جستجوی استاد راهنمای دانشگاهی

• Applied Statistics
• Biostatistics
• Time Series Analysis
• +۳ مورد دیگر

Dr. Carolina Euan is a Lecturer in Statistics at the School of Mathematical Sciences, Lancaster University, with affiliations to the Data Science Institute and STOR-i Centre for Doctoral Training. Her research focuses on time series analysis, spatio-temporal modeling, and their applications in environmental data science and brain data analysis. School of Mathematical Sciences Data Science Institute STOR-i Centre for Doctoral Training Biostatistics Research Group Centre of Excellence in Environmental Data Science Her work spans multiple disciplines: Environmental Data Science: Marine heatwaves, solar irradiance modeling, particle number size distribution Brain Data Analysis: Neural connectivity, brain signal clustering, coherence-based inference Statistical Methodology: Spatio-temporal extremes, spectral estimation, functional data analysis Recent publications demonstrate expertise in: 2025: Neural connectivity modeling, functional data analysis, particle distribution 2024: Virtual collaboration frameworks, precipitation regime modeling, spectral estimation 2023: Brain signal clustering, source apportionment, directional wave spectra Supervision includes: Kajal Dodhia: STOR-i (extreme sea temperatures) Jordan Hood: Bayesian modeling (COVID prevalence) Carla Pinkney: STOR-i

• Graphene Electronics
• Microwave Engineering
• Terahertz Technology
• +۳ مورد دیگر

Omid Habibpour is an Assistant Professor at the Microwave Electronics lab within Chalmers University of Technology. His research focuses on graphene-based devices and MMICs for high-frequency applications. B.Sc.: Electrical Engineering (Telecommunication Systems), Sharif University of Technology (2002) M.Sc.: Optical Telecommunication Systems (with honors), Amirkabir University of Technology (2004) His research spans Graphene Electronics , Microwave Engineering , and Terahertz Technology , emphasizing material characterization, device modeling, and MMIC design for high-data-rate communication systems. Recent work includes voltage-dependent mobility studies and zero transconductance resistance analysis in graphene FETs. Projects include the Graphene Core Project 3 (European Commission) and Quad Band Infrared Detector (VINNOVA). Publications trend toward graphene integration in microwave/THz systems and SiC substrate applications.

Christopher Hassallمشاهده پروفایل

دانشیار

• Entomology
• Mimicry
• Camouflage
• +۸ مورد دیگر

Dr. Christopher Hassall is an Associate Professor of Animal Biology at the School of Biology, University of Leeds , with research spanning entomology, climate change impacts, urban ecology, and conservation science. He leads the Hassall Lab and the BioDAR Project , which uses weather radar for biodiversity monitoring. Research Interests Quantifying insect abundance via weather radar (BioDAR/PestDAR/DRUID projects) Urban freshwater ecosystems and socio-ecological dynamics Evolution of insect camouflage/mimicry and flight behavior Climate change impacts on pollinators and invasive species Shadow diversity and extinction studies (Leverhulme DTP Co-Director) Scientific Awards Fellow of the Higher Education Academy Fellow of the Royal Entomological Society Advising : Supervises postgraduate researchers including Sicily Fiennes , Isabella Flowers , and Mx Solanum Foulstone , with a focus on interdisciplinary extinction studies and urban entomology.

Rafael Sebastianمشاهده پروفایل

استاد

• Cardiac Computational Modeling
• Artificial Intelligence in Medicine
• Medical Imaging
• +۴ مورد دیگر

