جست‌وجوی استاد و پژوهشگر

Keenan Craneمشاهده پروفایل

دانشیار

• Computer Science
• Mathematics
• Scientific Computing
• +۷ مورد دیگر

Keenan Crane is the Michael B. Donohue Associate Professor of Computer Science and Robotics at Carnegie Mellon University , with membership in the Center for Nonlinear Analysis and mentorship in the Geometry Collective . His research bridges differential geometry and computer science to develop fundamental algorithms for geometric data processing. Education : BS from University of Illinois at Urbana-Champaign, PhD from Caltech Fellowships : Google PhD Fellow, NSF Mathematical Sciences Postdoctoral Fellow Research focuses on Discrete Differential Geometry , addressing PDE solutions, mesh processing, and geometric modeling through methods like: Walk on Spheres for PDEs Intrinsic Triangulations for robust geometry Repulsive Energy formulations for collision avoidance Recent publications span 2025–2021 , emphasizing grid-free algorithms , anisotropic mesh generation , and differentiable systems . Scientific accolades include Packard Fellowship and NSF CAREER Award . Students include Nicole Feng , Olga Gutan , and Zoë Marschner . During his 2024 sabbatical at Roblox , he does not accept new researchers. Key software contributions include Penrose (math diagram generation) and I♥Mesh (domain-specific language for mesh algorithms).

• Signal Processing
• Electronics
• Biomedical Engineering
• +۵ مورد دیگر

Marek Miśkowicz serves as a Professor at the Department of Metrology and Electronics within the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków, Poland. His primary institutional contact is miskow@agh.edu.pl, with physical location in building B-1, room 212. His research spans signal processing, biomedical engineering, and electronics, specializing in event-based sampling methodologies, time-to-digital conversion techniques, and reconstruction of bandlimited signals from nonuniform samples. Key contributions include QRS detection algorithms for ECG monitoring, POCS-based reconstruction frameworks, and event-driven control systems for industrial IoT applications. His work emphasizes resource efficiency in embedded systems and mobile health monitoring through approximate computing and adaptive sampling strategies. Recent publications (2022-2025) demonstrate consistent focus on signal reconstruction from irregular samples, with growing emphasis on spiking neural networks for event classification and industrial IoT optimization. Biomedical applications (particularly ECG analysis) and industrial control systems represent dominant application domains, while methodological innovations center on iterative reconstruction algorithms and temporal accuracy evaluation in noisy environments. No scientific awards were referenced in the source materials. No information regarding student advising or research grants was available in the provided documentation. The source texts contained no details about laboratory facilities, research teams, or collaborative groups associated with Professor Miśkowicz.

• Signal Processing
• Engineering Education
• Frequency Domain Radars
• +۳ مورد دیگر

Lars Magne Lundheim is a Professor at the Department of Electronic Systems at Norwegian University of Science and Technology (NTNU). He holds an MSc and PhD in electrical engineering from NTNU. His career includes roles as Associate Professor (2002–2010), Research Scientist at SINTEF, and part-time academic positions. He belongs to the Signal Processing Group and focuses on signal processing for radar systems, power consumption optimization, and engineering education. His research spans technical domains like multicarrier communication systems and pedagogical innovations such as project-based learning and curriculum design. Notable contributions include work on sustainability integration in STEM education and competency development through active learning strategies. Lundheim is a Senior Member of IEEE and has reviewed for major journals/conferences including IEEE Transactions on Signal Processing. Recent publications emphasize educational frameworks linking mathematics with engineering practice, while earlier works address technical challenges in radar distortion correction and power-efficient multiplier design. He has contributed to 16 PhD examination committees and teaches courses like TTT4270 (Electronic System Design) and TTT4260 (Electronic System Design and Analysis). Lundheim’s outreach includes presenting at events like the Læringsfestivalen and collaborating on national initiatives like the 'Fremtidens teknologistudier' educational strategy report. His interdisciplinary work bridges technical innovation with pedagogical excellence in electrical engineering education.

