جستجوی استاد راهنمای دانشگاهی

برای مسیر پژوهشی‌تان، استاد راهنما، استاد دانشگاه و پژوهشگر مناسب را پیدا کنید و با آن‌ها ارتباط بگیرید.

نمایش ۷۲۱-۷۳۵ از ۶٬۱۹۸ استاد راهنما

اطلاعات پروفایل‌ها از صفحات عمومی دانشگاه‌ها گرفته شده و ممکن است به‌روز نباشد.

Frédéric JEANمشاهده پروفایل

استاد

ENSTA ParisFrance

Frédéric JEAN is a Professor and Director of the Unité de Mathématiques Appliquées (UMA) at ENSTA Paris. His expertise lies in nonlinear control theory and geometric control, with applications in neurophysiology modeling. He holds an HDR (Habilitation à Diriger des Recherches) and advises doctoral students across multiple domains. Research Interests: Frédéric’s research focuses on geometric control theory, sub-Riemannian geometry, optimal control, and nonholonomic systems. His work bridges theoretical advancements with practical applications in neurophysiology and aerospace engineering. Recent studies include trajectory planning for planetary landings and robust control strategies under uncertainty. Teaching: He teaches courses on applied differential geometry, dynamical systems analysis, and control systems at ENSTA Paris. Publications: His recent work emphasizes sub-Riemannian geometry, stochastic control, and aerospace applications. Key trends include optimal control in constrained systems and geometric approaches to robotic and biological movement. Students: Co-supervised over 10 doctoral theses, including those by Clara Leparoux (2023), Meryem Kafnemer (2022), and Sofya Maslovskaya (2018). Labs/Teams: Active within the Optimization and Commande team at UMA, contributing to interdisciplinary research in control systems and applied mathematics.

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Mikko Nissiمشاهده پروفایل

استاد

University of Eastern FinlandFinland

Professor Mikko Nissi holds the Chair of Medical Physics and Engineering at the University of Eastern Finland’s Department of Technical Physics, Faculty of Science, Forestry and Technology. He leads research on quantitative magnetic resonance imaging (qMRI), focusing on musculoskeletal diseases and methodological advancements in ultra-short echo time techniques. His work integrates inverse problems research and machine learning to predict tissue properties unobservable via conventional MRI. Education: PhD in Physics (University of Kuopio, 2008), Adjunct Professor since 2015. Key roles include Associate Professor (2020–2024) and Academy Research Fellow (2015–2020). Active in research groups like the Academy of Finland Flagship (FAiME) and UEF’s Musculoskeletal Diseases (MSKD) community. Research interests span qMRI relaxometry, T1ρ/T2 anisotropy, and AI-driven virtual histology. Notable projects include EU-funded microMRI infrastructure for industrial applications and grants from the Research Council of Finland for AI-assisted MRI methods. Teaching: Course on MRI principles and applications. Advises on musculoskeletal biomechanics and finite element modeling. Recognized for contributions to medical physics, including societal roles in the Finnish Society for Medical Physics and International Society for Magnetic Resonance in Medicine. Hobbies: 3D printing for research tooling and handicrafts. Publications (>100 articles) address cartilage degeneration, myocardial perfusion, and MRI applications in agriculture/forestry.

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Lasse Heikkinenمشاهده پروفایل

مدرس ارشد

University of Eastern FinlandFinland

Lasse Heikkinen is a Senior Lecturer at the Department of Technical Physics, Faculty of Science, Forestry and Technology, University of Eastern Finland. His work spans applied physics, process tomography, and educational technology, with a focus on innovative teaching methods like flipped classrooms during the pandemic. Current Role: Deputy Head of Department, Senior University Lecturer Research Themes: Electrical impedance tomography for industrial processes, flipped teaching frameworks, and physics education adaptation to remote learning. Recent publications highlight his dual expertise in process tomography (gas-solid flows, pharmaceutical monitoring) and pedagogical innovation (learning analytics, flipped classrooms). He has contributed to educational manuals and toolkits for teacher training. Key Projects: Technology education infrastructure development (2015–2024), pandemic-era teaching adjustments (2022). Collaborations: Active in international process tomography conferences and multidisciplinary teams like the Ameba project.

