runner-up PhD Award of AFIA (French Association for Artificial Intelligence) in 2024 Google PhD Fellowship in 2022 Ecole polytechnique Grand Prize of Research Internships in 2018 As an advisor, Marion currently supervises PhD student Yu-Han Wu (since 2024), with whom he has co-authored multiple publications on large learning rates and denoising score matching. Previously, he co-supervised several Master's students including Seorim Park, Yerkin Yesbay, and Nathan Doumèche. Marion has been actively involved in the machine learning community through conference organization (NeurIPS@Paris meetups), session chairing (ICSDS 2022), and extensive reviewing activities. He has served as a reviewer for top journals including JASA and Annals of Statistics, and conferences including NeurIPS and ICLR, where he was recognized as a top reviewer at NeurIPS 2023. Marion is a member of the Sierra research team at INRIA Paris, which focuses on machine learning theory and applications. He has also collaborated with researchers at CREST (Center for Research in Economics and Statistics), as evidenced by his participation in seminars organized by Anna Korba and Karim Lounici. His work often bridges theoretical computer science, statistics, and applied mathematics, reflecting the interdisciplinary nature of modern machine learning research.

• Deep Learning Theory
• Optimization of Neural Networks
• Generative Models
• +2 more

Pablo Durango-Cohen is an Associate Professor of Civil and Environmental Engineering at Northwestern University, located in Evanston, IL. He holds a Ph.D. in Industrial Engineering and Operations Research from UC Berkeley, following an M.S. from the same program and a B.S. in Industrial and Systems Engineering from the University of Southern California. His research focuses on developing and analyzing optimization and econometric models for transportation infrastructure systems, integrating environmental design, life-cycle assessment, and policy analysis to address decarbonization challenges in freight systems. He also explores dynamic segmentation models for nonprofit fundraising strategies. Education: Ph.D. Industrial Engineering and Operations Research, University of California, Berkeley (2006) M.S. Industrial Engineering and Operations Research, University of California, Berkeley B.S. Industrial and Systems Engineering, University of Southern California Research Interests: Prof. Durango-Cohen’s work bridges transportation engineering, environmental science, and operations research. He emphasizes infrastructure management through data-driven frameworks, including statistical process control for condition monitoring and predictive maintenance. His recent projects address decarbonization of freight rail systems, electric vehicle impacts on road infrastructure, and optimal auction designs for road concessions. He also applies mathematical models to analyze donor behavior and fundraising efficiency in universities, aiming to improve nonprofit resource allocation strategies. Awards: NSF Faculty Early CAREER Development Award (2006) Young Author Prize, 2007 World Congress on Transport Research Matthew G. Karlaftis Best Paper Awards (2020–2025) Advising & Grants: He advises current PhD candidates including Jing Yu, Adrian Hernandez, and Callahan Skiles, while mentoring former students across sustainability, infrastructure, and fundraising analytics. His research is supported by agencies like the National Science Foundation, Department of Energy (through ARPA-E), and Department of Transportation. He co-leads the LOCOMOTIVES project with ANL researchers, focusing on decarbonizing rail networks, and founded the Virtual Inter-university Symposium on Infrastructure Management (VISIM) to foster academic collaboration. Labs & Teams: As Principal Investigator (PI) on major initiatives like LOCOMOTIVES and VISIM, he collaborates with multidisciplinary teams at Northwestern and Argonne National Laboratory. His group develops tools such as the Locomotives interactive dashboard and a computational framework for input-output lifecycle assessments, accessible via repositories like CivEnv304 .

• Transportation Infrastructure Management
• Optimization
• Econometrics
• +5 more

