جست‌وجوی استاد و پژوهشگر

• International Finance
• Corporate Governance
• Institutional Economics
• +۷ مورد دیگر

John Goodell is a Professor in the Department of Finance at the College of Business, The University of Akron. He holds a PhD from Kent State University and is a leading scholar in international finance, institutional influences, ESG, and fintech. He serves as Editor-in-Chief of Research in International Business and Finance and Associate Editor for multiple top journals. Education: PhD, Kent State University MCIS, Cleveland State University MBA, Miami University His research focuses on the interplay between national culture, institutions, and financial systems. He explores topics such as ESG, cryptocurrency markets, energy finance, corporate governance, and behavioral finance. His work frequently examines how macro-level factors like culture, policy, and climate risk influence financial decision-making and market outcomes. His recent publications reveal a strong trend toward interdisciplinary research, combining finance with environmental science, political economy, and digital innovation. He frequently employs advanced econometric methods, including spillover analysis, quantile regression, and bibliometric modeling, to study market connectedness, risk transmission, and sustainability. Scientific Awards: Stockholm School of Economics/Women in the Academy of International Business Award for Increased Gender Awareness in International Business Research (2011) Dr. Goodell has secured research grants, including from the National Natural Science Foundation of China, and has advised on projects involving financial big data and agent-based modeling. He has been a frequent speaker at international conferences and his research has been featured in Washington Post , PBS NewsHour , and Bloomberg Businessweek . He has served on editorial boards and professional associations such as the Academy of International Business and the Southwestern Finance Association. He teaches courses in International Finance, Investments, and Managerial Finance, and has been consistently active in professional service, including editorial leadership and conference presentations.

• Hamiltonian Systems
• Dynamical Systems
• Bifurcation Theory
• +۳ مورد دیگر

Henk Broer is a Professor at the Bernoulli Institute within the Faculty of Science and Engineering at the University of Groningen. His work spans key areas in dynamical systems, Hamiltonian mechanics, and bifurcation theory, with contributions to KAM theory, integrable systems, and singularity theory. Fields of Interest: Hamiltonian systems, bifurcation theory, KAM theory, integrable systems, singularity theory, dynamical systems. Broer’s research focuses on nonlinear dynamics, parametrized KAM theory, and the interplay between phase space structures and parameter spaces. His recent work explores double Hopf bifurcations and monodromy in integrable systems. Recent publications highlight his expertise in Hamiltonian systems, bifurcation analysis, and quasi-periodic invariant tori. Broer has supervised 22 research projects, though specific student names are not listed. He contributes to addressing UN Sustainable Development Goals through mathematical modeling and theoretical advancements.

Tamas Koltaiمشاهده پروفایل

استاد

• Operations Management
• Industrial Engineering
• Production Planning
• +۱۲ مورد دیگر

Tamas Koltai is a Professor at the Budapest University of Technology and Economics, affiliated with the Department of Management and Corporate Economics within the Faculty of Economic and Social Sciences. He holds a PhD in Industrial Engineering and is actively contributing to research in operations management, production planning, and industrial engineering. His research interests include: Operations Management Industrial Engineering Production Planning Assembly Line Balancing Human-Robot Collaboration Data Envelopment Analysis (DEA) Supply Chain Management Learning Curves Flexible Manufacturing Systems Performance Evaluation His recent publications focus on applying mathematical programming models (MILP, CP), simulation, and DEA to optimize assembly lines, particularly under learning effects and human-robot collaboration. He explores workload distribution, cycle time optimization, and efficiency evaluation in both manufacturing and service sectors, including healthcare and business simulation games. His work bridges theoretical models with practical industrial applications, supporting managerial decision-making under uncertainty. While no specific scientific awards are listed, his extensive publication record (over 50 papers) and high citation count reflect significant academic impact. He frequently collaborates with researchers such as Imre Dimény, Noémi Kalló, Viola Gallina, and Rita Dénes. There is no public information on PhD students advised or grants received. His work does not mention specific labs or research teams, but his focus on applied operations research suggests strong industry collaboration potential.

