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نمایش ۷۲۱-۷۳۵ از ۱٬۳۳۹ استاد راهنما

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

Jacek Brodzkiمشاهده پروفایل

استاد

University of SouthamptonUnited Kingdom

Professor Jacek Brodzki, a faculty member at the University of Southampton since at least 2002, holds the title of Professor of Pure Mathematics and serves as Head of the School of Mathematical Sciences . He co-founded and directs the Centre for Geometry, Topology, and Applications (CGTA) , while leading the Southampton node of the £10M EPSRC-sponsored "Mathematical Foundations of Intelligence: An 'Erlangen Programme' for AI . His research spans topological data analysis , noncommutative geometry , and K-theory , with applications in medicine , physics , chemistry , and computer science . His work includes theoretical advancements in CAT(0) cubical spaces and applied projects like "Joining the Dots: From Data to Insight" , which integrates TDA, machine learning, and statistics for multidisciplinary challenges. Supported by grants from the EPSRC , Leverhulme Trust , and Alan Turing Institute , his research has produced groundbreaking applications in metasurface design , asthma biomarker discovery , and neural network analysis . Recent publications highlight his expertise in topological methods for disordered materials (2023), time-series analysis (2022), and stability of Mapper-type algorithms (2020). His theoretical work on the Baum-Connes conjecture and exactness of groups continues to influence geometric group theory. Scientific awards include Fellowship at the Alan Turing Institute (2019) . He actively supervises PhD students and collaborates across disciplines, spanning mathematics , physics , and precision medicine , and participates in the U-BIOPRED consortium for asthma research.

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Yu Tongمشاهده پروفایل

استادیار

Duke UniversityUnited States

Yu Tong is an Assistant Professor in the Department of Mathematics and Electrical and Computer Engineering at Duke University, and a member of the Duke Quantum Center. His research bridges quantum computation, many-body physics, and mathematical algorithms for quantum systems. Education: Ph.D. in Applied Mathematics, University of California, Berkeley (2022) B.S. in Computational Mathematics, Peking University (2017) Research Focus: Professor Tong develops theoretical and algorithmic frameworks for quantum computation. His work spans: 1) Quantum algorithms for simulating fermionic/bosonic systems and solving quantum chemistry problems; 2) Tensor network methods for analyzing quantum many-body systems; 3) Quantum embedding techniques for scalable simulations; and 4) Quantum learning theory for Hamiltonian identification and conservation law discovery. His research targets both near-term devices and fault-tolerant architectures. Publication Trends: Recent articles focus on quantum simulation efficiency, error mitigation, Hamiltonian learning, and ground-state preparation, with consistent applications in quantum chemistry and condensed matter physics. Methodological innovations include Heisenberg-limited scaling approaches and tensor network optimizations. Academic Service: Program Committee: QCTIP 2023/2025, TQC 2023, QSIM 2025 Editorial Board: Quantum journal

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Matthieu Puigtمشاهده پروفایل

استاد

University of the Littoral Opal CoastFrance

Matthieu Puigt is a Professor at Université du Littoral Côte d'Opale, specializing in signal and image processing with a focus on statistical machine learning , low-rank approximations , and sparse component analysis . His research extends to hyperspectral data fusion, unmixing, and restoration, as well as applications in chemistry and computational imaging . He leads the SPECIFI research team. Research Themes : Blind source separation, compressive learning, sensor calibration, and big data analysis Applications : Audio signal processing, environmental monitoring via drones, and urban air quality assessment His recent work includes developing VAE-based hyperspectral image emulators, tensor decomposition methods for multisensor data, and frameworks for butterfly species recognition. He advised Valentin Mullet's 2022 thesis on blockchain traceability systems and actively contributes to IEEE and GRETSI conferences.