Rafael Sebastian is a Full Professor at Universitat de Valencia and General Director for Science and Research of the Generalitat Valenciana. He leads the Computational Multiscale Simulation Lab (CoMMLab) and collaborates with institutions like Oxford University and Yale University. Department of Computer Science, Universitat de Valencia CoMMLab Founder Spanish Network of Excellence in Cardiac Modeling His research focuses on multi-scale computational models and artificial intelligence for patient-specific cardiac simulations , aiming to improve arrhythmia risk stratification and therapy planning . Key topics include cardiac conduction system modeling , scar-related ventricular tachycardia , and machine learning pipelines for clinical applications. Recent publications emphasize automata-based simulations for atrial arrhythmias, machine learning in arrhythmia localization, and 3D geometric characterization of aortic diseases. Trends show integration of computational modeling with clinical data and medical imaging . Scientific Awards: Best Poster Award, Functional Imaging and Modeling of the Heart (2021) Cum Laude Award, SPIE Medical Imaging (2009) Student Presentation Award (2011) He has supervised 7 PhD/Master students and led grants exceeding €1 million, including projects like iSARC-GENETICS and iCardioTwins , focusing on digital twin technology and cardiac disease stratification .

• Computer Architecture
• Embedded Systems
• High-Performance Computing
• +۳ مورد دیگر

Zhiqiang Que is a Research Associate in the Department of Computing at Imperial College London, affiliated with the Faculty of Engineering and the Centre for High-Throughput Digital Electronics and Machine Learning. His research focuses on computer architecture, embedded systems, high-performance computing, and CAD tools for hardware design optimization. His research interests include FPGA-based acceleration of machine learning models, hardware-software co-design, and real-time signal processing for scientific applications such as particle physics and gravitational wave experiments. He has contributed to projects involving low-latency graph neural networks (GNNs), Bayesian neural networks, and efficient stream processing on FPGAs. Recent work includes advancements in trustworthy design flows for deep learning acceleration, reconfigurable architectures for recurrent neural networks, and optimizing FPGA-based systems for high-energy physics experiments at the HL-LHC.

Taylor Bartonمشاهده پروفایل

دانشیار

• Electromagnetics
• RF Engineering
• Microwave Engineering
• +۴ مورد دیگر

Taylor Barton is an Associate Professor and Lockheed Martin Faculty Fellow at the University of Colorado's Department of Electrical, Computer, and Energy Engineering. His research focuses on electromagnetics, RF/microwave engineering, and integrated circuits, with an emphasis on power amplifier design, GaN-based systems, and high-frequency electronics. He leads a lab conducting cutting-edge work in antenna integration, nonlinear circuit analysis, and energy-efficient RF solutions. His contributions include innovations in load modulation techniques, multi-band amplifier architectures, and superconducting frequency conversion. Barton's work intersects with applications in 5G communications, aerospace systems, and wireless power transfer. His research interests span RF power amplification, semiconductor device modeling, and high-efficiency transmitter design. Notable areas include GaN MMICs, outphasing architectures, and adaptive impedance matching networks. Recent projects explore kinetic inductance-based frequency conversion and compact charge digitizer systems for beam monitoring. Publications highlight advancements in wideband amplifiers, thermal sensor integration in GaN circuits, and novel methods for distortion reduction. His work frequently addresses trade-offs between power efficiency, bandwidth, and linearity in modern RF systems. Barton collaborates with industry partners like Lockheed Martin to bridge academic research with practical aerospace and defense applications. His lab website showcases ongoing projects in reconfigurable RF components and multi-functional microwave systems.

Javier F. Medinaمشاهده پروفایل

استاد

• Neural Circuits
• Computational Neuroscience
• Prediction Machines
• +۳ مورد دیگر

Javier F. Medina, Ph.D., is a Professor and Brown Foundation Chair in Neuroscience at Baylor College of Medicine. His research focuses on understanding how neural circuits implement supervised learning to make predictions, particularly in the context of motor control and cognitive functions. His laboratory combines experimental techniques such as electrophysiology, optogenetics, and two-photon imaging with computational modeling and artificial neural networks to uncover the computational principles underlying brain function. Education Postdoctoral Fellowship in Neuroscience, UCSF (2007) Ph.D. in Neuroscience, University of Texas, Houston (2000) Bachelor of Science in Computer Science and Physics, Drexel University, Philadelphia (1993) Research Interests Medina’s work integrates computational neuroscience with experimental approaches to study prediction and error correction in neural circuits. His lab investigates how the cerebellum encodes temporal information, processes sensory input, and adapts through learning. Key topics include supervised learning algorithms, neural coding in Purkinje cells, and the role of prediction errors in motor adaptation. Publications His research spans themes in cerebellar circuitry, motor learning, and computational modeling. Recent work includes deep learning strategies for cell-type identification and studies on cerebellar modules for predictive control. Grants & Labs Medina leads a lab at Baylor College of Medicine, focusing on experimental and theoretical neuroscience. His work has implications for motor disorders linked to learning deficits, such as ataxia and Parkinson’s disease.

