جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Machine Learning
• Complex Systems
• +۱۰ مورد دیگر

Peter Andras is a Professor and the Dean of the School of Computing Engineering and the Built Environment at Edinburgh Napier University , a position he has held since August 2021. Prior to this, he served as Head of the School of Computing and Mathematics at Keele University (2017-2021) and was a Professor of Computer Science and Informatics there. His academic journey also includes significant roles at Newcastle University in both the School of Computing and the Department of Psychology. His educational background includes a PhD in Mathematical Analysis of Artificial Neural Networks, an MSc in Artificial Intelligence, and a BSc in Computer Science, all from Babes-Bolyai University, Romania. Peter's research is highly interdisciplinary, focusing on Artificial Intelligence, Machine Learning, Complex Systems, Agent-Based Modelling, Software Engineering, Systems Theory, Neuroscience, and the Modelling and Analysis of Biological and Social Systems . His work often bridges computer science with fields like psychology, chemistry, and engineering. His recent publications demonstrate a strong trend towards applying AI and machine learning in diverse contexts, including energy demand forecasting, cloud computing resilience, Internet of Things (IoT) and medical applications, and the modeling of biological and social behaviors. He has a particular interest in the theoretical and practical aspects of federated learning and privacy-preserving technologies. His research impact is substantial, with publications cited over 2,400 times and an h-index of 25. He has secured around £2.5 million in research funding. Scientific Awards and Honors: Fellow of the Royal Society of Biology Senior Member of the Institute of Electrical and Electronics Engineers (IEEE) Member of the Board of Governors of the International Neural Network Society (INNS) Advising and Grants: Professor Andras is actively involved in PhD supervision, currently serving as Director of Studies or Second Supervisor for several doctoral candidates. He is a prominent figure in the research community, serving on the EPSRC Peer Review College , the Royal Society International Exchanges Panel , and the Royal Society APEX Awards Review College . He is also a regular reviewer for major funding bodies including the Leverhulme Trust, MRC, BBSRC, and various EU funding agencies . His current projects, funded by the British Council, focus on international collaboration in AI, robotics, and sustainable construction. Labs and Teams: He is affiliated with the Centre for Artificial Intelligence and Robotic at Edinburgh Napier University, which serves as a hub for his research activities in AI and machine learning.

David Kleinfeldمشاهده پروفایل

استاد

• Neuroscience
• Neurobiology
• Biophysics
• +۵ مورد دیگر

David Kleinfeld is a Distinguished Professor at the University of California San Diego, holding dual appointments in Neurobiology and Physics. His primary research focuses on bridging neural circuitry across scales, from molecular mechanisms to behavior, with a focus on active sensation and brain vasculature. He leads the 'Low&High' Team BRAIN Circuits Program under the NIH initiative and holds the Dr. George Feher Experimental Biophysics Endowed Chair. Kleinfeld has pioneered neurotechnology tools like adaptive-optics imaging and all-optical histology. Education includes a PhD in Physics from UCSD (1984) and postdoctoral work at Bell Labs. His honors include NIH Pioneer Award (2010), Packard Foundation Award (1999), and Fellowships from AAAS and the American Academy of Arts and Sciences. He advises on neurotechnology startups and chairs academic committees, including the UCSD Academic Senate Distinguished Research Award. Current roles span neuroimaging, vascular dynamics, and sensorimotor systems, with active collaborations at institutions like the Salk Institute and Gwangju Institute of Science and Technology.

