جستجوی استاد راهنمای دانشگاهی

Weiqiang Zhuمشاهده پروفایل

استادیار

• Seismology
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

Weiqiang Zhu is an Assistant Professor at the University of California, Berkeley, specializing in earthquake physics and geophysical signal analysis. His research integrates machine learning, deep learning, and cloud computing to analyze large seismic datasets, focusing on understanding complex earthquake sequences, fault zone structures, and subsurface processes like fluid migration and volcanic unrest. He develops advanced computational methods such as neural operators and physics-informed models to improve seismic inversion, earthquake simulation, and early warning systems. Key contributions include the development of tools like PhaseNet for seismic phase picking, QuakeFlow for scalable earthquake monitoring, and DeepShake for shaking intensity prediction. His work bridges artificial intelligence with geophysical applications, emphasizing high-resolution earthquake cataloging and fault zone mechanics. Collaborative projects include imaging fault zones using fiber optics and studying magmatic systems beneath Hawaii. Zhu’s research also explores the interplay between fluid dynamics and seismic activity, particularly in simulating earthquake sequences with inertia, viscoelasticity, and fault zone fluid migration. He advocates for open-source tools, contributing datasets like the STanford EArthquake Dataset (STEAD) to advance AI-driven seismology.

Sam Nallaperuma-Herzbergمشاهده پروفایل

دانشگاهی

• Machine Learning
• Artificial Intelligence
• Optical Communication Systems
• +۵ مورد دیگر

Dr. Sam Nallaperuma-Herzberg is an Assistant Research Professor at the University of Cambridge's Department of Computer Science and Technology. He holds prestigious fellowships including the Borysiewicz Interdisciplinary Fellowship, Bye Fellowship at Murray Edwards College, and the Schmidt/Accelerate Fellowship in AI for Science and Society. His research focuses on machine learning applications in optical communication systems, neural networks for medical diagnostics, and reinforcement learning for network optimization. Affiliations: Department of Computer Science and Technology, Murray Edwards College Key Research Themes: Machine Learning, AI for Healthcare, Optical Fiber Networks, Reinforcement Learning Algorithms His work bridges computational methods with real-world challenges in telecommunications and healthcare. Notable achievements include developing graph neural networks for insomnia modeling and optimizing ultra-wideband fiber-optic systems using deep learning. He has been recognized with multiple interdisciplinary research fellowships, reflecting his cross-cutting contributions. Research outputs span over 30 peer-reviewed articles since 2017, with recent emphasis on AI-driven solutions for optical network optimization and circRNA-RBP interaction prediction. His projects include the BrainTwin initiative applying AI to sleep disorder analysis. Awards: Borysiewicz Interdisciplinary Fellow Bye Fellow, Murray Edwards College Schmidt/Accelerate Fellow in AI for Science and Society Current research explores hybrid neural architectures for medical diagnostics and Gaussian process modeling for optical signal optimization. He collaborates extensively on EU-funded initiatives and industry partnerships in AI and telecommunications.

• Computational Neuroscience
• Systems Neuroscience
• Neural Engineering
• +۷ مورد دیگر

Matthew A. Smith is a Professor in the Department of Biomedical Engineering and the Carnegie Mellon Neuroscience Institute, where he serves as Co-Director of the Center for the Neural Basis of Cognition. His research bridges computational and experimental neuroscience to understand visual perception, cognition, and motor control. Dr. Smith's research focuses on neural engineering, visual perception, cognition, eye movements, and neural circuits . His laboratory investigates how groups of neurons interact to construct visual perception and translate it into cognitive processes and motor outputs. The lab employs a multi-scale approach combining single-neuron electrophysiology with global signals like EEG and near-infrared imaging, examining both normal and disease states of the brain. Analysis of his recent publications reveals strong trends in brain stimulation optimization (e.g., MiSO/OMiSO frameworks), neural population dynamics during cognitive tasks, visual cortex plasticity , and non-invasive neurotechnology development. His work spans fundamental neuroscience questions about working memory and perception while developing practical tools for brain monitoring and intervention. NIH K99/R00 Pathway to Independence Award Research to Prevent Blindness Career Development Award Dr. Smith has secured substantial funding from NIH, NSF, Research to Prevent Blindness, Schaffer Foundation for Glaucoma Research, and Hillman Foundation to support his research program. His laboratory actively develops novel methodologies for neural recording and stimulation while investigating fundamental mechanisms of visual processing and cognition. The lab maintains strong collaborative ties within Carnegie Mellon's neuroscience ecosystem through the Center for the Neural Basis of Cognition.