• Applied Mathematics
• Computational Methods
• Control Theory
• +۵ مورد دیگر

Sara Grundel is a leading researcher at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany. Her work focuses on computational methods in systems and control theory, particularly in model order reduction, gas network simulation, and optimization of energy systems. Education: Diplom in Mathematics, ETH Zurich (2005) PhD in Mathematics, Courant Institute of Mathematical Sciences, New York University (2011) Research Interests: Sara’s research encompasses mathematical control theory, stability analysis, and numerical methods for differential-algebraic equations. She applies these techniques to gas and energy networks, epidemic modeling, and multi-agent systems. Her interdisciplinary work bridges computational mathematics with real-world engineering and public health challenges. Recent Publications: Her 15 most recent articles (2024–2012) demonstrate expertise in parametrized PDEs, model reduction for coupled systems, and control strategies for SARS-CoV-2 containment. Key subtopics include adaptive meshing, stability-preserving algorithms, and optimization of nonlinear network dynamics. Scientific Contributions: Developed clustering-based model reduction techniques for networked systems Investigated hyperbolic discretization methods using Riemann invariants Advanced polynomial root radius optimization with affine constraints Collaborations: Sara frequently collaborates with researchers like Peter Benner and Martin Gersen on energy grid simulations and control theory. She participates in international conferences (GAMM, IEEE CDC, MTNS) and contributes to edited volumes in applied mathematics.

Linda Olafsenمشاهده پروفایل

دانشیار

• Photonics
• Semiconductor Optoelectronics
• Mid-Infrared Lasers
• +۲۱ مورد دیگر

Linda Olafsen is an Associate Professor in the Department of Electrical & Computer Engineering at Baylor University’s School of Engineering and Computer Science. She holds a PhD in Physics from Duke University and has held academic positions at the University of Kansas and Baylor University since 1999. Her research spans photonics, semiconductor optoelectronics, and biomedical device development. PhD, Physics, Duke University (1997) MA, Physics, Duke University (1994) AB, Physics, Princeton University (1991) Her research focuses on mid-infrared semiconductor lasers, optoelectronic devices, graphene-semiconductor integration, and biomedical applications such as endovascular navigation and glucose monitoring. She leads the Semiconductor Optoelectronics Laboratory, where her team develops advanced mid-IR lasers, characterizes 2D/3D material interfaces, and engineers sensors for medical use. Her work bridges fundamental physics with practical engineering solutions in healthcare and defense. The recent publications show a strong trend toward mid-infrared optoelectronics , particularly interband cascade lasers , graphene-based contacts , and biomedical sensing . Her team integrates optical, thermal, and electrical characterization to improve laser efficiency and device performance. Applications span infrared countermeasures, chemical sensing, and endovascular robotics. Scientific Awards and Professional Leadership: Chair, Congressional Visit Day Subcommittee, MRS (2012–2016) Chair, Policy Outreach Working Group, MRS Government Affairs Committee Book Review Board Chair, MRS Bulletin Editorial Board Member, MRS Bulletin Lifetime Member, American Physical Society, OSA, and SPIE Senior Member, IEEE and IEEE Photonics Society Linda Olafsen actively mentors undergraduate and graduate students, involving them in research on laser development, material characterization, and biomedical device design. She has secured funding for projects in mid-IR lasers and sensing technologies. Her lab offers opportunities in device fabrication, graphene integration, beam profiling, and LabVIEW programming. She leads the Semiconductor Optoelectronics Laboratory , which focuses on: Mid-infrared semiconductor laser development Graphene-semiconductor optoelectronic devices Endovascular navigation using shape-memory alloys In vivo infrared and ultrasound sensing Beam and thermal profiling Device fabrication and characterization

