جست‌وجوی استاد و پژوهشگر

Karl Krushelnickمشاهده پروفایل

استاد

• Basic relativistic plasma studies
• Table-top particle accelerators
• Ultra-strong magnetic fields
• +۳ مورد دیگر

Karl Krushelnick is a Professor in the Department of Nuclear Engineering and Radiological Sciences at the University of Michigan, serving as Director of the Center for Ultrafast Optical Science (CUOS) and Associate Director for High Field Science. His research focuses on high-intensity laser-plasma interactions, relativistic electron beams, and applications in radiation generation, magnetic reconnection, and biomedical sensing. Research Interests: Basic relativistic plasma studies Table-top particle accelerators Ultra-strong magnetic fields Ultrafast laser technology Quantum electrodynamics (QED) in extreme light regimes Key Trends in Publications: Krushelnick’s recent work investigates zettawatt-equivalent laser experiments, orbital angular momentum effects on laser absorption, magnetic reconnection dynamics, and neutron generation mechanisms. His team explores laser wakefield acceleration, betatron X-ray diagnostics, and filamentation control for advanced applications in physics and engineering.

Ron Van der Meydenمشاهده پروفایل

استاد

• Computer Security
• Logic in Computer Science
• Logic of Knowledge and Belief
• +۹ مورد دیگر

Professor Ron Van der Meyden is a faculty member at the School of Computer Science and Engineering at the University of New South Wales, Sydney. His work focuses on the intersection of logic, security, and distributed systems, with particular expertise in blockchain technology and smart contracts. He leads the UNSW Interest Group in Blockchain, Smart Contracts and Cryptocurrency and organizes related seminar series. Professor Van der Meyden's research spans formal methods, computer security, and distributed systems. His work on epistemic logic has been influential in understanding knowledge-based systems and security protocols. He has made significant contributions to the formal verification of blockchain protocols and smart contracts, bringing rigorous mathematical approaches to these emerging technologies. His recent work explores the application of knowledge-based reasoning to consensus protocols and intersection management in autonomous systems. ACM Distinguished Scientist, 2009 As an advisor, Professor Van der Meyden has mentored numerous PhD and Masters students who have gone on to successful careers in academia and industry. His research is supported by grants including Australia's Economic Accelerator Grant for developing a commercial version of a software model checker and an AFOSR/DST Australia grant for verification and synthesis of fault-tolerant autonomous systems. He has received multiple ARC Discovery and Linkage grants over the years. Professor Van der Meyden leads the UNSW Interest Group in Blockchain, Smart Contracts and Cryptocurrency, fostering interdisciplinary research in this area. He has played key roles in major research centers including Smart Internet CRC and National ICT Australia (NICTA), where he established and led the Formal Methods program. His work on the formal verification of the seL4 microkernel and the Goanna static analysis tool has had significant practical impact.

• Machine Learning
• Optimization
• Federated Learning
• +۸ مورد دیگر

Peter Richtarik is a Professor at the King Abdullah University of Science and Technology (KAUST), specializing in Machine Learning, Optimization, and Federated Learning. He actively teaches courses such as Stochastic Gradient Descent Methods and mentors PhD and MS students like Konstantin Burlachenko, Kai Yi, and Lukang Sun. His research spans distributed optimization, parameter-efficient fine-tuning, and theoretical frameworks for non-convex and non-smooth problems. Recent work includes 2025 contributions to Bernoulli-LoRA (theoretical PEFT) and Gluon (LMO-based optimizers). He co-developed Thanos (block-wise pruning) and BurTorch (CPU-optimized training framework). His publications focus on communication efficiency ( ATA , LoCoDL ), differential privacy ( DP-RBCD ), and stochastic proximal methods. Richtarik received the Charles Broyden Prize for his work on quasi-Newton methods. He frequently presents at workshops like MLSS in Senegal and FLOW seminars.