Enyue Annie Luمشاهده پروفایل

استاد

• High Performance Computing
• Computer and Communication Networks
• Algorithm Design and Analysis
• +۵ مورد دیگر

Dr. Enyue (Annie) Lu is a Professor of Computer Science at Salisbury University, affiliated with the Department of Computer Science within the Richard A. Henson School of Science & Technology. She holds a Ph.D. from the University of Texas at Dallas. Her research focuses on High Performance Computing, including parallel and distributed processing, cloud computing, and GPU computing. She also explores Computer and Communication Networks, such as network security and wireless sensor networks, alongside Algorithm Design and Graph Theory applications in bioinformatics and medical imaging. Dr. Lu has led and contributed to numerous research projects, including the REU (Research Experience for Undergraduates) program, which emphasizes emerging computing in science and engineering. Her work spans interdisciplinary areas like medical imaging algorithms and stable matching problems, leveraging GPU acceleration and parallel computing techniques. She has advised over 20 students in these areas and collaborates with institutions like the University of Maryland Eastern Shore and Johns Hopkins University. Her publications emphasize practical applications of theoretical computer science, such as improving tomographic imaging and network intrusion detection through machine learning and distributed systems. Despite not listing explicit awards, her extensive contributions to undergraduate research and algorithm development highlight her scholarly impact.

Borja Blancoمشاهده پروفایل

پژوهشگر

• Developmental Neuroscience
• Infant Cognition
• Functional Brain Imaging
• +۳ مورد دیگر

Dr. Borja Blanco is a Research Associate at the Department of Psychology, University of Cambridge, specializing in developmental neuroscience and infant cognition. His research employs advanced neuroimaging techniques like high-density diffuse optical tomography and fNIRS to investigate functional brain development during early childhood. Research interests focus on: Neuroplasticity and bilingualism effects in infant brains Sleep-state modulation of neural connectivity Cross-cultural studies of cognitive development Clinical applications for ASD/ADHD early detection Wearable neurotechnology for infant monitoring Dr. Blanco's work has pioneered methods for cot-side neuroimaging and home-based assessment of infant brain function. His publications demonstrate consistent focus on functional connectivity patterns, bilingual exposure effects, and clinical applications of optical neuroimaging techniques.

• Biomedical Imaging
• Medical Imaging Technology
• MRI Acquisition
• +۳ مورد دیگر

Yun Jiang, Ph.D., is an Assistant Professor in the Department of Radiology and Biomedical Engineering at the University of Michigan Medical School. Their research focuses on developing novel MRI acquisition technologies to enhance disease diagnosis and patient care. Key innovations include Magnetic Resonance Fingerprinting (MRF), which provides accurate, accelerated tissue property measurements. Dr. Jiang's work integrates engineering principles to address clinical challenges, such as improving imaging quality, accelerating scans, and extracting quantitative biomarkers for diseases like cancer and neurological disorders. Education: Ph.D. in Biomedical Engineering Affiliations: Radiology BME, University of Michigan Medical School Research Interests: The lab emphasizes MRI technology development, particularly MRF for simultaneous T1/T2 mapping, diffusion-weighted imaging, and applications in prostate cancer, multiple sclerosis, and cardiovascular imaging. Projects include optimizing MRI hardware, improving reconstruction algorithms, and extending MRF to low-field systems and clinical workflows. Publications: Over 50 peer-reviewed articles highlight advancements in MRF's clinical feasibility, multi-vendor compatibility, and applications in liver, brain, and prostate imaging. Recent work explores deep learning integration and high-field phosphorus MRI for metabolic profiling. Labs/Teams: Leads the Jiang Lab, collaborating on translational MRI research with clinical partners to deploy MRF in diagnostic settings.

