جست‌وجوی استاد و پژوهشگر

Malte Wincklerمشاهده پروفایل

پژوهشگر ارشد

• Numerical Analysis
• Partial Differential Equations
• Maxwell's Equations
• +۶ مورد دیگر

Dr. Malte Winckler is a research-active academic in the Faculty of Mathematics at the University of Duisburg-Essen, Germany. His work centers on the numerical analysis of partial differential equations with applications in superconductivity and electromagnetic field modeling. His research focuses on numerical analysis of PDEs , particularly Maxwell's equations , variational inequalities , and shape optimization . He applies advanced mathematical techniques to model physical phenomena in type-II superconductors , including Bean's critical-state model and H(curl)-elliptic systems. His methodological expertise lies in finite element methods, adaptive approximation schemes, and optimal control. The recent publications show a consistent focus on developing and analyzing numerical schemes for non-smooth and nonlinear PDEs arising in superconductivity. Key themes include adaptive edge elements , fully discrete approximations , and shape optimization under physical constraints . These works appear in top-tier applied mathematics journals such as SIAM Journal on Numerical Analysis and Computational Optimization and Applications . Dr. Winckler is involved in the DFG SPP 1962 Priority Programme as a contributor to project 22 on non-smooth distributed parameter systems. He teaches courses such as Introduction to Numerical Methods and collaborates closely with Prof. Dr. Irwin Yousept. He has no listed scientific awards in the provided text. He maintains active research profiles on Google Scholar , ResearchGate , ORCID , and GitLab , indicating engagement with the broader scientific community. He advises no students listed in the material and is not indicated as part-time, retired, or former staff.

Boo Chun Jungمشاهده پروفایل

استاد

• Labor Accounting
• ESG (Environmental, Social, and Governance)
• CSR (Corporate Social Responsibility) Disclosure
• +۳ مورد دیگر

Boo Chun Jung is a Professor of Accounting at the Shidler College of Business, University of Hawaii at Manoa , holding the Shidler Distinguished Professorship since 2018. His research bridges Labor Economics and Accounting , with a focus on ESG and CSR disclosure, stock analyst behavior, inventory management, and international accounting standards. PhD, Accounting, University of Colorado at Boulder (2007) MBA & BBA, Seoul National University (2001 & 1999) His recent work explores how IFRS adoption affects inventory efficiency, and how labor unions influence earnings smoothing and post-acquisition integration. Publications in Contemporary Accounting Research and The Accounting Review highlight his contributions. He has received the Shirley Lee Faculty Research Award (2015) and was honored with the Lazaridis Institute Prize (2019) for co-authored work on investor relations. His teaching includes ACC 210, 648, and 660, covering accounting analysis and decision-making.

Rob Broekmeulenمشاهده پروفایل

استادیار

• Operations Management
• Retail Supply Chains
• Perishable Inventory Control
• +۴ مورد دیگر

Rob Broekmeulen is an Assistant Professor at the Department of Industrial Engineering and Innovation Sciences at Eindhoven University of Technology (TU/e). He also teaches Demand & Supply Chain Management at Erasmus University Rotterdam, though no explicit part-time designation is mentioned. His research focuses on operations management in retail supply chains with a specialization in perishable goods. Retail supply chain optimization Perishable inventory control Warehouse management systems His work addresses challenges in balancing cost-effectiveness with sustainability in retail environments. Key contributions include the Estimated Withdrawal & Aging (EWA) policy for perishables and analyses of excess shelf space utilization to smooth supply chain workloads. His research integrates microbiology knowledge to address food quality issues in logistics design. Scientific publications demonstrate expertise in inventory modeling, including stochastic demand systems , batch ordering , and lost sales approximation methods. Articles show consistent focus on reducing food waste through improved inventory policies while maintaining service levels.

