جست‌وجوی استاد و پژوهشگر

• Incoherent Scatter Radar
• Ionospheric Propagation
• Equatorial Electrodynamics
• +۵ مورد دیگر

Erhan Kudeki is a Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. He holds a PhD (1983) and BS (1978) in Electrical Engineering from Cornell University. His research focuses on ionospheric physics and radar remote sensing, including incoherent scatter radar theory, equatorial electrodynamics, atmospheric turbulence, and space science applications. He mentors undergraduate students in ionospheric radar signal processing projects. Education: PhD, Electrical Engineering, Cornell University (1983) BS, Electrical Engineering, Cornell University (1978) Research Interests: Professor Kudeki investigates ionospheric phenomena using radar techniques, specializing in plasma instabilities, equatorial electrodynamics, and atmospheric wave interactions. His work bridges experimental radar diagnostics with theoretical modeling of space weather phenomena, including 150-km echoes and spread-F development. Recent Publications: His 15 most recent articles (2020-2023) demonstrate evolving focus areas: ionospheric irregularities detection using advanced radar systems, quantum physics education for engineers, and immersive STEM pedagogy. Recurring themes include Jicamarca radar experiments, equatorial plasma dynamics, and innovations in engineering education. Awards: Teaching : George Anner Excellence Award (2018), Ronald W. Pratt Teaching Award (2013), College of Engineering Advisor recognition Research : NASA Group Achievement Awards (2004 Equis II, 1998 Cocqui II), NSF CEDAR Prize Lecture (2006) Consistently ranked excellent by students since 1987 Courses: Teaches core electrical engineering courses including ECE 210 (Analog Signal Processing), ECE 329 (Fields and Waves), ECE 350 (Fields and Waves II), and ECE 458 (Radio Wave Propagation).

Daniel Boleyمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Data Mining
• Machine Learning
• +۵ مورد دیگر

Daniel Boley is a Professor and Distinguished University Teaching Professor at the University of Minnesota, within the College of Science and Engineering, Department of Computer Science and Engineering. He serves as the Director of Graduate Studies for the Graduate Program in Data Science, which offers a Master's of Science and a Post-Baccalaureate Certificate. His office is located in Kenneth H. Keller Hall at 4-225C. Professor Boley's research spans computational methods in linear algebra, scalable data mining algorithms, and applications in systems biology and bioinformatics. His work focuses on scalable algorithms for convex optimization in machine learning, analysis of networks and graphs from metabolic biochemical networks, and wireless device networks. He has made significant contributions to numerical linear algebra methods for control problems, parallel algorithms, and iterative methods for matrix eigenproblems. His research interests also include algebraic models in systems and evolutionary biology, and biochemical metabolic networks. His recent publications demonstrate a strong focus on applying graph theory and network analysis to diverse domains including robot swarms, medical imaging (particularly for glioblastoma and COVID-19 diagnosis), and metabolic network analysis. His work bridges theoretical computer science with practical applications in biology and medicine, with a consistent emphasis on developing scalable computational methods. The trend shows increasing interdisciplinary collaboration, particularly with medical researchers. Distinguished Member by the ACM Top university award for post baccalaureate, graduate and professional education Distinguished University Teaching Professor title Professor Boley has advised numerous PhD students including Tatiana Lenskaia (2021), Shaozhe Tao (2018), Ham Ching Lam (2014), and others dating back to 1994. His research has been supported by various grants enabling work on scalable computation of elementary pathways through metabolic networks, Markov models of viral evolution, and scalable data mining algorithms for text analysis. He has developed software tools for clustering, dot plot visualization, and educational graphics. Professor Boley directs the Graduate Program in Data Science and has been involved in projects such as the Principal Direction Divisive Partitioning (PDDP) Project. His research group develops practical implementations of theoretical advances, including the PDDP clustering algorithm, Dot.py genome viewer, and various educational graphics tools for introductory programming courses. He maintains active collaborations across disciplines, particularly in bioinformatics and medical imaging applications.

