برای مسیر پژوهشیتان، استاد راهنما، استاد دانشگاه و پژوهشگر مناسب را پیدا کنید و با آنها ارتباط بگیرید.
نمایش ۱-۱۵ از ۴۹۱ استاد راهنما
اطلاعات پروفایلها از صفحات عمومی دانشگاهها گرفته شده و ممکن است بهروز نباشد.
Massimo Mischi is a Full Professor at the Faculty of Electrical Engineering of the Eindhoven University of Technology (TU/e) and chairs the Signal Processing Systems (SPS) Division , the largest division at TU/e with over 250 researchers. He founded the Biomedical Diagnostics (BM/d) Lab in 2012, which now includes 180 researchers and clinical/industrial advisors, focusing on biomedical signal processing for diagnostics and monitoring.
استادیار
Nicholas Ruozzi is an Assistant Professor of Computer Science at The University of Texas at Dallas (UTD), affiliated with the Erik Jonsson School of Engineering and Computer Science. His research focuses on machine learning, statistical inference, and probabilistic graphical models, with applications in virtual reality (VR) training, computer vision, and explainable AI. He has contributed to areas such as tractable probabilistic modeling, activity recognition in videos, and user tracking in VR systems. His work often bridges theoretical foundations with practical applications, such as developing algorithms for data privacy in VR training sessions and enhancing deep learning models through hybrid approaches with graphical models. Recent research trends include exploring multimodal interaction, distributionally robust models, and novel instance detection techniques in computer vision. Ruozzi's publications span topics like user identifiability in VR, predictive task guidance in AR, and systematic analysis of device interactions in VR systems. While no specific awards or grants are listed, his contributions reflect a strong emphasis on interdisciplinary applications of machine learning and probabilistic methods.
Prof. Dr. Johan Robertsson is a Full Professor of Applied Geophysics and Head of the Exploration and Environmental Geophysics (EEG) Group at ETH Zürich's Department of Earth and Planetary Sciences. He holds a MSc from Uppsala University (1991) and a PhD in Geophysics from Rice University (1994). Before joining ETH in 2012, he spent 15 years at Schlumberger in R&D roles, leading projects that revolutionized marine seismic data acquisition. His research focuses on wave propagation physics, seismic data inversion, and applications in exploration and environmental geophysics. He pioneered the use of Distributed Acoustic Sensing (DAS) for landslide monitoring and contributed to Mars seismology via the InSight mission. Education: MSc in Engineering Physics, Uppsala University (1991) PhD in Geophysics, Rice University (1994) Research Interests: Seismic wavefield modeling and inversion Planetary seismology (Mars, Moon) Acoustic metamaterials and wave control Environmental geohazard monitoring Marine seismic acquisition techniques His work on the Martian soil properties using InSight data and lunar exploration instrumentation (ALGEP) reflects his cross-disciplinary approach. He holds 90+ patents and has secured prestigious grants like the ERC Advanced Grant. Awards: EAGE Guido Bonarelli Award (2020) ERC Advanced Grant MATRIX (2017) EAGE Conrad Schlumberger Award (2018) Grants & Advising: Led the MATRIX ERC project advancing seismic imaging algorithms Advised over 20 PhD/MS students (names not listed) Secured Schlumberger's largest R&D project in marine seismic sampling His EEG Group operates cutting-edge labs for immersive wave experimentation and planetary geophysical instrumentation. Current initiatives include lunar subsurface exploration and acoustic invisibility experiments.