• Stochastic Processes
• Probability Theory
• Mathematical Biology
• +۳ مورد دیگر

Ilie Grigorescu is an Associate Professor at the University of Miami , affiliated with the College of Arts and Sciences and the Department of Mathematics . He also collaborates with the Computer Science division within the same college. Research Interests: Stochastic Processes Probability Theory Mathematical Biology Evolutionary Modeling Interacting Particle Systems Applied Mathematics Recent Publications focus on branching diffusions, evolutionary fixation times, stochastic game theory, and neuronal phase transitions. His work connects probabilistic models to biological and network systems, including studies on hydrodynamic limits and risk-averse optimal stopping. Contact: Email at i.grigorescu@miami.edu or call (305) 284-2146.

Mao Xiaojieمشاهده پروفایل

دانشیار

• Causal inference
• Data-driven optimization decision-making
• Machine Learning
• +۳ مورد دیگر

Mao Xiaojie is an Associate Professor at the Department of Management Science and Engineering, Tsinghua University's School of Economics and Management . Holding a PhD in Statistics and Data Science from Cornell University (2021) and a bachelor's in Mathematical Economics and Finance from Wuhan University (2016), Mao specializes in causal inference and data-driven optimization decision-making . PhD: Cornell University (2016-2021) Bachelor: Wuhan University (2012-2016) Mao's research bridges machine learning , statistics , and operations research to address challenges in contextual optimization, algorithmic fairness, and robust causal inference. Recent work focuses on data combination , surrogate variables , and minimax methods for handling unobserved confounding and limited outcome data. Key trends in Mao's publications include: Advancing bandit algorithms for efficient contextual decision-making Developing debiased machine learning frameworks for quantile treatment effects Designing robust optimization models under noisy and incomplete covariates Scientific recognition includes: Applied Probability Society Best Student Paper Competition Finalist (2020) Multiple teaching excellence awards at Tsinghua University (2022-2024) Research grants from the National Natural Science Foundation of China Mao currently teaches Empirical Methods in Management Science (PhD), Data Analysis: Inference and Decision Making (Master), and Probability Theory and Mathematical Statistics (Undergraduate). Research collaborations span institutions like Cornell and MIT, with publications in top venues including NeurIPS , ICML , and Operations Research .

• Differential Geometry
• Lorentz Manifolds
• Isometric Group Actions
• +۳ مورد دیگر

Eötvös Loránd University's Faculty of Science researcher Dávid Szeghy has been affiliated with the Department of Geometry since 2006. His work focuses on differential geometry, mathematical physics, and geometric analysis of Lorentz manifolds. PhD in Mathematics (2008, ELTE) Publications span 2003–2023 with emphasis on horizon differentiability, isometric group actions, and pseudo-Riemannian conjugate loci Key collaborations include J. Szenthe and A. Fothi Research trends show deep engagement with Lorentzian geometry , including studies on: Orbit type theorems for isometric actions Normalizable vs. non-normalizable orbits Horizon smoothness in general relativity Exponential mapping properties in spacetime His work appears in journals like Annales Henri Poincaré , Classical and Quantum Gravity , and Geometriae Dedicata , with citations across mathematics and physics domains.

Henryk Zähleمشاهده پروفایل

استاد

• Stochastics
• Quantitative Risk Management
• Statistical Robustness
• +۴ مورد دیگر

Henryk Zähle is a Full Professor of Stochastics at Saarland University's Department of Mathematics, where he has held a W3 position since 2014. He previously served as a W2 Professor (2013-2014) and W1 Junior Professor (2010-2012) at Saarland, and earlier at TU Dortmund University (2007-2010). He earned his Ph.D. in Mathematics from Technical University Berlin (2004) and a Diploma in Mathematics from University of Göttingen (2000). His research focuses on statistical robustness of risk measures asymptotic theory for empirical processes quantitative risk management Markov decision models insurance and financial mathematics with methodological contributions to bootstrapping, quasi-Hadamard differentiability, and sensitivity analysis. Article trends show sustained engagement with stochastic process theory nonparametric estimation robust statistical functionals applications to insurance and finance asymptotic error distributions time series analysis spanning both theoretical and applied domains. Scientific awards include Marie Curie Fellowship (University of Warwick, 2001) DFG Fellowship (2000-2003) He has supervised numerous Ph.D., Master's, and Bachelor's theses on topics like risk measure asymptotics empirical process convergence copula robustness Markov decision sensitivity nonparametric risk estimation statistical bootstrap methods and serves as Associate Editor for Metrika .

