جست‌وجوی استاد و پژوهشگر

Martin v. Mohrenschildtمشاهده پروفایل

دانشیار

• Control Systems
• Signal Processing
• Simulation
• +۵ مورد دیگر

Dr. Martin v. Mohrenschildt is an Associate Professor in the Department of Computing and Software at McMaster University's Faculty of Engineering. He holds academic positions as Undergraduate Advisor for Mechatronics and Mechatronics Coordinator. His expertise spans control systems, signal processing, hybrid systems, and immersive simulation. He earned a Dr. sc. math. ETH and Dipl. math. ETH (Swiss Federal Institute of Technology), and is a Professional Engineer (P.Eng.). Research focuses include Model Predictive Control, vibration analysis for industrial machinery, and multisensory integration in motion simulation. His Motion Simulator Laboratory features a 6-DOF simulator with advanced visualization and physiological measurement capabilities. The Embedded Systems Laboratory explores mechatronics and robotics, including alternative fuel injection systems. Teaching includes courses like CAS-748 (Time Series Analysis) and SFWR ENG 2SO3. Recent work emphasizes sensor fusion (LiDAR/camera/thermal), biofeedback systems for muscle interventions, and cognitive effects of motion cues in virtual environments. Collaborations with Dr. Shedden investigate perceptual neuroscience in immersive simulators. His lab facilities support interdisciplinary projects combining engineering, computer science, and cognitive science. Publications span 30+ years, covering hybrid systems theory, control algorithms, and applied sensor technologies. Current research directions include autonomous vehicle perception systems and aging-related changes in multisensory integration.

Alessandro Chiusoمشاهده پروفایل

استاد

• Control Systems
• Machine Learning
• System Identification
• +۳ مورد دیگر

Professor Alessandro Chiuso is a Full Professor in the Department of Information Engineering at the University of Padova, Italy. His research focuses on control systems, machine learning, system identification, and their applications to neuroscience and medical imaging. He has contributed to advancing data-driven control methodologies, regularization techniques, and brain network modeling. His work bridges theoretical control systems with practical applications, including predictive control under uncertainty, nonlinear system identification, and understanding brain dynamics through fMRI and PET data. Notable areas include developing algorithms for adaptive control, optimizing brain region interactions, and analyzing metabolic-functional couplings in neurological conditions. Recent publications highlight advancements in policy gradient methods for LQR control, sparse dynamic causal models for brain aging studies, and predictive control frameworks for stochastic systems. His research emphasizes the integration of machine learning with classical control theory to solve complex real-world problems.

Nianbo Dongمشاهده پروفایل

استاد

• Quantitative Methods
• Educational Evaluation
• Causal Inference
• +۶ مورد دیگر

Nianbo Dong is the Guy B. Phillips Professor in Education at the University of North Carolina at Chapel Hill (UNC-CH). His expertise lies in quantitative methodologies for educational evaluation, focusing on causal inference, multilevel experiments, and program effectiveness analysis. He holds a Ph.D. from the University of Pennsylvania (2009) and prior roles at the University of Missouri-Columbia and Vanderbilt University. Dr. Dong's research develops statistical tools for experimental design and causal analysis, including the PowerUp! software suite for multilevel experiment planning. His work evaluates teacher training programs, early childhood education interventions, and educational policies. He has received $4.3M+ in NSF and IES grants, highlighted by the NSF Early Career Award (2017). His methodological contributions include cost-effectiveness analysis frameworks, moderation/mediation effect designs, and addressing nested data structures. Recent work explores disparities in K-8 academic outcomes and trauma impacts on randomized trials. He advises on grant proposals and serves as Associate Editor for Educational Evaluation and Policy Analysis . Publications span 200+ peer-reviewed articles in journals like Journal of Educational Measurement and Journal of Research on Educational Effectiveness . His work bridges statistical rigor with real-world educational challenges, emphasizing practical tools for researchers and policymakers.