Khong Wei Leongمشاهده پروفایل

مدرس

• Biomedical Engineering
• Image and Signal Processing
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Khong Wei Leong is a Lecturer in the Department of Common Engineering at the Malaysia School of Engineering, Monash University Malaysia. He is a registered graduate engineer with the Board of Engineers Malaysia (BEM) and actively involved in research on biomedical imaging, image/video processing, smart healthcare, and non-contact vital signs measurement. His current work focuses on developing non-contact methods for measuring human vital signs, such as heart rate and blood pressure using video imaging and LabVIEW-based tools. Dr. Khong holds a PhD in Electrical & Electronics Engineering (2018), MEng in Modelling and Estimation of Vehicle Tracking (2013), and BEng (Hons) in Computer Engineering (2009), all from Universiti Malaysia Sabah. He coordinates the unit ENG1013: Engineering Smart Systems and serves as a reviewer for the Elsevier journal Biomedical Signal Processing & Control . His research interests span biomedical engineering, artificial intelligence, and telehealth solutions. Notable projects include the 'Catch me before I fail' initiative to improve student academic performance and a LabVIEW-based biomedical toolkit for ECG analysis. His work aligns with UN Sustainable Development Goals, particularly in advancing education and health technologies. Key collaborations include work on robot vision systems, vehicle tracking algorithms, and mobile health applications. Dr. Khong has contributed to over 26 peer-reviewed publications and actively participates in international conferences.

Горан Љ. Ђорђевићمشاهده پروفایل

استاد

• Computer Networks
• Wireless Communications
• Localization Systems
• +۳ مورد دیگر

Goran Lj. Djordjevic is a Professor at the Department of Electronics, Faculty of Electronic Engineering, University of Nis, Serbia. Appointed full professor in 2009 after progressive promotions from assistant professor (1999) to associate professor (2004), he represents a cornerstone of the institution where he completed all academic degrees. His three-decade career exemplifies deep institutional commitment and scholarly excellence in electronic engineering. His academic foundation was built entirely at the University of Nis: Diploma Engineer in Electronics (1989) Master of Science in Electronics (1994) Doctor of Philosophy in Electronics (1998) Professor Djordjevic's research forms three interconnected pillars: Networks-on-Chip (NoC) innovation with breakthroughs in deflection routing and port allocation; UWB localization systems solving multipath challenges in complex indoor environments through multi-algorithm fusion; and error control coding for storage systems with applications in optical media. His work consistently bridges theoretical rigor and practical implementation, evidenced by 22 impact-factor journal publications spanning VLSI design, wireless communications, and parallel computing. Analysis of his publication timeline reveals strategic evolution: recent work (2021-2022) focuses on real-world NoC optimization and robust indoor positioning, mid-career research (2015-2005) established CDMA bus architectures and fault-tolerance frameworks, while foundational contributions (2001, 1996) in constraint coding and task scheduling underpin his later breakthroughs. This trajectory demonstrates exceptional continuity in advancing communication reliability across hardware and software domains. Scientific recognition includes: No formal awards documented in source materials Regarding academic mentorship, while specific students aren't listed, his sustained research output implies active graduate supervision. Project funding shows zero current national grants, though international collaborations remain unspecified. His 22 impact-factor publications across IEEE, Elsevier, and Springer journals demonstrate consistent productivity through completed research initiatives, with recent work indicating ongoing laboratory activity in wireless and NoC domains.

• Signal Processing
• Image Analysis
• Medical Imaging
• +۳ مورد دیگر

Jeffrey J. Rodriguez is a Professor in the Department of Electrical and Computer Engineering at The University of Arizona, where he has been a faculty member since 1990. He currently directs the Signal and Image Laboratory. His research focuses on signal/image/video processing, automated image analysis, and medical imaging applications. Rodriguez holds a B.S. and Ph.D. from The University of Texas at Austin (1984, 1990) and an M.S. from MIT (1986), all in electrical engineering. He has served in leadership roles, including Co-Director of the NSF-funded Connection One research center (2003–2008) and General Chair of major conferences like the 2016 IEEE Southwest Symposium on Image Analysis. His work spans algorithm development for medical imaging (e.g., MRI segmentation, cell nuclei detection), computer vision (vehicle tracking, aerial imagery analysis), and optics (super-resolution imaging). Awards include the 2016 Best Poster Award from the American Otological Society. Rodriguez teaches courses such as Digital Image Processing (ECE 533/OPTI 533), Digital Signal Processing (ECE 429/529), and Circuit Theory (ECE 320A). His research and grants emphasize automated image analysis tools with applications in healthcare, surveillance, and environmental monitoring.