Artur Dąbrowskiمشاهده پروفایل

استاد

• Nonlinear Dynamics
• Chaos Theory
• Mechanical Engineering
• +۳ مورد دیگر

Dr. hab. Artur Dąbrowski is a Professor at the Department of Machine Dynamics - Division of Dynamics, Lodz University of Technology, Poland. His research focuses on Nonlinear Dynamics , Chaos Theory , and Mechanical Engineering , with a particular emphasis on Lyapunov exponent estimation, synchronization in chaotic systems, and control optimization. Key research areas include: Nonlinear Dynamics and Chaos Quantification Control Theory and Optimization Mechanical Systems and Oscillators Lyapunov Exponent Algorithms Surface Micro-Geometry in Bearings Vehicle Crashworthiness and Energy Flow His publications reveal a consistent focus on analyzing complex systems with reduced data, developing novel methods for chaos detection, and applying these insights to mechanical engineering challenges.

Ranit Deمشاهده پروفایل

پژوهشگر

• Global carbon cycle
• Eddy covariance
• Model-Data assimilation
• +۶ مورد دیگر

Ranit De is a Doctoral Researcher in the Department of Biogeochemical Integration at the Max Planck Institute for Biogeochemistry in Jena, Germany. De is affiliated with the International Max Planck Research School for Global Biogeochemical Cycles (IMPRS-gBGC) and works in the Model-Data Integration research group under Dr. Nuno Carvalhais. De's educational background includes: Ph.D. candidate (2021-present) at the International Max Planck Research School for Global Biogeochemical Cycles M.Sc. in Geo-information Science and Earth Observation (Water Resources and Environmental Management) from the University of Twente, Netherlands (2019-2021) B.Tech. in Agricultural Engineering from Sam Higginbottom University of Agriculture, Technology and Sciences, India (2015-2019) De's research focuses on understanding the global carbon cycle through advanced hybrid modeling techniques that combine physically based models with machine learning approaches. De specializes in simulating gross primary production at sub-daily scales using eddy-covariance data and light use efficiency-based models, with particular expertise in understanding interannual variability in terrestrial ecosystem fluxes. Analysis of De's recent publications reveals a strong research trajectory examining how temporal variation of hydrological parameters can improve model performance, investigating parametric uncertainties in land surface models, and exploring the relationship between spatiotemporal variability of model parameters and their controlling factors. De's work bridges the gap between traditional physical modeling and modern data-driven approaches. De has presented research at notable conferences including the EGU Mary Anning conference in June 2025 and the ICOS Science Conference 2024, demonstrating active engagement with the global scientific community. De's methodological innovations in model-data integration contribute significantly to improving biogeochemical models for better climate predictions and understanding carbon cycle dynamics under changing environmental conditions.