Calogero Maria Oddoمشاهده پروفایل

دانشیار

• Bionic tactile sensing
• Neurophysiology of touch
• Artificial intelligence in tactile systems
• +۵ مورد دیگر

Calogero Maria Oddo is an Associate Professor at Sant’Anna School of Advanced Studies, affiliated with the BioRobotics Institute and Department of Excellence in Robotics and Artificial Intelligence. He leads the Neuro-Robotic Touch Laboratory, coordinating a team of 25+ researchers. His work bridges neuroscience and engineering, focusing on tactile sensing, neuroprosthetics, and biomedical robotics. He holds a national qualification as Full Professor of Bioengineering and serves in academic governance roles, including Vice-Coordinator of the BioRobotics PhD program and President of the University Committee for Equal Opportunities. His research has been featured in top journals like Nature Machine Intelligence and recognized by prestigious awards such as the Feltrinelli Prize under 40 (2023). Education BSc in Electronic Engineering (University of Pisa, 2005, 1st in cohort) MSc in Electronic Engineering (University of Pisa, 2007, Excellence Track) PhD in Innovative Technologies (Sant’Anna School of Advanced Studies, 2011) Graduate of Sant’Anna Honours College (2008, 2006) Research Interests His lab develops tactile sensors inspired by human neurophysiology, with applications in bionic prostheses, robotic surgery, and healthcare. Key areas include neuromorphic engineering, mechanotransduction, and sensory feedback systems. Collaborations span institutions like the National Research Council, University of Pisa, and international partners such as École Normale Supérieure. Grants & Awards Coordinator of €10M+ in research funding from EU, Italian ministries, and private foundations Contributions to technology transfer via RoboIT and the ARTES 4.0 Competence Center Labs & Teams Leads the Neuro-Robotic Touch Lab and collaborates with the N2Lab (National Research Council), focusing on clinical translation of tactile technologies.

• Optimization
• IoT
• Sensors
• +۳ مورد دیگر

ZGHAL Mourad is a Researcher-Lecturer at CESI LINEACT, holding an HDR (2008) from Sup’Com, Carthage University and a PhD in Electrical Engineering (2000) from University Tunis Manar. He specializes in Optimization, IoT, Sensors, and Smart Healthy Cities , with a strong focus on Photonic Crystal Fibers and Nonlinear Optics . Education: HDR in Engineering (2008), Sup’Com, Carthage University PhD in Electrical Engineering (2000), University Tunis Manar Engineering Degree in Telecommunications (1995), Sup’Com, Carthage University Research Interests: Mourad’s work bridges IoT sensor networks with optical communication systems . He pioneers mid-infrared supercontinuum generation in chalcogenide fibers and explores optical mode multiplexing for high-speed communications. His recent work integrates federated learning for intrusion detection in smart grids and optimizes photovoltaic energy systems for building decarbonization . Publications Trends: His 2023–2025 work emphasizes AI-driven energy management , cybersecurity for IoT , and federated learning frameworks . Earlier contributions (2016–2019) focused on nonlinear optical effects in photonic fibers and high-bit-rate networks . Awards: Elected Vice-Präsident of the International Commission for Optics Fellow Optica (ex OSA) and SPIE Associate scientist at ICTP (UNESCO Category 1 Institute) Advising & Grants: Supervised 9 PhD students (e.g., Z. MONLA’s work on BIM/VR in building maintenance). Active member of the LINEACT Scientific Council and CTI Commission des Titres d’Ingénieurs. Labs & Teams: Leads the Engineering and Numerical Tools research team at CESI LINEACT. Collaborates with IMT Télécom SudParis as an Adjunct Professor.

• In-Memory Computing
• AI Hardware Acceleration
• Edge Computing
• +۵ مورد دیگر

Shaahin Angizi is an Assistant Professor in the Department of Electrical and Computer Engineering at the New Jersey Institute of Technology (NJIT). His research focuses on next-generation computing systems, emphasizing energy-efficient architectures, in-memory computing, and secure AI hardware. He leads multiple National Science Foundation (NSF)-funded projects, including initiatives on robotic vision systems, in-cache AI acceleration, and edge computing. His work spans hardware security, emerging memory technologies, and interdisciplinary applications of AI in healthcare and IoT. Notable projects include developing secure imaging sensors (SenGuard/iSEW), adversarial attack defenses for neural networks, and AI-driven accelerator design automation using large language models (LLM-IMC/TPU-Gen). Angizi has authored over 136 publications and secured 4 active NSF grants between 2022-2027. His research bridges device-level innovations (e.g., magnetoelectric FETs) with system-level designs, aiming to reduce computational carbon footprint while enhancing reliability. Key Projects: Infrared Retinomorphic Vision (2024-2027) Toward Opportunistic In-Cache AI Acceleration (2023-2025) Integrated Sensing & Normally-off Computing (2022-2026) Research Themes: Processing-in-Memory Architectures Edge Intelligence Hardware Hardware Security & Trust Non-Volatile Memory Systems Angizi’s contributions include pioneering sensor-embedded watermarking (iSEW), adversarial attack analysis on LLMs, and energy-efficient in-sensor processing frameworks (PISA/HyperSense). His work frequently explores synergies between photonics, analog computing, and AI to create sustainable high-performance systems.