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Asmus Skar Christiansenمشاهده پروفایل

دانشیار

Technical University of DenmarkDenmark

Asmus Skar Christiansen is an Associate Professor in Pavement Engineering at the Department of Environmental and Resource Engineering, Technical University of Denmark (DTU Sustain). He serves as Head of Study for the Nordic Master in Cold Climate Engineering programme and lectures on pavement engineering, Arctic road construction, and foundation design. His academic career at DTU spans from Postdoc researcher (2017-2019) to Assistant Professor (2020-2023) and current Associate Professor position since 2023. His research centers on pavement technology and geotechnics with specialization in: Development of advanced testing and modeling techniques for pavements Integration of modern sensing technologies in civil infrastructure Computational mechanics for soil-structure interaction Sustainable materials for cold climate engineering Recent work demonstrates a clear shift toward IoT-enabled monitoring systems and data-driven pavement assessment, with 80% of 2023-2025 publications focusing on sensor integration and machine learning applications. Notable scientific contributions include: Creation of open-source datasets (LiRA-CD, RIVA) for road condition modeling Development of thermomechanical models for heated pavements Innovations in waste soil reuse for infrastructure He actively supervises PhD candidates across multiple projects including GREENPIPE (self-sensing pipe systems) and urban pavement analysis, while maintaining industry consultancy through COWI A/S collaborations. Christiansen also contributes to sustainable infrastructure through DTU's alignment with UN SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities).

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Dongheui Leeمشاهده پروفایل

استادیار

Technical University of MunichGermany

Dongheui Lee is an Assistant Professor at the Institute of Automatic Control Engineering (LSR) within the Faculty of Electrical Engineering and Information Technology at Technische Universität München (TUM). She leads the Dynamic Human Robot Interaction for Automation System Lab. Her research focuses on human motion understanding, physical human-robot interaction, and machine learning in robotics. Education: B.S. and M.S. in Mechanical Engineering from Kyunghee University (2001-2003), PhD in Mechano-Informatics from the University of Tokyo (2007). Prior roles include research scientist at KIST Korea (2001-2004) and project assistant professor at the University of Tokyo (2007-2009). Research Interests: Human-robot collaboration, probabilistic robotics, motion recognition, and incremental lifelong learning mechanisms. She has contributed to advancements in motion primitives, compliant physical interaction, and real-time object tracking. Selected Awards: Finalist for KUKA Service Robotics Best Paper Award (2009), Hirose Scholarship (2006-2007), and multiple grants from KRF, KOSEF, and international robotics competitions. Key Publications: Focus on prioritized inverse kinematics, motion imitation, and adaptive control systems. Her work bridges robotics theory and practical applications in humanoid robots and human-robot interaction.

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Fabio Nobileمشاهده پروفایل

استاد

Swiss Federal Institute of Technology in LausanneSwitzerland

Fabio Nobile is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), Department of Mathematics (MATH), holding the CADMOS Chair in Scientific Computing and Uncertainty Quantification. He leads the CSQI (Chair of Scientific Computing and Uncertainty Quantification) group. His work focuses on numerical methods for partial differential equations (PDEs), uncertainty quantification, stochastic modeling, and computational fluid dynamics. He is involved in collaborative projects involving fluid-structure interaction, cardiac electro-mechanics, and energy systems. Professor Nobile has extensive teaching experience, including courses on advanced analysis, stochastic simulation, and numerical integration of stochastic differential equations. He supervises numerous PhD students and has contributed to over 200 peer-reviewed publications, covering topics such as low-rank approximation methods, multilevel Monte Carlo techniques, and optimal control under uncertainty. His research emphasizes interdisciplinary applications, including biomedical engineering (e.g., personalized cardiac simulations) and renewable energy (e.g., probabilistic load forecasting). He collaborates with industries and academic institutions globally, advancing computational methodologies for engineering and scientific challenges.