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Mustafa Gökhan UZGÖRENمشاهده پروفایل

استاد

Beykent UniversityTurkey

Mustafa Gökhan UZGÖREN is a Professor in the Department of Electrical and Electronics Engineering at Beykent University. He holds a distinguished academic career with previous appointments as Professor at Istanbul Ayvansaray University, Gedik University, Istanbul Cultural University, Kadir Has University, and Istanbul University. His administrative roles include Department Head, Vice Rector, and Dean across these institutions. His educational background includes a Doctorate (1982), Master's (1977), and Bachelor's (1975) in Electrical Engineering from Istanbul Technical University. UZGÖREN's research focuses on electromagnetic theory , wave diffraction , and scattering phenomena . His work spans analytical methods in wave interactions with complex surfaces, antenna design, and high-frequency electromagnetic modeling. Key areas include dielectric interfaces, impedance boundaries, and curved structures. His publications (1984–2010) predominantly explore wave diffraction mechanics, scattering at discontinuities, and electromagnetic boundary problems, with consistent applications in antenna technology and waveguide systems. Students Advised: 9 graduate theses supervised (1994–2005), including research on 3D object modeling, network security, diffraction theory, and circuit design. He maintains an IEEE membership since 1979 and has authored/co-authored 15+ textbooks on electromagnetic fields, antenna theory, and mathematical methods in engineering.

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Kenny Erlebenمشاهده پروفایل

استاد

University of CopenhagenDenmark

Kenny Erleben is a Professor and Head of the IMAGE research section at the Department of Computer Science, University of Copenhagen . His work integrates physics-based simulation with data-driven approaches, advancing fields like robotics, machine learning, and computer graphics. He leads a team of 10 within IMAGE, a section housing 10 faculty, 2 postdocs, and 20 PhDs, with research foci in computer vision, medical image analysis, and numerical methods. Education: PhD in Computer Science, University of Copenhagen (2005) Erleben’s research interests span physics-based modeling and simulation , robotics automation , data-driven modeling , and digital twins . His team develops numerical methods for interactive simulation, bridging real-world data with synthetic models for applications in surgical simulators and computer games. Scientific production trends include differentiable simulation pipelines, collision detection algorithms, and friction modeling. Key applications involve NVIDIA’s PhysX/Isaac engines, surgical robotics, and medical imaging. His awards include four best paper prizes, reflecting his impact on simulation and robotics. Supervision: 15 PhDs, 8 Postdocs, and 170+ theses Grants: HORIZON-RIA (€6.19M), EU MSCA-ITN RAINBOW (€4M+), and other Innovationsfond/DFF awards Erleben’s community leadership includes coordinating the Danish Academy of Digital Interactive Entertainment (DADIU) and reviewing for 300+ scientific papers. He has held roles like Deputy Head of Department (2012-2015) and Scientific Advisor at Alexandra Institute.

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Lennard Hilgendorfمشاهده پروفایل

پژوهشگر

University of CopenhagenDenmark

Lennard Hilgendorf is a Researcher at the Department of Computer Science , University of Copenhagen . His work focuses on Machine Learning with applications in quantum computing, medical imaging, natural language processing, and environmental sustainability. Research Trends: His recent publications highlight interdisciplinary work at the intersection of quantum mechanics and machine learning, efficient AI architectures for environmental sustainability, explainable models for medical diagnostics, and multimodal approaches to ecological monitoring. Key keywords include Machine Learning , Quantum Computing , Medical AI , and Environmental Science . Labs & Collaborations: Affiliated with the SCIENCE AI Centre and the TreeSense Centre , which specialize in foundational machine learning research and remote sensing for global tree resources, respectively.

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Daniel Hendrik Malzمشاهده پروفایل

