جستجوی استاد راهنمای دانشگاهی

Reidar B. Bratvold is a Visiting Professor at the University of Stavanger and Norwegian Institute of Technology. His research focuses on decision analysis, project valuation, and reservoir management, integrating machine learning and stochastic modeling. He has extensive industry experience, including roles as Vice President at Landmark Graphics and Managing Director at Roxar. Bratvold holds a PhD in Petroleum Engineering and an MSc in Mathematics from Stanford University, complemented by management education from INSEAD. His work emphasizes decision quality, addressing behavioral biases and uncertainty in energy investments. Notable contributions include frameworks for geosteering optimization and real-options valuation. He has authored numerous papers and books, including Making Good Decisions , and received awards such as the 2024 Energy Professional of the Year and 2017 SPE Management & Information Award. Bratvold’s research bridges academia and industry, promoting systematic decision-making methodologies. Awards: Energy Professional of the Year (2024) SPE Management & Information Award (2017) Three-time SPE Distinguished Lecturer Key Contributions: Machine learning in geosteering Value-of-information analysis Real-options valuation for oil projects His recent work explores reinforcement learning for decision-making and Bayesian methods for production optimization. Bratvold’s interdisciplinary approach combines petroleum engineering, economics, and data science to enhance industry decision-making.

• Decision Analysis
• Project Valuation
• Portfolio Analysis
• +۷ مورد دیگر

Dr. Yue Wu is a Lecturer in the Department of Mathematics and Statistics at the Faculty of Science, University of Strathclyde. She is actively engaged in research and teaching, with a strong focus on stochastic and numerical analysis. She is affiliated with the Alan Turing Institute as a Visiting Researcher and collaborates internationally on advanced mathematical and data science projects. Research Interests: Numerical analysis for stochastic (partial) differential equations (SDEs/SPDEs) Random periodic solutions and their numerical approximation Rough path theory and signature methods Applications in machine learning, data science, and engineering systems Her recent work bridges pure stochastic analysis with practical applications in AI, autonomous systems, and industrial diagnostics. She employs advanced mathematical tools such as log-signatures and randomized numerical schemes to solve complex real-world problems. Publication Trends: Dr. Wu's recent publications (2022–2025) show a strong trend toward integrating stochastic numerics with machine learning. Her work spans theoretical convergence analysis of numerical schemes, feature extraction using rough paths, and PDE-informed deep learning. There is a clear interdisciplinary focus, connecting mathematics with engineering and computer science. Scientific Awards: Strathclyde & TU Braunschweig Joint Collaborative Funding Recipient (2023) ICIAM2023 Financial Support Scheme 2 Recipient (2023) Turing Network Development Award: Trailblazers Competition Recipient (2022) Lower Saxony – Scotland Tandem Fellowship Recipient (2022) Advising and Grants: Dr. Wu is accepting PhD students and offers projects in stochastic numerics and rough path applications. She has secured funding as Principal Investigator (e.g., International Exchanges Round 3) and Co-investigator (e.g., AI-based asteroid navigation project with ESA). Her grants reflect strong international collaboration and interdisciplinary innovation. Labs and Teams: While no specific lab name is mentioned, Dr. Wu is part of active research networks including the Alan Turing Institute and collaborates with teams in aerospace, data science, and applied mathematics. She organizes seminars and workshops, indicating leadership in her research community.