Monica Fabianiمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Human Memory
• Aging
• +۵ مورد دیگر

Monica Fabiani is a Professor at the University of Illinois Urbana-Champaign, affiliated with the Neuroscience Program and the Beckman Institute for Advanced Science and Technology. She holds academic roles in Psychology and Neuroscience. Her research focuses on cognitive neuroscience of human memory and aging, neuroimaging tool development (e.g., EROS), and cerebrovascular contributions to brain aging. Prof. Fabiani’s work is funded by NIH (NIA) and MindPortal, emphasizing interdisciplinary integration of behavioral, electrophysiological, and neuroimaging data. Education: Ph.D. in Biological Psychology/Cognitive Neuroscience from the University of Illinois Urbana-Champaign. She serves as Editor of Psychophysiology and has received prestigious awards, including the Association for Psychological Science Fellowship (2006). Research Interests: Cerebrovascular health, neurophysiological bases of cognitive control, and multimodal imaging techniques. Key projects include studying arterial elasticity (pulse-DOT), cognitive aging mechanisms, and combining EEG, MRI, and optical methods. Notable Achievements: 181+ publications, including high-impact studies on white matter microstructure and the effects of physical activity on brain health. Her lab develops innovative tools like trimodal imaging and optical signal analysis for clinical and cognitive applications. Grants & Collaborations: Active projects funded by NIA and MindPortal, with international collaborations in brain aging and neuroimaging. She leads efforts to bridge basic science and translational research in cognitive health.

Xenofon Strakosasمشاهده پروفایل

استادیار

• Organic Bioelectronics
• Conductive Hydrogels
• Bioelectronic Medicine
• +۴ مورد دیگر

Xenofon Strakosas is an Assistant Professor at Linköping University's Department of Science and Technology (ITN), affiliated with the Laboratory of Organic Electronics (LOE) within the Faculty of Science and Engineering. His research focuses on organic bioelectronics, particularly the integration of electronic systems with biological tissues. Key projects include developing conductive hydrogels for 3D bioprinting, enzymatic polymerization of organic conductors on lipid membranes, and in vivo fabrication of soft electrodes for electronic medicine. Recent breakthroughs include growing electrodes in living tissue using injectable gels and achieving precise drug delivery via proton-trapping ion pumps. Supported by a SEK 10 million donation from the Stig Wadström Foundation, his work bridges technology and biology to address neurological diseases and human-machine interfaces. His lab collaborates across disciplines, leveraging organic electronics for biosensors, neural interfaces, and sustainable energy solutions. Publications emphasize advanced materials, electrochemical platforms, and biomedical applications. Current research trends prioritize biocompatibility, in vivo compatibility, and precise control of electronic-ionic interactions. Awards and recognitions include Physics World's 2023 major breakthrough designation for electrode growth in living tissues. Future directions include scalable bioelectronic systems and next-generation medical therapies.

• Microwave Engineering
• RF Systems Design
• Power Amplifier Design
• +۷ مورد دیگر

