جست‌وجوی استاد و پژوهشگر

• Structural Engineering
• Protective Materials and Structures
• Impact Mechanics
• +۳ مورد دیگر

Professor Wensu Chen is a Director of the Centre for Infrastructural Monitoring and Protection (CIMP) and holds the position of ARC Future Fellow at Curtin University's School of Civil and Mechanical Engineering. He leads research in protective structures and materials, structural resilience, and multi-hazard mitigation. With a BE/MSc from Tianjin University, ME from the University of Melbourne, and a PhD from the University of Western Australia, his career spans academia and industry. His research focuses on novel metaconcrete materials, blast-resistant designs, and modular construction. He has secured multiple ARC grants (Discovery, DECRA, Linkage) and collaborates with industry/government bodies like DEMIRS WA and Engineers Australia. Research interests include metamaterials, structural strengthening, and dynamic response analysis under impact and blast loads. Key awards include the Curtin Early-Career Researcher of the Year and Western Australian Premier’s Science Award nominations. He supervises over 20 PhD students and teaches courses in structural analysis and dynamics. His work emphasizes sustainable, resilient infrastructure with applications in building envelopes, seismic control, and tunnel safety under explosive threats.

Zhewei Wangمشاهده پروفایل

استادیار مهمان

• Deep Learning
• Machine Learning
• Medical Image Analysis
• +۵ مورد دیگر

Zhewei Wang is a Visiting Assistant Professor in Artificial Intelligence at the School of Electrical Engineering and Computer Science (EECS) within the Russ College of Engineering and Technology at Ohio University. He holds a Ph.D. and M.S. in Computer Science and Biomedical Engineering from Ohio University (2020 and 2019, respectively), as well as an M.S. in Electronic Engineering from Wuyi University (2011) and a B.S. in Telecommunications Engineering from the University of Electronic Science and Technology of China. Prior to his current role, he conducted research at Massachusetts General Hospital of Harvard Medical School from 2021 to 2023. His research interests span deep learning, machine learning, medical image analysis, computer vision, natural language processing, reinforcement learning, and graph networks. Notable contributions include work on plasmonic nanoparticles for medical diagnostics, semi-supervised learning for medical imaging, and spiking neural networks for speech enhancement. Recent publications highlight advancements in medical imaging, speech processing, and neural network architectures. Key areas of focus include vessel segmentation in X-ray angiography, MRI conversion techniques, and Alzheimer’s disease staging through metric learning. His interdisciplinary research bridges AI with biomedical applications, emphasizing practical solutions in healthcare and diagnostics. No scientific awards or grants are explicitly listed in the provided information. His academic advising and student mentorship details are not detailed here. He has contributed to collaborative projects at Ohio University and Harvard Medical School, reflecting his commitment to cutting-edge AI research.

• Civil Engineering
• Geotechnical Engineering
• Transportation Engineering
• +۵ مورد دیگر

Hakan Yasarer is an Associate Professor of Civil Engineering at The University of Mississippi (Ole Miss). He holds a Ph.D. in Civil Engineering from Kansas State University (2013). His research focuses on geotechnical engineering, pavement performance modeling, groundwater hydrology, and machine learning applications in civil infrastructure systems. He specializes in developing predictive models using artificial neural networks (ANN) and integrating geospatial data with environmental monitoring systems. Education: Ph.D. Civil Engineering, Kansas State University (2013) Dr. Yasarer’s work emphasizes sustainable infrastructure solutions, including pavement deterioration modeling for state transportation departments (e.g., MDOT), groundwater stress zone mapping using GRACE satellite data, and innovative material applications like recycled glass for soil stabilization. He has pioneered open-source frameworks for downscaling GRACE data and transforming hydrology tools into web APIs. His research bridges civil engineering challenges with advanced computational methods, addressing both terrestrial (e.g., Mississippi Delta groundwater systems) and extraterrestrial construction scenarios (e.g., moon-to-Mars infrastructure). His publications consistently apply machine learning to civil engineering problems, such as predicting geotechnical parameters from seismic data, optimizing pavement maintenance strategies, and analyzing synthetic groundwater datasets. While no specific awards are listed, his contributions to geospatial hydrology and ANN-based modeling reflect impactful scholarly work. Dr. Yasarer’s advising and grant activities are integral to his role, though specific grant details are not provided in the text. His lab likely focuses on computational civil engineering tools and sustainability initiatives, supported by collaborations with state agencies and NASA-funded projects like the 'In-Situ, Resilient, and Sustainable Moon to Mars Construction' campaign.