Xue Fengمشاهده پروفایل

دانشیار

• Ecohydrology
• Climate Change
• Plant Hydraulics
• +۳ مورد دیگر

Xue Feng is an Associate Professor in the Department of Civil, Environmental, and Geo- Engineering at the University of Minnesota , affiliated with the St. Anthony Falls Laboratory. Their research focuses on understanding how water mediates ecosystem responses to climate change, with applications in Earth system modeling and climate predictions. Research Interests Xue Feng's work spans ecohydrology, plant hydraulics, peatland hydrology, and urban watershed management. Key projects include studying seasonal rainfall dynamics, plant water use strategies under drought, and the tradeoffs of urban green infrastructure in the Minneapolis-St. Paul metropolitan area. Peatland carbon cycling under climate change Stomatal optimization and plant hydraulic modeling Urban heat island mitigation via vegetation Scientific Awards McKnight Land-Grant Professorship NSF CAREER Award Advising and Funding As PI of NSF- and DOE-funded projects, Feng mentors graduate students and postdocs, emphasizing external grant support and fellowship applications. Current projects include collaborations with the LTER program and Minnesota Stormwater Research Council.

Jarmo Malinenمشاهده پروفایل

مدرس

• Applied Mathematics
• Numerical Analysis
• Mathematical Systems Theory
• +۳ مورد دیگر

Jarmo Malinen is a Lecturer at the Department of Mathematics and Systems Analysis, Aalto University , School of Science. He leads the speech modeling group and participates in the Comspeech consortium, collaborating with institutions like the University of Helsinki and Turku University Hospital. His research spans mathematical systems theory, numerical analysis, and computational speech science, focusing on MRI-based vocal tract modeling, glottal flow dynamics, and speech acoustics. He develops physical models for speech production and contributes to educational technologies like the STACK system. Key publication trends include applications of finite element methods to vowel formants, inverse filtering for glottal flow estimation, and eigenvalue problem solutions for coupled systems. His work intersects applied mathematics, biomedical imaging, and speech physiology. Malinen advises master's and PhD students in mathematical modeling and speech research, though specific student names are not listed in the provided texts. He has developed tools for MRI data post-processing and vocal tract resonance optimization.

George Nakhlaمشاهده پروفایل

استاد

• Environmental Engineering
• Water and Soil Pollution Control
• Biological Wastewater Treatment
• +۶ مورد دیگر

Professor George Nakhla holds the Salamander Chair in Environmental Engineering at the Department of Chemical and Biochemical Engineering, Faculty of Engineering, Western University. With a Ph.D. from the University of Illinois at Urbana-Champaign, his career spans decades of groundbreaking research in biological wastewater treatment and bioremediation. Education: Ph.D. (University of Illinois at Urbana-Champaign, 1989), M.Sc. (University of Illinois at Urbana-Champaign, 1987), B.Sc. (University of Khartoum, 1983) His research focuses on biological nutrient removal , anaerobic digestion , and advanced oxidation processes , with recent work exploring vacuum-assisted fermentation systems, micro-aeration for sulfide control, and biochar-enabled co-digestion technologies. Collaborative projects include: Development of the first Communal Biological Nutrient Removal System in Ontario Patented fluidized bed nutrient removal processes Membrane bioreactor systems for biological nutrient removal Investigation of lignin accumulation impacts in cattle manure digesters Optimization of hydrogen production from industrial wastes Dr. Nakhla's work has expanded to include process modeling , bioreactor design , and micropollutant management , with significant contributions to understanding the interplay between operational parameters and microbial ecology in wastewater systems.

Christophe Kervazoمشاهده پروفایل

استادیار

• Sparse Blind Source Separation
• Nonnegative Matrix Factorization
• Hyperspectral Imaging
• +۴ مورد دیگر

Christophe Kervazo is an Assistant Professor (Maître de Conférences) at Télécom Paris, France, affiliated with the IMAGES group under the Image, Data, Signal (IDS) department . His research focuses on sparse blind source separation, nonnegative matrix factorization, hyperspectral imaging, and optimization techniques for remote sensing and biomedical applications. Education: Engineering degree from Supélec (2015), Master of Science from Georgia Institute of Technology (2016), PhD in Signal and Image Processing from Université Paris Saclay (2019). His work spans deep learning for inverse problems (including deep unrolling techniques), remote sensing (hyperspectral imaging and SAR), and uncertainty quantification . Recent publications address synthetic data training for medical imaging, distributed sparse BSS, and nonlinear component separation. Collaborators include institutions like CEA Saclay, Université de Mons, and ONERA. Current students include PhD candidates working on topics such as digital breast tomosynthesis, hyperspectral unmixing, and SAR image reconstruction. Former students and interns have contributed to projects involving plug-and-play methods, unrolling algorithms, and implicit regularization. He is involved in teaching and research projects, including collaborations with Airbus and ONERA on hyperspectral imaging and spectral band optimization. His lab, LTCI (Information Processing and Communication Laboratory), supports interdisciplinary work in signal and image processing.