• 2D Semiconductors
• Van der Waals Heterostructures
• Quantum Materials
• +۵ مورد دیگر

Andrew Mannix is an Assistant Professor in the Department of Materials Science and Engineering at Stanford University's School of Engineering. His teaching portfolio includes core courses such as Structure and Symmetry (MATSCI 184/214), Materials Science Colloquium , and specialized offerings like Introduction to Materials Science, Energy Emphasis (MATSCI/ENGR 50E) and Ethics and Broader Impacts in Materials Science (MATSCI 232). He supervises extensive independent research across all academic levels through MATSCI 100/150/200/299/300 and ME 392/398. His research focuses on the synthesis, characterization, and application of two-dimensional materials, particularly transition metal dichalcogenides and van der Waals heterostructures. Key areas include: Quantum phenomena in 2D semiconductors Advanced characterization techniques (TERS, hyperspectral microscopy) Strain and interface engineering Machine learning for materials optimization Ferroelectricity in twisted 2D systems Nanoelectronic device fabrication Recent work demonstrates breakthroughs in contact engineering for monolayer transistors, deterministic moiré superlattice fabrication, and flexible in-sensor computing architectures. His publications reveal a strong emphasis on experimental innovation combined with computational approaches, particularly in the last two years where over 30 articles address 2D material synthesis challenges, quantum property manipulation, and device integration. The research trajectory shows increasing focus on scalable fabrication methods and quantum applications. Scientific recognition includes: NSF CAREER Award for Tailoring van der Waals Ferroelectricity in 2D Layered Semiconductors Professor Mannix actively mentors students through multiple independent study pathways and leads research projects involving robotic assembly of van der Waals solids, deep learning-enhanced characterization, and novel synthesis techniques for borophenes and 2D polymers. His work bridges fundamental quantum phenomena with practical nanoelectronic applications, particularly in energy-efficient computing and quantum technologies.

• Cardiovascular Imaging
• Machine Learning in Medical Imaging
• Congenital Heart Disease
• +۶ مورد دیگر

Vivek Muthurangu is Professor of Cardiovascular Imaging and Physics at University College London's Institute of Cardiovascular Science. He serves as Head of the Research Department of Children's Cardiovascular Disease, which incorporates all ICS researchers based at Great Ormond Street Hospital (GOSH), and as Deputy Director of the UCL Institute of Cardiovascular Science. As lead for the Centre of Translational Cardiovascular Imaging, he has established a multidisciplinary team of physicists, engineers, computer scientists and clinicians that have pioneered the development of new imaging technologies with significant clinical impact. His educational background includes: Doctor of Medicine from King's College London (2007) Member of the Royal College of Paediatrics and Child Health (2002) Bachelor of Medicine, Bachelor of Surgery from University of Edinburgh (1999) Bachelor of Science (Honours) from University of Edinburgh (1996) Professor Muthurangu's research spans both engineering and clinical domains. His engineering-oriented research focuses on fast MR imaging using non-Cartesian sampling, iterative reconstruction, machine learning for image reconstruction and processing, and machine learning augmented computational modeling. Clinically, he specializes in imaging in pulmonary hypertension (for both children and adults), exercise imaging in cardiovascular disease, and imaging in congenital heart disease. Through his development of ultra-rapid imaging techniques, he was able to almost triple the number of cardiac MRI scans performed at GOSH with no increase in staff or infrastructure. Recently, he has pioneered Machine Learning techniques to further speed up MRI, reduce post-processing burden, massively accelerate computational fluid dynamics, and enable autonomous robotic catheterization. His recent publication portfolio (2024-2025) demonstrates a strong focus on cardiovascular imaging, particularly in congenital heart disease and Fontan circulation. Many papers incorporate machine learning approaches to improve imaging techniques, automate measurements, and enhance diagnostic capabilities. His work spans both technical development of imaging methods and clinical applications for specific patient populations, particularly children with congenital heart conditions, showing clear progression from method development to clinical implementation. Professor Muthurangu serves as the programme director of the 4-year BHF PhD programme at UCL and co-leads the MSc and iBSc module in congenital heart disease. He also teaches on multiple other MSc and iBSc modules and chairs the iBSc board of examiners at UCL Faculty of Population Health Science. As head of the Centre of Translational Cardiovascular Imaging, Professor Muthurangu leads a multidisciplinary team that bridges the gap between technical innovation and clinical application, with several developments having direct impact on patient care at Great Ormond Street Hospital.