• Cellular and molecular imaging
• Multimodality probe and reporter molecule development
• MR oximetry
• +۴ مورد دیگر

Vikram Kodibagkar is a Professor in the School of Biological and Health Systems Engineering at Arizona State University, with additional affiliations to the School of Medicine and Advanced Medical Engineering. He leads the Prognostic Bioengineering (ProBE) Lab, conducting cutting-edge research in cellular and molecular imaging, magnetic resonance physics, and biomedical engineering. Education: Ph.D. in Physics, Washington University, St. Louis (2002) M.Sc. in Physics, Indian Institute of Technology-Mumbai (1997) B.Sc. in Physics, University of Mumbai, India (1995) Research Focus: Professor Kodibagkar's research centers on developing advanced imaging technologies for medical applications. His work encompasses cellular and molecular imaging , multimodality probe development , and magnetic resonance oximetry . A key focus is the development of novel contrast agents and imaging techniques for detecting hypoxia in tumors and brain injuries. His lab also works on compressed sensing accelerated magnetic resonance spectroscopic imaging (MRSI) and functional imaging of implants . The ProBE Lab emphasizes comprehensive understanding of both theory and practical techniques to train the next generation of imaging leaders. Current research activities include developing non-invasive methods for real-time monitoring of engineered cells and tissues, investigating tumor oxygenation dynamics, and creating novel MRI nanosensors for various medical applications. Research Funding and Grants: Professor Kodibagkar has secured significant funding from major organizations including: National Institutes of Health (NIH) - Multiple R01 grants National Science Foundation (NSF) - CAREER Award US Department of Defense (DOD) DARPA/BTO Flinn Foundation Texas Higher Education Coordinating Board Teaching and Mentorship: He teaches various courses including BME 350 Signals & Systems for Bioengineers, BME 465/565 Magnetic Resonance Imaging, and supervises honors theses and research projects. His teaching spans undergraduate to doctoral levels, focusing on biomedical engineering and imaging technologies. Laboratory and Team: Professor Kodibagkar directs the Prognostic Bioengineering (ProBE) Lab at Arizona State University. The lab conducts interdisciplinary research combining engineering, physics, and medicine to develop next-generation imaging technologies for clinical applications.

• Turbulence Modeling
• Computational Fluid Dynamics (CFD)
• Large Eddy Simulation (LES)
• +۷ مورد دیگر

Lars Davidson is a Professor in the Department of Fluid Dynamics at Chalmers University of Technology. His research focuses on numerical simulations of fluid flow and heat transfer, with an emphasis on turbulence modeling for Large Eddy Simulation (LES) and hybrid LES/RANS methods. He has developed computational codes CALC-BFC and CALC-LES based on finite-volume techniques, and recently integrated machine learning to enhance wall functions and turbulence models. Key projects include Hybrid LES/RANS for wall-bounded flows Machine learning applications in fluid dynamics Aeroacoustic noise reduction in automotive and aerospace systems Wind turbine load analysis in forested regions . His publications span 302 articles in journals and conferences, with recent work on Neural networks for turbulence closure Plasma actuators for drag reduction Lattice Boltzmann wall-modeled LES . Collaborations include teams at Volvo, Siemens, and international research groups.

• Traffic Data Acquisition
• Traffic Data Analysis
• Video-Based Microscopic Traffic Flow Simulation
• +۲ مورد دیگر

Dr. Adrian Fazekas is a Lecturer at the Institute of Highway Engineering, RWTH Aachen University, and collaborates with the Federal Highway Research Institute (BASt). He holds a Dr.-Ing. in Computer Science from RWTH Aachen (2005–2011), specializing in Media Engineering. His professional trajectory includes roles as a Research Assistant at RWTH Aachen and industry experience as a Software Developer at Continental AG. Research interests focus on traffic data acquisition , microscopic traffic flow simulation , and intelligent transportation systems . Key projects include: DROVA: Drone-based traffic analysis for infrastructure optimization ESIMAS: Real-time tunnel safety management Digital Twin Road: Physical-informational mapping of future highways AUTUKAR: Automated tunnel monitoring systems His publications emphasize real-time traffic detection , safety analytics , and data-driven modeling , with recent work exploring thermal-camera nudging systems and weigh-in-motion accuracy. He actively contributes to the Research Association for Roads, Earth and Tunneling (SETAC). No awards or student advising roles are documented.