• Stochastic Analysis
• Numerical Analysis
• Stochastic Differential Equations
• +۸ مورد دیگر

Hening Liu is Professor of Finance at Alliance Manchester Business School (AMBS), University of Manchester, where he also serves as Director of the PhD Programmes in Accounting and Finance. He joined AMBS as a Lecturer in 2008, was promoted to Reader in Finance in 2015, and became Professor of Finance in 2017. Hening Liu earned his PhD in Economics from Northern Illinois University. His academic journey at AMBS has spanned over 15 years, during which he has established himself as a leading researcher in finance. Professor Liu's research spans theoretical and empirical asset pricing, financial econometrics, and portfolio choice. His recent work focuses on ambiguity preferences in asset pricing, structural estimation of asset pricing models, and general equilibrium models with nonstandard utility preferences. His research fingerprint reveals strong expertise in volatility, asset pricing, risk premium, investors behavior, capital market returns, Bayesian methods, financial economics, and portfolio choice. Hening Liu's publications demonstrate a consistent focus on advanced asset pricing models and financial decision-making under uncertainty. His recent work combines sophisticated econometric techniques with theoretical financial models to address fundamental questions about market behavior, investor preferences, and risk assessment. The recurring themes across his publications include the application of Bayesian methods, analysis of long-run risk models, and examination of ambiguity aversion in financial contexts. Regular reviewer for American Economic Review Regular reviewer for Journal of Finance Regular reviewer for Journal of Financial and Quantitative Analysis Regular reviewer for Journal of Political Economy Regular reviewer for Management Science Regular reviewer for Review of Financial Studies As Director of PhD Programmes in Accounting and Finance, Professor Liu supervises several doctoral students including Bo Huang, Ke Wang, Shaokai Ding, and Junhong Yu. His PhD students have secured placements at prestigious institutions worldwide including University of Bath, University of Chicago, London Business School, and Fudan University. Beyond academia, Professor Liu is an amateur expert in Go (Weiqi), appreciating the game's strategic depth which resonates with his research in asset pricing and decision-making under uncertainty.

• Theoretical Asset Pricing
• Empirical Asset Pricing
• Financial Econometrics
• +۹ مورد دیگر

Simon Tavaré is a Professor in the Departments of Statistics and Biological Sciences at Columbia University and Director of the Irving Institute for Cancer Dynamics. He holds affiliations with the Program for Mathematical Genomics. His research focuses on computational statistics, bioinformatics, and evolutionary approaches to cancer biology. Tavaré’s work integrates statistical methods with genomic data analysis, particularly in studying cancer genomics, tumor evolution, and therapeutic resistance. Education: PhD in Probability and Statistics from the University of Sheffield (1979). Key career milestones include roles at the University of Southern California (Kawamoto Chair in Biological Sciences), University of Cambridge (Director of the Cancer Research UK Cambridge Institute), and his current position at Columbia since 2018. He has been recognized with prestigious awards, including Fellowship of the Royal Society and election to the U.S. National Academy of Sciences. Research interests include statistical inference in cancer genomics, mutational signatures, and single-cell sequencing technologies. His lab develops computational tools for analyzing genomic diversity and tumor heterogeneity, with applications to esophageal adenocarcinoma, glioblastoma, and prostate cancer. Notable contributions include the CamGFR model for renal function estimation in cancer patients and advancements in approximate Bayesian computation methods. Grants and collaborations span institutions like the National Institutes of Health and Cancer Research UK. He leads interdisciplinary teams exploring the intersection of mathematics, biology, and medicine. His work emphasizes translating genomic insights into clinical diagnostics and therapeutic strategies.

• Computational Biology
• Bioinformatics
• Cancer Genomics
• +۳ مورد دیگر

Alberto Sorrentino serves as an Associate Professor in the Department of Mathematics at the University of Genoa, actively teaching Mathematical Methods for Bioengineering, Numerical Calculation, and Reverse Problems courses for Mathematics and Bioengineering degree programs through the 2025-2026 academic year. His research specializes in Numerical Analysis and Inverse Problems , developing computational frameworks for Medical Imaging and Neuroscience applications. Key contributions include probabilistic source localization tools for M/EEG, validation methodologies for electrical source imaging, and Bayesian approaches to neuroimaging inverse problems under extreme conditions like space environments. Recent publications (2023-2024) reveal a consistent focus on uncertainty quantification in brain imaging, advanced EEG analysis pipelines for space medicine, and optimization of regularization techniques for MEG connectivity. His work bridges computational mathematics with biomedical challenges through interdisciplinary collaboration. Information regarding academic advising, research grants, laboratory facilities, or team structures is not documented in the available materials.

