جست‌وجوی استاد و پژوهشگر

David Towersمشاهده پروفایل

استاد

• Optical Sensing Technologies
• Fluid Mechanics
• Structural Assessment
• +۶ مورد دیگر

Professor David Towers is the Head of the School of Engineering at the University of Warwick and holds the position of Professor of Mechanical Engineering. With over 30 years of combined academic and industry experience, he leads a dynamic research group focused on optical sensing technologies, fluid mechanics, and structural assessment. His work bridges engineering, biology, and clinical practice, with significant contributions to malaria control strategies through mosquito behavior analysis. Education: BSc (1st Class) in Mechanical Engineering Science from the University of Warwick PhD in Optical Engineering from the University of Warwick Royal Society Fellowship at ETH Zürich Research Interests: Professor Towers’ research spans three core areas: fluid mechanics (turbulent flows, sprays, multi-phase mixing), structural assessment (optical interferometry for deformation and stress measurement), and clinical optical systems (mosquito tracking for malaria intervention). His multi-disciplinary approach emphasizes collaboration with end-users, particularly in developing novel optical instruments for industrial and healthcare applications. Grants & Projects: AI for Mosquito Trajectory Understanding (£X, 2022–Present) Bill & Melinda Gates Foundation: Next-Gen LLIN Development (£X, 2019–2023) MRC: Indoor Mosquito Behavior Mapping (£X, 2019–2022) Awards: Athena SWAN Silver Award (2021) for promoting gender equality in STEM. Labs & Teams: Leads the Applied Optics Laboratory and collaborates with the Vector Control Research Group, focusing on AI-driven solutions for vector-borne disease control.

Yue Guoمشاهده پروفایل

استاد

• Energy Storage Systems
• Automotive Engineering
• Lithium-Ion Batteries
• +۴ مورد دیگر

Yue Guo is a Professor of Battery Systems at Coventry University, affiliated with the Institute of Future Transport and Cities and the Centre for E-Mobility and Clean Growth. He holds a BEng in Electrical Engineering and Automation from Harbin Institute of Technology (China), an MSc in Management of Information Technology from the University of Nottingham, a PhD in Model-Based Design for Automotive Electronic Systems from the University of Warwick, and an MBA in Global Energy Industry from Warwick Business School. His research focuses on energy storage systems, battery thermal management, and automotive electronics. He has collaborated extensively with the automotive industry on projects involving system-of-systems modeling, Li-ion battery testing, and low-carbon transport technologies. Notable roles include former Project Manager and Deputy Head of the Energy Innovation Centre at WMG, University of Warwick, and current Chair of Battery Systems at C-ALPS. Professional memberships include Chartered Engineer, IEEE Senior Member, and SAE International Battery Thermal Management Committee. Key research trends in his publications include predictive battery diagnostics, thermal runaway prevention, and LCA analysis of battery production. His work emphasizes real-time monitoring technologies and sustainable energy storage solutions. Projects address challenges in battery aging, environmental impacts, and integration into low-carbon vehicle systems. Awards and recognitions include his academic appointments and industry collaborations, though no specific scientific awards are listed. He has advised on numerous research initiatives and contributed to advancing battery technologies through innovative thermal management systems and model-based testing frameworks. Labs and teams: Active in the Centre for E-Mobility and Clean Growth, leading interdisciplinary projects at Coventry University's Institute of Future Transport and Cities. Collaborations span academic and industrial partners globally, focusing on next-generation energy storage solutions.

• Remote Sensing
• Land Cover Classification
• Deep Learning
• +۴ مورد دیگر

Huiran Jin is an Associate Professor in the School of Applied Engineering and Technology at New Jersey Institute of Technology (NJIT). Her research focuses on remote sensing, land cover classification, and environmental modeling, with a strong emphasis on integrating multi-sensor data and machine learning techniques. She has secured federal grants from NSF and USGS for projects addressing riparian ecosystem dynamics and harmful algal blooms. Her work spans applications from solar image generation using GANs to geospatial education frameworks promoting diversity and inclusion in STEM. Her research interests include environmental monitoring using remote sensing technologies such as VIIRS, Landsat, and LiDAR, as well as cognitive strategies in engineering education. Notable contributions include fusion of optical/radar/LiDAR data for land cover mapping and assessing spatiotemporal variations of environmental parameters like PM2.5 and PM10 in China. Her articles reflect interdisciplinary trends in geospatial science, combining environmental data analysis with educational innovations. Recent projects highlight deep learning applications for flood monitoring and subpixel urban mapping. Jin has collaborated internationally on ecosystem dynamics and hydroclimate variability studies. Her work has been supported by grants totaling over $1M, including a 7-year NSF project (2018-2025) and a 2023 USGS-funded study on harmful algal blooms in NJ lakes. She has published extensively in journals like ISPRS Journal of Photogrammetry and Remote Sensing, with over 500 citations and an h-index of 11.