• Embedded Machine Learning
• Neural Network Hardware Acceleration
• Quantized Neural Networks
• +۳ مورد دیگر

Lukas Einhaus is a Researcher and PhD student in the Embedded Systems department at the University of Duisburg-Essen since April 2020, affiliated with the Intelligent Embedded Systems (IES) research group and contributing to initiatives including Elastic AI and the IoT Garage. His academic background includes: Bachelor of Science from University of Duisburg-Essen, thesis focused on programming abstractions for concurrent embedded systems Master of Science from University of Duisburg-Essen, specializing in distributed and reliable systems with thesis research on quantizing neural networks Einhaus's research centers on designing neural networks for efficient hardware implementation on FPGAs, with primary expertise in quantized or low-precision neural networks that reduce bit depth (typically 1-3 bits) for computations and information flow. This work enables energy-efficient AI solutions for embedded and IoT devices where resource constraints are critical. His publication record from 2021-2025 reveals consistent innovation in FPGA-based neural network optimization, with applications spanning fluid flow estimation, time-series analysis, and real-time stream processing. Core themes include Elastic AI for adaptive systems, precomputation techniques for convolutional layers, and hardware-aware neural architecture design. He previously contributed to the BMBF-funded project "KI-Sprung: LUTNet" (until March 2022), developing energy-efficient AI networks using elementary lookup tables for FPGA deployment. Einhaus actively mentors students through the IoT Garage initiative, supervising practical projects including drink-mixing machines, exoskeletons, and ball-challenge systems.

• Bayesian Machine Learning
• Medical Image Analysis
• Graph-based Learning
• +۳ مورد دیگر

Raghavendra Selvan, an Assistant Professor (Tenure Track) at the University of Copenhagen, holds joint appointments in the Machine Learning Section (Department of Computer Science), Kiehn Lab (Department of Neuroscience), and the Data Science Laboratory. His academic journey includes a PhD in Medical Image Analysis (2018), MSc in Communication Engineering (2015), and BSc in Electronics and Communication Engineering (2009). PhD - Medical Image Analysis, University of Copenhagen (2018) MSc - Communication Engineering, Chalmers University (2015) BSc - Electronics and Communication Engineering, BMS Institute of Technology, India (2009) His research focuses on Bayesian Machine Learning with emphasis on Medical Image Analysis, Graph-based Learning, Tensor Networks, Approximate Inference, and Multi-Object Tracking Theory. Recent publications highlight his contributions to environmentally sustainable AI practices, efficient deep learning in medical imaging, and novel applications of tensor networks. Key research areas: Green AI and Environmental Sustainability Medical Image Analysis Graph Neural Networks Crystal Structure Prediction Model Compression Materials Science Applications

• Optical Imaging
• Biomedical Device and Instrumentation
• Medical Image Analysis
• +۲ مورد دیگر

Jungeun (Jenny) Won is an Assistant Professor of Research in the Department of Biomedical Engineering at the School of Engineering and Applied Sciences, University at Buffalo. Her research focuses on optical imaging , biomedical device development , medical image analysis , and artificial intelligence in OCT . She leads the Translational Biophotonics Laboratory , where she develops advanced OCT techniques for medical applications such as diabetic retinopathy , otitis media , and biofilm analysis . Contact: 215J Bonner Hall, Buffalo NY 14260, jungeunw@buffalo.edu Related Links: CV PDF , Google Scholar , Lab Website Her recent work involves high-resolution OCT for longitudinal studies on retinal degeneration, VISTA OCTA for blood flow analysis, and 3D motion correction algorithms to enhance image quality. She also explores multimodal imaging combining OCT with Raman spectroscopy for bacterial differentiation and microplasma-based therapies for ear infections.

