جستجوی استاد راهنمای دانشگاهی

Sherryse Corrowمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Developmental Psychology
• Face Perception
• +۵ مورد دیگر

Dr. Sherryse Corrow is a Professor of Psychology at Bethel University, affiliated with the Department of Psychology within the College of Arts and Sciences. She joined Bethel in 2017 and is actively involved in the university's faculty development team. Her research focuses on cognitive neuroscience, particularly face perception, prosopagnosia, and developmental disorders. She teaches courses in cognitive neuroscience and research methodologies, emphasizing student engagement in both classroom and lab settings. Education: B.A. in Psychology, Minnesota State University, Moorhead (2008) M.A. in Child Psychology, University of Minnesota (2011) Ph.D. in Child Psychology, University of Minnesota (2013) Postdoctoral training in neuroscience (neurology/ophthalmology and visual sciences), University of British Columbia, Vancouver (2013–2017) Research Interests: Dr. Corrow’s work examines face recognition impairments in developmental and psychiatric disorders, prosopagnosia (face blindness), and the intersection of cognitive neuroscience with education. She explores topics such as the role of auditory and visual cues in face processing, multisensory integration, and the psychological impact of prosopagnosia on individuals and families. Her research also extends to pedagogical innovations, such as course-based undergraduate research experiences (CUREs) in statistics. Publications: Her recent work highlights trends in understanding face perception deficits, the neural mechanisms underlying prosopagnosia, and the application of perceptual learning techniques for rehabilitation. She has also contributed to educational pedagogy, advocating for efficient teaching strategies and student-centered learning. Professional Contributions: As a member of Bethel’s faculty development team, Dr. Corrow supports colleagues in curriculum design and effective teaching practices. Her research has practical implications for addressing challenges in facial recognition disorders and enhancing undergraduate research opportunities. Labs/Teams: While no specific lab is named, her work aligns with cognitive neuroscience research groups focused on sensory integration and developmental disorders.

Hang Dinhمشاهده پروفایل

دانشیار

• Quantum computing
• Post-quantum cryptography
• Computer security
• +۳ مورد دیگر

Hang Dinh is an Associate Professor in the Department of Computer and Information Sciences at Indiana University South Bend. She joined the faculty in 2010 as a full-time member and has since contributed significantly to teaching and research in theoretical computer science. Her research focuses on quantum computing, post-quantum cryptography, algorithm analysis, and heuristic search methods. Dinh holds a Ph.D. in Computer Science from the University of Connecticut, an M.S. from the Asian Institute of Technology, and a B.S. from Vietnam National University, Hanoi. Education: Ph.D. in Computer Science & Engineering, University of Connecticut (2010) M.S. in Computer Science, Asian Institute of Technology (2002) B.S. in Computer Science, Vietnam National University, Hanoi (2000) Research Interests: Quantum computing and post-quantum cryptographic systems Algorithm design and complexity analysis Heuristic search optimization Theoretical computer science education Awards & Honors: Best Paper Award at ComManTel 2015 Taylor L. Booth Memorial Scholarship (2008-2009) AAAI Student Travel Award (2007) Vietnamese Government Fellowship (2002) Canadian Government Scholarship (2000) Grants & Projects: 2013: $8,500 for 'Quantum Algorithms for Post-quantum Cryptography' 2011: $8,500 for 'Empirical Analysis of A* Search for Hard Problems' 2011: $700 for conference travel (QIP 2011) Teaching Contributions: Courses include Discrete Structures, Security in Computing, and Foundations of Computing Theory. She has developed new courses like CSCI-B401 and CSCI-C250 to strengthen the computer science curriculum. Professional Activities: Co-founder of ExtentWorld, a social media platform emphasizing privacy. Maintains an academic profile on social media and actively publishes in journals like Quantum Information & Computation and Journal of Artificial Intelligence Research .

Da Huمشاهده پروفایل

استادیار

• Smart Infrastructure Systems
• Robotics and Sensing
• Infrastructure Inspection and Management
• +۷ مورد دیگر