Nir Dayanمشاهده پروفایل

پژوهشگر

• Materials Science
• Biomedical Engineering
• Nuclear Magnetic Resonance (NMR)
• +۳ مورد دیگر

Nir Dayan is a Researcher in the Department of Chemical Engineering and Materials Science at Michigan State University's College of Engineering. His work focuses on advanced spectroscopic techniques including electron spin resonance, nuclear magnetic resonance, and cryo-electron microscopy, with applications in materials science, biomedical engineering, and structural biology. Dr. Dayan is affiliated with the Bioengineering Building and holds a position emphasizing experimental and numerical analysis of novel materials and biological systems. His research interests span the development of microfluidic ESR systems, solid-state NMR methodologies, and structural characterization of viral particles such as the IKe phage. Recent work includes pulsed ESR applications for biodosimetry and ultra-miniature resonator technologies. These studies integrate interdisciplinary approaches across physics, chemistry, and bioengineering. Key technical contributions include innovations in nonuniform NMR sampling, cryo-EM structural analysis, and advanced resonator design for single-crystal studies. While no specific grants or awards are listed, his publication record demonstrates sustained contributions to high-impact areas of materials characterization and biophysical research.

Paul Rosenمشاهده پروفایل

دانشیار

• Computer Graphics
• 3D Visualization
• Interactive Rendering
• +۳ مورد دیگر

Paul Rosen is an Associate Professor at the University of South Florida within the Department of Computer Science and Engineering. His research focuses on computer graphics, visualization, and geometric modeling, with significant contributions in camera models, 3D rendering, and interactive visualization techniques. Academic Rank: Associate Professor Affiliation: University of South Florida, Department of Computer Science and Engineering Research Areas: Computer Graphics, 3D Visualization, Geometric Modeling, Image Processing, Human-Computer Interaction. His publications span topics including Bézier curves, nonpinhole camera approximations, and volumetric display perception studies. While he collaborates with institutions like Purdue University (CGVLab), he is currently an active faculty member at USF, not part-time, retired, or former staff.

Melanie Kircheisمشاهده پروفایل

استادیار

• Numerical Mathematics
• Fast Fourier Transform
• Sampling Theory
• +۲ مورد دیگر

Dr. Melanie Kircheis is a Junior Professor in Numerical Mathematics at the Faculty of Mathematics, Chemnitz University of Technology. She maintains an active research program focused on numerical methods for Fourier analysis and sampling theory, with significant contributions to the field of nonuniform fast Fourier transforms. Her research expertise spans numerical mathematics with particular emphasis on Fast Fourier Transform algorithms, sampling theory, and their applications in signal processing and medical imaging. Dr. Kircheis has developed innovative methods for the inversion of nonequispaced fast Fourier transforms and has investigated regularization techniques for Shannon sampling formulas. Her work bridges theoretical foundations with practical implementations, addressing computational challenges in reconstructing Fourier transforms from irregularly sampled data. Analysis of Dr. Kircheis's publication record reveals a sustained research trajectory with increasing impact. Her work demonstrates progression from theoretical foundations of sampling theorems to sophisticated algorithmic implementations. A significant portion of her research addresses the mathematical and computational challenges of reconstructing Fourier transforms from nonequispaced samples, with direct applications in medical imaging technologies such as MRI. Her recent publications indicate continued innovation in parameter optimization for sampling methods and extension of these techniques to multivariate settings. Dr. Kircheis has established a productive research collaboration with Professor Daniel Potts at Chemnitz University of Technology, as evidenced by their co-authored publications spanning multiple years. Her research has been published in reputable journals including Linear Algebra and its Applications and Sampling Theory, Signal Processing, and Data Analysis, with her 2019 paper on direct inversion of the nonequispaced fast Fourier transform accumulating 34 citations.

