جستجوی استاد راهنمای دانشگاهی

Yi Shenمشاهده پروفایل

دانشیار

• Applied Probability
• Stochastic Processes
• Extreme Value Theory
• +۳ مورد دیگر

Yi Shen is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, Canada. He holds affiliations with the Institute of Quantum Computing. His research focuses on applied probability, stochastic processes, and their symmetries, including stationarity, self-similarity, and exchangeability. He explores applications in extreme value theory, financial mathematics, quantum information, and statistical physics. Education: PhD in Operations Research (Cornell University, 2013) under Gennady Samorodnitsky; Ingenieur in Quantitative Economics and Finance (École Polytechnique, 2008); BS in Mathematics and Physics (Tsinghua University, 2005). Research emphasizes random locations of stochastic processes (e.g., supremum locations) and their connections to probabilistic symmetries. Recent work bridges probability with quantum mechanics, econometrics, and machine learning. His publications address topics like regression discontinuity designs, operator-scaling Gaussian fields, and quantum system Hamiltonian inference. Key contributions include analyzing self-similar processes, ergodic theory applications, and separability-entanglement classification in quantum systems. His work spans theoretical probability to applied domains like finance and quantum computing.

Emanuele Borgonovoمشاهده پروفایل

استاد

• Sensitivity Analysis
• Decision Analysis
• Risk Assessment
• +۶ مورد دیگر

Emanuele Borgonovo is a Full Professor and Director of the Department of Decision Sciences at Bocconi University. He holds a PhD from MIT and an MSc in Nuclear Engineering from Politecnico di Milano. As a Research Affiliate at MIT's Department of Nuclear Science and Engineering, he contributes to interdisciplinary research. His roles include Co-editor-in-Chief of the European Journal of Operational Research and former President of the Decision Analysis Society of INFORMS. His research focuses on sensitivity analysis methodologies, including the development of Differential Importance Measures adopted by NASA. Applications span risk assessment (e.g., space systems, climate models), machine learning, and optimal transport theory. Key contributions include methods for feature importance, interaction quantification, and probabilistic sensitivity measures. Education: PhD, Massachusetts Institute of Technology, 2001 MSc in Nuclear Engineering (Mathematics/Physics focus), Politecnico di Milano Recipient of the 2020 Bocconi Teaching Innovation Award, Borgonovo teaches courses in quantitative methods, analytics, and computer experiments across undergraduate, graduate, and PhD programs. He pioneered the Bachelor in Economics, Management, and Computer Science at Bocconi. His work bridges theoretical advancements with practical applications in sustainability, business analytics, and policy, leveraging collaborations with institutions like NASA and the Silvio Tronchetti Provera Foundation.

• Image Analysis
• Statistical Methods
• Determinantal Point Processes
• +۶ مورد دیگر

Agnes Desolneux holds dual roles as a Research Professor (CNRS Directrice de recherche) at the Centre Borelli and a Professor attached to the Department of Mathematics at ENS Paris-Saclay. Her affiliations include the Centre Borelli (ex-CMLA) and CNRS laboratories, focusing on advanced interdisciplinary research in mathematical imaging and stochastic methods. Education and Background: PhD in Applied Mathematics (2000), ENS Cachan Habilitation à Diriger des Recherches (HDR) in Applied Mathematics (2010), Université Paris Descartes Research Interests: Her work combines statistical analysis, stochastic processes, and mathematical modeling to address challenges in image analysis. Key areas include: Statistical methods ( a contrario framework, maximum entropy models) Image restoration and texture synthesis Determinantal point processes (DPP) and optimal transport Geometric analysis of random fields (curvature, Euler characteristics) Perceptual modeling of Gestalt principles Recent Contributions: Her research explores cutting-edge topics such as Gromov-Wasserstein distances in Gaussian mixture models, anisotropy analysis of random fields, and federated learning techniques. Her methodologies bridge theoretical probability and applied image processing. Awards and Recognition: No specific awards listed, but her extensive publication record in top journals like SIAM Journal on Imaging Sciences and Annals of Applied Probability highlights her scholarly impact. Grants and Collaborations: Active in projects like StoRI (Stochastic Recipes in Imaging) and the Master MVA program, emphasizing interdisciplinary training in mathematical data science. Her work often involves collaborations with institutions like the EDMH doctoral school and the Université Paris-Saclay. Labs and Teams: Leads research within the Centre Borelli, a CNRS-ENS Paris-Saclay hub for mathematics and computer science, fostering innovation in imaging and stochastic modeling.