Alessandro Ferrariمشاهده پروفایل

استادیار

• Macroeconomics
• International Trade
• International Macroeconomics
• +۳ مورد دیگر

Alessandro Ferrari is an Assistant Professor of Economics at the University of Zurich and an Affiliated Professor at the Barcelona School of Economics. He holds a PhD from the European University Institute in Florence. His research focuses on firms’ roles in macroeconomics and trade, international macroeconomics, and the implications of supply chains, fiscal unions, and multinational corporate strategies. He is a Research Affiliate at the Centre for Economic Policy Research (CEPR). His academic work spans key areas such as the transmission of demand shocks through global supply chains, the geography of multinational firms under profit-shifting frictions, and the welfare implications of currency versus fiscal unions. He also investigates macroeconomic fragility arising from firm heterogeneity and market power dynamics. Education: PhD in Economics from the European University Institute (Florence) Recent research highlights include analyzing the role of inventories in shock propagation, subsidy competition in the U.S., and the causal effects of EU trade agreements on non-trade outcomes. His work bridges theoretical models with empirical evidence, often employing structural estimation and synthetic control methods. He has contributed to understanding systemic risk in banking through funding structure analysis and has explored policy frameworks for risk-sharing in monetary unions. His interdisciplinary approach addresses contemporary challenges in global economics, trade, and financial stability.

Ricardo Vinuesaمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Fluid Mechanics
• Sustainability
• +۷ مورد دیگر

Ricardo Vinuesa is an Associate Professor at KTH Royal Institute of Technology, affiliated with the Department of Mechanics within the School of Engineering Sciences. He serves as the Principal Investigator (PI) for projects such as 'An AI-based framework for harmonizing climate policies and projects with the SDGs' and co-leads the 'Faster-than-real-time and high-resolution simulation of fluid flow in engineering applications' initiative. His work integrates artificial intelligence with fluid mechanics, sustainability, and climate policy. Education and Academic Roles: Vinuesa teaches courses including Data-driven methods in engineering mechanics, Mechanics I, and Turbulence, holding roles as examiner, course manager, and teacher. He is part of the Digital Futures Faculty and actively contributes to academic programs at KTH. Research Interests: His research focuses on AI-driven solutions for fluid mechanics challenges, turbulence modeling, sustainable development goals (SDGs), and climate policy alignment. He develops machine learning frameworks for flow control, sensor optimization, and climate risk assessment, emphasizing interdisciplinary applications. Projects and Applications: Key projects include AI-based frameworks for SDG harmonization, high-resolution fluid simulations for indoor climate, and turbulence control via reinforcement learning. His work bridges computational fluid dynamics with real-world sustainability challenges, leveraging deep learning and data-driven methodologies. Labs and Collaborations: Vinuesa leads the Artificial Intelligence Group, with a lab website at vinuesalab.com . His research is supported by collaborations with industry and academic partners, focusing on advancing fluid mechanics and environmental engineering through AI innovation.

• Automotive Engineering
• Railway Engineering
• Mechanical Engineering
• +۶ مورد دیگر

Vicente Díaz López is a Full Professor in the Department of Mechanical Engineering at Carlos III University of Madrid. He serves as Director of the Duque de Santomaró Institute of Motor Vehicle Safety and Director of the Master's Degree in Railway Engineering. Academic Department: Mechanical Engineering Research Group: Experimental Mechanics, Calculations and Transport Group (MECATRAN) Contact: vdiaz@uc3m.es Research Interests span automotive and railway engineering, focusing on: Intelligent tire systems for grip and friction estimation Vehicular dynamics and stability control Braking efficiency and safety analysis Structural mechanics in transport systems Sensor fusion and neural networks for vehicle parameter estimation Roadworthiness testing methodologies Recent Publications demonstrate expertise in: Brake force estimation techniques Intelligent tire development Vehicle roll stability control Structural failure analysis in transport Low-cost sensor systems for driver monitoring Projects include: Principal investigator of the fBrake software suite (2014–present) for braking efficiency analysis Multiple contracts with technical inspection agencies (ITV) and companies like Bombardier Patent development for driving style evaluation systems