Valery Nakariakovمشاهده پروفایل

استاد

• Solar Physics
• Magnetohydrodynamics
• Coronal Seismology
• +۲ مورد دیگر

Valery Nakariakov is Full Professor of Physics at the University of Warwick and holds honorary positions at Stanford University. His research in solar physics and plasma astrophysics focuses on magnetohydrodynamic (MHD) waves observed in solar atmospheres, solar wind, and planetary magnetospheres. He pioneered MHD Coronal Seismology, a diagnostic method for remote plasma analysis using wave properties. Nakariakov's work explores wave behavior in nonuniform, nonlinear space plasmas, examining phenomena like solitons, negative energy effects, and self-organization. His honors include the Royal Astronomical Society's Chapman Medal (2024) and Institute of Physics' Payne-Gaposchkin Medal (2015). He serves on editorial boards for MIT journal Grey Room and PMLA, and supervises graduate research in astrophysical plasma waves. His publications demonstrate consistent investigation into wave-based diagnostics of solar corona properties, oscillation mechanisms in coronal structures, and development of novel observation techniques. Research frequently examines energy transfer processes through MHD waves and their role in coronal heating.

Håkan Johanssonمشاهده پروفایل

استاد

• Signal Processing
• Communications Engineering
• Digital Filter Design
• +۳ مورد دیگر

Håkan Johansson is a Professor at the Department of Electrical Engineering (ISY) at Linköping University, Sweden, affiliated with the Division of Communication Systems (KS). This division conducts research and education in communications engineering, statistical signal processing, and network science, emphasizing strong industrial collaborations and practical research applications. His research spans Signal Processing , Communications Engineering , and Digital Filter Design , focusing on low-complexity and reconfigurable solutions for communication systems. Key areas include power amplifier linearization, advanced sampling techniques, and equalization algorithms for 5G/6G and massive MIMO systems, with strong emphasis on implementation efficiency and real-world applicability. Recent publications (2024-2025) reveal a concentrated effort on energy-efficient signal processing for next-generation wireless infrastructure. Dominant themes include linearization of power amplifiers in multi-antenna systems, reconstruction of complex signals from nonuniform sampling, and machine learning integration for UAV communications, reflecting industry-driven priorities for 6G development. No scientific awards were documented in the provided materials. Professor Johansson operates within a robust research ecosystem at ISY, supervising PhD students in the Communication Systems division alongside colleagues like Erik G. Larsson (Head of Division). While specific grant details are absent, his extensive publication record indicates sustained funding for high-impact telecommunications research. He contributes to Linköping University's Communication Systems division, a collaborative hub bridging theoretical signal processing with industrial applications. The team specializes in end-to-end communication solutions from algorithm design to hardware implementation, maintaining strong ties with Sweden's telecommunications sector for rapid technology transfer.

Tilo Arensمشاهده پروفایل

مدرس

• Inverse Problems
• Numerical Analysis
• Partial Differential Equations
• +۳ مورد دیگر

Dr. Tilo Arens is a Lecturer at the Institute for Applied and Numerical Mathematics , part of the Karlsruhe Institute of Technology (KIT) . His research focuses on numerical methods for scattering problems , particularly involving electromagnetic waves , chiral scatterers , and inverse scattering problems . He actively contributes to the Working Group 4: Inverse Problems and collaborates on projects within the CRC 1173 Wave Phenomena . His teaching portfolio includes courses such as Higher Mathematics II for engineering disciplines, Optimization Theory , and Scattering Theory . He also supervises bachelor’s and master’s theses on topics like Approximation of rotation-minimizing frames using B-spline curves and Numerical methods for integral equations . Dr. Arens has authored numerous publications on inverse scattering, integral equations, and computational methods. Notable works include studies on monotonicity-based shape reconstruction , high-order numerical methods for bi-periodic structures , and electromagnetic chirality measurement . His research intersects with applied mathematics, physics, and computational science. He previously collaborated on a student seminar with Heisenberg-Gymnasium Karlsruhe to bridge university and secondary education in mathematics. His habilitation (2010) and PhD (2000) are in electromagnetic and elastic wave scattering, respectively.

