جست‌وجوی استاد و پژوهشگر

François Orieuxمشاهده پروفایل

استادیار

• Inverse Problems
• Bayesian Methods
• Signal Processing
• +۵ مورد دیگر

François Orieux is an Assistant Professor at the University Paris-Saclay, affiliated with the Laboratoire des Signaux et Systèmes (L2S) within CentraleSupélec. His research focuses on Bayesian methods for inverse problems, computational imaging, and signal processing, with applications in astronomy, microscopy, and embedded AI systems. He holds a PhD in Astrophysical Instrumentation (2009) and an HDR in Computational Imaging (2024). Key research areas include: Development of optimization algorithms for hyperspectral and multispectral fusion High-dimensional sampling techniques for large-scale data GPU-accelerated convolution methods for real-time processing Instrument modeling for radio astronomy and terahertz spectroscopy Recent work emphasizes hardware-aware AI implementations on embedded systems and radio interferometric imaging pipelines. He collaborates with institutions like the Institut d'Astrophysique de Paris and leads projects funded by ANR and EU initiatives. Teaching activities include computational imaging and signal processing courses at CentraleSupélec. His open-source contributions include the Q-MM Python toolbox for optimization algorithms.

Riccardo Marinمشاهده پروفایل

پژوهشگر

• Spectral Shape Analysis
• Geometric Deep Learning
• Virtual Humans
• +۳ مورد دیگر

Riccardo Marin is a Post-Doctoral Researcher at the University of Tuebingen, supported by the Humboldt Post-Doctoral Fellowship. He is part of the Real-Virtual Humans group led by Prof. Gerard Pons-Moll. Previously, he conducted post-doctoral research at Sapienza University of Rome in the GLADIA group under Prof. Emanuele Rodolà, contributing to the SPECGEO ERC project. He earned his Ph.D. in Computer Science from the University of Verona under Prof. Umberto Castellani, winning the Best PhD Thesis Award by EG-Italy. He holds a Master's degree in Computer Science and Engineering from the University of Verona (2017). His research focuses on Spectral Shape Analysis, Geometric Deep Learning, and Virtual Humans, with contributions to top-tier conferences and journals (NeurIPS, IJCV, CVPR). Key achievements include a Best Student Paper Award at 3DV 2020 and five Outstanding Reviewer Awards. He actively serves as a reviewer for leading journals and conferences (PAMI, TCVG, CVPR, NeurIPS) and has organized events like Siggraph RCD and 3DV 2018. Collaborations include institutions like École Polytechnique, Max-Planck Institute, INRIA Strasbourg, UCL, and the University of Milan. His work is supported by grants from Google and the European Research Council (ERC). His research bridges geometry processing with machine learning, emphasizing spectral methods and shape correspondence. Notable projects include MoMaS (real-time procedural texturing), FARM (3D human body registration), and contributions to latent space analysis for shape modeling. He maintains an active GitHub profile with repositories like NICP and InstantRecoveryFromSpectrum .

• Cybersecurity
• Data Leak Prevention
• Steganography
• +۵ مورد دیگر

Dr. Abdulaleem Al-Othmani is a Senior Lecturer in Computer Science at De Montfort University (DMU), serving as Programme Manager for APU/DMU dual award computing programmes. He holds a PhD in Computer Science from Universiti Teknologi Malaysia (UTM) and has held academic roles at University Kuala Lumpur (UniKL) and Asia Pacific University (APU) in Malaysia. His research focuses on cybersecurity, including steganography, digital watermarking, data leak prevention, and machine learning applications in security. Education: BSc in Computer Engineering, University of Baghdad (2002) Master of Computer Science (Information Security), UTM (2010) PhD in Computer Science, UTM (2016) Research Interests: Data Leak Prevention frameworks for educational institutions Cybersecurity in IoT and telemedicine systems Machine learning-driven threat detection E-learning security during pandemics Biometric authentication systems for healthcare Awards & Grants: Principal Investigator: RM99,380 FRGS grant for data leak prevention (2020–2022) UTM International Doctoral Fellowship (2011–2012) Certificate of Excellence for PhD thesis (2016) Advising & Professional Roles: Supervised over 20 PhD/Master students and 50+ bachelor dissertations Current PhD supervision: Safari Ismail Mussa (web app vulnerability scanners) External Examiner for MSc programmes at Birmingham City University (2021) Member of IEEE, BCS, ACM, and AIS professional bodies He is affiliated with the Cyber Technology Institute (CTI) at DMU and contributes to editorial boards of journals/conferences.