Can Atacaمشاهده پروفایل

دانشیار

• Computational Materials Science
• Quantum Monte Carlo
• Density Functional Theory
• +۳ مورد دیگر

Can Ataca is an Associate Professor in the Department of Physics at the University of Maryland, Baltimore County (UMBC), affiliated with the College of Natural and Mathematical Sciences. He directs the STEAM Lab (Simulation, Theory and Engineering of Advanced Materials), focusing on computational investigations of materials for energy, device, and filtration applications using methods spanning density functional theory (DFT), quantum Monte Carlo (QMC), and molecular dynamics (MD). He holds a Ph.D. in Physics from Bilkent University's National Nanotechnology Research Center (Turkey), with double undergraduate degrees in Physics and Electrical Engineering. Prior to UMBC, Ataca was a postdoctoral associate at MIT's Department of Materials Science and Engineering and a Senior Research Scientist at Brown University's Department of Chemistry. Ataca's research targets low-dimensional materials, concrete, electrode materials, and molecular systems, emphasizing electronic, magnetic, and thermal properties under extreme conditions. His publications demonstrate extensive use of machine learning for materials property prediction, surface functionalization studies, and quantum mechanical simulations of 2D systems. His group develops computational methodologies bridging multiple length/time scales. No specific awards, students, or grants are detailed in the provided materials. The STEAM Lab collaborates with national laboratories including NASA and welcomes students/postdocs interested in computational condensed matter physics.

David Doermannمشاهده پروفایل

استاد

• Document Image Understanding
• Video Analysis
• Pattern Recognition
• +۹ مورد دیگر

David Doermann is a Professor and Department Chair of the Department of Computer Science and Engineering at the University at Buffalo's School of Engineering and Applied Sciences. His research focuses on document image understanding, video analysis, pattern recognition, computer vision, media forensics, and artificial intelligence. Education: PhD in Computer Science, University of Maryland, College Park (1993) MS in Computer Science, University of Maryland, College Park (1989) BS in Computer Science/Mathematics, Bloomsburg University of PA (1986) Research Interests: Dr. Doermann's work spans computer vision, document analysis, and AI, with recent emphasis on histopathology image synthesis, motion prediction, and personalized vision-language models. His publications highlight advancements in neural architecture search, federated learning, and binary neural networks. Scientific Awards: Award for Excellence from the Under Secretary of Defense (2017) DARPA Results Matter Award (2016) IEEE Fellow (2014) IAPR Fellow (2014) Royal Academy of Engineering Fellowship (2011-2013) Honorary Doctorate from University of Oulu (2002) Future Work: He is exploring the integration of generative AI in software development and advancing techniques for human-centric video-depth generation.

Darko Zibarمشاهده پروفایل

استاد

• Machine Learning in Photonic Systems
• Optical Communication
• Quantum Communication
• +۳ مورد دیگر

Darko Zibar is a Professor at the Department of Electrical and Photonics Engineering, Technical University of Denmark (DTU), and leads the Machine Learning in Photonics Systems (MLiPS) group. He holds a M.Sc. in Telecommunication and a Ph.D. in Optical Communications from DTU (2004, 2007). As a Visiting Professor, he has contributed to research at Politecnico di Torino, Friedrich Alexander University of Erlangen, University of California Santa Barbara, and University of Colorado, Boulder. Research Interests: Machine learning applications in optical communication systems Digital signal processing for classical and quantum photonics Optimization of Raman amplifiers and frequency combs Nonlinear fiber optic transmission modeling Photonic reservoir computing with silicon microring resonators Scientific Contributions: His work includes record-breaking achievements in optical phase noise measurement (approaching quantum limits) and programmable gain Raman amplifier design (S+C+L band). He has received prestigious awards such as the ERC Consolidator Grant (2017), Humboldt Bessel Research Award (2021), and Villum Investigator Award (2023).