Chun-Hua Guoمشاهده پروفایل

استاد

• Matrix analysis
• Scientific computing
• Numerical linear algebra
• +۳ مورد دیگر

Chun-Hua Guo is a Professor in the Department of Mathematics and Statistics at the University of Regina, Faculty of Science. His research focuses on matrix analysis, scientific computing, and applications in tensor computations and nonlinear matrix equations. He teaches advanced courses such as MATH 869 Numerical Analysis. Dr. Guo’s recent work emphasizes iterative methods for solving eigenvalue problems of nonnegative tensors, matrix equations arising in nano research, and convergence analysis of numerical algorithms. His publications address topics like Newton-Noda iteration, modified Newton methods for Z-eigenpairs, and algebraic Riccati equations associated with M-matrices. His research trends highlight advancements in computational techniques for matrix functions (e.g., matrix pth root), tensor Perron pairs, and stability analysis of iterative algorithms. These contributions bridge theoretical linear algebra with practical computational challenges in engineering and scientific domains. Dr. Guo’s advising and grants involve developing efficient numerical methods for complex systems. His work has implications for fields requiring high-precision matrix computations, such as control theory, stochastic modeling, and nano-material simulations.

Joshua San Miguelمشاهده پروفایل

دانشیار

• Approximate computing
• Processor microarchitecture
• Intermittent computing
• +۴ مورد دیگر

Associate Professor Joshua San Miguel leads research in computer architecture and systems at the University of Wisconsin-Madison, with an affiliate role in Computer Sciences. His work focuses on energy-efficient computing for IoT devices, microarchitecture innovations, and networks-on-chip. He holds a PhD (2017) and BASc (2012) from the University of Toronto. Education: PhD in Electrical & Computer Engineering, University of Toronto (2017) BASc in Engineering Science (ECE), University of Toronto (2012) Research Interests: Approximate computing for energy harvesting systems Branch prediction and value prediction in processors Cache architectures and networks-on-chip for many-core processors Intermittent computing resilience His recent work emphasizes value-level parallelism (Carat/uSystolic), RTL simulation acceleration (TaroRTL), and personalized neural network inference (CAP’NN). His research has been recognized with the NSF CAREER Award (2021) and multiple IEEE Micro Top Picks. Grants & Advising: Active in supervising advanced independent studies and master’s/dissertation research. Extensive grant funding includes the NSF CAREER Award and the Grainger Faculty Scholarship. Labs & Teams: Leads research groups focused on approximate computing and energy-efficient architectures within the Electrical & Computer Engineering department.

• Algorithmic Personalization
• Digital Identity
• Critical Political Economy of the Web
• +۵ مورد دیگر

Tanya Kant is an Associate Professor in Media and Cultural Studies (Digital Media) at the University of Sussex’s School of Media, Arts and Humanities. Her research focuses on algorithmic personalization, digital identity, and the socio-economic implications of data-driven systems. She authored the influential book *Making It Personal: Algorithmic Personalization, Identity and Everyday Life* (2020) and co-manages REFRAME, an open-access publishing platform. Her work bridges qualitative cultural studies with critical political economy, examining how algorithmic systems shape identities, creativity, and socio-material realities. Key research areas include generative AI ethics, algorithmic autobiographies, data profiling, and the impact of AI on PR/communications industries. Kant has collaborated with Ofcom, MIT, and Full Fact on data ethics and misinformation. Her teaching roles include convening MA Digital Media and supervising PhDs in critical AI studies. Recent grants include a £9,600 AHRC Impact Accelerator Award (2023) for studying ethical AI use in communications sectors. Professional activities include advisory roles at Open Rights Group and Full Fact, as well as frequent media engagements discussing AI’s societal implications. Her work emphasizes creative resistance strategies against algorithmic systems, such as participatory methods for ‘writing with’ digital selves through algorithmic autobiography projects. Notable Grants: Sussex KE Fellowship (2025), AHRC Impact Accelerator Award (2023) Key Collaborations: MIT, Ofcom, Full Fact, Magenta Associates Teaching Portfolio: Convenes 10+ modules including ‘Digital Cultures’ and ‘Gender, Sexuality and Digital Culture’