Tarek Elgoharyمشاهده پروفایل

دانشیار

• Astrodynamics
• Optimal Control
• Robotics
• +۴ مورد دیگر

Tarek Elgohary is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of Central Florida (UCF). He serves as Director of the Astrodynamics and Space Robotics Laboratory, focusing on space systems research. Prior to joining UCF, he held postdoctoral and visiting scholar positions at Texas A&M University and the University of California, Irvine. His research spans astrodynamics, optimal control, robotics, and space situational awareness. Ph.D. in Aerospace Engineering – Texas A&M University M.S. in Aerospace Engineering – Texas A&M University B.S. in Mechanical Engineering – American University in Cairo Elgohary's research employs advanced computational methods including radial basis functions, machine learning, and analytical perturbation techniques. His work addresses space navigation, uncertainty propagation, and robotic systems for spacecraft motion emulation, with recent focus on cislunar orbit modeling and space debris tracking. His publications demonstrate trends in machine learning applications for orbital mechanics, uncertainty quantification , and space situational awareness . Key methodologies include physics-informed neural networks, octree-based coverage algorithms, and adaptive variational iteration techniques. Heep Fellowship (2013-14) Aerospace Engineering Graduate Fellowship (2012-13) Regents Fellowship (2008-09) Elgohary's laboratory develops algorithms for space exploration missions, including lunar dust mitigation strategies and autonomous spacecraft control systems. His team works on hardware-in-the-loop testing for flight validation and parallel computing architectures for space domain awareness.

Janusz A. Starzykمشاهده پروفایل

استاد

• Machine Learning
• Neural Networks
• Robotics
• +۷ مورد دیگر

Janusz A. Starzyk is a Professor of Electrical Engineering and Computer Science at Ohio University's Russ College of Engineering and Technology, and holds a concurrent professorship at the University of Information Technology and Management in Rzeszów, Poland. He earned his M.Sc. and Ph.D. in Electrical Engineering from Warsaw University of Technology and a habilitation from Silesian University of Technology. His research focuses on embodied intelligence, machine learning, neural networks, and VLSI design, with notable contributions to self-organizing systems and reinforcement learning. Starzyk has supervised 47 M.Sc. and 16 Ph.D. students, and his work spans over 190 peer-reviewed publications. He leads the Embodied Intelligence Lab at Ohio University and collaborates with institutions worldwide, including Nanyang Technological University and the Institute for Artificial Intelligence in Switzerland. His awards include the Best Research Paper Award from Ohio University and nominations for IEEE Fellow. He has secured $3.8 million in research grants, with projects in GPS signal processing, radar target recognition, and machine learning applications. His patents include innovations in object identification systems and self-organizing learning hardware. Starzyk's expertise bridges academia and industry, with roles as a consultant for companies like Magnolia Broadband and Sarnoff Research.

• Artificial Intelligence
• Robotics
• Automation
• +۴ مورد دیگر

Dr. Salem Ameen is a Lecturer in Artificial Intelligence, Robotics, and Automation at the University of Salford’s School of Science, Engineering & Environment. He is affiliated with the Centre for Future Engineering. He earned his Ph.D. in 2017 from the University of Salford, focusing on optimizing deep learning with multi-armed bandits, and his Master’s in Computer Science and Engineering (2007–2009). Research Interests : Deep Learning Optimization (e.g., multi-armed bandit algorithms) Neural Network Pruning for resource-constrained environments AI in healthcare diagnostics and patient care Robotics integration with AI (e.g., healthcare automation) Educational technology leveraging AI Teaching & Supervision : He teaches courses across levels 3–7, including Artificial Intelligence, Robotics, Mathematics, and Mechatronics. He supervises Master’s projects in areas like Computer Vision, Deep Reinforcement Learning, and Robotics. He offers PhD supervision in topics such as Explainable AI, Federated Learning, and Soft Robotics. Affiliations & Labs : He is part of the Centre for Future Engineering, focusing on innovative automation and smart technologies.