استادیار

University of CopenhagenDenmark

Daniel Hendrik Malz is an Assistant Professor in the Department of Mathematical Sciences at the University of Copenhagen. His research focuses on theoretical aspects of quantum technology, with particular emphasis on quantum information theory, quantum optics, and many-body physics. He is embedded in the Quantum for Life center and QMATH research group, and maintains strong collaborations with experimental groups at the nearby Niels Bohr Institute. Dr. Malz received his PhD from the University of Cambridge (2015-2018) under the supervision of Andreas Nunnenkamp, following his BA and MMath at Trinity College, Cambridge (2011-2015). He held postdoctoral positions at the Max Planck Institute of Quantum Optics (2018-2022) working with Ignacio Cirac, and at the Technical University of Munich (2022-2023) with Johannes Knolle, before joining the University of Copenhagen as an Assistant Professor in April 2023. His research program spans several interconnected areas in quantum physics. In quantum optics, he investigates many-body phenomena in open quantum systems, particularly collective effects like superradiance in waveguide QED and chiral systems. His work in quantum information theory focuses on the computational complexity of quantum many-body problems, random quantum circuits, and the boundary between classically simulable and quantum-computationally hard problems. He has made significant contributions to tensor network state preparation, developing efficient methods for creating these important quantum states. In quantum simulation, he explores novel approaches to simulating complex quantum systems, particularly using atomic arrays and superconducting circuits. Dr. Malz's publication record shows a strong focus on theoretical quantum physics with increasing engagement with experimental implementations. His recent work demonstrates a trend toward more applied research that bridges theoretical concepts with experimental feasibility, particularly in the areas of photonic quantum computing and superconducting circuit implementations. The breadth of his work spans fundamental theoretical questions to potential applications in quantum simulation and computation. Dr. Malz is actively building his research group at the University of Copenhagen and is hiring both PhD students and postdoctoral researchers. He maintains strong connections with experimental groups, particularly in the quantum optics section of the Niels Bohr Institute, and is part of Copenhagen's vibrant quantum research ecosystem, which includes the Novo Nordisk Quantum Computing Programme. His research group operates within the Department of Mathematical Sciences but is deeply integrated with Copenhagen's broader quantum research community, including collaborations with the Niels Bohr Institute and participation in the Quantum for Life center. This interdisciplinary environment allows for close interaction between theoretical and experimental researchers across multiple quantum platforms.

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Davide Girolamiمشاهده پروفایل

دانشیار

Polytechnic University of TurinItaly

Davide Girolami is an Associate Professor at the Department of Applied Science and Technology (DISAT) , Politecnico di Torino . He is affiliated with the Institute of Condensed Matter Physics and Complex Systems and leads the Nanophysics and Quantum Systems research group. His teaching portfolio includes delivering Introduction to Entanglement for the PhD program in Physics and Fisica I for Aerospace Engineering undergraduates. Research Interests focus on Quantum Information Science , Quantum Computing , and Quantum Correlation Theories . Key contributions include: Developing purity-based methods for entanglement quantification (2024) Establishing quantum-classical boundary through correlation suppression (2022) Formulating weighted distance metrics for many-body state comparison (2021) Advancing quantum metrology via local measurement protocols (2017) Creating information-theoretic coherence measures (2014) Article Trends reveal consistent focus on quantum resource theories and correlation quantification across 15 recent works. Topics span quantum state preparation complexity , causal inference in quantum dynamics , and scalable measurement protocols for experimental validation. Scientific Awards include: Universitas 21 Prize (2012) Graduate School Travel Prize (2011), both from The University of Nottingham

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Marion Menzelمشاهده پروفایل

استاد

Ingolstadt University of TechnologyGermany

Marion Menzel is a Professor and Vice Dean at the Faculty of Electrical Engineering and Information Technology (Technische Hochschule Ingolstadt). She specializes in Biomechatronics and Sensor Data Analysis, with a focus on Medical Imaging and Magnetic Resonance Imaging (MRI). Her research emphasizes quantitative MRI techniques, including MR fingerprinting, deep learning applications in medical imaging, and hyperpolarized MRI for metabolic analysis. Education highlights include a Ph.D. (1999–2002) and Diplom-Chemiker (1994–1999) from RWTH Aachen. She has held roles at GE Healthcare, Siemens AG, and Forschungszentrum Jülich before joining THI in 2021. Her work bridges engineering and clinical practice, addressing challenges in imaging reconstruction, parameter mapping, and AI-driven diagnostics. Research interests span MRI acceleration, uncertainty quantification, and multimodal data fusion. She has pioneered methods like StoDIP and MRI2Qmap, advancing 3D imaging and compressive sensing. Her contributions to hyperpolarized 13C MRI enable non-invasive metabolic profiling in oncology and diabetes research. Key awards include recognition as an Alumna of the German Academic Scholarship Foundation. Her lab collaborates on projects like CHAIMELEON, a Pan-European initiative for AI-driven cancer management tools. Recent grants focus on improving diagnostic accuracy through AI and spatiotemporal reconstruction algorithms. Menzel’s team develops open-source frameworks for MRI analysis and actively participates in standardizing medical imaging protocols. Her work has direct clinical impact in neuro-oncology, radiology, and precision medicine.