Bingzhe Liمشاهده پروفایل

استادیار

• DNA Storage Systems
• Machine Learning Systems
• Low-Power Computing
• +۳ مورد دیگر

Dr. Bingzhe Li is an Assistant Professor in Computer Science at UT Dallas' Erik Jonsson School of Engineering. His research at the Lab for Intelligent Storage and Computing (Lab4ISC) focuses on DNA storage systems, machine learning infrastructure, and energy-efficient computing architectures. Awarded the NSF CAREER Award (2025) and recognized for Best Paper nominations at leading conferences. Research spans DNA storage capacity optimization, reinforcement learning for hybrid SSDs, Kubernetes storage optimization, and stochastic computing architectures. Recent publications demonstrate innovations in out-of-core graph processing and blockchain storage systems. Leads multiple NSF/NASA-funded projects on DNA storage and convertible SSDs. Teaches Digital Logic and Computer Architecture courses. Supervises 8 PhD students and 3 master's candidates in storage systems and low-power computing research.

Vasilios Kelefourasمشاهده پروفایل

مدرس

• Compiler Optimizations
• Deep Neural Networks
• Matrix/Tensor Computations
• +۲ مورد دیگر

Dr. Vasilios Kelefouras is Lecturer in Computer Science at the University of Plymouth's School of Engineering, Computing and Mathematics. His research optimizes software applications for execution time, energy consumption, and memory footprint across CPUs, GPUs and FPGAs. Notable contributions include analytical methodologies accelerating convolution layers in Deep Neural Networks (x1.1-7.2 speedup over Intel oneDNN) and optimization techniques for image processing algorithms (x2.8-40 speedup over Intel IPP). His compression work for Deep Neural Networks includes Tensor Train Decomposition methodologies for edge devices. Honors include HiPEAC Technology Transfer Award (2022) and Best Paper at SAMOS XXII. Teaches Parallel Computing, Computer Systems, and Computing Practice. Current PhD supervision focuses on accelerating ML algorithms on heterogeneous architectures.

Antonio Rodríguez-Sánchezمشاهده پروفایل

دانشیار

• Explainable AI
• Computational Neuroscience
• Deep Learning
• +۴ مورد دیگر

Antonio Rodríguez-Sánchez is an Associate Professor in the Intelligent and Interactive Systems group at the Department of Computer Science, Universität Innsbruck (since 2019). He holds a PhD from York University (2010) and has held academic roles in Austria, Canada, and Spain. His research focuses on Explainable AI, computational neuroscience, deep learning, computer vision, robotics, and medical imaging. Education: PhD in Computer Science, York University (Canada), 2010 M.Sc. in Computer Science, Universidade da Coruña (Spain), 1998 B.Sc. in Computer Science, Universidad de Córdoba (Spain), 1996 3-year Bachelor in Biology, Autonomous University of Madrid (Spain), 1998–2001 Research Interests: His work bridges AI and neuroscience, emphasizing explainable systems, medical imaging analysis, robotics, and deep learning applications. Recent trends in publications highlight advancements in healthcare AI (REM sleep disorder prediction), robotic recycling, and computer vision for environmental monitoring. Teaching & Advising: Teaches courses like Deep Learning, Computer Vision, and Algorithms. Supervises PhD/MSc students (e.g., Safoura Rezapour-Lakani, Sebastian Stabinger). Active in EU-funded projects like PaCMan (FP7-ICT) and IntellAct. Labs & Projects: Leads research in the Intelligent and Interactive Systems group, focusing on interdisciplinary AI applications. Projects include automated avalanche detection and robotic recycling systems.

Marco Grangettoمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Artificial Intelligence
• +۵ مورد دیگر

Marco Grangetto serves as Full Professor in the Department of Computer Science at the University of Turin, coordinating research in image processing and computer vision. His expertise spans wavelets, image/video coding, data compression, error resilient video coding, and biomedical image processing, with significant contributions to ISO JPEG2000 standardization and editorial roles in IEEE Transactions on Multimedia. His educational background includes a PhD in Electrical and Communications Engineering (2003) and MSc in Telecommunications Engineering (1999), both from Politecnico di Torino. His research integrates Artificial Intelligence and Deep Learning with medical imaging and fundamental compression theory , producing innovations in neural network pruning, capsule networks, and entropy-based models. Recent work focuses on Covid-19 diagnosis from chest X-rays and efficient 3D scene modeling. His publication trends reveal dual trajectories: applied medical AI (Covid-19 diagnostics, lung cancer segmentation) and theoretical advances (learned compression, contrastive learning, bias mitigation). This bridges clinical validation with information-theoretic foundations, particularly in resource-constrained environments. Scientific recognition includes: Premio Optime by Unione Industriale di Torino (2000) Fulbright Grant for research at UC San Diego (2001) He maintains leadership through IEEE editorial positions, ISO standardization participation, and MPAI membership. His research group develops medical datasets (UniToChest, UniToPatho) while advancing neural network efficiency for clinical deployment. Current projects focus on entropy minimization techniques and unbiased representation learning for healthcare applications.