Christian Fager is a Full Professor at the Department of Microwave Electronics, Chalmers University of Technology, Sweden. He has been affiliated with Chalmers since completing his Ph.D. there in 2003. As Head of the Microwave Electronics Laboratory, his research focuses on nonlinear transistor modeling, energy-efficient power amplifier architectures, and distributed MIMO systems. He has co-invented 8 patents and published over 250 papers, including a seminal book on Nonlinear Transistor Model Parameter Extraction Techniques (Cambridge University Press, 2011). Dr. Fager holds editorial roles as Associate Editor of IEEE Microwave Magazine and member of the MTT-S Technical Coordination Committee on Wireless Communications. He is a Board Member of the European Microwave Association (EuMA) and has chaired multiple IEEE topical conferences. His awards include the Chalmers Supervisor of the Year (2018), inaugural Area of Advance Award (2010), and IEEE IMS Best Student Paper (2002). He leads research initiatives in distributed antenna systems, digital pre-distortion, and GaN/SiGe-based high-efficiency amplifiers, with projects involving testbed development for 5G/6G applications. His work bridges theoretical modeling and practical implementation in RF/microwave systems, emphasizing thermal and multi-physical simulation integration.

• Power Systems
• Smart Grids
• Renewable Energy Integration
• +۳ مورد دیگر

Prof. Ferdinanda Ponci is a Professor at RWTH Aachen University, leading the Department of Monitoring and Distributed Control for Energy Systems. Her work focuses on smart grid technologies, renewable energy integration, and control systems for modern power networks. She actively contributes to advancing grid resilience, EV charging infrastructure, and cybersecurity in energy systems. Key research interests include hybrid AC-DC grids, distributed energy resources coordination, and application of AI in grid management. She has pioneered frameworks like datafev for EV charging management and SMU platforms for synchronized measurements. Her interdisciplinary approach also addresses diversity in engineering and societal impacts of energy systems. Prof. Ponci’s laboratory activities involve hardware-in-the-loop testing, blockchain-based grid solutions, and development of educational toolboxes for emerging technologies. She collaborates on projects such as submarine transmission systems and grid interoperability testing, contributing to both academic and industrial advancements in power systems engineering.

Piya Palمشاهده پروفایل

استاد

• High Dimensional Statistical Signal Processing
• Compressive Sensing
• Tensor Methods
• +۴ مورد دیگر

Prof. Piya Pal is a Professor in the Department of Electrical and Computer Engineering at the University of California, San Diego. Her research focuses on high-dimensional statistical signal processing, energy-efficient sampling techniques, and covariance-driven inference. She previously held an Assistant Professor position at the University of Maryland, College Park, and was affiliated with the Institute for Systems Research. Education: Ph.D. in Electrical Engineering from California Institute of Technology (2013). Notable achievements include the NSF CAREER Award (2016) and the 2014 Charles and Ellen Wilts Prize for her thesis on sparse sampling and estimation. Her work emphasizes structured sampling and robust algorithms for undersampled data analysis, with applications in sensor arrays, compressive sensing, and optical imaging. Research Interests: Energy-efficient sparse array design Correlation-aware sparse estimation Covariance compression and statistical inference Tensor methods in machine learning High-resolution imaging systems Publications highlight advancements in sparse array geometries (nested/coprime samplers), Cramér-Rao bound analysis, and hybrid beamforming. Recent work explores super-resolution imaging and millimeter-wave channel sensing with learned empirical priors. Her contributions address fundamental trade-offs between sample size, resolution, and domain knowledge integration. Scientific Awards: NSF CAREER Award (2016) 2014 Charles and Ellen Wilts Prize (Caltech) Advising & Grants: Current research is supported by NSF CAREER funding. Her lab focuses on interdisciplinary projects combining signal processing with medical imaging and wireless communication challenges.