• Super-resolution Microscopy
• Structured Illumination Microscopy
• Biomedical Imaging
• +۳ مورد دیگر

PD Dr. Marcel Müller is a Senior Lecturer in the Faculty of Physics at Bielefeld University , affiliated with the AG Biomolekulare Photonik (Biomolecular Photonics Research Group). His academic work focuses on advanced optical imaging technologies and their applications in biomedical research. University: Bielefeld University College: Faculty of Physics Research Group: AG Biomolekulare Photonik Academic Rank: Senior Lecturer Dr. Müller's research explores cutting-edge developments in super-resolution microscopy , particularly through structured illumination microscopy (SIM) and optical fluctuation imaging . His recent work addresses challenges in improving spatial resolution , image denoising using machine learning, and cost-effective imaging solutions for live cell analysis. He specializes in optimizing camera technologies and real-time reconstruction algorithms to enhance microscopy capabilities. The 15 most recent publications highlight his contributions to optical nanoscopy , with particular emphasis on structured illumination , deep learning denoising , multiplane imaging , and noise-controlled reconstructions . His work spans from hardware development (e.g., image-splitting prisms , DMD devices ) to software innovations (e.g., real-time reconstruction algorithms ), all aimed at making super-resolution microscopy more accessible and efficient for biological applications. Key Research Areas: Structured Illumination Microscopy (SIM) Super-resolution Optical Imaging Deep Learning for Image Denoising Cost-effective Microscopy Systems Live Cell Imaging Camera Technology Optimization Dr. Müller's laboratory, the AG Biomolekulare Photonik , develops innovative optical imaging tools that bridge physics and biology. His team focuses on modular microscope designs and open-source reconstruction frameworks to democratize access to advanced microscopy techniques. Current projects include optimizing video-rate imaging and exploring nonfluorescent nanoparticle probes for cellular dynamics studies.

Lasse Lensuمشاهده پروفایل

استاد

• Computer Vision
• Pattern Recognition
• Machine Learning
• +۵ مورد دیگر

Lasse Lensu is a Professor of Machine Vision and Data Analysis at Lappeenranta-Lahti University of Technology LUT (LUT University), Finland. He is affiliated with the Department of Computational Engineering within the LUT School of Engineering Sciences. His research spans multiple domains including computer vision, pattern recognition, and machine learning with applications in marine biology, medical imaging, and spectroscopy. Prof. Lensu received his M.Sc. degree in data communications in 1991, followed by Lic.Sc. and D.Sc. degrees in computer science in 2001 and 2002 respectively, all from LUT. His academic journey has been deeply rooted in the same institution where he now serves as a professor. His primary research interests focus on computer vision and pattern recognition with machine learning approaches. Specifically, he has made significant contributions to plankton recognition using deep learning techniques, medical image analysis particularly in retinal imaging, and signal processing in Raman and CARS spectroscopies. Prof. Lensu has also contributed to energy forecasting and battery state-of-charge prediction using advanced machine learning models. Analysis of his recent publications reveals a strong trend toward applying computer vision and deep learning to specialized domains. His work spans from fundamental research in machine learning techniques to practical applications in marine biology (plankton recognition), medical diagnostics (retinal imaging), and chemical analysis (spectroscopy). A significant portion of his recent work focuses on addressing challenges in open-set recognition, domain adaptation, and uncertainty quantification in deep learning models. Prof. Lensu is an active member of the Computer Vision and Pattern Recognition Laboratory at LUT. He has been instrumental in technology transfer initiatives, contributing to the creation of multiple spin-off companies from the university. His research has practical implications across several industries including environmental monitoring, healthcare diagnostics, and energy management.