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Nicholas Mayhallمشاهده پروفایل

دانشیار

Virginia Polytechnic Institute and State UniversityUnited States

Nicholas J. Mayhall is an Associate Professor in the Department of Chemistry at Virginia Tech, affiliated with the College of Science. His research focuses on computational and quantum chemistry, particularly in developing novel quantum algorithms for molecular simulations and renewable energy applications. He holds a B.S. from the University of Southern Indiana (2006), a Ph.D. from Indiana University (2011), and completed a postdoc at UC Berkeley (2011–2015). Research Interests: Quantum computing methods, tensor product wavefunctions, adaptive variational algorithms (e.g., ADAPT-VQE), and strongly correlated systems. He leads the Mayhall Group, which contributes to software tools like FermiCG and CtrlQ for quantum chemistry simulations. Awards: Alfred P. Sloan Research Fellow (2021), NSF CAREER Award (2018), and ACS Postdoctoral Research Award (2014). His group includes 10+ graduate students/postdocs working on quantum algorithms, molecular dynamics, and quantum hardware optimization.

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Georgios Tzimpragosمشاهده پروفایل

استاد

University of Wisconsin-MadisonUnited States

Georgios Tzimpragos is a Professor in the Department of Electrical & Computer Engineering at the University of Wisconsin-Madison, with a focus on computer architecture and novel computing technologies. Previously, he was an Assistant Professor at the University of Michigan (2022–2025) and earned his PhD from UC Santa Barbara (2022), focusing on neuro-inspired temporal paradigms for superconducting computing. His research explores overcoming technology limitations in superconductors, quantum computing, and biological computing systems, aiming to bridge computation and verification for improved correctness and security. Education: PhD in Computer Science, University of California-Santa Barbara (2022) MS in Computer Science, University of California-Davis (2016) BS in Computer Science, National Technical University of Athens (2012) Research Interests: His work spans computer architecture, novel computing technologies (e.g., superconductors, flexible electronics), and brain-inspired computing. Recent projects include developing energy-efficient superconducting circuits, quantum computing hardware, and neuromorphic systems that integrate biological principles. His group emphasizes formal verification and semantic gap reduction in computing systems. Recent Publications Trends: His articles address topics like superconducting logic design, quantum circuit simulation, and neuromorphic hardware acceleration. Recent work highlights include clockless superconducting logic, evolutionary neural network optimization, and formal methods for circuit validation. Awards & Honors: James E. Smith Professorship (UW-Madison, 2025) ACM SIGARCH & IEEE CS TCCA Outstanding Dissertation Award (2023) Quantum Research Institute Fellowship (U-Michigan, 2024) Grants & Teaching: Teaches E C E 554 (Digital Engineering Laboratory) and has received grants supporting superconducting and quantum computing research. His lab collaborates with industry partners on low-power and scalable architectures.

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Yu Zhangمشاهده پروفایل

پژوهشگر

University of SheffieldUnited Kingdom

Dr. Yu Zhang is a Researcher at the School of Computer Science, University of Sheffield, affiliated with the Pervasive Computing research group. His work focuses on advanced machine learning techniques applied to healthcare and agricultural domains. He specializes in multi-task learning, spatio-temporal data analysis, and tensor methods. Key research areas include Alzheimer’s disease progression prediction using neuroimaging data and precision fertilization management through sustainable farming systems. His contributions bridge computational methods with real-world applications in healthcare analytics and environmental sustainability. Dr. Zhang’s research integrates temporal relation graphs, adaptive control systems, and interpretable AI frameworks to address complex challenges in disease modeling and agricultural optimization. He explores innovations like continuous incremental learning and randomized feature learning to enhance predictive accuracy and scalability. His work emphasizes practical applications, such as the ParallelFarm system for carbon-neutral farming and frameworks for medical decision-making based on MRI data analysis. His research trends show a strong focus on multi-task learning frameworks that adapt to temporal dynamics, with applications spanning healthcare biomarker identification, resource-efficient farming, and robust algorithm design. Collaborations likely involve interdisciplinary teams in computer science, neuroscience, and environmental science. Future directions may include expanding these methods to other healthcare domains and environmental sustainability initiatives. No scientific awards are explicitly mentioned in the provided text. Advising and grant details are not listed here, but his affiliations suggest involvement in collaborative research projects within the School of Computer Science. His work is rooted in the Pervasive Computing group, emphasizing embedded systems and AI-driven solutions for real-world problems.