Rasha Karakchiمشاهده پروفایل

مدرس

• High Performance Computing
• Reconfigurable Computing
• Embedded Systems
• +۳ مورد دیگر

Rasha Karakchi serves as a Lecturer in the Department of Computer Science and Engineering at the University of South Carolina's Molinaroli College of Engineering and Computing, where she teaches diverse courses while maintaining an active research program in hardware acceleration and embedded systems. Her academic foundation includes: Ph.D. in Computer Science and Engineering, University of South Carolina (2020) M.E. in Computer Engineering, University of South Carolina (2016) Dr. Karakchi's research centers on high-performance reconfigurable embedded systems, with particular focus on hardware acceleration for automata processing, spiking neural networks, and genomic sequence alignment. Her work bridges theoretical computer science with practical hardware implementation, emphasizing energy efficiency and real-time performance in security-critical applications. Analysis of her 2023-2025 publications reveals a dominant research trajectory applying machine learning to optimize hardware configurations for domain-specific tasks. Key thematic clusters include ML-enhanced automata processors for pattern matching, lightweight encryption engines for embedded security, and specialized architectures for spiking neural network acceleration—all demonstrating consistent innovation in hardware-software co-design for computationally intensive workloads. Her research excellence has been recognized through: SPARC Award (South Carolina's Program to Advance Research and Creativity)

• Pharmaceutical Engineering
• Drug Delivery Systems
• Continuous Manufacturing
• +۳ مورد دیگر

Dr. Rahamatullah Shaikh is an Assistant Professor in Industrial Pharmaceutics at the Centre for Pharmaceutical Engineering Science within the School of Pharmacy and Medical Sciences, University of Bradford. With extensive international research experience in academia and industry, his expertise spans drug formulation, continuous manufacturing, and particle engineering. He holds a PhD from the University of Limerick and an MPhil from Queen's University Belfast, where he developed patented buccal patch technology. His research focuses on: Continuous manufacturing processes via Quality by Design (QbD) Next-generation drug delivery systems for oral, mucosal, transdermal, and ophthalmic applications Novel biomaterials for regenerative medicine His publication portfolio demonstrates strong trends in pharmaceutical cocrystallization, continuous processing, and advanced drug delivery systems, particularly for ophthalmology and personalized medicine. Dr. Shaikh teaches across undergraduate and postgraduate programs and serves as Health and Safety Lead at the CPES. He has supervised 16 student research projects and actively engages in science outreach through initiatives like spectroscopy workshops.

Rickey Dubayمشاهده پروفایل

استاد

• Control Systems
• Robotics
• Advanced Manufacturing
• +۲ مورد دیگر

Rickey Dubay is Professor and Director of Graduate Studies in Mechanical Engineering at University of New Brunswick. His research spans control systems, robotics, and advanced manufacturing, with particular focus on unmanned marine vehicles. Research areas include: complex control systems, robotic learning control, intelligent systems, and unmanned vehicle control. He leads projects on 3D trajectory tracking for marine vehicles and cognitive robotic strategies for autonomous systems. He has supervised 7 PhD and 23 Master's graduates, with 3 PhD and 4 Master's students currently. His research includes patented innovations in nonlinear control methodologies and complex profile tracking, and he co-founded Eigen Innovations Inc. Course instruction includes advanced topics: Model-based Control Methods, System Identification, and Optimal Control Methods.

Roberto Menichettiمشاهده پروفایل

پژوهشگر

• Theoretical Physics
• Computational Physics
• Statistical Mechanics
• +۷ مورد دیگر

Roberto Menichetti is a theoretical physicist at the University of Trento , affiliated with the Department of Physics and the VARIAMOLS research group . His work integrates computational physics, statistical mechanics, and machine learning to model biomolecules and soft matter systems. Prior roles include postdoctoral research at the Max Planck Institute for Polymer Research (2016-2018) and academic training at the University of Rome 'La Sapienza'. PhD in Physics (La Sapienza, 2016) BSc/MSc in Physics (La Sapienza, 2012) His research spans: Coarse-Graining Methods for polymers, colloids, and biomolecules High-throughput Molecular Dynamics Simulations in drug-membrane permeability studies Integration of Machine Learning with physical models for multi-scale analysis Applications in Biophysics (e.g., chromatin condensates, protein dynamics) Article trends highlight his focus on computational frameworks like EXCOGITO and theoretical investigations of non-equilibrium systems and allosteric regulation . He actively develops tools for systematic model reduction and conformational sampling in biomolecular systems. Contact: roberto.menichetti@unitn.it