• Machine Learning
• Bioinformatics
• Materials Science
• +۷ مورد دیگر

Pengyu Hong is a Professor of Computer Science at Brandeis University's Michtom School of Computer Science and an affiliated faculty member at the Benjamin and Mae Volen National Center for Complex Systems. His expertise spans Machine Learning, Bioinformatics, Materials Science, and FinTech, with a focus on interdisciplinary applications in healthcare, molecular biology, and complex systems analysis. Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign M.E. in Computer Science, Tsinghua University B.Eng. in Computer Science, Tsinghua University Research Interests: Hong's lab develops advanced machine learning techniques for analyzing heterogeneous data (images, text, financial data), with notable contributions in glycomaterials analysis, clinical outcome prediction, and active nematics modeling. His work bridges computational methods with biomedical and material science challenges, including NMR spectroscopy analysis and molecular property prediction. Publications: Recent work focuses on machine learning applications in glycan sequencing, fairness analysis in medical algorithms, and optical flow techniques for fluid dynamics. The lab also maintains benchmark datasets like GlycoNMR for carbohydrate analysis. Labs & Teams: Hong leads research at the Volen National Center for Complex Systems, integrating computational approaches with experimental systems biology and materials science.

• Mechano-Genomics
• Nuclear Mechanics
• Genome Regulation
• +۳ مورد دیگر

Prof. G.V. Shivashankar is a Full Professor of Mechano-Genomics at ETH Zurich and holds a joint appointment at the Paul Scherrer Institute (PSI), Switzerland. He previously served as Deputy Director of the Mechanobiology Institute (MBI) at the National University of Singapore (NUS), where he also held the IFOM-NUS Chair Professorship. His research focuses on nuclear mechanics, genome regulation, and cancer diagnostics, integrating optical imaging, machine learning, and functional genomics. Educated at The Rockefeller University (PhD, 1994–1999) and with postdoctoral training at NEC Research Institute, he has led groundbreaking studies on nuclear mechanogenomics and mechano-driven cell fate transitions. Awards include the Birla Science Prize (2006), Swarnajayanthi Fellowship (2007), and EMBO membership (2019). His work bridges disciplines, exploring how mechanical forces influence nuclear architecture and gene regulation. He leads the Laboratory of Multiscale Bioimaging at PSI, collaborating with IFOM in Milan. Current projects include developing nuclear biomechanical markers for early cancer diagnosis and AI-driven analysis of chromatin dynamics. Grants include support from the Mechanobiology Institute, Ministry of Education (Singapore), and the IFOM-MBI Joint Lab. Notable achievements include detecting chemoresistant cancer cells via chromatin biomarkers and reprogramming fibroblasts for tissue regeneration. His lab actively mentors students and collaborates globally on mechanobiology applications in health and disease.

Jordan J. Williamsمشاهده پروفایل

استادیار

• Neural Engineering
• Optogenetics
• Biomedical Engineering
• +۵ مورد دیگر

Jordan J. Williams is an Assistant Professor and Director of the Neural Engineering & Modulation Laboratory (NEMo Lab) at the Joint Department of Biomedical Engineering, affiliated with Marquette University and the Medical College of Wisconsin. He holds MD and PhD degrees from Washington University in St. Louis and completed postdoctoral work at the University of Pittsburgh. Education: Postdoctoral Scholar (2013-2019), Systems Neuroscience Institute, University of Pittsburgh MD (2013), Washington University in St. Louis PhD (2013), Biomedical Engineering, Washington University in St. Louis B.S. (2005), Electrical Engineering and Engineering Physics, South Dakota State University His research focuses on neural engineering, optogenetics, and brain-machine interfaces. Key projects include peripheral optogenetic stimulation for motor recovery post-spinal cord injury, neuroprosthetics, and viral gene therapy applications. Recent publications highlight advancements in spatiotemporal nerve stimulation patterns, non-human primate studies, and neural interface biocompatibility. Dr. Williams teaches courses like Neural Engineering , Biomedical Signal Processing , and Control Systems for Biomedical Engineering . His funded grants include projects on functional motor recovery and improved spinal cord injury rehabilitation through optogenetics. Current research emphasizes the intersection of neuroscience and engineering to address paralysis, with a strong focus on translating preclinical findings into clinical applications. His work has been published in journals such as Journal of Neuroscience Methods , Science Robotics , and Frontiers in Neuroscience .