Antonio Ciconeمشاهده پروفایل

استاد

• Numerical Analysis
• Signal Processing
• Applied Mathematics
• +۵ مورد دیگر

Antonio Cicone is a Professor of Numerical Analysis at the University of L'Aquila, Italy. He holds the position of Research Associate at the Istituto di Astrofisica e Planetologia Spaziali (INAF) and Istituto Nazionale di Geofisica e Vulcanologia (Rome). His research focuses on signal processing, numerical linear algebra, and their applications in geophysics, astrophysics, medicine, and economics. He leads projects funded by the Italian Ministry and coordinates international initiatives like the MaSAG and NoSAG summer schools. He serves as an editor for journals such as Applied and Computational Harmonic Analysis and Frontiers in Applied Mathematics. His work includes developing algorithms like Fast Iterative Filtering (FIF) and the IMFogram for nonstationary signal analysis. Key collaborations involve universities and institutions worldwide, addressing challenges in plasma dynamics, GNSS signals, and cultural heritage preservation. Education: Holds an Abilitazione Scientifica Nazionale (ASN) for Full Professor in Numerical Analysis (2023) and Associate Professor (2020). Member of the Ph.D. program in Computer Science and Mathematics at the University of Insubria. Extensive teaching in signal processing and numerical analysis. Research Interests: Signal decomposition, time-frequency analysis, applications in geophysics/astrophysics, circular economy through signal processing, and interdisciplinary collaborations. His methods are applied to biomedical signals, optical fringe patterns, and environmental data analysis. Grants & Leadership: PI of the PRIN PNRR 2023 project on circular economy. Organized multiple international conferences and summer schools. Editorships and advisory roles in academic journals highlight his leadership in computational mathematics and signal processing. Labs/Teams: Involved with the Mathematics for Signal Processing group at the University of L'Aquila and collaborates with global institutions like Duke University and Georgia Tech.

• Neuroscience
• Visual Cortex
• Neural Circuits
• +۴ مورد دیگر

Kristina Nielsen is an Associate Professor of Neuroscience at Johns Hopkins University School of Medicine and a researcher at the Zanvyl Krieger Mind/Brain Institute. She investigates the function, development, and plasticity of higher-level visual cortex circuits. PhD, Max Planck Institute for Biological Cybernetics (Germany) Postdoctoral work at Salk Institute (2006-2012) with Ed Callaway and Rich Krauzlis Her research focuses on: Structure-function relationships in higher visual cortex Neural mechanisms of object recognition Developmental plasticity of visual circuits Two-photon microscopy applications in primates Viral vector-based circuit analysis Key article trends include primate visual cortex organization, motion and shape integration, and cortical development mechanisms. She has contributed to journals like Nature , Neuron , and Journal of Neuroscience . Scientific awards: CNRS 2025 bronze and silver medals Current lab members include graduate researchers Dallas Khamiss, Emmanuel Osikpa, and Brandon Nanfito. Her team has received ANR funding for projects like 'Earlier stages of development of the motion pathway' and 'Encoding of 2D and 3D shape in primate V4.'

Ben Wuمشاهده پروفایل

دانشیار

• Optical Communication
• Neuromorphic Computing
• Photonics
• +۳ مورد دیگر

Ben Wu, Ph.D., is an Associate Professor in the Department of Electrical & Computer Engineering at Rowan University’s Henry M. Rowan College of Engineering. He holds a B.S. from Nankai University (2008) and a Ph.D. from Princeton University (2015). His research focuses on photonics, including photonic neuromorphic computing, free-space optical communication, and optical imaging. Dr. Wu’s work emphasizes high-speed, low-latency photonic systems for applications in communication and artificial intelligence. He leads the Ben Wu Lab, which explores photonic neural networks, lidar signal processing, and secure optical communication systems. His research has been supported by grants from organizations like the National Science Foundation and NEC Laboratories America. Notable awards include the 2018 Inspira Health Award. Dr. Wu actively advises graduate students and postdoctoral researchers, with openings for scholars interested in optics, photonics, and wireless communication. His lab collaborates on projects such as noninvasive 3D medical imaging and secure key distribution systems.