• High-dimensional Statistics
• Machine Learning
• Signal Processing
• +۵ مورد دیگر

Karim Lounici is a Professor at the Centre de Mathématiques Appliquées (CMAP) of Ecole Polytechnique. His research focuses on high-dimensional statistics, machine learning, signal/image processing, probability theory, and optimization. He explores theoretical foundations and practical applications in areas like stochastic processes, quantum tomography, and dynamical systems. His work bridges mathematical rigor with computational methods, addressing challenges in data analysis, uncertainty quantification, and algorithmic efficiency. Recent contributions include equivariant representation learning, robust covariance estimation, and learning Koopman operators for dynamical systems. Publications span topics like neural conditional probability, spatio-temporal Monge alignment, and domain adaptation, reflecting expertise in both statistical theory and applied machine learning. No scientific awards explicitly mentioned. No advising or grant details provided in text. Affiliated with CMAP, a leading applied mathematics research center at Ecole Polytechnique.

Nikolay Tzvetkovمشاهده پروفایل

استاد

• Partial Differential Equations
• Nonlinear Waves
• Regularity Theory
• +۷ مورد دیگر

Nikolay Tzvetkov is a Professor of Mathematics at École Normale Supérieure de Lyon (since 2022), following roles as professor at CY Cergy Paris University (2009–2022) and the University of Lille 1 (2004–2009). He completed his PhD in 1999 under Jean-Claude Saut at Université Paris-Sud. His research focuses on partial differential equations, particularly nonlinear wave dynamics, regularity, and probabilistic well-posedness. Key contributions include studies on Gaussian measure transport under Hamiltonian flows, transverse instability of water waves, and invariant measures for equations like the Benjamin-Ono and nonlinear Schrödinger equations. Research interests span: nonlinear dispersive PDEs, well-posedness in low regularity spaces, statistical descriptions of wave systems, and probabilistic methods in PDE analysis. Collaborations include work with V. Banica, N. Burq, and N. Visciglia. His publications explore topics such as invariant measures, soliton dynamics, and dispersive equations on manifolds. Publications reflect deep analysis of Hamiltonian PDEs, including quasi-invariance of Gaussian measures, probabilistic well-posedness, and global dynamics. Talks and lectures (e.g., at IHES, Pisa, Rennes) highlight his engagement with the academic community. His work bridges deterministic and stochastic PDE approaches, emphasizing rigorous mathematical frameworks for nonlinear phenomena.