Nicole Eikmeierمشاهده پروفایل

دانشیار

• Network Science
• Higher-Order Interactions
• Dynamic Networks
• +۳ مورد دیگر

Nicole Eikmeier is an Associate Professor at Grinnell College , holding the Luebke-Sproehnle Endowed Chair in Computer Science . Her research focuses on network science , particularly higher-order interactions , spectral clustering , and dynamic network modeling . She teaches courses in algorithms, theory, and the societal implications of technology. Education: Ph.D. in Mathematics, Purdue University (August 2019) B.A. in Computer Science and Mathematics, Concordia College (May 2012) Nicole’s research examines the structure and dynamics of complex networks. She develops models for nonuniform hypergraphs , analyzes centrality measures in dynamic systems , and investigates the topological properties of higher-order networks . Her work bridges theoretical mathematics and applied computer science, with implications for social, epidemiological, and technological systems. Her publications span topics like COVID-19 transmission modeling in educational institutions , tensor decomposition for dynamic topic analysis , and stochastic processes on tree networks . She has presented at premier conferences including ACM SIGKDD and IEEE ICDM . Scientific Awards: NSF Grant Research Corporation for Science Advancement Grant Luebke-Sproehnle Endowed Chair Nicole actively explores educational frameworks integrating ethics into computer science curricula and investigates alliances and leadership in competitive networks . She maintains affiliations with institutions like the Alan Turing Institute .

• Analog Integrated Circuits
• Mixed-Signal VLSI Design
• High-Speed Data Converters
• +۷ مورد دیگر

Yan Zhu is a faculty member at the University of Macau , affiliated with the Analog and Mixed Signal VLSI Laboratory within the Faculty of Science and Technology. She has a strong research focus on high-performance analog and mixed-signal integrated circuits, particularly in data conversion and low-power design. Her research interests include: Analog and Mixed-Signal VLSI Design High-Speed Data Converters (ADCs) Noise-Shaping and Time-Domain Circuits PVT-Robust and Low-Power Circuit Techniques Compute-in-Memory and AI Hardware Acceleration The recent publications of Yan Zhu demonstrate a clear trend toward advanced ADC architectures such as time-interleaved, pipelined-SAR, and time-domain converters, with a strong emphasis on calibration, linearity, and energy efficiency. Her work frequently appears in top-tier journals like IEEE JSSC and conferences like ISSCC and CICC, indicating leadership in the field of analog circuit design. There is also a growing focus on machine learning hardware, particularly analog compute-in-memory systems for edge AI applications. No scientific awards or honors are mentioned in the provided text. Yan Zhu has made significant contributions through collaborative research, particularly with Chi-Hang Chan and Rui Paulo Martins , and has been involved in numerous projects related to ADC calibration, metastability, and high-speed sampling. While specific grant details are not listed, the volume and quality of publications suggest active funding support. She has not listed any advisees in the provided data. She is a core contributor to the Analog and Mixed Signal VLSI Laboratory at the University of Macau, where her team focuses on cutting-edge IC design for communication, sensing, and artificial intelligence applications.

Ben Adcockمشاهده پروفایل

استاد

• Mathematical Data Science
• Numerical Analysis
• Approximation Theory
• +۳ مورد دیگر

Ben Adcock is a Professor of Mathematics at Simon Fraser University (SFU), Department of Mathematics, Faculty of Science. He holds a PhD from the University of Cambridge (2010) and has held postdoctoral fellowships at SFU and Purdue University. His research focuses on mathematical data science, numerical analysis, approximation theory, compressed sensing, and machine learning applications in scientific computing. Education: PhD in Mathematics (2010, University of Cambridge), MMath (2006), BA (2005). Prior roles include Assistant Professor at Purdue University (2012–2014) before returning to SFU as faculty. Awards include the CAIMS/PIMS Early Career Award (2017), Sloan Fellowship (2015), and Leslie Fox Prize (2011). Research interests span sparse regularization techniques, compressed sensing theory, deep neural networks for scientific computing, and high-dimensional function approximation. Recent work emphasizes optimal sampling strategies, stability in deep learning for image reconstruction, and Hilbert-valued function approximation. Publications reflect contributions to compressive imaging, function approximation, and algorithmic stability. Notable books include Sparse Polynomial Approximation of High-Dimensional Functions (SIAM, 2022) and Compressive Imaging: Structure, Sampling, Learning (CUP, 2021). Supervised students include Juan M. Cardenas (2021 SIAM CSE poster award winner) and Yi Sui. He leads editorial roles at SIAM journals and co-organized the 2020 Foundations of Computational Mathematics conference.