Nan Chenمشاهده پروفایل

استاد

• Financial Engineering
• FinTech
• Quantitative Finance
• +۷ مورد دیگر

Prof. Nan Chen is a Professor in the Department of Systems Engineering and Engineering Management at The Chinese University of Hong Kong (CUHK). He holds a PhD from Columbia University (2006) and directs the Bachelor of Engineering in Financial Technology (FinTech) and the Master of Science in Financial Engineering at CUHK-Shenzhen. His research focuses on quantitative finance, systemic risk, Monte Carlo simulation, and reinforcement learning. He has authored over 40 papers in top journals like Operations Research and Review of Financial Studies . Education : BSc and MSc in Probability & Statistics, Peking University (1998–2001) MPhil/PhD in Operations Research, Columbia University (2005–2006) Research Interests : FinTech and quantitative finance Systemic risk modeling and network analysis Reinforcement learning and stochastic control Monte Carlo methods and computational finance Awards & Grants : Best Student Paper Award, INFORMS (2006) Exemplary Teaching Awards (2009, 2017) Over HK$7M in GRF grants (2008–2023) Second Prize, Jiangsu Province Social Science Award (2023) Public Service : Member of Hong Kong's Payment Systems Tribunal and Insurance Appeals Tribunal, and reviewer for major journals like Mathematical Finance and Operations Research . Key Contributions : Pioneered Ito-Taylor expansions for diffusion models, dual methods for stochastic control, and liquidity risk frameworks. Led development of Hong Kong's first FinTech undergraduate program.

Jonathan Ajo-Franklinمشاهده پروفایل

استاد

• Applied Geophysics
• Seismic Imaging
• Timelapse Seismology
• +۵ مورد دیگر

Jonathan Ajo-Franklin is a Professor in the Department of Earth, Environmental and Planetary Sciences at Rice University, specializing in applied geophysics. His research focuses on seismic acquisition and monitoring to address subsurface challenges in energy systems, CO2 mitigation, and water resource management. He leads the Ajo-Franklin Lab, leveraging distributed fiber optic sensing (DAS) and dark fiber infrastructure for dense seismic data collection. Education: B.A. in Computer Science/History (Rice University, 1998), M.S. and Ph.D. in Geophysics (Stanford University, 2003–2004). His work spans geothermal energy, carbon storage, and cryosphere dynamics, with projects like the EGS Collab initiative and collaborations with DOE and industry partners. Recent publications emphasize DAS applications in fracture monitoring, geothermal systems, and subsurface imaging. Key research areas include timelapse seismology, rock physics, and the integration of seismic data with environmental challenges. Ongoing projects explore geothermal reservoir imaging, CO2 leakage detection, and permafrost thaw monitoring using DAS. The lab’s tools include dark fiber networks, permanent seismic sources, and ambient noise analysis.

Jonathan Elliottمشاهده پروفایل

استادیار

• Industrial Organization
• Environmental Economics
• Energy Economics
• +۴ مورد دیگر

Jonathan Elliott is an Assistant Professor in the Department of Economics at Johns Hopkins University. He holds a PhD from New York University (2022) and a Bachelor's degree from the University of Kentucky. His research focuses on empirical industrial organization, environmental economics, and energy markets, with a particular emphasis on how market structure influences firm behavior in regulated industries like telecommunications and electricity. He investigates regulatory mechanisms to improve outcomes such as investment, emissions control, and service reliability. Teaching includes PhD-level Industrial Organization and undergraduate Environmental Economics courses at Johns Hopkins (2023–2025). His research has been published in top journals such as the Journal of Political Economy , with a focus on computational modeling and policy analysis. His GitHub repository demonstrates expertise in equilibrium simulations for telecommunications markets, using Python and advanced econometric tools.

Miki Nakajimaمشاهده پروفایل

استادیار

• Planetary Dynamics
• Planet Formation and Evolution
• Impact Cratering
• +۵ مورد دیگر

Miki Nakajima is an Assistant Professor of Earth and Environmental Sciences and Physics and Astronomy at the University of Rochester. She holds a PhD from the California Institute of Technology. Her dual appointments reflect her interdisciplinary research in planetary science, combining geophysics, astrophysics, and computational modeling. Education: PhD in Planetary Science, California Institute of Technology Research Interests: Planetary Dynamics: Focuses on impact processes, planetary interior evolution, and moon/exomoon formation mechanisms. Specializes in modeling Enceladus plumes and Earth-Moon system origins. Numerical Simulations: Develops advanced computational tools like NcorpiON for collisional system modeling and N-body integration. Early Solar System: Investigates accretion processes, mantle differentiation, and volatile retention during terrestrial planet formation. Research Trends in Publications: Recent work emphasizes giant impact simulations (Moon formation), tungsten isotope systematics, and exomoon formation limitations. Her articles consistently integrate high-fidelity numerical models with geochemical observations. Advising & Grants: No advisees listed; her funding sources likely support computational infrastructure and space mission collaborations. Active in planetary science communities. Labs/Teams: Affiliated with the University of Rochester's Earth and Environmental Sciences department, collaborating with astrophysics groups on interdisciplinary projects.