Marc Teyssierمشاهده پروفایل

پژوهشگر ارشد

• Human-Computer Interaction (HCI)
• Robotics
• Wearable Technology
• +۶ مورد دیگر

Marc Teyssier is a Post-Doctoral Research Fellow at the Human-Computer Interaction Lab within the Department of Computer Science at Saarland University. He holds a Ph.D. in Computer Science from Télécom Paris – Institut Polytechnique de Paris and a Master’s degree in Interaction Design. His research focuses on developing creative hardware and software that bridges technology and human nature, emphasizing anthropomorphic devices, sustainable materials, and affective communication. Education: PhD in Computer Science, Télécom Paris – Institut Polytechnique de Paris (France) Master’s in Interaction Design Research Interests: Teyssier explores how technology can be made more human-centric through projects like Skin-On Interfaces (artificial skin-covered devices), MobiLimb (robotic smartphone attachments), and Eyecam (an anthropomorphic webcam). He combines prototyping, digital fabrication, and user research to reimagine device design, emphasizing sustainability (e.g., bioplastics) and ethical implications of sensing technologies. Key Contributions: Developed open-source projects like PolySense (smart textiles) and Eyecam (critical design for surveillance devices). Published at top venues including CHI and ICRA, with work on human-like artificial skin sensors and emotion-conveying touch interfaces. Recipient of ERC Stg InteractiveSkin funding and EPSRC grants. Labs & Groups: Leads the Resilient Futures research group at De Vinci Innovation Center and collaborates with Saarland University’s HCI Lab.

• 3D Imaging
• Signal Processing
• Optical Engineering
• +۵ مورد دیگر

Dr. Daniel Lau is a Professor and Director of Graduate Studies in the Department of Electrical and Computer Engineering at the University of Kentucky, part of the Stanley and Karen Pigman College of Engineering. He holds a Ph.D. (1999) from the University of Delaware and a B.Sc. (1995) from Purdue University. His research focuses on 3D imaging systems, spectral imaging, machine vision, and signal processing, with notable contributions to structured light technologies, blue-noise halftoning, and QR code embedding systems. Prior to academia, he worked as a DSP Engineer at Aware, Inc., and an Image and Signal Processing Engineer at Lawrence Livermore National Laboratory. Key research areas include 3D fingerprint identification, multispectral color acquisition/display, and compressive spectral imaging using novel coded apertures. His recent work emphasizes real-time 3D reconstruction, lens distortion correction, and hypergraph signal processing frameworks. Lau has pioneered methods for blue-noise sampling on non-Euclidean domains and developed algorithms for phase-measuring profilometry. His innovations in QR code embedding (e.g., Colored-QRNet) bridge machine vision and information security. Publications span 30+ years, showing progression from early work on halftoning (green-noise models) to current advancements in AI-driven imaging systems. He leads projects funded by NSF and industry partners, focusing on applications in automotive inspection, medical imaging, and industrial automation. His lab develops both theoretical frameworks and hardware implementations, such as FPGA-based error diffusion systems.