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Nick O. Jaenssonمشاهده پروفایل

استادیار

Eindhoven University of TechnologyNetherlands

Nick O. Jaensson is an Assistant Professor (tenure track) in the Processing and Performance of Materials group at Eindhoven University of Technology (TU/e). He holds affiliations with the ICMS and is part of the Mechanical Engineering department. His research focuses on numerical methods for soft materials, including complex fluids like suspensions, emulsions, and polymeric liquids. He collaborates with industry on optimizing processes such as microfluidics and material processing. Academic Background: Master's in Biomedical Engineering (2012) and PhD in Polymer Technology (2016), both from TU/e. Postdoc at ETH Zürich (2018) before joining TU/e in 2020. Research Interests: Non-Newtonian fluid mechanics, interfacial rheology, uncertainty quantification, and physics-informed machine learning. His work bridges fundamental insights into material microstructure and industrial applications. Award: Walters Prize (2018) for contributions to non-Newtonian fluid mechanics. Teaching & Courses: Advanced computational continuum mechanics, interfacial transport phenomena, and design/programming principles. Supervised 19 students (no names listed). Projects: Leads the DAMOCLES project (2021–2025), focusing on data-augmented modeling for engineering systems. Labs/Teams: Head of the Processing and Performance of Materials group, collaborating with experimental and computational teams.

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Gonzalo Arceمشاهده پروفایل

استاد

University of DelawareUnited States

Dr. Gonzalo Arce is the Charles Black Evans Professor and JPMorgan-Chase Senior Faculty Fellow at the University of Delaware. His research spans computational imaging, signal processing on graphs, and machine learning, with applications in biomedicine, Earth science, and lithography. He leads interdisciplinary projects in computational compressive lidar, spectral tomography, and hypergraph neural networks, collaborating globally with institutions in Finland, China, and beyond. Dr. Arce has held the Nokia-Fulbright Distinguished Chair and is a Fellow of IEEE, SPIE, and the National Academy of Inventors. His research focuses on advancing imaging technologies through compressive sensing, generative AI, and hypergraph structures. Notable contributions include SpectralCam (low-cost spectral imaging) and super-resolution techniques for satellite LiDAR. He advises on patent litigation involving signal/image processing and QR codes. With over 800 publications and 30 patents, he actively contributes to the Data Science Institute and IFSAN at UD. Awards: National Academy of Inventors Fellow, IEEE Life Fellow, SPIE Fellow, AAIA Fellow Teaching: Courses on machine learning and deep learning in imaging Labs/Teams: Collaborates with global research groups in computational imaging and financial analytics

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Hanghang Tongمشاهده پروفایل

استاد

University of Illinois Urbana-ChampaignUnited States

Hanghang Tong is a Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign. He also holds affiliations with the National Center for Supercomputing Applications (NCSA) and the Center for Digital Agriculture. His research interests span machine learning, data mining, graph theory, and ethical AI, with a focus on network analysis, fairness, and large-scale data systems. Tong has been recognized with prestigious awards including the IEEE Fellow (2022), ACM Distinguished Member (2020), and NSF CAREER Award (2019). His recent work explores topics like LLM safety, time series analysis, and network alignment through tools like PLANETALIGN. Tong actively contributes to open-source libraries and benchmarks, advancing reproducibility and ethical standards in AI. Education : Not explicitly listed in provided texts. His research emphasizes practical applications of AI in climate science, healthcare, and social networks. Tong's work often addresses challenges in model fairness, scalability, and cross-domain adaptation. He collaborates widely, contributing to both foundational theory and real-world systems. Key projects include CLIMB (class-imbalanced learning benchmark) and Saffron-1 (LLM safety assurance). Tong’s lab at NCSA and Siebel School drives innovation in AI-driven data science and computational methods.

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