Hans Martin Kjerمشاهده پروفایل

دانشیار

• Ultrasound Imaging
• Super-Resolution Imaging
• Biomedical Image Analysis
• +۷ مورد دیگر

Hans Martin Kjer is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he is affiliated with the UltraSound and Biomechanics group within the Visual Computing Center and the Center for Fast Ultrasound Imaging. His research bridges engineering and medical imaging, with a strong emphasis on developing and validating advanced ultrasound techniques for biomedical applications. Research Interests: His work focuses on super-resolution ultrasound imaging, microvascular analysis, 3D reconstruction of biological structures, and image registration. He applies computational methods to improve the resolution and accuracy of ultrasound, particularly in renal and lymph node vasculature imaging. His research contributes to the UN Sustainable Development Goals in health and well-being through innovative diagnostic tools. Publication Trends: Over the past several years, Kjer has consistently published in high-impact journals and conferences in biomedical engineering and imaging. His recent work emphasizes the validation of super-resolution ultrasound against micro-CT, realistic 3D blood flow simulation, and the application of AI in enhancing imaging resolution. These studies reflect a strong trend toward quantitative, reproducible, and clinically relevant imaging solutions. Scientific Contributions: While no specific awards are listed, his leadership in major research projects and frequent collaborations with leading experts in ultrasound (e.g., Jørgen Arendt Jensen) underscore his significant role in the field. Advising and Funding: Kjer serves as a supervisor and principal investigator in several funded research initiatives, including AI for Extreme Super-Resolution CT , 3DIM: 3D Imaging Center , and QIM: Center for Quantification of Imaging Data from Max IV . He mentors PhD students and collaborates across disciplines, contributing to both biomedical and materials science imaging projects. Laboratories and Teams: He is an integral member of the Center for Fast Ultrasound Imaging and the Visual Computing Center at DTU. These teams focus on cutting-edge ultrasound technologies, image processing algorithms, and multimodal imaging integration, positioning Kjer at the forefront of computational biomedical imaging in Denmark.

Ilaria Caiazzoمشاهده پروفایل

استادیار

• High-Energy Astrophysics
• Compact Objects
• White Dwarfs
• +۱۱ مورد دیگر

Ilaria Caiazzo is an Assistant Professor at the Institute of Science and Technology Austria (ISTA), where she conducts research in high-energy astrophysics, focusing on compact objects such as white dwarfs, neutron stars, and black holes. She earned her Ph.D. from the University of British Columbia and was a Burke-Sherman Fairchild Fellow at Caltech before joining ISTA. Her work integrates theoretical modeling with observational data from major surveys and space missions. B.Sc. in Physics, University of Genoa M.Sc. in Physics, Università degli Studi di Milano Ph.D. in Astrophysics, University of British Columbia Her research interests lie at the frontier of extreme physics in stellar environments. She investigates white dwarf evolution, X-ray polarization from neutron stars, gravitational wave sources, and stellar dynamics in globular clusters. Using data from missions like IXPE, Gaia, and JWST, she explores how compact objects serve as natural laboratories for fundamental physics. Her work often combines multi-wavelength observations with theoretical modeling to understand emission mechanisms, magnetic fields, and stellar evolution. The recent publications reflect a strong focus on observational astrophysics, time-domain surveys, and mission-driven science. Key themes include the use of Gaia and ZTF for stellar dynamics and binaries, IXPE for X-ray polarimetry, and the development of future missions like Colibrì. Her research spans both stellar remnants and broader cosmological implications, including LIGO follow-up strategies and planetary system evolution around dead stars. Ilaria Caiazzo is actively involved in science policy and education. She co-developed a framework for Canadian space mission funding and has taught advanced astrophysics courses. She also organized the Simulating Stars Summer School in Beijing, engaging nearly 100 students. While no formal scientific awards are listed, her leadership in the Colibrì mission and her outreach efforts highlight her impact beyond research. She leads the Colibrì mission concept as Project Scientist—a Canadian-led X-ray telescope initiative aiming for high spectral and timing resolution to probe neutron stars and black holes. She collaborates with international teams, including the IXPE mission scientists, and mentors students in astrophysics and data analysis. Her interdisciplinary work also extends to filmmaking, where she produces science-inspired art through her company Sukiyaki Studio.