Joren Gijsbrechtsمشاهده پروفایل

استادیار مدعو

• Operations Management
• Supply Chain Management
• Machine Learning
• +۳ مورد دیگر

Joren Gijsbrechts serves as an Adjunct Assistant Professor in the Department of Operations and Data Science at Católica Lisbon School of Business and Economics, Catholic University of Portugal. His research focuses on data-driven decision making in Operations Management through Machine Learning and Prescriptive Analytics to optimize inventory and transportation systems. Academic background includes: Bachelor in Business Engineering, University of Antwerp, Belgium Master in Business Engineering, University of Antwerp, Belgium PhD, KU Leuven, Belgium His research spans Operations Management, Supply Chain Analytics, and Machine Learning applications for inventory control and transportation logistics. Collaborations include Kellogg School of Management, Olin Business School, Kuehne Logistics University, and Exeter University, with practical implementations for corporate inventory optimization. Recent publications reveal a concentrated trend in deep reinforcement learning for complex inventory challenges (dual sourcing, lost sales, multi-echelon systems) and synchromodal transportation planning. His work bridges theoretical advancements with industrial applications in global supply chain reshoring and real-time logistics decision-making. Gijsbrechts delivers guest lectures and industry workshops on Supply Chain Analytics, translating academic research into business practice. While the provided text contains no details about grants or student advising, his active publication record and corporate engagements demonstrate strong industry-academia integration.

• Numerical Analysis
• Virtual Element Methods
• Partial Differential Equations
• +۳ مورد دیگر

Andrea Borio is an Associate Professor at the Department of Mathematical Sciences 'GL Lagrange' (DISMA) at Politecnico di Torino. He is affiliated with the College of Mathematical Engineering and College of Mechanical, Aerospace and Automotive Engineering . His research focuses on Numerical Analysis , Virtual Element Methods , Partial Differential Equations , and Mesh Adaptivity . Research Interests: A posteriori error estimates, mesh adaptivity for polygonal/polyhedral grids, stabilization-free numerical methods, and applications of virtual element methods in geomechanics, biomedical simulations, and coupled flow problems. Recent Publications highlight advancements in stabilization-free virtual element methods for elliptic equations, divergence-free projections, and software frameworks like POLYDIM and GEOS for large-scale simulations. His work spans applications in underground fluid storage, brain tumor modeling, and discrete fracture networks. Teaching: Courses include Sobolev Spaces on Non-Smooth Domains , Solving PDEs on Polygonal Meshes , and modules on numerical models/methods across Mathematical, Mechanical, and Aerospace Engineering programs. Students: Supervises Davide Fassino, a PhD student in Pure and Applied Mathematics (38th cycle, 2022–ongoing). Skills: ERC sectors include numerical analysis, scientific computing, and mathematical applications in industry.

Arthur G Richardsمشاهده پروفایل

استاد

• Trajectory Optimization
• Unmanned Aerial Vehicles (UAVs)
• Model Predictive Control
• +۴ مورد دیگر