André Brinkmannمشاهده پروفایل

استاد

• Data Center Management
• Cloud Computing
• Storage Systems
• +۵ مورد دیگر

André Brinkmann is a full professor at the Department of Computer Science, Johannes Gutenberg University Mainz, leading the Efficient Computing and Storage Group. He previously served as head of the university's data center (2011–2021) and was an assistant professor at Paderborn University (2008–2011). He holds a Ph.D. in Electrical Engineering from Paderborn University (2004) and managed the Paderborn Centre for Parallel Computing (PC²). His research focuses on algorithm engineering for data center management, cloud computing, storage systems, and high-performance computing (HPC). Notable projects include: Development of ad hoc file systems like GekkoFS and IO-SEA for exascale architectures Optimization of storage systems (e.g., hybrid RAID, SSD garbage collection) Quantum computing compiler research for trapped-ion architectures Leadership in initiatives like the I/O Trace Initiative and BINARY (Big Data in Atmospheric Physics) He serves as Senior Associate Editor of ACM Transactions on Storage and co-chairs major conferences like FAST 2026 and ARCS 2026.

Hosameldin O. A. Ahmedمشاهده پروفایل

پژوهشگر ارشد

• Multimodal Medical Imaging
• Artificial Intelligence
• Extended Reality (XR)
• +۴ مورد دیگر

Dr. Hosameldin Ahmed is a Research Fellow at Aston Digital Futures Institute , Aston University , with a focus on interdisciplinary applications of artificial intelligence (AI) , extended reality (XR) , and data analytics in healthcare, industrial systems, and cultural heritage preservation. His collaborative work includes partnerships with Imperial College London and Brunel University (CEPROQHA Project).

Simone Brugiapagliaمشاهده پروفایل

دانشیار

• Mathematics of Data Science
• Computational Mathematics
• Numerical Analysis
• +۵ مورد دیگر

Simone Brugiapaglia is an Associate Professor in the Department of Mathematics and Statistics at Concordia University in Montréal, Canada. His academic journey includes a PhD in Mathematical Models and Methods from Polytechnic University of Milan (2016), an MSc in Mathematics from University of Pisa (2012), and a BSc in Mathematics from University of Pisa (2010), all earned cum laude . Prior to his current role, he held postdoctoral positions at École polytechnique fédérale de Lausanne (2016) and Simon Fraser University (2016-2019). Dr. Brugiapaglia's research bridges mathematics, data science, and computational methods. Key interests include: Foundations of deep learning and neural networks Compressed sensing and sparse recovery algorithms High-dimensional approximation theory Numerical methods for PDEs and diffusion equations Physics-informed machine learning Optimization techniques for large-scale problems His work develops rigorous mathematical frameworks for data-driven algorithms. His publications (30+ including two books) consistently focus on high-dimensional computation , featuring recent advances in neural network theory (e.g., generalization bounds, rank collapse), compressed sensing techniques (e.g., greedy algorithms, unrolled networks), and physics-informed learning. A strong trend involves combining traditional numerical methods with deep learning for PDE solutions. Awards & Fellowships: Concordia Research Fellow (2023) Leslie Fox Prize for Numerical Analysis (2nd place, 2019) PIMS Postdoctoral Fellowship (2016-2018) Multiple INdAM scholarships during graduate/undergraduate studies He has supervised over 20 trainees across postdoctoral, graduate, and undergraduate levels. While specific grants aren't detailed, his fellowship history indicates sustained research funding. No explicit research labs or teams are mentioned, but his supervision record and collaborative publications suggest active leadership in research groups focused on computational mathematics.

Philip Nelsonمشاهده پروفایل

استاد

• Acoustic Signal Processing
• Array Signal Processing
• Spatial Audio
• +۱ مورد دیگر

Philip Nelson is Professor of Acoustics at the University of Southampton and former CEO of the Engineering and Physical Sciences Research Council. A Fellow of the Royal Academy of Engineering, his research advances acoustic signal processing and spatial audio. Recent work applies neural networks to acoustic classification and develops methods for personalized sound field reconstruction. His industrial collaborations include Samsung, Huawei, and Qualcomm. Honors include a CBE for contributions to UK engineering science.