Fatemeh Ghoreishiمشاهده پروفایل

استادیار

• Machine Learning
• Bayesian Statistics
• Autonomous Systems
• +۴ مورد دیگر

Fatemeh Ghoreishi is an Assistant Professor at Northeastern University, holding joint appointments in the Department of Civil and Environmental Engineering and the Khoury College of Computer Sciences . Her research focuses on machine learning, Bayesian statistics, and decision-making under uncertainty, with applications in autonomous systems, transportation, and cyber-physical systems. She leads the Institute for Experiential AI and collaborates across disciplines to advance AI-driven solutions for complex systems. Education: Ph.D./M.Sc., Mechanical Engineering (Minor in Applied Statistics), Texas A&M University (2019/2016) M.Sc., Biomedical Engineering, Iran University of Science & Technology (2014) B.Sc., Mechanical Engineering, University of Tehran (2012) Research Interests: Dr. Ghoreishi develops data-driven methodologies for uncertainty quantification, reinforcement learning for autonomous systems, and human-AI collaboration. Her work integrates Bayesian inference, optimization, and sensor data to solve real-world challenges in transportation, robotics, and infrastructure security. Recent projects include AI for disaster response, privacy-preserving federated learning, and safe control of autonomous vehicles. Awards & Grants: $900,000 U.S. Army grant (2024): 'Interactive Human-Agent Teaming Via Multiple Feedback Types' $60,000 U.S. Army grant (2023): 'BRITE: Bayesian Inference and Preference Learning' IEEE Senior Member (2025) 2025 AIAA SciTech Best Paper Finalist 2024 MECC Conference Best Paper Award Advising & Teams: She mentors Ph.D. students (e.g., Negar Asadi) and undergraduates (e.g., Ise Okhiria, Alyssa Lee). Her lab emphasizes interdisciplinary collaboration, with affiliations at Northeastern’s Institute for Experiential AI and partnerships with the University of Utah and University of Michigan.

• Dependable Systems
• Probabilistic Verification
• Concurrency Theory
• +۵ مورد دیگر

Holger Hermanns is a Professor at Saarland University , holding the Chair for Dependable Systems and Software within the Faculty of Mathematics and Computer Science on the Saarland Informatics Campus. He has held academic positions at Universiteit Twente (Netherlands), INRIA Rhône-Alpes (France), and Universität Erlangen-Nürnberg (Germany), with part-time roles at INRIA (2007-2010) and Universiteit Twente (2001-2006). His research spans modeling and verification of concurrent systems , resource-aware embedded systems, compositional performance evaluation , and energy informatics . Recent work focuses on satellite network architectures, AI-driven system management, and ethical considerations in algorithmic accountability. Authored over 200 peer-reviewed papers (ha-index 92, h-index 56) Co-chaired program committees for CAV, CAV, CONCUR, TACAS, and QEST Keynote speaker at numerous international conferences Honors include the ERC Advanced Grant (2016), Preis des Fakultätentages Informatik (2013), and membership in Academia Europaea (2013). He serves on steering committees for ETAPS and TACAS, and has led Dagstuhl Foundation initiatives.

Markos Katsoulakisمشاهده پروفایل

استاد

• Generative Modeling
• Scientific Machine Learning
• Uncertainty Quantification
• +۳ مورد دیگر

Markos Katsoulakis is a Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst, serving as Director of the Applied Mathematics & Computation program and Applied Math MS Graduate Admissions. He holds leadership roles in the Center of Applied Mathematics and the UMASS TRIPODS Institute for Theoretical Foundations of Data Science. His research focuses on generative modeling, scientific machine learning, uncertainty quantification, and multi-scale methods, with applications in materials science, catalysis, and data-driven modeling. He contributes to editorial boards of SIAM journals and teaches advanced courses like Mathematics of Generative Artificial Intelligence. Education: Ph.D. in Applied Mathematics from Brown University (1993), B.A. in Mathematics from National and Kapodistrian University of Athens (1987). Research emphasizes foundational mathematics in generative AI, including trustworthiness, robustness, and integration with physical models. Recent work addresses high-dimensional control, likelihood-free inference, and generative particle algorithms for scarce data. His publications span machine learning, data science, and applied mathematics, with a focus on theoretical guarantees and algorithmic innovation. Leadership includes directing interdisciplinary initiatives and mentoring through the Applied Mathematics program. His work bridges computational science and data-driven methodologies, emphasizing model correctability and uncertainty quantification in complex systems.