• Numerical Analysis
• Inverse Problems
• Medical Imaging
• +۳ مورد دیگر

Lars Nørvang Andersen is an Associate Professor at the Department of Mathematics, Aarhus University, affiliated with the Stochastics research group. His academic journey includes a Ph.D. in applied probability theory (2009) and a master's degree in statistics from Aarhus University. Research focus: statistical learning, bioinformatics, stochastic modeling Former positions: Postdoctoral researcher at Bioinformatics Research Centre (Aarhus) and Stanford University's Department of Biology Academic service: Assessment committees and Danish Statistical Society treasurer His research spans statistical learning, machine learning, and stochastic modeling in bioinformatics and operations research. Recent publications highlight interdisciplinary work in Gaussian process models, population genetics, and differentially private learning algorithms. Key trends in publications include: Advancements in self-distillation techniques (2021) Applications of phase-type distributions in genetics Stochastic modeling for rare event simulations (2018) Optimization algorithms for logistics and machine learning Teaching activities cover applied statistics, theoretical statistics, and modern statistical learning at bachelor's and master's levels, with industry collaborations in student projects.

• Statistical Learning
• Bioinformatics
• Mathematical Modelling
• +۵ مورد دیگر

Eunshin Byon is a Professor and Richard Wilson Faculty Scholar in Industrial and Operations Engineering at the University of Michigan, serving as Director of Masters Programs. Her expertise spans reliability engineering, data-driven decision-making, and stochastic systems optimization for critical infrastructure including renewable energy grids and manufacturing processes. Education: Ph.D. in Industrial and Systems Engineering, Texas A&M University M.S. in Industrial and Systems Engineering, KAIST B.S. in Industrial and Systems Engineering, KAIST Dr. Byon's research pioneers digital twin calibration and uncertainty quantification methodologies, integrating stochastic simulation with big data analytics to solve complex reliability challenges. Her work bridges theoretical advances in sequential computer experiments with practical applications in energy sustainability and predictive maintenance, emphasizing variance reduction techniques and operational decision frameworks for interconnected systems. Recent publications reveal a concentrated focus on parameter calibration innovations and computational efficiency in stochastic modeling, demonstrating consistent advancement in digital twin technologies for energy systems. These works increasingly incorporate machine learning elements while maintaining rigorous statistical foundations for uncertainty propagation. Scientific Awards: teaching award from the QCRE division of IISE (2024) Richard Wilson Faculty Scholar (2023) 2022 Best Associate Editor award, INFORMS Journal on Data Science 2021 Department Faculty Award Dr. Byon has mentored multiple doctoral candidates to successful completion, with students placing at Clemson University and Zhejiang University. Her research is sustained by significant external funding including an NSF grant for digital twin calibration (2023-2025) and Ford Motor Company research support (2023-2025), focusing on real-world implementation of stochastic optimization frameworks. She leads a specialized research group developing next-generation reliability assessment tools, collaborating with industry partners on renewable energy integration challenges while advancing fundamental methodologies in stochastic simulation and data analytics for complex engineered systems.

• Reliability Engineering
• Fault Diagnosis
• Condition Monitoring
• +۸ مورد دیگر