Moïse Blanchard is an Assistant Professor and Tennenbaum Early Career Professor at the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology, having joined in August 2025. Previously, he was a Postdoctoral Fellow at Columbia University Data Science Institute. His academic journey includes a Ph.D. in Operations Research from MIT (2024), and M.Sc. and B.Sc. degrees in Applied Mathematics from École Polytechnique. Blanchard's research focuses on the intersection of machine learning theory, statistics, and optimization. His work addresses fundamental questions in universal learning, online algorithms, and convex optimization under memory constraints. His research program explores learnability under minimal assumptions, query complexity/memory tradeoffs, and decision-making in adversarial environments. This work has significant implications for theoretical computer science, operations research, and statistical learning theory. His publications reveal a strong emphasis on foundational aspects of learning theory and optimization. Recent work demonstrates expertise in universal learning frameworks, memory-constrained optimization, and probabilistic analysis of combinatorial problems. His research often bridges theoretical computer science with practical optimization challenges, particularly in contexts where traditional i.i.d. assumptions don't hold. Columbia DSI postdoctoral fellowship, 2024 INFORMS Transportation Science & Logistics (TSL) best student paper award, 2023 Air Force Office of Scientific Research Grant (AFOSR), with Prof. Patrick Jaillet, 2023 COLT 2022 Best student paper runner-up Bronze medal, Alibaba Global Mathematics Competition, 2022 Blanchard has received significant research funding including an Air Force Office of Scientific Research Grant. His work has been recognized with multiple prestigious awards, including the INFORMS TSL best student paper award for his research on the k-Traveling Salesman Problem. His extensive publication record in top venues demonstrates a strong research trajectory with impactful contributions to theoretical machine learning and optimization.

• Machine Learning Theory
• Online Algorithms
• Optimization
• +4 more

Marie-Colette van Lieshout is a Professor of Spatial Stochastics at the Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, and a Scientific Staff Member in the Stochastics group at Centrum Wiskunde & Informatica (CWI), Amsterdam. She has been active in research since 1997 and is a leading expert in stochastic geometry, spatial statistics, and image analysis. Her educational and professional background includes positions at the University of Warwick and the Free University Amsterdam. She is currently engaged in advanced research on point processes, random fields, and tessellation models, with applications in seismic hazard, fire risk, and machine learning. Her research interests include: Stochastic Geometry Spatial Statistics Image Analysis Point Process Modeling Seismic Risk Assessment Machine Learning for Spatial Data Her recent publications (2023–2025) focus on spatial intensity estimation, marked point processes, and data-driven risk modeling, showing a strong integration of classical spatial statistics with modern computational and machine learning techniques. Key themes include adaptive kernel smoothing, infill asymptotics, and applications in environmental and public safety domains. She has received significant recognition, including: Elected Fellow, International Statistical Institute (ISI) She has been awarded multiple research grants from NWO and other agencies, including the KLEIN grant for fire risk management and the DeepNL grant for seismicity prediction in Groningen. She has supervised or collaborated with researchers such as C. Lu, Z. Baki, and R. Markwitz. She is also active in academic service, serving on editorial boards (e.g., Methodology and Computing in Applied Probability), advisory boards (InHolland University), and councils of learned societies (Bernoulli Society, KWG). She leads and participates in research clusters such as STAR and contributes to outreach and education through courses and public lectures on earthquake modeling and spatial statistics.

• Stochastic Geometry
• Spatial Statistics
• Image Analysis
• +6 more

Ryan Browne is an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds a BMath (2004), MMath (2006), and PhD (2009) from the same institution. His research focuses on model-based clustering , classification , and measurement system quality assessment , with applications in multivariate analysis and statistical inference. He is particularly known for contributions to mixture models and their computational optimization. Education: BMath in Statistics, University of Waterloo (2004) MMath in Statistics, University of Waterloo (2006) PhD in Statistics, University of Waterloo (2009) Ryan’s work emphasizes flexible statistical methodologies , including advancements in skewed distributions, high-dimensional data analysis, and robust algorithms for clustering and classification. He has received the prestigious 2011 W.J. Youden Award from the American Statistical Association for his PhD research on measurement system evaluation. His research trends span computational statistics (e.g., sketching algorithms for big data) and model-based clustering innovations (e.g., mixtures of generalized hyperbolic distributions). Recent work explores parsimonious models, nested Gaussian structures, and efficient parameter estimation for complex datasets. Key Awards: 2011 W.J. Youden Award (American Statistical Association) Ryan collaborates on applied projects, including industrial ecology and sensory data analysis. He has developed R packages like mixture and MixGHD , which implement his methodological contributions.