Dr. Da Hu is an Assistant Professor in the Department of Civil and Environmental Engineering at Kennesaw State University (KSU), joining in Spring 2023. His research focuses on smart infrastructure systems, leveraging AI, robotics, and data analytics for disaster response, infrastructure inspection, and facility management. He holds a Ph.D. from the University of Tennessee, Knoxville, and has extensive postdoctoral and industry experience. His work spans robotic systems, geotechnical engineering, and building information modeling, with over 20 publications in top journals. Education: Ph.D. in Civil Engineering, University of Tennessee, Knoxville (2022) M.S. in Civil Engineering, Texas Tech University (2018) M.S. in Civil Engineering, University of the Chinese Academy of Sciences (2017) B.S. in Safety Engineering, Zhengzhou University (2014) B.B.A. in Business Administration, Zhengzhou University (2014) Research Interests: Robotics and Sensing, Infrastructure Inspection, Human-Robot Collaboration, VR/AR, Disaster Management, and AI-driven solutions for sustainable infrastructure. His lab develops technologies like drone-based damage assessment, GPR for subsurface mapping, and robotic disinfection systems for hospitals. Grants & Awards: NSF ERI Award ($199,642) as PI (2024–2026) SPCEET Outstanding Early Career Faculty Award (2025) Best Paper Award, Building and Environment (2020) Advising & Labs: Supervises PhD and master’s students in smart infrastructure and robotics. Active in the Geotechnical Lab, developing affordable learning resources like CE3708 lab videos. Former lab members include Tracy Le and Frank-Cedric Kadjo. Teaching: Teaches courses like CE 2003, ENGR 2214, and CE 3708 Geotechnical Engineering Lab.

• Intelligent Transportation Systems
• Traffic Engineering
• Reinforcement Learning
• +۳ مورد دیگر

Baher Abdulhai is an Adjunct Associate Professor at the Department of Civil Engineering , McMaster University . He is a leading figure in Intelligent Transportation Systems (ITS), with a focus on adaptive traffic control, reinforcement learning, and smart mobility solutions. His work integrates machine learning, data science, and engineering principles to address urban traffic challenges. Abdulhai’s research spans over two decades, emphasizing innovations in traffic signal control (e.g., MARLIN-ATSC framework), autonomous vehicle integration, and sustainable transportation systems. He leads the Toronto Intelligent Transportation Systems Centre and Testbed , providing a platform for experimental and applied research. His contributions include developing algorithms for real-time traffic optimization, electric bus fleet management, and cyber-physical-social platforms for smart cities. Research Themes: Reinforcement learning in traffic systems, adaptive signal control, urban mobility modeling, and smart infrastructure. Key Projects: eMARLIN+, ONE-ITS platform, and studies on electrified transit systems. His publications highlight advancements in deep reinforcement learning applications, sensor data fusion, and policy optimization for traffic networks. Abdulhai’s work bridges theoretical research and practical implementations, with applications in congestion management, autonomous vehicle coordination, and sustainable urban planning.

• Nuclear Physics
• Particle Physics
• Radiation Detection Technology
• +۳ مورد دیگر

Dr. David O'Donnell is a Senior Lecturer in the Nuclear Physics Group at the University of Strathclyde, affiliated with the School of Computing, Engineering and Physical Sciences. His research focuses on probing the structure of exotic nuclei using gamma-ray and charged particle spectroscopy, with a particular emphasis on octupole correlations in actinide nuclei and pear-shaped nuclei studies. He contributed to the design of the STFC-funded Light Ion Spectrometer Array and is actively involved in the Scottish Centre for the Application of Plasma-based Accelerators (SCAPA), providing nuclear physics expertise in interdisciplinary projects. Research interests include developing scintillation-based radiation detectors for medical tumor margin detection and space gamma-ray burst monitoring. He also conducts Monte Carlo simulations for radiation interaction modeling. Recent work involves collaborations on the West of Scotland PET Centre for fluorine-18 radioactivity studies and the HIE-ISOLDE superconducting recoil separator. Key projects include CP violation searches via Mössbauer spectroscopy of Ac-227 and Coulomb excitation studies of radium isotopes. His work spans experimental nuclear physics, detector technology, and applications in medicine and space science.

• Particle Physics
• High Energy Physics
• Quantum Chromodynamics (QCD)
• +۵ مورد دیگر

Juan Jose Saborido Silva is a Professor in the Department of Particle Physics at the University of Santiago de Compostela, affiliated with the Faculty of Physics and the Galician Institute of High Energy Physics (IGFAE). He leads the GAES High Energy Physics Group, focusing on experimental particle physics and collider-based studies. He earned his doctorate in 1995 with a thesis on 'Study of the polarized sum rules in QCD for the nucleon.' His research emphasizes precision measurements in B-meson decays, CP violation, and the spectroscopy of exotic particles. Key contributions include studies of rare decays, quarkonium production, and heavy flavor dynamics at the Large Hadron Collider (LHC). His work spans over 70 peer-reviewed articles since 2020, with recent emphases on: Bs meson decays and mixing phenomena Observation of new baryon resonances Searches for lepton-flavor violation CP asymmetry measurements in charm and beauty sectors His experimental contributions include analyses of data from proton-proton collisions at 5-13 TeV energies, with a focus on LHCb detector performance and jet substructure studies.