Christian A. Naesseth is an Assistant Professor of Machine Learning at the University of Amsterdam, where he is a member of the Amsterdam Machine Learning Lab and serves as lab manager of the UvA-Bosch Delta Lab 2. He is also an ELLIS member, actively contributing to the European AI research community. His work bridges theoretical machine learning with practical applications across scientific domains. University of Amsterdam - Faculty of Science Amsterdam Machine Learning Lab (AMLab) UvA-Bosch Delta Lab 2 (Lab Manager) ELLIS Institute member Naesseth's research focuses on generative modeling, uncertainty quantification, and probabilistic machine learning. His work spans diffusion models, flow matching techniques, stochastic differential equations, and their applications in scientific domains. He has made significant contributions to simulation-free training frameworks like SDE Matching, which eliminates the need for discretization and simulation when fitting latent SDE models to data. His research also addresses critical challenges in uncertainty quantification, including conformal prediction, risk monitoring in test-time adaptation, and multiple hypothesis testing. His recent publications demonstrate a consistent focus on improving efficiency and reliability in generative modeling while maintaining theoretical rigor. The work on SDE Matching represents a major advancement in training efficiency for latent stochastic differential equations, achieving speed improvements of several orders of magnitude. His research on risk monitoring and conformal prediction addresses practical deployment challenges for AI systems operating under distribution shift. Best Workshop Paper Award at AABI 2025 for SDE Matching 100% acceptance rate across major ML conferences in the 2024-2025 cycle (5/5 NeurIPS, 2/2 AISTATS, 2/2 ICML, 1/1 UAI) Naesseth actively mentors PhD students and postdocs, including Grigory Bartosh, Hany Abdulsamad, and several visiting researchers from institutions worldwide. He serves as program chair for AABI 2024 and has been involved in organizing multiple workshops at major conferences including ICML and NeurIPS. His lab has secured funding through collaborations with Bosch and likely other industry partners, supporting postdoctoral researchers and PhD students working at the intersection of theory and applications. He leads the UvA-Bosch Delta Lab 2, which focuses on advancing the theoretical foundations of machine learning while developing practical applications. The lab maintains strong connections with the broader Amsterdam Machine Learning ecosystem, including collaborations with ELLIS units in Amsterdam, Delft, and Nijmegen.

• Generative Modeling
• Uncertainty Quantification
• Probabilistic Machine Learning
• +۵ مورد دیگر

Denny Wu is a Faculty Fellow at the New York University Center for Data Science and affiliated with the Flatiron Institute 's Center for Computational Mathematics. He completed his PhD in Computer Science at the University of Toronto and the Vector Institute for Artificial Intelligence , advised by Assistant Professors Jimmy Ba and Murat A. Erdogdu . Prior, he earned an undergraduate degree in Computational Biology from Carnegie Mellon University as a research assistant under Ruslan Salakhutdinov . His research focuses on Theoretical Machine Learning , particularly Neural Network Optimization , Generalization Performance , and High-Dimensional Statistics . His work has been presented at top conferences like NeurIPS , ICML , and AISTATS , alongside publications in journals such as Nature Protocols and Journal of Statistical Mechanics: Theory and Experiment . Collaborative efforts include partnerships with RIKEN AIP 's Deep Learning Theory Team and Microsoft's Deep Learning Group. Borealis AI Fellowship 2023 UChicago Rising Star in Data Science Denny actively collaborates with institutions like the Vector Institute , RIKEN AIP , and Flatiron Institute , focusing on mathematical frameworks for understanding deep learning systems.

• Machine Learning
• Neural Networks
• High-Dimensional Statistics
• +۲ مورد دیگر