• High Performance Computing
• Energy Efficiency
• Parallel Computing
• +۶ مورد دیگر

Laura Carrington is a researcher at the University of California, San Diego, specializing in High Performance Computing (HPC) with a focus on energy efficiency, memory management, and performance optimization. She has contributed to the development of tools like PEBIL for binary instrumentation, ADAMANT for data movement analysis, and frameworks for power management in large-scale systems. Her research spans multiple domains including ARM processor evaluation, Xeon Phi vectorization, and communication reduction in graph algorithms. Key collaborations include work with Michael Laurenzano, Allan Snavely, Ananta Tiwari, and Pietro Cicotti. Laura's work addresses critical challenges in HPC such as DVFS configuration optimization, workload colocation, and energy-aware algorithm design. While no explicit academic rank is stated, her extensive publication record across 2002-2019 in top venues like SC, IPDPS, and IJHPCA establishes her as a significant contributor to HPC research. Her work has influenced practices in system-level power management, scientific application characterization, and energy-efficient computing for both CPU/DRAM domains and emerging memory technologies.

• Machine Learning
• Cyber-Physical Systems
• Intelligent Embedded Systems
• +۵ مورد دیگر

Cesare Alippi is a Professor at Politecnico di Milano, Italy, and Università della Svizzera italiana, Switzerland, with visiting appointments at the University of Kobe (Japan) and Guangzhou (China). His research focuses on machine learning in nonstationary environments, intelligent embedded systems, and cyber-physical systems. Alippi holds IEEE Fellow status and has received notable awards, including the 2016 Gabor Award and the 2018 IEEE CIS Outstanding Computational Intelligence Magazine Award. He has authored a monograph, 6 edited books, and over 200 papers. Alippi's academic roles include Vice-President for Education of the IEEE Computational Intelligence Society and Distinguished Lecturer (2014-2016). He has organized major conferences like IJCNN and IEEE SSCI and contributed to initiatives promoting education in computational intelligence. His research addresses challenges in adaptive systems, embedded intelligence, and fault diagnosis. Key projects include deploying wireless sensor networks for environmental monitoring and developing machine learning algorithms for real-world applications like landslide prediction and network security. Alippi's awards reflect his contributions to neural networks and adaptive learning, including the IBM Faculty Award (2013) and IEEE Young Engineer Award (2004).

• Model Predictive Control
• Nonlinear Systems Control
• Robust Control
• +۳ مورد دیگر

Dr. Johannes Köhler is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich, Switzerland. His research focuses on advanced control systems, particularly model predictive control (MPC), robust control of nonlinear systems, and data-driven control strategies. He holds a Master's degree in Engineering Cybernetics from the University of Stuttgart (2017) and a Ph.D. in Mechanical Engineering (2021), for which he received the 2021 European Systems & Control PhD Award. His work emphasizes safety, optimization, and adaptability in control systems, with applications ranging from autonomous navigation to energy-efficient building systems. Key academic achievements include the 2022 ETH Zurich Career Seed Award for his work on data-driven predictive control, and the 2020 Best Publication Award at the University of Stuttgart for applying MPC to combat the COVID-19 outbreak. He has authored over 60 peer-reviewed articles, with recent contributions in stochastic MPC, robust nonlinear control, and safety-aware exploration strategies. Education: Ph.D. in Mechanical Engineering, University of Stuttgart (2021) Master of Engineering Cybernetics, University of Stuttgart (2017) His research group at ETH Zürich develops algorithms for real-time control of complex systems, with a focus on embedded MPC for robotics and energy systems. Current projects include safe autonomous navigation, adaptive control under uncertainty, and data-driven methods for nonlinear systems.

Gilles Perrouinمشاهده پروفایل

استاد پژوهشی

• Software Engineering
• Testing
• Artificial Intelligence
• +۳ مورد دیگر

Gilles Perrouin is a Research Professor in the Faculty of Computer Science at the University of Namur, affiliated with the Research Center on Information Systems Engineering and the Namur Digital Institute. His expertise spans Software Engineering, Testing, Artificial Intelligence, and Machine Learning, with a focus on software product lines, configuration management, and automated testing techniques. He leads projects such as SQUAL.AI (Software Quality through Artificial Intelligence) and SmartTune (Hyper-parameter Optimization for Deep Learning). Perrouin has received awards including the Distinguished Reviewer Award (2023 and 2019) and the VAMOS 2024 ten-year Most Influential Paper Award. He actively participates in organizing conferences like the Belgium-Netherlands Software Evolution Workshop and contributes to initiatives promoting diversity in science, such as Women and Girls in Science events. His research involves advancing testing methodologies for complex systems, exploring energy-efficient neural architectures, and addressing fairness in machine learning. Collaborations span international institutions, reflecting his global impact in software engineering and AI.