• Particle Physics
• High Energy Physics
• Dark Matter
• +۴ مورد دیگر

Kevin Zhang is an Assistant Professor in the Department of Physics & Astronomy at The University of Utah. His research bridges Particle Physics, High Energy Physics, and Condensed Matter Physics, focusing on Effective Field Theories (EFTs) and their applications to dark matter detection and quantum field phenomena. He holds an ORCID identifier 0000-0001-8305-5581. His recent work, including 15 publications (2020–2023), explores topics such as dark matter direct detection via phonon/magnon excitations, anomaly cancellation in EFTs, and neural network effective theories . Projects emphasize interdisciplinary approaches, combining particle physics with condensed matter systems and computational frameworks like PhonoDark and exceed-dm. He has taught advanced courses in Quantum Field Theory I Quantum Mechanics Thesis Research (Ph.D. and Master's) Special Reading Topics and secured external funding from the National Science Foundation (2024–2027) for research on 'Effective Field Theories, Machine Learning and New Physics.'

• Porcine Reproductive and Respiratory Syndrome Virus
• Virus Immunology
• Viral Pathogenesis
• +۶ مورد دیگر

Ying Fang is a Professor in the Department of Pathobiology at the University of Illinois. Her research focuses on viral immunology, vaccine development, and innovative diagnostic technologies. She has collaborated internationally on studies involving porcine reproductive and respiratory syndrome virus (PRRSV), influenza A virus, and nanoparticle-based diagnostics. Key research interests include viral pathogenesis mechanisms, maternal immunity in swine, and the application of photonic crystal technologies for molecular diagnostics. Her work bridges veterinary medicine and biomedical engineering through interdisciplinary collaborations. Recent publications highlight advancements in concurrent PRRSV vaccination strategies, influenza virus-microbiome interactions in neonatal piglets, and deep learning-enhanced nanoparticle quantification. She has pioneered a photonic crystal microscopy technique for high-resolution diagnostics. Recipient of the 2024 NAI Fellowship, Dr. Fang has contributed to media discussions on coronavirus in pets and developed an all-species coronavirus test. Her lab integrates virology, nanotechnology, and computational methods to address global health challenges in animal and human medicine.

Tien Yeeمشاهده پروفایل

استادیار

• Water Resources Engineering
• Computational Fluid Dynamics (CFD)
• IoT and Sensor Systems
• +۵ مورد دیگر

Dr. Tien Yee is an Assistant Professor in the Department of Civil and Environmental Engineering at Kennesaw State University. He holds a Ph.D., MSc, and BSc in Civil Engineering from the University of Kentucky. His research focuses on hydraulics, computational fluid dynamics (CFD), and IoT technologies applied to water resources, transportation, and biofluid mechanics. He leads the Hydrodynamics and Systems Lab, which develops Digital Twin systems for infrastructure monitoring and collaborates with institutions like UC Berkeley and Georgia Tech. Key projects include flamingo feeding mechanics studies published in PNAS, short tunnel lighting simulations in collaboration with the University of Georgia, and bridge scour monitoring with machine learning. His work bridges civil engineering with interdisciplinary fields such as biofluid dynamics and computer science. Dr. Yee’s educational background includes degrees from the University of Kentucky, and he teaches courses in hydrology, fluid mechanics, and engineering mechanics at KSU. His lab engages students at all academic levels. Though no specific awards are listed, his contributions span multiple high-impact projects and peer-reviewed publications. Collaborations include partnerships with academic institutions and industry partners like Wi-Skies LLC. His research emphasizes practical applications of advanced technologies to solve real-world engineering challenges.