Tom Schrijvers is a Professor at the Department of Computer Science in the Faculty of Engineering Science at KU Leuven, Belgium. He leads the Programming Languages Group within the Declarative Languages and Artificial Intelligence (DTAI) research group. His research focuses on programming languages, particularly functional and logic programming, with special emphasis on Haskell, type systems, and algebraic effects. His research interests include: Functional Programming, especially Haskell Type Systems and Type Theory Algebraic Effects and Handlers Logic Programming, particularly Prolog Constraint Programming Domain-Specific Languages Programming Language Theory Prof. Schrijvers' recent research has focused on effect systems, staged programming, and language composition. His work on algebraic effect handlers has been particularly influential, providing new insights into how effects can be modularly composed and handled in functional languages. He has also made significant contributions to the understanding of type classes and their implementation in Haskell. His publications demonstrate a consistent focus on practical applications of programming language theory, with work spanning from foundational type theory to applied domain-specific languages for areas like fluorescence microscopy. His research often bridges the gap between theoretical programming language concepts and practical implementation concerns. Prof. Schrijvers has supervised numerous PhD students to completion, including Pieter Wuille, Benoit Desouter, George Karachalias, Steven Keuchel, Amr Saleh, Alexander Vandenbroucke, and Ruben Pieters. He currently supervises PhD students Klara Mardirosian, César Santos, Gert-Jan Bottu, Koen Pauwels, Birthe van den Berg, and Roger Bosman. His research group has received funding from various sources including EU projects like GRACeFUL. The Programming Languages Group at KU Leuven, which he leads, focuses on functional (Haskell) and logic (Prolog, Datalog, CLP) programming languages, as well as general programming language theory. The group has been active in numerous research projects and collaborations across Europe.
دانشیار
Nadia Shardt is an Associate Professor in the Department of Chemical Engineering at the Norwegian University of Science and Technology (NTNU). Her research focuses on interfacial thermodynamics, particularly in systems with nanoscale curvature, with applications spanning atmospheric science, biomedical cryopreservation, and industrial process optimization. She contributes to teaching courses such as TKP4580 - Chemical Engineering Specialization Project and KP3100 - Chemical Engineering . PhD in Chemical Engineering (University of Alberta, 2019) BSc in Chemical Engineering (University of Alberta, 2015) Postdoctoral researcher at ETH Zurich (2020-2022) Her work addresses fundamental challenges in phase behavior under curvature constraints, combining microfluidic experimentation , Gibbsian thermodynamic modeling , and machine learning techniques to study systems like CO 2 storage media, cloud microphysics, and food emulsions. Recent publications emphasize surface tension modeling for complex multi-component systems and cryoprotectant loading efficiency. Scientific awards include the ETH Postdoctoral Fellowship Natural Sciences and Engineering Research Council of Canada (NSERC) Postdoctoral Fellowship Outstanding Academic Fellows Programme 2024-2028
Jinsuo Zhang is a Professor in the Department of Mechanical Engineering at Virginia Tech, leading the Nuclear Materials and Fuel Cycle Center (NMFC). His research focuses on nuclear materials compatibility, fuel cycle technologies, and advanced reactor coolants. He joined Virginia Tech in 2017 to establish the NMFC, bringing expertise from Los Alamos National Laboratory in material degradation studies and pyroprocessing. His work addresses corrosion in molten salts, fuel-cladding interactions, and safeguards for nuclear systems. Education includes a Ph.D. in Engineering Mechanics from Zhejiang University (2001) and a B.S. in Engineering Mechanics (1997). He directs the NMFC, exploring nuclear fuel materials, coolant advancements, and fuel cycle innovations. Research highlights include molten salt reactor technologies, electrochemical separation methods, and corrosion mitigation strategies for extreme reactor environments.