• Control Engineering
• Process Control
• Optimal Control
• +۱۱ مورد دیگر

Torsten Wik is a Professor in Control Engineering at Chalmers University of Technology. He leads the Control Engineering research group and focuses on process control with theoretical and applied methodologies. Institution: Chalmers University of Technology Department: Control Engineering His research spans optimal control, model reduction, and systems with model uncertainties. Applications include energy-saving systems, environmental improvement, biological systems (water purification, recirculating fish farms, LED greenhouse lighting), and battery estimation/modeling/control. Recent work emphasizes battery degradation diagnosis, state estimation, fast charging, and reconfigurable systems. Key methodologies include physics-informed frameworks, machine learning integration, entropy-based predictive algorithms, and hypergraph modeling. Applications extend to electric vehicles, photovoltaic systems, fuel cells, and biofilm reactors. Publications highlight collaborations across engineering domains, focusing on control theory, electrochemical modeling, and real-time optimization. His work bridges theoretical advancements with industrial applications in energy systems, transportation, and sustainable agriculture.

Jimmy Liمشاهده پروفایل

پژوهشگر ارشد

• Applied Geophysics
• Geomechanics and Resources Geotechnical Engineering
• Petroleum and Reservoir Engineering
• +۵ مورد دیگر

Dr. Jimmy Li is a Postdoctoral Research Fellow at the School of Mechanical and Mining Engineering, The University of Queensland, with expertise in geophysics and geomechanics for resource and geoenergy exploration. His career spans over a decade in industry roles at Halliburton Energy Services and academic research since completing his PhD in 2021. Bachelor of Petroleum Engineering (2007) from China University of Petroleum Doctor of Philosophy in Petroleum Engineering (2021) from Curtin University Dr. Li's research focuses on theoretical and experimental studies in rock mechanics and geotechnical engineering for underground mining and drilling applications. Key areas include: Machine Learning in Geoscience for tasks like CMRR and UCS prediction Rock Physics involving seismic, microseismic, and ultrasonic wave propagation Weathering in Sedimentary Rock through experiments on fluid-rock interactions Petrophysics and Formation Evaluation using core analysis and laboratory measurements Drilling Engineering in hostile environments (HPHT), geosteering, and borehole stability His recent publications highlight trends in coal geomechanics, shale permeability, CO2 sequestration modeling, and AI applications in upstream geoenergy. He has collaborated on multi-frequency sonic logging tools and established the NATA-accredited Quality Management System for rock mechanics at UQ. Dr. Li is available for supervision and has secured funding from industry partners like Origin Energy, Normet Asia Pacific, and Lihir Gold Limited for projects on coal strength, grout durability, and roadway stability. He contributes to developing machine learning methods for reducing subjectivity in mining safety assessments and optimizing drilling technologies.

• X-ray astronomy
• Galaxy clusters
• Inverse Compton scattering
• +۱۰ مورد دیگر

Dan Wik is an Associate Professor in the Department of Physics & Astronomy at the University of Utah . His research focuses on observational X-ray astronomy, particularly galaxy clusters, inverse Compton scattering, X-ray binaries, and the X-ray background. He has extensive experience in data calibration, analysis tool development, and mission collaborations such as NuSTAR, Chandra, and XRISM. Wik holds a PhD in Astronomy from the University of Virginia (2010) and a BS in Astrophysics from Ohio University . His research interests span galaxy cluster mergers, nonthermal emission processes, high-energy astrophysics, and cross-calibration studies between X-ray observatories. Recent articles highlight his work on NuSTAR observations of galaxy clusters, X-ray binary populations in M31 and M33, inverse Compton emission constraints, and stray light background analysis techniques. His studies often integrate multiwavelength data and address cosmological implications of X-ray observations. Wik has received multiple grants from NASA for projects like Time Domain X-ray Studies of AGN and Hard Bandpass Extension of XRISM Cluster Observations . He supervises undergraduate and graduate researchers and teaches courses from general education to advanced graduate levels, including Foundations of Astronomy and High Energy Astrophysics .