Tobias Langlotzمشاهده پروفایل

استاد

• Visual Computing
• Augmented Reality
• Virtual Reality
• +۲ مورد دیگر

Tobias Langlotz is a Professor at the Department of Computer Science, Aarhus University. His primary research focuses on Visual Computing, Augmented Reality, Virtual Reality, Human-Computer Interaction, and Augmented Human applications. He explores advanced vision augmentations and wearable technologies to enhance human perception and interaction with digital environments. His recent work emphasizes understanding visual discomfort in augmented reality systems, developing novel display technologies for mixed reality, and refining presence measurement methodologies in virtual environments. Key contributions include studies on presence questionnaires, neural rendering techniques, and hybrid layered displays. No scientific awards are explicitly listed in the provided materials. His research publications primarily address technical challenges in AR/VR systems and human-centric design principles. No advising or grant information is available in the current text. His work contributes to both academic and applied aspects of augmented and virtual reality technologies.

Peter Bienstmanمشاهده پروفایل

استاد

• Photonics
• Neuromorphic computing
• Nanophotonics
• +۱۱ مورد دیگر

Peter Bienstman is a full professor at Ghent University, working in the Department of Information Technology (INTEC) where he has been since 1997. He is affiliated with the Photonics Research Group and also collaborates with imec. His research spans nanophotonics, neuromorphic computing, and biosensing applications. Bienstman received his electrical engineering degree from Ghent University in 1997 and completed his Ph.D. at the same institution in 2001. His doctoral work focused on "Rigorous and efficient modelling of wavelength scale photonic components." His research interests primarily revolve around nanophotonics and its applications, with specific focus areas including: Photonic Reservoir Computing for neuromorphic information processing Optical label-free biosensors based on ring resonators TE/TM biosensors for measuring conformational changes SiN biosensors operating in the visible spectrum Optical spiking neurons and neuromorphic architectures Nanophotonic information processing systems Analysis of his recent publications reveals a strong focus on advancing photonic reservoir computing for practical applications, particularly in communications signal processing and biomedical sensing. His work demonstrates how photonic systems can implement neuromorphic computing paradigms with energy efficiency advantages over traditional electronics. Recent trends show increasing integration of phase-change materials and exploration of quantum-inspired photonic computing approaches. Bienstman has received significant recognition for his work, most notably an ERC Starting Grant for the Naresco-project: "Novel paradigms for massively parallel nanophotonic information processing." This prestigious European grant supports his innovative research at the intersection of photonics and computing. As an advisor, Bienstman has supervised numerous doctoral students to completion and currently mentors a large research group with nine active PhD students and two postdoctoral researchers. His research is supported by multiple grants that enable the development of novel photonic computing architectures and biosensing platforms. The group's work bridges fundamental photonics research with practical applications in communications, healthcare, and computing. The Photonics Research Group at Ghent University, where Bienstman works, maintains state-of-the-art facilities for nanophotonic device design, fabrication, and characterization. The group collaborates extensively with imec and other international research institutions, creating a vibrant ecosystem for advancing photonic technologies from fundamental research to potential commercial applications.

Mujdat Cetinمشاهده پروفایل

استاد

• Computational Imaging
• Signal Processing
• Machine Learning
• +۵ مورد دیگر

Mujdat Cetin is a Professor of Electrical and Computer Engineering at the University of Rochester, serving as the Robin and Tim Wentworth Director of the Goergen Institute for Data Science and Director of the New York State Center of Excellence in Data Science. Previously, he held faculty positions at Sabanci University, Istanbul, Turkey, and MIT. He earned his BS from Bogazici University (1993), MS from the University of Salford (1995), and PhD from Boston University (2001). His research focuses on computational imaging, signal processing, and machine learning applications in biomedical imaging, radar systems, and neuroscience. Key areas include uncertainty quantification in imaging, probabilistic methods for image analysis, and brain-computer interfaces. His work bridges electrical engineering, computer science, and biomedical applications. Notable awards include the IEEE Signal Processing Society Best Paper Award and the Turkish Academy of Sciences Distinguished Young Scientist Award. He is an IEEE Fellow and has held editorial roles for journals like IEEE Transactions on Computational Imaging and SIAM Journal on Imaging Sciences. His contributions span conference leadership, including Technical Program Co-chair roles for major signal processing and fusion events. Cetin’s research group has advanced computational sensing, medical imaging algorithms, and EEG-based emotion/motor imagery classification. Ongoing work emphasizes integrating physics-based models with deep learning for robust imaging and signal analysis.