MARCOS FERNANDEZ PICHELمشاهده پروفایل

استادیار

• Health Information Credibility
• Natural Language Processing
• Information Retrieval
• +۳ مورد دیگر

Marcos Fernández Pichel is an Assistant Professor at the Department of Electronics and Computing, affiliated with the Higher Technical School of Engineering at the University of Santiago de Compostela. He holds a Doctorate from the same institution (2023) with a thesis focused on technologies for analyzing health-related online content credibility. His research focuses on health information credibility assessment, natural language processing (NLP), and misinformation detection. He collaborates with the Singular Center for Research in Intelligent Technologies (CiTIUS) and has developed systems like Depressmind for mental health surveillance on social media, and Social Minder for detecting pandemic-related misinformation. His work has been recognized by the Royal Galician Academy of Sciences' award for best young researcher article (2022). Key projects include analysis of search engine performance for health queries, LLM-based depression symptom assessment, and risk communication strategies for radon gas. He has contributed to TREC Health Misinformation Track competitions (2021-2022), demonstrating expertise in retrieval systems and misinformation detection methodologies. Pichel's research often bridges computational linguistics with public health applications, emphasizing real-time monitoring of dynamic web sources (e.g., eXtream system). His work frequently involves semi-supervised learning techniques and ethical considerations in AI-driven health communication systems.

Chris Pickardمشاهده پروفایل

استاد

• Electronic Structure Methods
• First Principles Structure Prediction
• Superconductivity
• +۴ مورد دیگر

Professor Chris Pickard, currently the Sir Alan Cottrell Professor of Materials Science at the University of Cambridge, is affiliated with the TCM Group at the Cavendish Laboratory and the Department of Materials Science and Metallurgy. His research focuses on electronic structure methods, first principles structure prediction, and the development of the CASTEP software for computational materials discovery. Research Emphasis: Superconductivity, high-pressure materials, machine learning in simulations, hydrogen absorption, and phase diagram engineering Laboratory: TCM Group, Cavendish Laboratory, Cambridge Recent publications highlight his work in predicting and synthesizing superconducting hydrides, understanding quantum anharmonicity effects, and advancing machine learning techniques for materials discovery. His methodologies span high-pressure physics, density functional theory, and computational screening of novel compounds. His articles demonstrate trends in: Superconductivity under extreme conditions High-pressure phase transformations Data-driven structure prediction Synthesis pathway modeling Computational validation of material stability Multi-scale simulations for practical applications

Antonio Norelliمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Deep Learning
• Explainable AI
• +۳ مورد دیگر

Antonio Norelli is a Research Fellow at Oxford University and a PhD student in Computer Science at Sapienza University of Rome, specializing in AI and deep learning within the GLADIA research group. His academic journey includes a BSc in Physics, an MSc in Computer Science, and the SSAS interdisciplinary honors program. He has held research internships at Spiketrap (San Francisco) and Amazon Lablet in Tübingen. His research focuses on bridging the gap between human and artificial intelligence, emphasizing symbolic reasoning, explainable AI, and multimodal learning. Notable contributions include the ASIF framework for unsupervised multimodal alignment and Olivaw, an Othello-playing AI that achieves expert-level performance without human knowledge. His key research interests span artificial scientific discovery, neural network explainability, and the theoretical foundations of intelligence. Recent work explores relative representation learning and zero-shot latent space communication, with applications in cross-modal retrieval and efficient model adaptation. Norelli’s interdisciplinary approach combines insights from philosophy of science (Popperian critical rationalism) with machine learning to develop systems capable of symbolic reasoning and hypothesis generation. Publications highlight advancements in: (1) Multimodal learning without training through coupled data alignment, (2) Game AI strategies for Othello using minimal resources, (3) Neuro-symbolic architectures for interpretable decision-making. His work has been presented at NeurIPS and IEEE Transactions on Games, among others. Current efforts aim to formalize intelligence as information-processing mechanisms grounded in symbol manipulation and semantic alignment.

• Astrophysics
• Gravitational Wave Astronomy
• Neutron Stars
• +۳ مورد دیگر

Keith Riles is a Professor in the Physics Department at the University of Michigan. He is actively involved in gravitational wave research, particularly focusing on neutron star astrophysics and multi-messenger observations. His work includes analyzing data from the LIGO-Virgo-KAGRA collaboration to detect gravitational waves from compact object mergers, pulsar emissions, and supernova remnants. Riles has contributed to searches for continuous gravitational waves from known pulsars, studies of neutron star properties via gravitational wave signatures, and investigations into ultralight dark matter using gravitational wave detectors. His research spans topics like Einstein's general relativity tests, cosmic expansion measurements, and instrument noise characterization in advanced interferometers. Key areas of expertise include analysis of gravitational wave data from observing runs O1-O4, studies of gravitational lensing effects, and electromagnetic follow-up strategies for transient events. He has also participated in developing detector sensitivity through quantum noise reduction techniques and environmental noise mitigation in LIGO facilities.