Zin Linمشاهده پروفایل

استادیار

• Quantum Photonics
• Electromagnetics
• Nanophotonics
• +۴ مورد دیگر

Zin Lin is an Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech, based at the Virginia Tech Research Center in Arlington. His research focuses on inverse design principles in nanophotonics, computational modeling, and scientific machine learning, with applications in quantum photonics, electromagnetics, and optical imaging. He leads the Inverse Design and Discovery Group, emphasizing large-scale optimization for physical systems and novel device discovery through physics-based AI. Education: Postdoc in Applied Mathematics at MIT (2018–2022), Ph.D. in Applied Physics from Harvard University (2018), and a B.A. in Physics and Mathematics from Wesleyan University (2012). He is a recipient of the National Science Foundation Graduate Fellowship (2014–2018). Research interests include inverse design of nanophotonic devices, topology optimization, quantum optics, and computational imaging. His group explores cutting-edge topics like metasurface engineering, terahertz wave generation, and bio-chemical sensing through physics-driven optimization frameworks. Recent work emphasizes scalable optical systems, such as end-to-end optimized metalenses and meta-optics for imaging, as well as quantum control in graphene-based metasurfaces. Key contributions span nonlinear frequency conversion, high-energy particle detection via nanophotonic scintillators, and topology-optimized multi-layered optical systems. Notable awards include the NSF Graduate Fellowship. His team actively pursues interdisciplinary projects at the intersection of wave physics, machine learning, and high-performance computing, with open positions for PhD students and postdocs.

Jan De Beenhouwerمشاهده پروفایل

استاد

• X-ray Imaging
• Computed Tomography
• Image Processing
• +۳ مورد دیگر

Prof. Jan De Beenhouwer is a faculty member at the University of Antwerp, affiliated with the Department of Physics and the imec Vision Lab. His research focuses on advanced computational imaging techniques, particularly in X-ray tomography, phase contrast imaging, and reconstruction algorithms for medical and industrial applications. His primary research interests include: Development of novel X-ray imaging methodologies like edge illumination phase contrast Advanced CT reconstruction algorithms for sparse-view and dynamic systems Integration of deep learning with tomographic reconstruction Industrial applications including defect detection and material characterization Biomedical imaging such as bone structure analysis and tissue modeling Analysis of recent publications (2024-2025) reveals strong emphasis on: Innovations in phase contrast imaging hardware and simulation tools Advanced reconstruction techniques for motion compensation and sparse data AI-powered approaches for industrial inspection and biomedical research Development of open-source tools (CAD-ASTRA) for the tomography community He leads research at imec Vision Lab, focusing on both fundamental imaging physics and practical applications. The lab collaborates extensively with industrial partners on non-destructive testing solutions.

David Hysellمشاهده پروفایل

استاد

• Ionospheric Physics
• Space Plasma Physics
• Aeronomy
• +۳ مورد دیگر

David Hysell is a Professor in the Department of Earth & Atmospheric Sciences at Cornell University. His research focuses on ionospheric physics, plasma turbulence, and equatorial aeronomy. He leads projects utilizing HF radar networks, satellite data (e.g., ICON), and advanced modeling techniques to study ionospheric instabilities and space weather phenomena. Key tools include the Jicamarca Radio Observatory and collaborative initiatives like the SPARTA Center for space weather forecasting. His work integrates fluid/plasma simulations, automatic differentiation for parameter estimation, and hybrid kinetic models to understand Farley-Buneman instabilities, equatorial spread F, and sporadic E-layer dynamics. Recent efforts emphasize ionospheric forecasting using multi-instrument data fusion and machine learning approaches. Hysell actively participates in international observatory upgrades and geospace radar development to enhance global ionospheric monitoring capabilities. Scientific contributions include advancing ionospheric specification methods using HF beacon networks, investigating solar minimum conditions at equatorial altitudes, and studying plasma wave interactions in high-latitude regions. His research bridges theoretical plasma physics with applied space weather monitoring, addressing both fundamental science and operational needs.