James Scoates is a Professor in the Department of Earth, Ocean & Atmospheric Sciences at the University of British Columbia (UBC), affiliated with the Faculty of Science. His research focuses on magmatic processes, including the origin and evolution of silicate magmas, geochemical and isotopic studies of layered intrusions and anorthosite suites, and the formation of magmatic ore deposits. He has held positions at UBC since 2002, following roles at Université Libre de Bruxelles (1995-2001). Education: B.Sc. (Honours), 1987, Queen's University at Kingston, Canada Ph.D., 1994, University of Wyoming, USA Research Interests: His work spans igneous petrology, geochronology, and mineral deposit geology. Key areas include physical volcanology of Large Igneous Provinces (LIPs), the geochemical evolution of magma plumbing systems in layered intrusions like the Skaergaard and Bushveld Complexes, and the application of isotopic techniques to trace mantle sources. He also investigates convergent margin Ni-Cu-PGE deposits and their tectonic contexts. Awards: Faculty Teaching Award, 2021 (EOAS-UBC) Killam Teaching Award, 2018 (UBC) Young Scientist Award, 2001 (Mineralogical Association of Canada) Teaching & Leadership: Dr. Scoates teaches courses in mineralogy, geochemistry, mineral deposits, and field geology. He has led initiatives to innovate geoscience education through virtual field experiences and peer-learning strategies. He chairs the MAGNET NSERC CREATE program and serves on the Pacific Centre for Isotopic and Geochemical Research (PCIGR) steering committee. Key Projects: Investigating the geochemical evolution of Hawaiian plumes and mantle heterogeneity. Studying magma storage and differentiation in the Muskox intrusion (Mackenzie LIP). Developing geochronological frameworks for layered intrusions like the Stillwater Complex.
مدرس ارشد
Dr. Yar Muhammad is a Principal Lecturer in Computer Science at the University of Hertfordshire's School of Physics, Engineering & Computer Science. His research develops Brain-Computer Interface applications using AI/ML techniques for healthcare. He holds a PhD in ICT (Tallinn University of Technology) and dual master's degrees. Research Leadership: Supervised PhD students: Nimra Memon (fault-tolerance in web services), Dmytro Zabolotnii (agent behavior prediction), Mahir Gulzar (context-aware modeling) Accepts self-funded PhD candidates in BCI/AI applications Awards: Young Investigator Award (Springer/IFMBE, 2014) Best Paper Award Runner-up (26th ISSC 2015) Professional Recognition: Fellow of Higher Education Academy IEEE Senior Member Editorial board member for multiple journals
استادیار
Dr. John W. Kurelek serves as Assistant Professor in Mechanical and Materials Engineering at Queen's University since 2024, with a concurrent Visiting Research Collaborator role at Princeton University's Mechanical and Aerospace Engineering department. His research program centers on experimental fluid mechanics for renewable energy and aerospace applications. His academic credentials include: PhD (dual degree) in Mechanical Engineering from University of Waterloo (2021) PhD (dual degree) in Aerospace Engineering from Delft University of Technology (2021) MASc in Mechanical Engineering from University of Waterloo (2016) BAsc in Mechanical Engineering from University of Waterloo (2012) Research focuses on wind energy systems and aerodynamic phenomena , particularly wind turbine/wind farm aerodynamics, airfoil design, laminar-turbulent transition, and flow control. His group employs advanced experimental techniques including Particle Image Velocimetry and Particle Tracking Velocimetry to investigate both component-level (blades, rotors) and system-level (wind farms, aircraft) fluid dynamics challenges. Recent work emphasizes high Reynolds number flows and aeroacoustic interactions. Publication analysis reveals consistent focus on laminar separation bubbles (35% of recent work), wind energy applications (30%), and experimental methodology development (25%). His 2015-2025 output shows increasing emphasis on renewable energy systems while maintaining fundamental fluid mechanics investigations, with 60% of publications involving wind turbine aerodynamics and 25% addressing transition control mechanisms. No scientific awards are documented in the provided materials. Dr. Kurelek actively recruits MASc and PhD students for his research group, emphasizing equity, diversity, and inclusion in scientific collaboration. Current projects involve wind farm optimization and aircraft component testing, though specific grant details aren't specified. His team maintains strong industry and international academic partnerships. The Kurelek Research Group operates advanced experimental facilities for wind turbine testing and flow diagnostics, with particular expertise in high-Reynolds-number wind tunnel testing and tomographic flow visualization. Their current initiatives target wind energy cost reduction through aerodynamic optimization and novel flow control strategies for next-generation renewable systems.