Jiannan Yangمشاهده پروفایل

مدرس

• Digital Twins
• Uncertainty Quantification
• Probabilistic Engineering
• +۲ مورد دیگر

Dr. Jiannan Yang is a Lecturer in Digital Engineering at the University of York's School of Physics Engineering and Technology. His research develops innovative engineering design methodologies integrating digital twins, uncertainty quantification, and sustainable principles. Research objectives include: Developing adaptive design processes for digital twins Optimizing sensor placement for system identifiability Creating validation metrics for predictive accuracy Investigating probabilistic/non-probabilistic uncertainties Implementing dynamic visualizations for collaboration

Stephen Connorمشاهده پروفایل

مدرس

• Applied Probability
• Coupling Theory
• Perfect Simulation
• +۴ مورد دیگر

Dr. Stephen Connor is a Lecturer in the Department of Mathematics at the University of York. He obtained his undergraduate and PhD degrees from the University of Warwick and joined York in 2008. His research focuses on applied probability, coupling theory, and convergence properties of Markov processes. Research Interests: Specializes in Markov process convergence, stochastic stability, perfect simulation algorithms, and cutoff phenomena in random walks. Key applications include card shuffling, queueing systems, and interacting particle systems. Publication Trends: Recent articles demonstrate theoretical advances in stochastic processes with experimental validation. Works frequently combine mathematical rigor with computational methods to solve complex probabilistic models. Recurring themes include optimal coupling strategies, mixing time analysis, and algorithm development. Professional Activities: Editor for Applied Probability Trust journals Academic Steering Group member - Scottish Mathematical Sciences Training Centre Founder - Probability in the North East research network Research Grants Committee - London Mathematical Society Advising & Outreach: Currently supervises PhD students Georgia Sowerby and Wachiraphong Ratiphaphongthon. Leads school outreach programs on mathematical topics including card shuffling probabilities and cryptography. Served as LMS Holgate Session Leader (2017-2020).

Florian Dörflerمشاهده پروفایل

استاد

• Distributed Control Systems
• Optimization
• Cyber-Physical Systems
• +۷ مورد دیگر

Florian Dörfler is a Full Professor at ETH Zurich's Department of Information Technology and Electrical Engineering and Deputy Head of the Automatic Control Laboratory. He holds a Ph.D. in Mechanical Engineering from the University of California, Santa Barbara (2013), and a Diplom in Engineering Cybernetics from the University of Stuttgart (2008). Prior to ETH, he was an Assistant Professor at UCLA (2013–2014). His research focuses on distributed control and optimization in complex systems, including smart grids, robotic coordination, and social networks. Key areas include power grid stability, online feedback optimization, and data-driven control. He has advised students who received or were finalists for prestigious awards at major conferences such as the European Control Conference and American Control Conference. Notable awards include the IFAC Manfred Thoma Medal (2020), European Control Award (2020), and IEEE Circuits and Systems Best Paper Award (2016). His work spans theoretical advancements and practical applications, with contributions to stability analysis, decentralized control, and energy systems. Led the Automatic Control Laboratory and contributes to ETH Zurich’s broader efforts in cyber-physical systems. Active in academic service, including editorial roles and conference organization. His lab develops cutting-edge solutions for grid resilience, distributed optimization, and networked systems.

Christos Christodoulouمشاهده پروفایل

استاد

• Antennas and Electromagnetics
• High Power Microwaves
• Wireless Communications
• +۲ مورد دیگر

Christos Christodoulou is a Professor and Director of COSMIAC (Aerospace Research Center) at the University of New Mexico’s School of Engineering. He leads the Department of Electrical and Computer Engineering and previously served as Department Chair (1999-2005) and Dean of the School of Engineering (2017-2023). His research focuses on antennas, high-power microwaves, wireless communications, and machine learning applications in electromagnetics. Education: PhD in Electrical Engineering (North Carolina State University, 1985), MS in Electrical Engineering (North Carolina State University, 1981), and BS in Physics and Mathematics (American University in Cairo, 1979). Research interests include cognitive radio antennas, reconfigurable systems, and electromagnetic shielding. He has published over 600 papers, 18 book chapters, and 9 books. Awards include IEEE Fellow and Distinguished Lecturer (2007-2010). He advises students in doctoral and master’s programs, with over 40 PhD and 75 MS graduates. His work at COSMIAC emphasizes aerospace research, including antenna array design and millimeter-wave technologies.