Daniel Rönnowمشاهده پروفایل

استاد

• Electrical Engineering
• RF Technology
• Microwave Components
• +۷ مورد دیگر

Daniel Rönnow is a Professor of Electronics at the University of Gävle since 2011. Previously, he held roles such as lecturer at the University of Gävle (2004–2006), researcher at Acreo (1998–2000), and postdoc at the Max Planck Institute (1996–1998). He earned his MSc and PhD in Engineering Physics from Uppsala University (1991–1996). His research focuses on RF/microwave components, including phase noise characterization, nonlinear system linearization, metamaterials, and measurement technology. Key areas include MIMO systems, thin-film optics, and seismic sensors. Recent work emphasizes ultrawideband radar for nondestructive testing and antenna design for industrial applications. Rönnow has authored/co-authored over 50 publications, with notable contributions in IEEE Transactions and other journals. His work bridges theoretical models (e.g., behavioral amplifiers) with practical applications like sensor development and radar systems.

Swastik Karمشاهده پروفایل

استاد

• Nanophysics
• Materials Science
• Quantum Materials
• +۳ مورد دیگر

Swastik Kar is a Professor of Physics at Northeastern University, specializing in experimental nanophysics and materials science. His research focuses on the electronic, optical, and electrochemical properties of graphene and 2D quantum materials, with applications in energy storage, sensing, and quantum technologies. He holds a BS from Presidency College, MS and PhD from the Indian Institute of Science, followed by postdoctoral research at Universität Karlsruhe and Rensselaer Polytechnic Institute. His academic journey includes roles as Assistant Professor (2010–2016) and Associate Professor (2016–2023) before becoming a full Professor in 2023. His work bridges experimental physics with advanced fabrication techniques, emphasizing scalable synthesis and characterization of atomically thin materials. Research interests span 2D heterostructures, quantum materials manufacturing, and AI-driven materials discovery. Key projects include the Laboratory for 2D Quantum Materials (2DQM) and the Experiential Quantum Advancement Laboratories (EQUAL), focusing on quantum materials and sensing technologies. Collaborative efforts with industry and academia aim to commercialize innovations like radiation detectors and color-recognition systems. Publications highlight advancements in moiré physics, defect engineering, and optoelectronic devices. His work on graphene-based sensors and nanomaterial color identification systems has been widely recognized. Grants include an NSF award for radiation detector development and TIER 1 funding for interdisciplinary quantum initiatives. Labs and affiliations include the Northeastern Center for Renewable Energy Technology and the Quantum Materials and Sensing Institute (QMSI), where he serves as Deputy Director. His research integrates computational modeling, machine learning, and advanced characterization to accelerate quantum material commercialization.

• Optoelectronics
• Liquid Crystal Technologies
• Visible Light Communication (VLC)
• +۴ مورد دیگر

Juan Carlos Torres Zafra is a Visiting Professor at Universidad Carlos III de Madrid. His research focuses on optoelectronics, liquid crystal technologies, visible light communication (VLC), and sensor systems. His work spans interdisciplinary areas including energy-harvesting IoT nodes, indoor positioning systems, and optical communication interfaces for high-definition media transmission. Torres Zafra has contributed to advancements in semiconductor materials (e.g., perovskites), low-cost sensor networks, and hybrid RF-VLC positioning systems. His research also extends to educational technology, exploring tools like Telegram and Google Workspace for improving student engagement in cybersecurity engineering programs. Notable projects include the GUTI group's work on optical vortices using liquid crystal devices and the development of tunable resonators based on liquid crystal capacitance. His publications from 2020–2025 highlight trends in VLC system optimization, AI-driven disinformation detection (SmartVote-AI initiative), and medical studies on amyloidosis therapy outcomes. Torres Zafra’s work often emphasizes practical applications in robotics, automotive systems, and sustainable energy solutions. While no formal awards are listed, his extensive publication record (over 80 entries from 2004–2025) reflects sustained contributions to photonics, sensor engineering, and liquid crystal device innovation. His research integrates hardware design, algorithm development, and material science to address challenges in modern communication systems and assistive technologies for visually impaired patients.