Dan Casasمشاهده پروفایل

دانشیار

• Computer Graphics
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Dan Casas is a Senior Applied Scientist at Amazon in Seattle and an Associate Professor (Profesor Titular) on leave from King Juan Carlos University in Spain. His research spans the intersection of Computer Graphics, Computer Vision, and Machine Learning with a focus on 3D reconstruction, modeling, and animation of virtual humans and clothing. He has authored over 40 high-impact publications in top venues including SIGGRAPH, CVPR, and NeurIPS, and holds 3 international patents. Dr. Casas received his M.Sc. degree (2009) from Universitat Autònoma de Barcelona (Spain), including a research visit at Carnegie Mellon University. He earned his Ph.D. in Computer Graphics (2014) from the University of Surrey (UK), supervised by Prof. Adrian Hilton. He completed postdoctoral research at the University of Southern California's Institute for Creative Technology (2014-2015) and the Max Planck Institute in Saarbrücken (2015-2016). His research interests center on creating realistic virtual humans and digital clothing through advanced techniques in computer vision and machine learning. Casas has pioneered methods for 3D reconstruction of humans and garments from video input, physics-based simulation of soft-tissue deformations, and data-driven approaches to character animation. His work bridges the gap between theoretical computer graphics and practical applications in virtual reality, digital fashion, and immersive communication. Analysis of his recent publications reveals a consistent focus on human digitization, with increasing emphasis on machine learning approaches. His work has evolved from traditional computer graphics techniques toward neural representations and diffusion models, particularly in the areas of 3D garment simulation and human avatar creation. The trend shows growing integration of physics-based modeling with data-driven approaches to achieve both realism and computational efficiency. Marie Skłodowska-Curie Individual Fellowship (2015) FBBVA Leonardo Fellowship (2021) Medal from the Royal Academy of Engineering of Spain for Young Researcher Award (2023) i3 certification (outstanding researcher) from Spanish Ministry of Universities (2022) Winner of 2021 IEEE Retail Digital Transformation Grand Challenge Multiple Outstanding Reviewer Awards at top conferences (CVPR, BMVC, 3DV) Dan Casas has successfully advised multiple PhD students including Suzanne Sorli, Cristian Romero, Raquel Vidaurre, and Igor Santesteban (now at Meta Reality Labs), with several ongoing students including Melania Prieto-Martin, Gonzalo Gómez-Nogales, and Andrés Casado-Elvira. He has secured significant research funding as Principal Investigator, totaling over €1.2 million from Spanish Ministry of Science projects, EU H2020 programs, and industry fellowships including the FBBVA Leonardo Fellowship. His leadership extends to conference organization as Area Chair for ICCV 2023 and General Chair for ACM i3D 2020. Dr. Casas leads research in digital human modeling with applications in virtual reality, fashion technology, and immersive communication. His team develops advanced techniques for creating personalized 3D avatars from minimal input (like smartphone videos), addressing challenges in geometry, appearance, and physical simulation of virtual humans and their clothing.

Bersimis Sotiriosمشاهده پروفایل

دانشیار

• Statistical Process Control
• Public Health Surveillance
• Health Analytics
• +۵ مورد دیگر

Sotirios Bersimis is an Associate Professor at the University of Piraeus, Department of Business Administration. He holds additional roles as an elected member of the board of directors of the National Statistical Institute (Greece) and representative for FenSTATs and ECAS. Previously, he served as President of the Hellenic Organization for Health Care Services (EOPYY) and as President of the European Healthcare Fraud & Corruption Network (EHFCN). His education includes a PhD in Statistics from the University of Piraeus, an MSc in Statistics from Athens University of Economics and Business, and a BSc in Statistics and Insurance Science from the University of Piraeus. His research focuses on stochastic models for process monitoring, statistical process control, and health analytics. He has published over 60 peer-reviewed articles in journals like Journal of Quality Technology , Statistics in Medicine , and Annals of the Institute of Statistical Mathematics . His work emphasizes applications in healthcare surveillance, quality management, and fraud detection. Notable contributions include the development of the Process Monitoring Group and the 'Multivariate Statistical Process Control Charts: An Overview' paper, which remains highly cited in the field. Bersimis has received awards such as the 2018 ENBIS Best Manager Award and a public honor from the Greek Prime Minister. He actively collaborates with healthcare institutions, pharmaceutical companies, and international organizations, contributing to projects on health expenditure modeling and anti-fraud initiatives. His teaching spans undergraduate and postgraduate programs in statistics, biostatistics, and data science.