Arthur G Richards serves as Professor of Robotics and Control within the Dynamics and Control department at the University of Bristol's School of Engineering Mathematics and Technology. His research specializes in trajectory optimization for aerospace applications, focusing on UAV autonomy, spacecraft rendezvous, and air traffic management through advanced optimization techniques. His educational foundation includes an M.Eng. from the University of Cambridge and S.M./Ph.D. degrees from MIT. Research interests center on solving complex aerospace challenges through: Non-convex optimization for obstacle avoidance in cluttered environments Robust model predictive control for real-time disturbance compensation Distributed optimization enabling large-scale vehicle cooperation Scalable algorithms for high-traffic scenarios with minimal fuel consumption Analysis of his 132 research outputs reveals evolving emphasis on reliability-aware UAV path planning, interpretable reinforcement learning for aircraft control, and swarm robotics with real-world validation. Recent work increasingly integrates machine learning with traditional control theory while addressing practical constraints like sensor noise and system failures. Professor Richards has supervised 22 research students and secured funding for 11 projects, including the active Aerial Robotics for Search and Rescue (2022-2026) and PORTAL (2022-2024) initiatives. His industry collaborations with Thales and focus on technology transfer demonstrate strong academic-industrial integration. He actively contributes to the Smart Networks for Sustainable Futures and Robotics research groups, developing frameworks for multi-robot systems that balance theoretical rigor with practical deployment requirements in conservation, inspection, and exploration scenarios.

• Changepoint Detection
• High-dimensional Time Series
• Statistical Inference
• +۳ مورد دیگر

Haeran Cho is Professor of Statistical Science in the School of Mathematics at the University of Bristol, holding a BSc and PhD in Statistics. Her research focuses on developing foundational methodologies for detecting structural changes in complex high-dimensional data streams, with applications spanning finance, environmental monitoring, and biomedical engineering. Her primary research interests include changepoint detection in non-sparse regression frameworks, factor model diagnostics for tensor time series, and robust nonparametric segmentation techniques. She pioneers approaches that handle heavy-tailed distributions, temporal dependence, and high-dimensional scaling—addressing critical limitations in classical change point theory through adaptive covariance scanning and multiscale inference frameworks. Professor Cho received the Research Prize in 2013 for her contributions to statistical theory. She currently leads the £1.2M EPSRC-funded project Statistical Foundations for Detecting Anomalous Structure in Stream Settings (DASS, 2024-2029), developing real-time anomaly detection systems for industrial applications. Previous projects include quantile factor modeling for high-dimensional time series (2019). Her software implementations ( CptNonPar , mosum , fnets ) have become standard tools in statistical computing, with over 500 citations. She actively collaborates through Horizon Europe initiatives and supervises postgraduate researchers in statistical methodology development.

Bin Gaoمشاهده پروفایل

دانشیار

• Riemannian Optimization
• Tensor Computation
• Parallel/Distributed Computing
• +۵ مورد دیگر

Bin Gao is an Associate Professor at the Academy of Mathematics and Systems Science (AMSS), Chinese Academy of Sciences. He holds a Ph.D. in Applied Mathematics (2019, University of Chinese Academy of Sciences) and a B.Sc. in Mathematics (2014, Sichuan University). His postdoctoral experience includes positions at UCLouvain (2019-2021) and the University of Münster (2021-2022). Research Interests: Riemannian optimization, tensor computation, parallel/distributed algorithms for orthogonality constraints, machine learning applications. Key Contributions: Development of retraction-free methods on Stiefel manifolds, preconditioned Riemannian algorithms, and geometric frameworks for symplectic eigenvalue problems. Article Trends: Recent work focuses on overcoming the curse of dimensionality via manifold-based optimization, including distributed algorithms for Stiefel manifolds, graph-regularized tensor completion, and second-order methods for symplectic structures. Keywords span numerical analysis, quantum information, and machine learning. Scientific Awards: 2021 Zhong Jiaqing Mathematics Award 2018 Best Student Paper Award (CSIAM) 2018 CAS Special President Scholarship 2017 National Scholarship for Doctoral Students (China) 2016 Honor Student Award (International Workshop on Modern Optimization and Application) Advising & Collaborations: Collaborates with researchers from UCLouvain, University of Münster, and AMSS. Mentors students in Riemannian optimization and tensor computation. Leads the popman research group.