Laura Lechuga Gómezمشاهده پروفایل

استاد پژوهش

• Nanotechnology
• Biosensors
• Medical Diagnostics
• +۳ مورد دیگر

Dr. Laura Lechuga Gómez is a CSIC Research Professor and Group Leader at the Institute of Nanoscience and Nanotechnology (ICN2), focusing on NanoBiosensors and Bioanalytical Applications . Her research emphasizes plasmonic and photonic biosensors for clinical, biomedical, and environmental diagnostics. Key projects include label-free detection of pathogens, autoimmune markers, and environmental pollutants. She leads an interdisciplinary team advancing point-of-care platforms for conditions such as tuberculosis, cancer, and preeclampsia. Research Interests span nanotechnology, biosensor design, and analytical chemistry. Her work bridges fundamental physics and applied biomedical engineering, with a focus on translating lab innovations into real-world diagnostic tools. Recent advances include biomimetic sensors for immunotherapy evaluation and veterinary SARS-CoV-2 detection. Publications (2025–2022) highlight cutting-edge contributions in Biosensors & Bioelectronics , Analytical Chemistry , and Nanomaterials , addressing challenges in sensor sensitivity, integration, and clinical validation. Her team collaborates globally on EU-funded projects, advancing nanotechnologies for health and environmental monitoring. Laura’s group develops portable platforms using silicon photonics and plasmonic systems, enabling rapid diagnostics in resource-limited settings. Ongoing work explores mRNA cleavage for splicing analysis and microfluidic integration for automated assays.

Greg Zaharchukمشاهده پروفایل

استاد

• Neuroimaging
• Radiology
• Artificial Intelligence in Medical Imaging
• +۴ مورد دیگر

Greg Zaharchuk is a Professor of Radiology specializing in Neuroimaging and Neurointervention at Stanford University's School of Medicine. His academic roles include leadership in clinical and translational imaging research. He holds an MD from Harvard Medical School, a PhD in Applied Physics from Harvard/MIT, and dual undergraduate degrees in Materials Science & Engineering and German Studies from Stanford. Education: MD, Harvard Medical School (2000) PhD, Harvard/MIT (1999) BS & BA, Stanford (1990) Research Focus: Dr. Zaharchuk's work centers on advancing neuroimaging techniques for cerebrovascular diseases, stroke, and neurodegenerative disorders. He pioneers AI-driven innovations in MRI/PET fusion, low-dose imaging, and deep learning models to enhance diagnostic accuracy and clinical decision-making. Key areas include stroke outcome prediction, amyloid PET reconstruction, and cross-modality imaging integration. Research Trends: His recent articles emphasize AI applications in neuroimaging, such as improving low-field MRI quality and PET reconstruction using MRI priors. He also explores stroke lesion segmentation, CBF quantification, and the ethical implications of AI in neuroradiology. Affiliations & Labs: He leads the Center for Advanced Functional Neuroimaging at Stanford, advancing cutting-edge imaging technologies. His research bridges clinical practice and innovation, addressing challenges in ischemic stroke, Alzheimer's imaging, and radiation dose reduction. Grants & Collaborations: His work is supported by NIH grants and industry partnerships, focusing on multimodal imaging systems and AI-driven diagnostics. Collaborations include international neuroradiology societies and interdisciplinary teams in neurology and computer science.

Vincent Humphreyمشاهده پروفایل

مدرس

• Climate Change
• Earth System Models
• Water and Carbon Cycles
• +۳ مورد دیگر

Dr. Vincent Humphrey is a Lecturer at ETH Zurich's Department of Environmental Systems Science, specializing in climate dynamics and Earth system modeling. His research focuses on climate change impacts, satellite observations, and machine learning applications in environmental science. He holds a PhD in Land-Climate Dynamics from ETH Zurich (2017) and completed postdoctoral fellowships at institutions including Caltech and the University of Zurich. Education PhD (2014-2017): ETH Zurich, Land-Climate Dynamics MSc (2011-2014): University of Lausanne, Environmental Geosciences BSc (2008-2011): University of Lausanne, Geosciences and Environment Research Interests Humphrey’s work addresses climate feedback mechanisms, terrestrial water and carbon cycles, and the integration of satellite data (e.g., GRACE, GPS) into Earth system models. He develops machine learning tools to analyze time series data and improve model accuracy. Key projects include CONSTRAIN (constraining climate projections) and GRUN (global runoff dataset). Awards ETH Medal (2018) Advising & Grants Humphrey has contributed to major initiatives like the Global Sea Level Budget Group and collaborated on datasets such as GRACE-REC and GRUN. His research bridges observational and modeling approaches to understand climate system interactions. Labs/Teams He is affiliated with ETH Zurich’s Climate Physics group and collaborates with institutions like NASA’s Jet Propulsion Laboratory (via Caltech).