Won Namgoongمشاهده پروفایل

استاد

• RF/Analog Circuits
• Signal Processing Systems
• Cognitive Radios
• +۲ مورد دیگر

Won Namgoong is a Professor in Electrical and Computer Engineering at the University at Albany (SUNY). His research centers on DSP-assisted analog/RF circuits for cognitive radios, high-speed links, and sensor systems. An NSF CAREER Award recipient, he previously worked at UT Dallas, USC, and Atheros Communications. Namgoong's innovations include digital distortion cancellation for SAW-less receivers, sub-Nyquist cognitive radios, and noise-suppressed PLL designs. He has served on editorial boards for IEEE Transactions on Circuits and Systems and the Journal of Signal Processing Systems. His industry-academia hybrid expertise bridges signal processing theory with practical RF/analog implementations.

Unal Sakogluمشاهده پروفایل

دانشیار

• Signal Processing
• Image Processing
• Machine Learning
• +۶ مورد دیگر

Dr. Unal 'Zak' Sakoglu is an Associate Professor of Computer Engineering at the University of Houston-Clear Lake's College of Science and Engineering. He holds a B.S., M.S., and Ph.D. in Electrical and Computer Engineering from the University of New Mexico (UNM). His research focuses on signal/image processing, medical imaging analysis, machine learning, and neuroimaging applications. He has contributed to the development of non-uniformity correction algorithms for infrared sensors, dynamic functional connectivity analysis in fMRI, and space-filling curve methodologies for multidimensional data analysis. His work has been supported by grants from DOD, AFOSR/AFRL, and private industry. Education: Ph.D. in Electrical and Computer Engineering (UNM), Albuquerque, NM Postdoctoral Work: UNM Neurology Department BRAIN Imaging Center, UT Southwestern Medical Center, Abbott Laboratories Dr. Sakoglu's research interests include advancing brain function understanding through machine learning applied to neuroimaging data, with applications in schizophrenia, Gulf War Illness, and multiple sclerosis. He has pioneered techniques for improving fMRI classification accuracy and developing novel sampling methods for data visualization. Recent articles highlight advancements in lesion segmentation using U-Net architectures, fMRI-based connectivity analysis, and infrared sensing for environmental monitoring. He has received teaching accolades including UHCL Piper Teaching Award nominations and multiple AFOSR/AFRL fellowships. Grants: DOD CDMRP Grant (2016-2018), NCSI XSEDE EMPOWER Grants (2020-2021) Lab Focus: Neuroimaging software development, medical data analysis tools, and interdisciplinary collaborations in neuroscience and engineering

• Functional Analysis
• Artificial Intelligence
• Computational Mathematics
• +۴ مورد دیگر

Anders Christian Hansen is a Professor at the University of Oslo and a Lecturer at the University of Cambridge. He holds positions as Royal Society University Fellow and Marie Curie Fellow. His research focuses on Functional Analysis, Artificial Intelligence, and Computational Mathematics, with emphasis on numerical analysis, inverse problems, and medical imaging. Hansen has authored numerous high-impact publications in journals like PNAS and SIAM, addressing challenges in compressed sensing, spectral computations, and AI stability. Education Ph.D., Mathematics, University of Cambridge (2008) M.A., Mathematics, University of California, Berkeley (2005) Siv. Ing., Norwegian University of Science and Technology (2002) Research Interests Develops mathematical frameworks for stable signal recovery Advances compressed sensing theory for real-world applications Explores computational barriers in AI and deep learning Specializes in spectral computations and operator theory Awards Royal Society University Research Fellowship Marie Curie Fellowship Labs/Teams Engaged with computational mathematics and operator algebra research groups at the University of Oslo and University of Cambridge.