Manolis Zampetakisمشاهده پروفایل

استادیار

• Theoretical Machine Learning
• Statistics
• Optimization
• +۳ مورد دیگر

Manolis Zampetakis is an Assistant Professor of Computer Science at Yale University. Previously, he was a postdoc at UC Berkeley's EECS Department working with Michael Jordan, and earned his PhD from MIT's EECS Department under Constantinos Daskalakis. His research spans Theoretical Machine Learning, Statistics, Optimization, Computational Complexity, Game Theory, and Mechanism Design. He has received the ACM SIGEcom Doctoral Dissertation Award and a Google PhD Fellowship. Current affiliation: Yale University (Assistant Professor) Prior affiliations: UC Berkeley (Postdoc), MIT (PhD student), NTUA (Undergraduate) His research focuses on algorithmic game theory, robust statistics, and optimization challenges in machine learning. He explores computational complexity in multi-player games, truncated linear regression, and strategy-proof mechanisms. Recent work includes backdoor attacks in neural networks and jailbreaking black-box LLMs, with publications in top venues like NeurIPS, COLT, FOCS, and STOC. Notable scientific contributions have been recognized through awards and special issues. He co-organized workshops at FOCS 2018, WALE 2019, and WALE 2022. His students include Anay Mehrotra, Jane Lee, Katerina Mamali, Shuchen Li, and Nikolaos Koumpis, often co-advised with prominent researchers like Amin Karbasi and Tuomas Sandholm.

Jennyمشاهده پروفایل

استاد

• Turbulent Combustion Modeling
• Computational Fluid Dynamics
• Rarefied Gas Kinetics
• +۴ مورد دیگر

Professor Jenny leads the Jenny Research Group at ETH Zürich, specializing in turbulent reactive flows, rarefied gas kinetics, and biomedical fluid dynamics. Her work bridges fundamental research with industrial applications in energy systems and fluid mechanics. Develops advanced turbulence models (TDDM, hybrid LES/RANS) for multi-scale flows Pioneers data assimilation frameworks for RANS simulations using adjoint methods Advances particle-based stochastic algorithms for fractured porous media transport Her recent publications emphasize adaptive time integration techniques, probabilistic modeling of non-linear transport phenomena, and optimized simulation tools for hydrogen storage systems. The group's methodological innovations focus on reducing computational costs while maintaining physical accuracy through novel regularization strategies. Key applications include combustion device optimization, high-pressure tank filling analysis, and fractured reservoir simulations. Current projects integrate machine learning with traditional CFD methods to address challenges in droplet clustering, flame surface density propagation, and supersonic spray dynamics. The research framework spans from direct numerical simulations of fundamental flow physics to industrial-scale hybrid modeling implementations.

Stefano Vigognaمشاهده پروفایل

دانشیار

• Machine Learning
• Statistical Learning Theory
• Harmonic Analysis

Stefano Vigogna is an Associate Professor in the Department of Mathematics at the University of Rome Tor Vergata with significant contributions to theoretical machine learning. He is affiliated with the Rome Center on Mathematics for Modeling and Data Sciences (RoMaDS), focusing on the mathematical foundations of learning algorithms. His research expertise spans: Machine Learning Statistical Learning Theory Harmonic Analysis Professor Vigogna's publication record demonstrates deep theoretical work connecting advanced mathematics to machine learning. His research investigates the spectral properties, geometric structure, and convergence behavior of neural networks using functional analysis and harmonic analysis techniques. Notable publications include his 2022 ICML paper on multiclass learning with exponential convergence rates and numerous works exploring the mathematical properties of deep learning systems through reproducing kernel spaces. He teaches Statistica for the Master's program in Environmental Biology and Statistical Learning for the Master's program in Pure and Applied Mathematics, reflecting his dual expertise in mathematical theory and practical data science applications. Professor Vigogna maintains active collaborations with leading researchers including Lorenzo Rosasco and Ernesto De Vito, advancing our fundamental understanding of learning algorithms through rigorous mathematical analysis. His work represents an essential bridge between pure mathematics and the theoretical foundations of modern artificial intelligence.