Stanislav Volkovمشاهده پروفایل

استاد

• Discrete Probability
• History-dependent Random Walks
• Time-inhomogeneous Markov Chains
• +۲ مورد دیگر

Stanislav Volkov is a Professor of Mathematical Statistics at Lund University, serving as Deputy Head of Division. He holds a Diploma and PhD in Mathematics from Moscow State University, and M.A. and M.Sc. in Economics from New Economic School and University of Wisconsin-Madison. Previously, he was a postdoctoral fellow at the Fields Institute (Canada) and EURANDOM (Netherlands), and held a permanent position at the University of Bristol (UK). His research focuses on discrete probability, history-dependent random walks, and percolation theory. Education: Diploma and Ph.D. in Mathematics, Moscow State University M.A. in Economics, New Economic School M.Sc. in Economics, University of Wisconsin-Madison Research interests include advanced stochastic processes, with emphasis on history-dependent systems and percolation. Recent work explores transience in random walks, limit distributions in social dynamics models, and generalizations of forest fire processes. His contributions span theoretical foundations and applied probability models. Professional activities include teaching courses on mathematical modeling, Markov processes, and probability theory. He advises doctoral students and actively contributes to the Probability group at Lund University’s Centre for Mathematical Sciences.

Can Liمشاهده پروفایل

استاد

• Physical Chemistry
• Environmental Catalysis
• Chiral Catalysis
• +۴ مورد دیگر

Can Li is a Full Professor at the Dalian Institute of Chemical Physics, affiliated with the Chinese Academy of Sciences (elected in 2003) and holds memberships in prestigious institutions such as the Academy of Sciences for Developing World (TWAS, 2005) and Academia Europaea (2008). His academic career spans decades, including leadership roles as Director of the State Key Laboratory of Catalysis (1998–2015), President of the International Association of Catalysis Societies (2008–2012), and Chairman of the Catalysis Society of China (2005–2012). Dr. Li’s research focuses on broad areas of catalysis and physical chemistry, including Environmental Catalysis , Chiral Catalysis , Photo(electro)catalysis , and Biocatalysis . His work also involves advanced analytical techniques like In-situ Characterization of Catalytic Reactions and Time-resolved Spectroscopy . While recent Google Scholar publications appear to focus on computer science topics like AI fairness and safety, his core research remains rooted in chemical sciences. His scholarly contributions have been recognized with multiple awards, including the National Catalysis Achievement Award of China (2014) , Natural Science Award of China (2011) , and the International Catalysis Award (2004) . He has held visiting professorships at institutions in Belgium, the USA, the UK, Japan, and France. 2014 National Catalysis Achievement Award of China 2011 Natural Science Award of China 2005 Outstanding Science and Technology Achievement Prize of the CAS 2005 HoLeungHoLee Prize 2004 International Catalysis Award 1998 National Award for Outstanding Young Scientists in China 1997 Hong Kong Qiu-Shi Outstanding Young Scientist Award

Xingjie Liمشاهده پروفایل

دانشیار

• Numerical Analysis
• Partial Differential Equations
• Mathematical Physics
• +۳ مورد دیگر

Xingjie (Helen) Li is an Associate Professor in the Mathematics & Statistics Department at the University of North Carolina at Charlotte. Her research focuses on numerical analysis, partial differential equations, and mathematical physics with an emphasis on nonlocal models, multiscale methods, and computational mechanics. She develops advanced numerical schemes for stochastic systems and explores applications in materials science, image processing, and epidemiological modeling. Her work bridges theoretical analysis and practical computation, addressing challenges in coarse-graining stochastic dynamics, atomistic-to-continuum coupling, and probabilistic collocation methods for heterogeneous materials. Recent studies include innovative approaches to nonlocal diffusion problems, peridynamic fracture modeling, and data-driven time-stepping algorithms for ergodic systems. She has also applied mathematical techniques to analyze real-world scenarios such as pandemic dynamics in Singapore and Japan. Key contributions include energy-preserving finite difference schemes, feature-oriented imaging compression frameworks, and inference-based adaptive methods. Her research portfolio reflects interdisciplinary collaboration spanning computational physics, applied mathematics, and data science.