Christian Schlichtkrullمشاهده پروفایل

استاد

• Algebraic Topology
• Homotopy Theory
• Topological Hochschild Homology
• +۳ مورد دیگر

Christian Schlichtkrull is a Professor at the Department of Mathematics, University of Bergen, Norway. His contact information includes email (christian.schlichtkrull@uib.no), phone (+47 55582828), and addresses: Visitor address at Allégaten 41, 5007 Bergen, and Postal address at P.O. Box 7803, NO-5020 Bergen. His primary research focuses on advanced topics in algebraic topology, with emphasis on: Homotopy theory including stable homotopy and parametrized homotopy Topological Hochschild homology (THH) and its logarithmic variants Symmetric spectra and diagram spaces Algebraic K-theory connections via cyclotomic trace Generalized Thom spectra and virtual vector bundles Schlichtkrull's publications (2005-2020) demonstrate sustained exploration of spectra, homology theories, and K-theory connections. Recent work emphasizes multiplicative structures in parametrized homotopy and logarithmic THH, frequently collaborating with researchers from institutions like Radboud University and University of Bonn. His research has been supported by the Research Council of Norway (Grant 213458). No advising relationships, laboratory affiliations, or scientific awards are documented in the provided materials.

Daniel Axehillمشاهده پروفایل

استاد

• Autonomous Systems
• Optimization-Based Control
• Sensor Fusion
• +۴ مورد دیگر

Professor Daniel Axehill is affiliated with the Department of Electrical Engineering (ISY) at Linköping University, specializing in planning and optimization-based control for autonomous systems. His research bridges theoretical developments and industrial applications. His recent work focuses on robust motion planning for autonomous vehicles, optimal task and motion planning algorithms, execution-time analysis for model predictive control, and high-performance solvers for multi-parametric quadratic programming. Key methods include lattice-based planning, disturbance estimation, and real-time optimization. He contributes to the Wallenberg Autonomous Systems Program (WASP), collaborating on advancements in robotics, sensor fusion, and complex network control systems.

• Applied Probability
• Insurance Mathematics
• Finance
• +۳ مورد دیگر

Mogens Bladt is a Professor of Applied Probability and Insurance Mathematics at the Department of Mathematical Sciences, University of Copenhagen. He has held positions since 2018 after 24 years as Principal Researcher at the National University of Mexico (1994-2018), with visiting professorships at Technical University of Denmark and University of Copenhagen since 2001. Research: Focuses on time-inhomogeneous phase-type distributions, matrix-oriented life insurance models, heavy-tailed distributions, and diffusion bridge simulation Teaching: Offers graduate/undergraduate courses in Applied Probability, Stochastic Processes, Risk Theory, and Numerical Analysis Scientific Contributions: Developed R packages for Markov jump processes, phase-type distributions, and diffusion bridges. Holds grants from Mexico and Denmark, including Danish Research Council funding (2007–2008). Supervised 5 PhD, 8 Master’s, and 13 Bachelor’s theses Organized academic workshops and served as Associate Editor for Stochastic Models since 1997

Joshua Speagleمشاهده پروفایل

استادیار

• Astrostatistics
• Data Science
• Machine Learning
• +۷ مورد دیگر

Joshua Speagle is an Assistant Professor jointly appointed in the Department of Statistical Sciences and the David A. Dunlap Department of Astronomy & Astrophysics at the University of Toronto. He is also an Associate Member of the Dunlap Institute for Astronomy & Astrophysics and a Member of the Data Sciences Institute. His research lies at the intersection of statistics, astronomy, and computer science, focusing on astrostatistics and data-intensive astrophysics. His research interests include astrostatistics, data science, machine learning, statistical inference, and Bayesian methods. He develops novel statistical learning techniques to extract insights from large, complex datasets, particularly from astronomical surveys. His work emphasizes interpretability, robust inference, and computational efficiency, with applications to galaxy formation, stellar photometry, and 3D dust mapping. The trends in his recent publications reflect a strong focus on interdisciplinary methodologies, particularly in Bayesian inference, nested sampling, and machine learning applied to astrophysical problems. His work consistently bridges theoretical statistics with practical applications in astronomy, emphasizing open-source software and reproducible research. Banting Postdoctoral Fellowship Dunlap Fellowship Joshua Speagle is deeply committed to mentorship and collaboration. He co-leads the Astrostatistics Research Team (ART) with Gwen Eadie, mentoring students and postdocs across disciplines. He is involved in graduate and undergraduate research programs, including the Astronomy & Astrophysics Summer Undergraduate Research Program (SURP). He teaches courses in statistics and astronomy and serves on committees within the University of Toronto and professional societies such as the AAS, ASA-AIG, and SSC-DSA. He co-leads the interdisciplinary Astrostatistics Research Team (ART), which fosters a collaborative, inclusive environment focused on cutting-edge research at the intersection of statistics and AI. The team emphasizes open and accessible science, releasing open-source tools like dynesty and brutus , and mentoring the next generation of data scientists.