• Numerical Solution of Partial Differential Equations
• Spectral Element Methods
• Reduced Basis Methods
• +۳ مورد دیگر

Einar Malvin Rønquist is a Professor and Head of the Department of Mathematical Sciences at NTNU since August 2013. He holds a MSc from NTNU (1980) and a PhD from MIT (1988). His research focuses on numerical solutions of partial differential equations, spectral element methods, reduced basis methods, and computational fluid dynamics. He has been a leader in several research initiatives, including the Computational Science and Visualization program at NTNU (2003–2011). Rønquist is a member of prestigious academies: NTVA (since 2005) and DNKVS (since 2010). He has supervised 8 PhD students and 25 MSc students. His work spans computational science, with notable contributions to parametric modeling, parallel computing, and fluid dynamics simulation. His administrative roles include Vice President of R&D at Nektonics, Inc. (1991–1999) and Deputy Head of NTNU’s Department of Mathematical Sciences (Fall 2012). His publications highlight advancements in numerical methods for PDEs, including spectral element techniques, reduced basis approaches, and high-order approximations for complex geometries.

• Fluid network dynamics
• Partial differential equation solvers
• Hydraulic network characterization
• +۴ مورد دیگر

Dr. Aaron Zecchin is a Senior Lecturer in Environmental Engineering at the University of Adelaide, affiliated with the School of Architecture and Civil Engineering under the Faculty of Sciences, Engineering and Technology. His research focuses on fluid network dynamics, hydraulic system modeling, and evolutionary algorithm applications in engineering problems. His expertise includes transient analysis in pipeline networks, fault detection using statistical methods, and optimization techniques for water distribution systems. He actively investigates fluid-structure interactions, Laplace-domain characterizations, and sparse linear solvers for steady-state simulations. His work integrates advanced computational methods with practical engineering challenges, emphasizing infrastructure resilience and sustainability. Research Themes: Hydraulic transients, pipeline condition assessment, system identification Key Tools: Method of characteristics, Kalman filtering, ant colony optimization Applications: Water distribution systems, flood mitigation, wildfire risk modeling Recent publications emphasize anomaly detection in pipe systems, burn probability prediction using dimensionality reduction, and optimizing municipal infrastructure rehabilitation. His work bridges theoretical modeling with real-world infrastructure challenges, contributing to safer and more sustainable engineering solutions.

Michael Sørensenمشاهده پروفایل

استاد

• Statistical Inference for Stochastic Processes
• Stochastic Differential Equations
• Financial Mathematics
• +۳ مورد دیگر

Michael Sørensen is a Professor at the Department of Mathematical Sciences, University of Copenhagen. His primary research focuses on statistical inference for stochastic processes, particularly stochastic differential equations and jump processes, with applications in finance, physics (e.g., wind-blown sand dynamics), and biology. He has authored/co-authored influential books such as Exponential Families of Stochastic Processes and edited volumes on empirical process techniques and statistical methods for stochastic differential equations. His work bridges theoretical statistics with applied problems in natural sciences and finance. Research interests include modeling turbulence, sand transport dynamics, and protein structure evolution. Collaborations with earth scientists like Keld Rømer Rasmussen have advanced understanding of aeolian processes. His methodologies emphasize likelihood-based inference and estimating functions, with contributions to high-frequency data analysis and diffusion bridge simulations. A comprehensive CV and full publication list are available on his profile. Key contributions span stochastic modeling in physics (e.g., sand dune dynamics), financial econometrics, and computational statistics. He has pioneered techniques for analyzing multi-modal diffusions and developed frameworks for mixed-effects stochastic differential equations. His work is widely cited in both theoretical and applied statistical literature.