Eric Laber serves as James B. Duke Distinguished Professor of Statistical Science at Duke University, with cross-appointments as Professor of Biostatistics & Bioinformatics in the School of Medicine and Research Professor of Global Health. He co-directs the Duke Computing Initiative and maintains active roles in the Department of Statistical Science. His educational background includes: Ph.D. in Statistics, University of Michigan, Ann Arbor (2011) M.A. in Statistics, University of Michigan, Ann Arbor (2007) Laber's research pioneers statistical reinforcement learning for dynamic decision-making in complex health environments. He specializes in developing methodologies for Sequential Multiple Assignment Randomized Trials (SMARTs) to optimize personalized treatment sequences in precision medicine. His work bridges machine learning with clinical applications, focusing on cancer therapeutics, mental health interventions, and resource-constrained global health settings. Recent innovations address network interference in policy learning and risk-sensitive optimization for digital health platforms. Analysis of his 15 most recent publications reveals strong trends in adaptive clinical trial design, with increasing emphasis on mHealth applications (40% of recent work), PTSD management in cancer survivors (25%), and network-based reinforcement learning (20%). Key methodological threads include Thompson sampling variants (33%), functional data analysis for truncated outcomes (15%), and causal inference under interference (25%). His scientific recognition includes: James B. Duke Distinguished Professorship (premier faculty honor at Duke) Laber secures substantial grant funding for high-impact health research, currently leading 10 active projects totaling over $15M. Major awards include NIH/NCI funding for next-generation SMARTs in cancer therapeutics (2023-2028), NSF support for risk-sensitive statistical learning (2024-2027), and multiple NIH trials for digital mental health interventions. His collaborative approach spans oncology, psychiatry, and global health teams, with special focus on vulnerable populations including cancer survivors and adolescents. As Co-Director of the Duke Computing Initiative, he leads interdisciplinary teams developing scalable computational frameworks for precision medicine. Current efforts integrate reinforcement learning with electronic health records systems to create real-time clinical decision support tools, particularly for resource-limited settings in infectious disease management and chronic pain treatment.

• Statistical Reinforcement Learning
• Precision Medicine
• Dynamic Treatment Regimes
• +۳ مورد دیگر

Dr. Nikolas Kantas is a Reader in Statistics at Imperial College London's Department of Mathematics. He completed his undergraduate studies and PhD at the University of Cambridge's Signal Processing Group. His research focuses on developing numerical methods for complex problems in inference, optimisation, filtering, and control. Research Interests: Kantas specializes in computational statistics and stochastic processes, with expertise in particle filtering, Sequential Monte Carlo, and Markov Chain Monte Carlo methods. His work bridges theoretical foundations with applications in data assimilation, optimization under uncertainty, and high-dimensional statistical modeling. Publication Trends: Recent work (2022-2025) demonstrates strong focus on optimization algorithms, stochastic differential equations, and Monte Carlo methods. Key themes include multi-objective optimization, privacy-preserving algorithms, Langevin dynamics, and distributed computing. Methodological innovations frequently address high-dimensional and real-time computational challenges. Student Advising & Grants: Currently supervises 4 PhD students and has graduated 9 doctoral candidates. Research funding includes JP Morgan AI Faculty Research Awards and support from the National Physical Laboratory (NPL). Academic Leadership: Co-organizes the annual Greek Stochastics workshop on Statistics and Applied Probability. Coordinates PhD programs through the Mathematics Research program, MFC CDT, and Statistics and Machine Learning CDT.

• Computational Statistics
• Stochastic Processes
• Numerical Methods
• +۷ مورد دیگر

Renato Zanetti is an Associate Professor in the Department of Aerospace Engineering & Engineering Mechanics at the University of Texas at Austin, where he has served as core faculty since January 2017. His affiliations include: Core Faculty, Center for Autonomy Affiliate, Jah Decision Intelligence Group His research develops cutting-edge statistical methods for aerospace applications, focusing on three interconnected domains: Autonomy systems : Specializing in GPS-denied navigation and spacecraft rendezvous Inverse problems : Creating hybrid physics-machine learning approaches Scientific machine learning : Advancing Sequential Monte Carlo and Gaussian Mixture Models Before academia, he accumulated significant industry experience: Designed Orion spacecraft navigation systems at NASA Johnson Space Center Led the Vehicles Dynamics group at Draper Laboratory Developed navigation for Cygnus ISS resupply missions