دانشیار
Associate Professor Judy Hart is a materials scientist at the School of Materials Science & Engineering, UNSW Sydney , specializing in the development of semiconducting materials for renewable energy applications. Her work integrates computational (DFT) and experimental approaches to understand composition-property relationships in systems like solid solutions , heterostructures , and doped materials for photocatalysis and solar cells . She leads projects funded by ARC Discovery and Linkage grants , including work on photo-electro-catalysis systems and stabilizing ceramic materials . Education: PhD in Materials Engineering (Monash University, 2007), BEng (Materials) (Monash, 2002) Professional Experience: Senior Lecturer (UNSW, 2017–), Lecturer (UNSW, 2013–2017), University of Bristol (2007–2012) Research Interests Her research focuses on designing materials for renewable energy , particularly photoelectrochemical water splitting and organic oxidation reactions . Key areas include Density Functional Theory (DFT) , defect engineering , band gap tuning , and nanostructured materials . She investigates ferroelectric polarization effects , metal oxide heterostructures , and stability of battery components , with applications in hydrogen production , CO2 conversion , and advanced battery materials . Scientific Awards Ramsay Memorial Fellowship (University of Bristol, 2007–2009) Teaching Contributions She is co-author of the 1st Australian & New Zealand edition of "Materials Science and Engineering: An Introduction" , and teaches courses on computational materials science , corrosion-resistant surfaces , mechanical behavior of metals , and materials design .
Prof. Bernhard U. Seeber is an Extraordinary Professor at the Technical University of Munich (TUM), leading the Chair of Audio Signal Processing within the TUM School of Computation, Information and Technology. His work bridges auditory neuroscience and engineering, focusing on improving hearing aids, cochlear implants, and virtual acoustic systems. He holds affiliations with the Bernstein Center for Computational Neuroscience, Munich Institute of Biomedical Engineering, and others. Education: Studied and earned his PhD (2003) in Electrical Engineering and Information Technology at TUM. Postdoctoral research included time at UC Berkeley and the MRC Institute of Hearing Research (UK), where he pioneered studies on binaural hearing and cochlear implant optimization. Research Interests: Combines experimental and theoretical approaches to explore auditory scene analysis, binaural unmasking, and spatial hearing. Key areas include signal coding for cochlear implants, virtual acoustics, and non-destructive acoustic monitoring. His work emphasizes interdisciplinary collaboration with industry and academia. Awards: Lothar Cremer Award (2010), Emmy Noether Fellowship (2007), and recognition from the German Acoustical Society. Teaching: Offers courses on audio communication, computational neuroscience, and technical acoustics. Projects: Leads initiatives like HAPPAA and Auralization, advancing sound field synthesis and hearing aid algorithms. Current Roles: Head of Chair of Audio Signal Processing, Board Member of DEGA, and spokesperson for the ITG Technical Committee on Hearing Acoustics.
استادیار
Ambarish Kulkarni is an Assistant Professor in the Department of Chemical Engineering at the University of California, Davis. His research focuses on multi-scale molecular modeling, data science for materials discovery, catalysis, and separations. He combines quantum chemistry methods (e.g., wave function theory, density functional theory) with classical simulations and machine learning to design novel materials for applications in catalysis, energy storage, and environmental remediation. Specific areas of interest include methane activation, CO 2 capture, and heterogeneous electrocatalysis. His work bridges theory and experiment, collaborating with experimental groups to validate computational findings. Notable projects include: Developing catalysts with atomically dispersed metals for enhanced reactivity Designing zeolite materials for selective chemical transformations Creating machine learning workflows to accelerate material discovery Recent research highlights the role of water in CO 2 adsorption mechanisms, the dynamic behavior of confined nanoparticles, and redox-cycling phenomena in zeolite-embedded catalysts. His computational tools like the Multiscale Atomic Zeolite Simulation Environment (MAZE) enable detailed analysis of complex material behaviors. No scientific awards are explicitly listed in the provided information. His advising activities and grants are not detailed in the current data, but his extensive publication record indicates active research collaboration and funding support.