Matt J. Rutherfordمشاهده پروفایل

دانشیار

• Autonomous Systems
• Embedded Systems
• Software Engineering
• +۳ مورد دیگر

Matt J. Rutherford is an Associate Professor in the Department of Computer Science at the University of Denver, with a joint appointment in the Department of Electrical and Computer Engineering. He is Deputy Director of the Unmanned Systems Research Institute and a faculty fellow of Project X-ITE. His research focuses on autonomous systems, embedded systems, and software engineering, with extensive contributions to UAV navigation, control systems, and robotics. Rutherford holds a Ph.D. in Computer Science from the University of Colorado Boulder (2006), an MS (2001), and a BS in Civil Engineering from Princeton University (1996). His work emphasizes practical applications of software engineering principles in distributed and embedded systems. Notable projects include radar-based collision avoidance for UAVs, self-leveling landing platforms, and studies on electric vehicle charging impacts on power grids. Rutherford's research bridges theoretical computer science with real-world engineering challenges, particularly in unmanned systems and robotic autonomy. Key publications explore UAV flight control using neural networks, ground/ceiling effects in rotorcraft, and GPU-based real-time pose estimation. His contributions to model-driven systems and distributed testbed automation highlight long-term engagement with software reliability and scalable experimentation frameworks. Rutherford collaborates widely, including with institutions like the University of South Carolina and Politecnico di Torino. His interdisciplinary approach integrates robotics, aerospace engineering, and software engineering to advance autonomous system capabilities.

• Computer Science
• Electrical Engineering
• Machine Learning
• +۴ مورد دیگر

Marian Verhelst is a Professor at KU Leuven's Faculty of Engineering Science, renowned for her research in hardware-efficient computing and dedication to STEM education. Her work spans hardware acceleration for machine learning, edge AI, and in-memory computing, with a focus on energy optimization and algorithm-hardware co-design. Her research interests include: Designing flexible hardware for ultra-low-power edge AI systems Optimizing sparsity-aware architectures for deep learning workloads Advancing chiplet-based and 3D memory technologies Co-designing algorithms and hardware for probabilistic AI Pioneering STEM outreach through KU Leuven InnovationLab Recent publications (2023–2025) demonstrate strong trends in: Hardware-software co-optimization for edge ML systems Efficient data movement in heterogeneous accelerators Low-precision and sparse computation techniques RISC-V based customizable SoCs Sustainable AI accelerator design Awards & Honors: Young Academy of Europe Award (2021) for science communication and STEM advocacy She leads significant educational initiatives, including the KU Leuven InnovationLab which has engaged 150 schools and 13,000 students since 2014. The program develops hands-on STEM projects (e.g., AI-powered wheelchairs, sustainable energy systems) and provides teacher training to inspire youth in engineering.

Joshua Garciaمشاهده پروفایل

استادیار

• Software Engineering
• Software Architecture
• Software Vulnerability Management
• +۵ مورد دیگر

Joshua Garcia is an Assistant Professor in the Informatics Department at the University of California, Irvine (UCI), within the Donald Bren School of Information and Computer Sciences. His research focuses on software architecture, automated testing, and cybersecurity, particularly in autonomous systems and mobile applications. He leads projects like DeltaDroid, Doppelgänger Test Generation, and Darcy, which address software vulnerability management, architectural consistency, and safety-critical systems. Key achievements include an NSF CAREER Award (2025), an NSF CRI Grant (2018), and a DARPA competition win (2024). His work is adopted by organizations like Boeing, Google, and NASA. Garcia collaborates internationally, involving institutions in Padova and researchers like Luca, Jessy Ayala, and Philipp. Research Interests: Software architecture evolution, automated exploit generation, autonomous vehicle testing, and accessibility in software development Grants: NSF CAREER ($500K+), NSF CRI ($1M+) Labs/Teams: HexHive Group, Autonomous Systems Testing Lab