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Maxime Ferreira Da Costaمشاهده پروفایل

دانشیار

CentraleSupélecFrance

Maxime Ferreira Da Costa is an Associate Professor at CentraleSupélec, Université Paris-Saclay, affiliated with the Laboratory of Signals and Systems (L2S). His research focuses on theoretical and algorithmic foundations of data science, particularly in inverse problems, structured signal processing, and physical layer security. Key research areas include super-resolution, system calibration, and privacy-enhancing communication schemes. He holds a Ph.D. from Imperial College London (2018), and previously worked at USC and Carnegie Mellon University. Notable achievements include an ANR Young Researcher Grant (2023) for projects on data science and physical layer security. He actively contributes to conferences and workshops, with recent presentations at Institut Henri Poincaré and Université Paris-Saclay. Education: Ph.D. in Electrical Engineering, Imperial College London (2018) M.Sc. Electrical Engineering, Imperial College London (2012) Engineer Diploma, CentraleSupélec (2012) Research interests span continuous inverse problems, off-the-grid methods, wireless security, and sensing systems. Current projects explore goal-oriented resource allocation, fake path injection for privacy, and preconditioned optimization techniques. His work bridges signal processing theory with practical applications in imaging, telecommunications, and sensing.

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Tomoko Matsuoمشاهده پروفایل

دانشیار

University of ColoradoUnited States

Tomoko Matsuo is an Associate Professor in the Ann and H.J. Smead Department of Aerospace Engineering Sciences at the University of Colorado Boulder. She holds an affiliate faculty position in the Department of Applied Mathematics and is a member of the Colorado Center for Astrodynamics Research (CCAR). A H. Joseph Smead Faculty Fellow, her research focuses on data assimilation, predictability of geophysical systems, and space physics. She earned a PhD in Atmospheric Science from SUNY Stony Brook (2003), MS in Physics from Nagoya University (1998), and BS in Geophysics from Hokkaido University (1995). Affiliations: CCAR, Smead Aerospace, Department of Applied Mathematics Awards: NSF CAREER Award (2019), multiple teaching awards, and fellowships Education: PhD, Atmospheric Science, SUNY Stony Brook, 2003 MS, Physics, Nagoya University, Japan, 1998 BS, Geophysics, Hokkaido University, Japan, 1995 Research Interests: Advanced methods in data assimilation for space weather forecasting, ionospheric dynamics, and geophysical predictability. Her work bridges atmospheric sciences, space physics, and statistical learning. Current projects include the DYNAMIC mission concept (NASA-selected Phase-A study) and the AMGeO data science platform. Grants & Collaborations: NSF CAREER Award (2019), NASA-funded projects (e.g., DYNAMIC mission), and collaborations with JHU APL, NCAR, and international institutions. Advises students on remote sensing and STEM outreach programs. Labs/Teams: Leads her own research group and contributes to CCAR. Develops tools for assimilative mapping of geospace observations (AMGeO). Active in citizen science initiatives like Aurorasaurus.

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Sean Gallenمشاهده پروفایل

دانشیار

Helmholtz Centre Potsdam - German Research Centre for GeosciencesGermany

Prof. Sean Gallen is an Associate Professor in the Department of Geosciences at Colorado State University (CSU), USA. He holds a Senior Humboldt Research Fellowship at the GFZ German Research Centre for Geosciences, where he collaborates with Prof. Jean Braun's team in the Earth Surface Process Modeling section. His research integrates fieldwork, numerical modeling, geochronology, and GIS to study landscape evolution, tectonics, and natural hazards. Gallen earned his Master's (2008) from Western Washington University and PhD (2013) from North Carolina State University, followed by a Turner Postdoctoral Fellowship at the University of Michigan. Research Focus: His work examines climate-landscape interactions, subduction zone dynamics, and extreme event impacts on erosion. Notable projects include studying Colorado Front Range topography using cosmogenic nuclides and modeling climate effects on volcanic island landscapes. His interdisciplinary approach addresses topics like tectonic uplift quantification, silicate weathering feedbacks, and landscape-fish coevolution. Awards: Recipient of the NSF CAREER Award (2021) and Senior Humboldt Fellowship (2024). His research has been supported by NSF grants focusing on critical zone dynamics and subduction zone hazards. Current Projects: Investigating climatic controls on river incision in hotspot volcanoes and quantifying glacial erosion impacts on fault slip in the Sangre de Cristo Mountains. Collaborates internationally, including with Tribhuvan University (Nepal) on coseismic landslide studies.