Steven W McLaughlinمشاهده پروفایل

استاد

• Communications
• Information Theory
• Error Control Coding
• +۶ مورد دیگر

Dr. Steven W. McLaughlin serves as the Provost and Executive Vice President for Academic Affairs at Georgia Tech, overseeing all academic units. He is also a Professor in the School of Electrical and Computer Engineering. McLaughlin holds degrees from Northwestern University (B.S.E.E.), Princeton University (M.S.E.), and the University of Michigan (Ph.D.). His research focuses on communications and information theory, with contributions to error control coding, data security, and quantum key distribution. He co-founded CREATE-X, a program that has launched 72 student-led companies and engaged over 1,500 students in entrepreneurship. McLaughlin has authored over 250 publications, holds 36 U.S. patents, and is a Fellow of the IEEE. Awardees include the Chevalier dans l'Ordre National du Mérite (France’s second-highest civilian honor) and the Presidential Early Career Award for Scientists and Engineers (PECASE). His leadership roles include Dean of the College of Engineering and Vice Provost for International Initiatives. He has pioneered initiatives in global engagement and educational innovation, emphasizing interdisciplinary collaboration and technology-driven solutions.

Bacha Rehmanمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +۵ مورد دیگر

Bacha Rehman is a Senior Lecturer in the Department of Computing (AI & Data Science) at Solent University, affiliated with the Science and Engineering Research Group. His research focuses on artificial intelligence, machine learning, medical imaging, and metamaterials design, with a strong emphasis on practical applications in healthcare technology, computer vision, and optoelectronics. He has contributed to over 16 peer-reviewed publications from 2011 to 2025, exploring topics such as brain tumor detection through CNNs, infrared ship tracking, and machine learning-driven metamaterial optimization. His work bridges theoretical advancements with real-world challenges, such as developing robust solar energy systems using nanostructured materials and enhancing facial expression recognition frameworks. Collaborations span international research networks, though specific partnerships remain unspecified in the provided data. No academic awards are documented, though his consistent output reflects sustained research engagement. Rehman’s email is bacha.rehman@solent.ac.uk , and his ORCID is 0000-0003-2081-5728 . His research trajectory demonstrates a commitment to interdisciplinary innovation within AI, materials science, and computational engineering.

• Nonlinear Optics
• Fiber Optics
• Supercontinuum Generation
• +۳ مورد دیگر

Mikko Närhi is a Researcher at the Faculty of Engineering and Natural Sciences, specializing in nonlinear optics, fiber optics, and ultrafast laser dynamics. His work focuses on optical fiber fabrication, modulation instability, supercontinuum generation, and rogue wave phenomena. He has contributed to advancements in coherent beam combining, reference-free dual-comb spectroscopy, and real-time measurement techniques for ultrafast processes. Education: Master of Science (Technology) in Science and Engineering (2014), and a Lower-Degree Level Tertiary Education in Technology (2013). Research Interests: Mikko's research explores the intersection of nonlinear optics and fiber-based technologies, including the development of novel optical fibers doped with nanoparticles, analysis of extreme events in optical systems using machine learning, and the design of high-power laser systems through coherent beam combining. His work emphasizes practical applications such as ultrafast pulse generation and spectroscopic tools for environmental and biomedical diagnostics. Articles highlight his contributions to fiber laser dynamics, real-time measurement techniques, and machine learning-driven analysis of nonlinear phenomena. Key areas include Er 3+ -doped tellurite fibers, power scaling of optical vortices, and the optimization of Mamyshev regenerators for ultrafast pulse shaping. Grants: He held an outgoing grant from 15.09.2015 to 15.01.2016. His work has been supported by collaborative projects with institutions globally, focusing on fiber optics and laser technologies. Labs/Teams: His research involves collaborations on advanced optical systems, with contributions to cutting-edge projects in nonlinear fiber optics and high-power laser development.