MARIA ALONSO PENAمشاهده پروفایل

استادیار

• Nonparametric Statistics
• Circular Regression
• Statistical Modeling
• +۳ مورد دیگر

María Alonso-Peña is an Assistant Professor at the University of Santiago de Compostela , affiliated with the Faculty of Biology and the Department of Statistics, Mathematical Analysis, and Optimization . Her research focuses on nonparametric statistical methods, particularly in circular regression models and their applications in neuroscience and animal behavior analysis. She holds a PhD in Statistics from the University of Santiago de Compostela (2022), with a thesis titled New approaches to nonparametric circular regression models . Education: PhD in Statistics (2022), University of Santiago de Compostela Research Interests: Nonparametric statistics, circular regression, optimization, and statistical modeling in neuroscience and ecology Her work bridges theoretical statistics with applied problems, such as analyzing neuronal spike counts and animal escape behavior using advanced circular regression techniques. She has collaborated with institutions like KU Leuven and Universidad de Granada. Recent research includes a grant from the Xunta de Galicia (ED481A-2019/139) for neuroscientific applications. Key contributions include frameworks for circular local likelihood regression and parametrically guided kernel density estimators for spherical data. These methods address challenges in modeling directional and multimodal datasets, with implications for fields like neuroscience and environmental science.

Bernard Haasdonkمشاهده پروفایل

استاد

• Model Reduction
• Parametrized Partial Differential Equations (PDEs)
• Kernel Methods
• +۷ مورد دیگر

Bernard Haasdonk is a Professor at the University of Stuttgart, affiliated with the Institute of Applied Analysis and Numerical Simulation (IANS), part of the Faculty of Mathematics and Computer Science. His research focuses on model reduction techniques for parametrized partial differential equations (PDEs), kernel-based methods, numerical analysis, and machine learning applications in scientific computing. He leads a research group in numerical mathematics and has contributed to software tools like RBMatlab and KerMor. Affiliations: Institute of Applied Analysis and Numerical Simulation, University of Stuttgart Roles: Academic Researcher, Software Developer, Grant Principal Investigator His work bridges numerical simulation, machine learning, and reduced basis methods, addressing challenges in optimal control, fluid dynamics, and biomechanics. Haasdonk has held multiple funded projects, including those on kernel methods for model reduction and certified RB-ML-ROM surrogate models. Research Interests: Model reduction for PDEs, kernel methods, greedy algorithms, numerical analysis, optimal control, and applications in fluid dynamics and porous media. He emphasizes structure-preserving methods for Hamiltonian systems and data-driven approaches for surrogate modeling. Publications: Over 200 articles in journals like SIAM, BIT Numerical Mathematics, and Physica D, focusing on convergence analysis, kernel-based approximation, and reduced-order modeling. Recent trends include adaptive greedy algorithms, symplectic model reduction, and energy-conserving surrogates. Awards: IEEE PerCom 2017 Best Paper Award, Teaching Excellence Awards (2012-2017), and early-career research grants. Grants: DFG-funded projects on model reduction, SimTech Cluster contributions, and collaborations on fuel cells and biomechanics. Teams: Leads the Numerical Mathematics Research Group at IANS, collaborating with interdisciplinary teams on projects like MORCOS (Model Order Reduction of Coupled Systems) and KerMor (Kernel Methods for Model Reduction).