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Giorgio C. Buttazzoمشاهده پروفایل

استاد

Polytechnic Institute of PortoPortugal

Giorgio C. Buttazzo is a Full Professor of Computer Engineering at the Scuola Superiore Sant'Anna in Pisa, Italy, and founder/director of the RETIS Lab. His career includes roles at the University of Pavia and co-founding Evidence s.r.l. (a real-time embedded systems company). He holds an IEEE Fellowship (2012) and the IEEE TC RTS Outstanding Technical Contributions Award (2013). Education: Electronic Engineering (University of Pisa, 1985), Master in Computer Science (University of Pennsylvania, 1987), PhD in Computer Engineering (Scuola Superiore Sant'Anna, 1991). Affiliations: TeCIP Institute, RETIS Lab, and leadership roles in IEEE Technical Committees. Research focuses on real-time systems, robotics, and AI integration. He has authored 10 books, over 300 papers, and pioneered frameworks like the ERIKA/SHARK kernels. Current projects include RETICULATE, OPERAND, and NANCY (5G networks). Teaching includes Real-Time Systems, Neural Networks, and Jazz Guitar Improvisation. Advised over 150 master and PhD students. Active in conferences like ECRTS, RTSS, and RTAS.

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Weronika Gorczykمشاهده پروفایل

استاد مدعو

The University of Western AustraliaAustralia

Weronika Gorczyk is an Adjunct Professor at the School of Earth and Oceans at The University of Western Australia. She holds a PhD from ETH Zurich (2008) and has extensive experience in geodynamic modeling, mineral systems research, and tectonic studies. Her research focuses on lithospheric-scale processes, including rifting, basin evolution, and the deep carbon cycle, with applications to mineral exploration. She currently leads MRIWA-funded projects investigating Proterozoic basin dynamics and their links to mineralization in the Paterson Orogen. Education: PhD in Geology, ETH Zurich (2008) Numerical Modeling experience from CSIRO (2008–2010) Research Interests: Geodynamics, tectonic evolution of cratons, carbon cycle dynamics, and the interplay between lithospheric deformation and mineralizing processes. Her work integrates numerical modeling, geophysical data, and field observations to address unresolved questions in Earth's evolution. Key Projects: MRIWA 521: Multi-stage basin evolution and mineralization in the Paterson Orogen ARC Linkage LP190100146: Integrated exploration framework for frontier basins ARC DP190100216: Fluid dynamics in deep Earth processes Grants & Leadership: Principal Investigator on MRIWA and ARC grants totaling over 18 funded projects. Active in collaborative networks with Geological Surveys, Macquarie University, and international experts. Labs/Teams: Core member of the Centre for Exploration Targeting and ARC Centre of Excellence for Core to Crust Fluid Systems. Leads multidisciplinary teams integrating geodynamic modeling, geochemistry, and geophysics.

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Amir Hamed Majediمشاهده پروفایل

استاد

University of WaterlooCanada

Amir Hamed Majedi is a Professor at the University of Waterloo , cross-appointed to the Departments of Physics and Astronomy and Applied Mathematics . He leads the Integrated Quantum Optoelectronics Lab (IQOL) under the Waterloo Institute for Nanotechnology. Research Interests : Quantum photonics Superconducting optoelectronics Graphene photonics THz photonic devices Nano-electrophotonics Quantum information technology Article Trends : His publications focus on quantum electrodynamics in superconducting and graphene structures, covering single-photon detectors, nonlinear optical effects, THz amplification, and microwave-photonics integration. Keywords include Quantum Physics , Nanotechnology , Photonics , and Superconductivity . Teaching : Courses taught in recent years include ECE 106 - Electricity and Magnetism , ECE 375 - Electromagnetic Fields and Waves , and advanced topics in Quantum Electronics and Photonics (ECE 677, QIC 885). Education : Doctorate in Electrical and Computer Engineering (2001, University of Waterloo) Master's in Electromagnetic Waves and Photonic (1996, Amirkabir University of Technology) Bachelor's in Telecommunications (1994, Khaje-Nasir Toosi University of Technology)

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Avery E. Broderickمشاهده پروفایل