• Model-based Clustering
• Classification
• Measurement Models
• +4 more

Dr. Andrew Curtis is a Professor in the Department of Population and Quantitative Health Sciences at Case Western Reserve University's School of Medicine. He also holds a joint appointment in the Department of Anthropology, College of Arts and Sciences. His research focuses on spatial epidemiology, context-driven spatial data collection, and spatial confidentiality, with methodological expertise in geospatial data collection and analysis. He previously directed the WHO Collaborating Center for Remote Sensing and Public Health and has advised numerous public health agencies internationally. His work addresses health disparities at neighborhood scales, disaster response, and spatial syndromic surveillance. Dr. Curtis has mentored 10 PhD graduates now working in academia and public health sectors globally. Education includes a PhD in Geography from SUNY Buffalo (1995), MA in Geography from SUNY Buffalo (1991), and a BA from Portsmouth Polytechnic (1987). He has taught courses in GIS for health, medical geography, and global health. His advisory roles include collaborations with the CDC, Red Cross, and public health departments in Ohio and internationally. His research has been applied to cholera outbreaks, opioid overdose patterns, and pandemic response strategies. He co-edited a special issue on health geography and serves on the editorial board of the Annals of the Association of American Geographers. Dr. Curtis' spatial video methodologies have enabled fine-scale mapping in informal settlements, disaster zones, and conflict-affected areas. His recent work includes geospatial support for Ohio hospitals during the pandemic and energy vulnerability analysis. He emphasizes participatory research and context-enriched data collection to bridge gaps between health research and actionable policy.

• Spatial Epidemiology
• GIS Applications in Public Health
• Spatial Data Collection in Challenging Environments
• +5 more

Tommy Löfstedt is an Associate Professor at Umeå University , affiliated with the Department of Computing Science and the Department of Mathematics and Mathematical Statistics. His research focuses on machine learning , computer vision , and medical image analysis , with applications in life sciences, radiation therapy, and biomedical imaging. He leads multiple research projects, including AI-driven delineation in radiation therapy, quantitative MRI for radiotherapy, and machine learning for plant nutrient uptake. Current research emphasizes structured regularization methods to improve model interpretability and robustness. Key applications include medical image segmentation , Alzheimer's classification , and uncertainty estimation in MRI . Recent publications highlight his work on morphological regularization , adversarial attack mitigation , and multi-task learning in medical imaging contexts. His projects span 2022–2026 with funding for pediatric oncology automation and gynecological cancer staging. Affiliated with both computing and mathematical departments, he bridges algorithm development with applied mathematical frameworks in medical and life science domains.

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +4 more

Prof. Dr. Erik Rodner is a faculty member at the University of Applied Sciences Berlin (HTW Berlin), where he serves as a Professor for Machine Learning and Data Science. He also contributes to the School of Engineering Sciences - Technology and Life. His research spans computer vision, machine learning, and biomedical applications, with a focus on learning with limited data, robust visual recognition models, and medical image analysis. He has developed innovative methods for medical diagnostics, industrial classification, and anomaly detection. Recent publications (2025-2016) highlight his expertise in visual in-context learning, semi-weakly segmentation, and active learning frameworks. He has collaborated with institutions such as ZEISS Group, Friedrich Schiller University Jena, and UC Berkeley. Scientific Awards: Award for Excellent Teaching (2023)

• Machine Learning
• Computer Vision
• Data Science
• +5 more

Bo Markussen is a Professor at the University of Copenhagen within the Department of Mathematical Sciences . He is also a member of the Data Science Laboratory , where he contributes to statistical methodology and interdisciplinary collaborations. His academic journey began with a Cand.Scient (MSc) and PhD in Statistics from the University of Copenhagen, awarded in 1998 and 2002 respectively. 2012–present: Professor, Department of Mathematical Sciences, University of Copenhagen 2009–2012: Associate Professor, Department of Basic Sciences and Environment, University of Copenhagen 2006–2009: Assistant Professor, Department of Basic Sciences and Environment, University of Copenhagen Bo Markussen's research focuses on applied statistics , particularly in functional data analysis and multiple testing corrections in genetics . His work spans diverse domains including environmental science, agriculture, and public health. Recent research output highlights applications in Arctic climate data analysis, fire risk modeling, plant stress phenotyping, and nutritional biomarker prediction. His recent publications demonstrate a strong trend toward machine learning integration with statistical modeling , addressing challenges in high-dimensional data analysis and environmental risk assessment. Collaborations span institutions in Denmark and internationally, reflecting his engagement in pan-Arctic climate studies and tropical agricultural research. 2018–present: Associate Editor, Scandinavian Journal of Statistics 2017–2019: Chair, Danish Society for Theoretical Statistics 2015–2017: Board Member, Danish Society for Theoretical Statistics As a central figure in the Data Science Laboratory , Markussen leads statistical consultancy initiatives and contributes to methodological advancements. His expertise bridges theoretical statistics with real-world applications, particularly in handling complex datasets across biological and environmental domains.

• Applied Statistics
• Statistical Consultancies
• Functional Data Analysis
• +5 more