• Autonomy
• Inverse Problems
• Scientific Machine Learning
• +۲ مورد دیگر

Chevaleyre Yann is a Professor of Computer Science at LAMSADE, Paris-Dauphine PSL University, where he has been working since 2017. Previously, he served as Professor of Computer Science at Paris-Nord University from 2009 to 2017 and as Director of the "data science" team at the LIPN laboratory. His academic journey includes a Lecturer position at LAMSADE, Paris-Dauphine University from 2002 to 2009, a Habilitation thesis at Paris-Dauphine University in 2009, and a Doctorate at Pierre and Marie Curie University under the supervision of Jean-Daniel Zucker from 1998 to 2001. Professor Chevaleyre's research spans multiple areas within artificial intelligence and computer science, with particular expertise in multi-agent systems, computational social choice, and machine learning. His work on preference modeling, voting theory, and resource allocation has significantly contributed to the field of computational social choice. More recently, his research has expanded into adversarial machine learning, robust classification, and generative models, reflecting the evolving landscape of AI research. His publication record demonstrates consistent contributions across multiple domains, with recent work focusing on precision-recall optimization in generative models, the role of randomization in adversarial robustness, and novel approaches to graphical bilinear bandits. His research shows a clear trajectory from foundational work in multi-agent systems toward more contemporary challenges in machine learning security and evaluation. Professor Chevaleyre has maintained active collaborations with researchers across France and internationally, evidenced by his extensive co-authorship network. His work bridges theoretical computer science with practical applications in areas ranging from robotics to bioinformatics.

• Artificial Intelligence
• Multi-Agent Systems
• Computational Social Choice
• +۳ مورد دیگر

Philippe VANHEEGHE serves as a Lecturer at Centrale Lille Institute's School of Engineering, maintaining offices at Centrale Lille (C319) and ESPRIT (S2.49) within the Scientific City campus. He is actively affiliated with the SIGMA research team at CRIStAL laboratory. His research specializes in statistical signal processing applications, particularly for railway infrastructure monitoring and electrophysiological measurement systems. Key contributions include developing non-destructive evaluation methods for railways, optimizing sensor management in multi-target tracking, and creating novel visual function assessment techniques for young children using sequential Monte Carlo methods. Dr. VANHEEGHE has supervised five doctoral theses between 2016-2021, with research spanning real-time sensor management implementation, state space exploration improvements, and AI-driven railway data analysis. His work consistently bridges theoretical signal processing with industrial railway applications through the SIGMA team's framework.

• Signal Processing
• Visual Tracking
• Sensor Management
• +۳ مورد دیگر

Yang Li serves as Associate Professor of Marketing and Associate Dean for the MBA Program at Cheung Kong Graduate School of Business (CKGSB). Holding a PhD in Marketing from Columbia Business School alongside dual master's and bachelor's degrees from Columbia and Peking University respectively, he bridges advanced statistical methodologies with practical business applications. His research centers on statistical machine learning and Bayesian nonparametrics applied to consumer behavior analysis, with specialization in online personalization, text mining, and choice modeling. Recent work demonstrates significant focus on fragmented attention economies, ethical AI frameworks, and NFT network dynamics, reflecting contemporary digital market challenges. Management Science Marketing Science Journal of Marketing Research Journal of Consumer Research Harvard Business Review Professor Li's publications reveal evolving expertise from foundational pricing elasticity studies toward cutting-edge AI applications in consumer contexts. His work increasingly integrates generative models and graph neural networks to decode complex consumer collection behaviors and digital ecosystem dynamics. Scientific recognition includes being a Finalist for the 2021 Paul E. Green Best Paper Award. Industry impact is demonstrated through executive education programs and strategic consultancies with Tencent, Haier, and Tmall. As Associate Dean for MBA Programs, he oversees curriculum development while maintaining active corporate governance roles on boards of publicly traded companies across China and Hong Kong, directly applying his research insights to strategic decision-making in digital transformation initiatives.

• Statistical Machine Learning
• Bayesian Nonparametrics
• Scalable Optimization
• +۵ مورد دیگر