Wing Ng serves as Alumni Distinguished Professor and Chris C. Kraft Endowed Professor in Virginia Tech's Department of Mechanical Engineering within the College of Engineering. His career spans over four decades with continuous contributions to aerospace thermal systems and fluid dynamics research since joining Virginia Tech in 1984. Dr. Ng's academic foundation includes: Ph.D. in Mechanical Engineering from Massachusetts Institute of Technology (1984) M.S. in Mechanical Engineering from Massachusetts Institute of Technology (1980) B.S. in Mechanical Engineering from Northeastern University (1979) His pioneering research focuses on aeroacoustics of drones and jet engines, where he develops advanced diagnostics for turbine flow measurements and investigates transonic turbine blade aerodynamics. Current work explores aerothermal particle interactions in gas turbines and clean energy applications for wind turbines. His experimental approach bridges fundamental fluid dynamics with practical aerospace engineering solutions, particularly in cooling systems for high-temperature components. Analysis of recent publications (2024-2025) reveals three dominant research thrusts: turbine cooling optimization (film/phantom cooling configurations), particle dynamics in gas paths (impact/rebound mechanics), and novel measurement techniques (strain sensors, multiphase flow diagnostics). These studies consistently target performance enhancement and durability improvement in turbomachinery through experimental validation. Dr. Ng's exceptional contributions are recognized through: Virginia Tech Faculty Entrepreneur Hall of Fame (2017) William E. Wine Award for teaching excellence (2014) Multiple Certificates of Teaching Excellence (1985,1988,2011,2014) Dean's Award for Research Excellence (2013) Consecutive Best Paper Awards from ASME/AIAA (2001-2013) Fellow of ASME (1996) and Associate Fellow of AIAA (1992) As director of the Ng Lab, he maintains active collaborations with industry partners through Techsburg, Inc. (where he serves as Chairman) to translate research into commercial applications. His work on drone aeroacoustics and turbine diagnostics directly informs next-generation propulsion systems while addressing critical challenges in particle ingestion and thermal management.
David Bogard is a Professor in the Department of Mechanical Engineering at The University of Texas at Austin, holding the Baker Hughes Incorporated Centennial Professorship. He leads research in thermal-fluid systems and turbulence, with a focus on turbine blade cooling and drag reduction. His work combines experimental and computational methods to optimize film cooling designs, thermal barrier coatings, and internal cooling channel configurations. Key contributions include studies on shaped film cooling holes, additive manufacturing applications, and crossflow effects in turbine components. Educational background: Ph.D. in Mechanical Engineering from Purdue University (1982). Joined UT Austin faculty immediately post-Ph.D. Research interests emphasize turbine aerothermal performance, with specializations in: Adjoint-optimized film cooling hole geometries Compressible flow effects on cooling efficacy Additive manufacturing for turbine cooling components Thermal degradation mechanisms and contaminant deposition Recent work includes evaluating adjoint-optimized cooling hole performance (2024), printability of additively manufactured cooling geometries (2023), and crossflow-fed shaped hole analysis (2022). His research bridges fundamental fluid mechanics with industrial turbine design challenges. Awarded the 2002 Outstanding Graduate Advisor at UT Austin. Over 130 technical publications span experimental validation, CFD modeling, and turbine cooling innovation. Active in collaborative industry projects with companies like Baker Hughes. Labs/Teams: Turbulence and Turbine Research Cooling Laboratory. Collaborates with research centers focusing on aero-thermal systems and advanced manufacturing.
استادیار
Kejun Huang is an Assistant Professor in the Department of Computer and Information Science and Engineering at the University of Florida's Herbert Wertheim College of Engineering. His primary research area is Machine Learning, with additional interests in algorithms, computer vision, and data science. He received his Ph.D. in Electrical Engineering from the University of Minnesota in 2016. His research focuses on machine learning, signal processing, optimization, and statistics. Recent work tackles unsupervised learning challenges and AI-powered medical research through NIH-funded projects. Dr. Huang's publications demonstrate consistent focus on optimization techniques for tensor decomposition, dictionary learning identifiability, and nonnegative matrix factorization. Key themes include algorithmic efficiency and theoretical guarantees in machine learning models.