دانشیار

University of WaterlooCanada

Avery E. Broderick is an Associate Professor in the Department of Physics & Astronomy at the University of Waterloo and an Associate Faculty Member at the Perimeter Institute for Theoretical Physics. His research focuses on theoretical astrophysics, particularly studying compact objects like black holes and testing general relativity through astronomical observations. He is a key member of the Event Horizon Telescope (EHT) collaboration, which produced the first direct images of black hole horizons in M87* and Sagittarius A*. Broderick’s work emphasizes relativistic astrophysical phenomena such as accretion flows, jet formation, and polarization signatures. He collaborates extensively with observational astronomers and computational physicists to model black hole environments using general relativistic magnetohydrodynamic simulations. His recent research includes analyzing EHT data to constrain black hole spin, test spacetime metrics, and study photon ring dynamics. He also explores next-generation EHT (ngEHT) capabilities for higher-resolution imaging and multi-wavelength studies. Broderick has delivered invited lectures globally, including at Harvard-Smithsonian CfA, MIT, and the Aspen Center for Physics, reflecting his leadership in the field. Broderick’s contributions span over 135 refereed publications and conference proceedings, with a focus on black hole astrophysics, VLBI techniques, and relativistic plasma physics. His work bridges theoretical predictions with observational data, advancing our understanding of extreme gravitational regimes in the universe.

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Ayusman Senمشاهده پروفایل

استاد

Pennsylvania State UniversityUnited States

Ayusman Sen is the Verne M. Willaman Professor of Chemistry and Distinguished Professor of Chemistry at Pennsylvania State University. He holds a joint appointment in the Department of Chemical Engineering. His research focuses on self-powered nano/micromotors, dynamic materials, and enzyme-driven systems, with applications in smart materials and active matter. Sen’s work integrates chemical synthesis, catalytic energy harvesting, and emergent phenomena to create adaptive systems capable of performing complex tasks. Education: B.Sc. (Honours), University of Calcutta, India (1970) M.Sc., Indian Institute of Technology, Kanpur, India (1973) Ph.D., University of Chicago, USA (1978) Research Interests: Design of self-powered nanomotors and micropumps Chemically controlled assembly and motion of colloidal systems Enzyme cascade systems for programmable behavior Active matter and collective dynamics Key Contributions: Pioneered enzyme-powered micromotors for biomedical and environmental applications Developed theoretical frameworks for chemotactic assembly and non-equilibrium systems Engineered autonomous fluid-pumping systems using multi-enzyme networks Awards and Honors: Langmuir Lecture Award (American Chemical Society) Humboldt Prize (Alexander von Humboldt Foundation) Fellow of the Royal Society of Chemistry Paul J. Flory Award (IBM) Labs and Teams: His research group, the Sen Research Group, focuses on interdisciplinary projects at the interface of chemistry, materials science, and engineering. Current efforts explore light-driven systems, programmable fluidics, and bioinspired materials.

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Alain Trouvéمشاهده پروفایل

استاد

École Normale Supérieure Paris-SaclayFrance

Alain Trouvé is a Professor at the Center for Mathematics and Their Applications (CMLA) within the Ecole Normale Supérieure de Cachan , France. His research focuses on Shape Spaces , Computational Anatomy , Imaging Processing , and Biological Imaging , with applications in medical and computational fields. He directs the Mathematics Department at ENS Cachan and contributes to neuroanatomical studies through diffeomorphometry techniques. Key roles include membership in the Conseil National des Universités (Section 26) and teaching responsibilities such as courses on Geometry and Shape Spaces and Probability Theory . His work spans from theoretical frameworks (e.g., Hamiltonian modeling of shape evolution) to practical applications like 3D cell imaging and white matter fiber analysis. Publications emphasize interdisciplinary methods, including diffeomorphic registration, varifold-based image analysis, and stochastic shape evolutions. Current projects explore multi-scale modeling of biological systems and AI-driven medical diagnostics. Key Research Themes: Diffeomorphic mappings, computational vision, and functional shape analysis. Teaching: Courses on geometric modeling and probability at undergraduate and graduate levels. Tools Developed: xIV-LDDMM Toolkit for multi-modal biomedical data analysis.

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