• Computer Science
• Quantum Computing
• Machine Learning
• +۲ مورد دیگر

Atul Kumar is a Researcher in computer science with publications spanning quantum computing, machine learning, and cybersecurity. His work appears in journals including IEEE Transactions on Visualization and Computer Graphics, Knowledge-Based Systems, and IEEE Access. His research focuses on quantum machine learning algorithms, medical image analysis, hardware security, and chaotic encryption systems. Recent investigations explore quantum support vector machines, hardware Trojan attacks, and uncertainty-aware neural representations for scientific visualization. Analysis of his publication portfolio reveals consistent methodological innovation in applying quantum computing principles to traditional computing problems, particularly in image processing and classification tasks. His security-focused research examines vulnerabilities in hardware systems and develops cryptographic solutions using chaotic maps.

Slavche Pejoskiمشاهده پروفایل

استاد

• Wireless Communication Networks
• Energy Harvesting
• Federated Learning
• +۳ مورد دیگر

Slavche Pejoski is a Full Professor at the Faculty of Electrical Engineering and Information Technologies (FEIT) of the Ss. Cyril and Methodius University of Skopje, specializing in the Department of Telecommunications. He holds a PhD in Telecommunications (2015), Master's (2010), and Bachelor's (2007) degrees from the same institution. His academic roles include Assistant (2014–2016), Assistant Professor (2016–2020), Associate Professor (2020–2025), and Full Professor (since 2025). His research focuses on wireless communication networks , energy harvesting , federated learning , intelligent reflecting surfaces (IRS) , and network optimization . He has pioneered work on optimizing resource allocation in IRS-assisted networks, covert communications, and delay-minimization in federated learning. Publications emphasize ALOHA protocols , UAV-based communication systems , and RF energy harvesting . Recent work (2022–2025) highlights advancements in covert communications and IRS-enhanced networks . He received the Best Paper Award at TELFOR 2015 for MRI imaging research. His grants and collaborations span industry partnerships, emphasizing practical network implementations. Labs/Teams: Active in FEIT's Institute of Telecommunications , leading projects on next-generation wireless systems and AI-driven networking.

• Real-Time Systems
• Scheduling Algorithms
• Network Protocols
• +۳ مورد دیگر

Radu Dobrin is a Senior Lecturer and Head of Department at the Department of Computer Science and Software Engineering, Mälardalen University. He is associated with the Academy of Innovation, Design and Technology. His research focuses on real-time systems, scheduling algorithms, network protocols, and embedded systems. He also contributes to curricular development in computer science education, as evidenced by his work on curriculum mapping frameworks and ethics integration. Dobrin's recent work includes advancements in TSN (Time-Sensitive Networking), fault-tolerant scheduling, and probabilistic analysis of real-time systems. Despite no explicit mentions of awards or grants, his extensive publication record indicates substantial contributions to both theoretical and applied aspects of computer science. His research trends emphasize improving scheduling efficiency in real-time environments, handling cache-related preemption delays, and enhancing network resilience through adaptive frameworks like MALOC. Collaborations with industry are evident in projects like HUBLINKED and curriculum mapping initiatives. While specific student advisees are not listed, his academic role suggests involvement in mentoring graduate students in computer science and engineering disciplines. Technical responsibilities include managing the department's academic operations and contributing to Mälardalen University's digital infrastructure, as seen through his involvement in accessibility improvements and web-related projects. No specific grants or lab affiliations are detailed in the provided texts, though his research areas imply potential involvement in EU-funded or industry-sponsored projects.