Michael Zurelمشاهده پروفایل

پژوهشگر ارشد

• Quantum Information
• Quantum Computation
• Quantum Contextuality
• +۷ مورد دیگر

Michael Zurel is a NSERC Postdoctoral Fellow in the Department of Mathematics at Simon Fraser University, working under Dr. Nadish de Silva, Canada Research Chair in the Mathematics of Quantum Computation. His research focuses on foundational aspects of quantum computation, quantum information, and nonclassical physics. Key interests include quantum contextuality, negativity in quasiprobability representations, and classical simulation algorithms for quantum systems. He holds a PhD, MSc, and BSc in Physics and Mathematics from the University of British Columbia (2024, 2020, 2019), all supervised by Dr. Robert Raussendorf. His doctoral work explored classical descriptions of quantum computations via hidden variable models and quasiprobability representations. His master’s thesis addressed hidden variable models and classical simulation algorithms for quantum computation with magic states on qubits. Research interests emphasize bridging quantum foundations with computational efficiency, particularly how nonclassical features like contextuality enable quantum advantage. Collaborators include prominent figures such as Robert Raussendorf, Juani Bermejo-Vega, and Cihan Okay. His scientific achievements include the NSERC Postdoctoral Fellowship. Advising and grants are not explicitly detailed, but his work is supported by foundational research grants. He collaborates actively within quantum information theory and computational physics communities.

• Resource Optimization
• Telecommunication Networks
• VLSI Circuit Design
• +۵ مورد دیگر

Professor Arunabha Sen is a faculty member at Arizona State University (ASU), affiliated with the School of Computing and Augmented Intelligence and the College of Health Solutions as a Health Solutions Ambassador. He joined ASU in 1987 and holds a Ph.D. in Computer Science from the University of South Carolina (1987). His research focuses on resource optimization in telecommunication networks, VLSI circuits, hardware-software co-design, and network security. Key areas include algorithm design, combinatorial optimization, and network processor systems. His work spans wireless, optical, and sensor networks, with contributions to video transmission over mobile ad-hoc networks and interference-aware channel assignment. Notable projects include robust network design against WMD attacks and tools for resilient communication networks. He has served on multiple technical committees for conferences like IEEE and IFIP, and contributed to academic initiatives such as capstone courses on network processors. Grants include NSF, DOD-DTRA, and Motorola Labs funding, emphasizing interdisciplinary research in network science and communications. Teaching responsibilities include courses on algorithms, game theory, and network design. His service roles include Associate Editor for IEEE Transactions on Mobile Computing and leadership in graduate program committees. Research outputs include over 30 peer-reviewed publications, with recent work in algorithmic network design and social computing data mining.

Anthony Rizzoمشاهده پروفایل

استادیار

• Integrated photonics
• Quantum photonics
• Neuromorphic photonics
• +۴ مورد دیگر

Anthony Rizzo is an Assistant Professor of Engineering at Dartmouth College, leading the Rizzo Integrated Photonic Systems Laboratory. His research focuses on integrated photonics, quantum photonics, and neuromorphic photonics, with applications in high-speed data communication, quantum computing, and energy-efficient systems. He holds a PhD in Electrical Engineering from Columbia University (2022), an MS from Columbia (2019), and a BS in Physics from Haverford College (2017). His work has been funded by organizations such as the Toyota Research Institute and the U.S. Army Research Laboratory. Key research areas include silicon photonics for ultra-low energy optical interconnects, photonic-electronic integration, and novel photonic materials like aluminum nitride. Notable achievements include the first demonstration of a Kerr comb-driven silicon photonic link and advancements in 3D photonic integration for terabit-scale data links. His lab explores applications in quantum sensing, neuromorphic computing, and visible wavelength photonics for atomic systems. Funding Sources: Toyota Research Institute, U.S. Army Research Laboratory, Thayer School of Engineering, AIM Photonics Courses Taught: ENGS 23 (Distributed Systems and Fields), ENGS 60 (Solid-State Electronic Devices) Recent publications highlight breakthroughs in ultra-low loss waveguides, scalable photonic linear neurons, and energy-efficient modulators. His work bridges fundamental photonics research with practical applications in next-generation communication and computing systems.