Sai Mounika Errapotuمشاهده پروفایل

استادیار

• Cybersecurity
• Privacy Preservation
• Smart Grid Security
• +۶ مورد دیگر

Sai Mounika Errapotu is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Texas at El Paso (UTEP). She leads the Cyber Physical Systems Privacy and Security Enhanced Computing (CyPSEC) Lab, focusing on developing practical security and privacy solutions for hardware and software systems in distributed networks, IoT, and smart grids. Her research integrates cryptography, differential privacy, and optimization to address emerging challenges in cyber-physical systems. Education: Ph.D. in Electrical and Computer Engineering (University of Houston, 2018), B.Tech. in Electronics and Communication Engineering (Jawaharlal Nehru Technological University, 2013). Postdoctoral work: AI, Networking Technologies, and Security Lab, University of Houston (2018–2019). Her research interests span cybersecurity, privacy-preserving protocols, trust modeling, and optimization in smart grids, IoT, and wireless networks. She emphasizes application-specific solutions that balance security, privacy, and scalability. Recent articles highlight advancements in intrusion detection via GANs and neural networks, smart home IoT security protocols, and privacy-preserving techniques in healthcare and transportation systems. Major Grants: $782,500 ARL grant for GAN-based intrusion detection, $1.1M DOE NNSA consortium for power systems, and $140K PNNL grant for healthcare synthetic data. Awards: Miguel Izquierdo Teaching Excellence Award (2021), Rising Stars Award, and Best Dissertation Award (2018). Advising highlights include mentoring students like Humaira (CPS Rising Star), Nicholas Lopez (M.S. grad in IoT security), and Ismael Holguin (LLNL intern). The CyPSEC Lab actively publishes in IEEE and NAPS conferences, addressing vulnerabilities in protocols like DNP3 and smart home IoT systems. Labs/Teams: CyPSEC Lab focuses on bridging theory and practice in CPS security, collaborating with industry partners and national labs.

• Bayesian Statistics
• Machine Learning
• Medical Imaging
• +۵ مورد دیگر

Dr. Yanan Fan is a Senior Principal Research Scientist at CSIRO's Data61 and an Adjunct Professor of Statistics at the University of New South Wales (UNSW). His research focuses on Bayesian models, computational methods for real-world problems, and interdisciplinary applications in fields like medical imaging, cosmology, and climate science. He holds a PhD in Statistics from the University of Bristol and has over 20 years of academic experience at UNSW's School of Mathematics and Statistics. Education: PhD in Statistics, University of Bristol, UK Undergraduate Degree in Mathematics, University of Melbourne Research Interests: Fan develops Bayesian semiparametric models, approximate Bayesian computation (ABC), and scalable computational methods for medical imaging (e.g., PET), cosmology, and climate modeling. He also investigates gender bias in educational evaluations and leads initiatives like the Data4Good stream of UDASH. His work emphasizes practical problem-solving through advanced statistical techniques. Leadership & Contributions: As Team Leader of Bayesian Computational Methods and Applications at Data61, he drives innovation in statistical methodologies. He has served on the Scientific Committee of MATRIX research institute and as an Associate Editor for major statistical journals. His projects include probabilistic climate projections and bias analysis in student evaluations. Labs & Groups: Active member of the StatML Group and leader of the Bayesian Computational Methods team, focusing on integrating machine learning and statistical computing.

Hans-Martin Schwabمشاهده پروفایل

استادیار

• Biomedical Engineering
• Ultrasound Imaging
• Photoacoustics
• +۳ مورد دیگر

Hans-Martin Schwab is an Assistant Professor at the Biomedical Engineering Department of Eindhoven University of Technology (TU/e), affiliated with the Eindhoven MedTech Innovation Center and the Photoacoustics and Ultrasound Laboratory. His research focuses on advanced ultrasound and photoacoustic imaging techniques, including multi-perspective imaging, model-based artifact reduction, and deep learning-assisted signal processing. He contributes to UN Sustainable Development Goals through innovations in medical imaging technologies. His expertise spans transducer engineering, circumferential strain imaging, and ultrasound reconstruction methods. Recent collaborations involve international teams working on applications like vascular strain analysis and tissue regeneration using ultrasound phased arrays. Over 28 publications since 2020 highlight contributions to photoacoustic imaging systems, numerical simulations, and clinical ultrasound advancements. Prof. Schwab teaches courses on ultrasound imaging, clinical measurements, and blood oxygenation imaging. He has supervised numerous projects, including DBL initiatives on light/sound integration for medical imaging. His work emphasizes translating theoretical models into practical clinical tools, with a focus on improving diagnostic accuracy and patient outcomes.