Holger Meyerمشاهده پروفایل

استاد

• Experimental High Energy Particle Physics
• Neutrino Physics
• Nuclear Physics
• +۳ مورد دیگر

Dr. Holger Meyer is a Professor of Physics at Wichita State University's Fairmount College of Liberal Arts and Sciences, within the Department of Mathematics, Statistics, and Physics. He holds a Ph.D. from Virginia Tech (2002) and specializes in experimental high-energy particle physics, with a focus on neutrino physics. His research involves collaborations with major institutions like Fermi National Accelerator Laboratory (Fermilab) and Brookhaven National Laboratory, contributing to the NOvA and DUNE long-baseline neutrino experiments. Dr. Meyer also explores space-based neutrino detection in collaboration with NASA's Marshall Space Flight Center and other universities, aiming to detect neutrinos in near-solar environments. His research interests span neutrino oscillation studies, CP violation, and astrophysical applications such as supernova neutrino detection. He actively participates in detector development for DUNE, including liquid argon time projection chambers (LArTPCs) and software computing frameworks. Dr. Meyer has contributed to cross-section measurements in neutrino interactions and pioneered methods for neutrino interaction vertex reconstruction using machine learning. Publications highlight his work on DUNE's supernova pointing capabilities, Fermilab accelerator upgrades, and NOvA's oscillation parameter analyses. He is a key figure in international physics collaborations, advancing neutrino physics through both terrestrial and space-based experimental initiatives.

• Virtual Reality
• Human-Computer Interaction
• Affective Computing
• +۳ مورد دیگر

Ifigeneia Mavridou is a Researcher at Bournemouth University's Faculty of Media and Communication , affiliated with the Centre of Digital Entertainment and collaborating with Emteq Ltd. Her work focuses on Virtual Reality and Affective Human-Computer Interaction (HCI) , leveraging physiological signals like EEG, EMG, and PPG for emotion detection. Education : MA in Art, Virtual Reality and Multi-user Systems (University Paris-8 & Athens School of Fine Arts, 2014); PhD in Affective State Recognition in Virtual Reality (Bournemouth University, 2021) Her research explores the intersection of New Media Art and VR to identify emotional features that enhance immersive experiences and VR content re-playability . Current projects involve developing OCOsense™ smart glasses for facial expression recognition and AVEL (Affective Virtual Environment Library) for emotion analysis. Recent publications highlight trends in VR-based emotion detection , wearable sensor integration (optomyography, EMG, PPG), and applications in mental health and clinical research . She is supported by an EPSRC grant for her work on affective VR systems. Collaborative efforts include supervision by Dr. Emili Balaguer-Ballester, Dr. Alain Renaud, Dr. Anna Troisi, Dr. Ellen Seiss, and Dr. Charles Nduka. She has participated in over 20 new media art exhibitions across Europe since 2005.

Raviv Raichمشاهده پروفایل

استاد

• Signal Processing
• Machine Learning
• Optimization
• +۴ مورد دیگر

Raviv Raich is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University. He holds a B.Sc. and M.Sc. from Tel-Aviv University (1994, 1998) and a Ph.D. from Georgia Institute of Technology (2004). His research focuses on signal processing, machine learning, optimization, and probabilistic modeling with applications in adaptive sensing, manifold learning, and sparse signal reconstruction. He has advised numerous graduate students and contributed to impactful work in bioacoustics, hyperspectral imaging, and medical diagnostics. Notable awards include the NSF CAREER Award (2013) and multiple best paper awards. His academic leadership includes editorial roles at IEEE Transactions on Signal Processing and contributions to the IEEE Signal Processing Society. Education: B.Sc. Electrical Engineering, Tel-Aviv University (1994) M.Sc. Electrical Engineering, Tel-Aviv University (1998) Ph.D. Electrical Engineering, Georgia Tech (2004) Research Interests: Statistical signal processing and machine learning frameworks Adaptive sensing strategies for optimization in imaging and tracking Manifold learning for high-dimensional data analysis Sparse representations in signal reconstruction and inverse problems Publications: Over 100 peer-reviewed articles in top venues like IEEE Transactions on Signal Processing, IEEE Sensors Journal, and Pattern Recognition Letters, emphasizing theoretical guarantees and practical applications in bioacoustics, medical imaging, and environmental monitoring. Grants: Active funding from NSF, DARPA, and industry partnerships supporting research in optimization, multi-instance learning, and hyperspectral sensing.

Philip Fergusonمشاهده پروفایل

دانشیار

• Satellite guidance and control
• CubeSats
• Drone control and navigation
• +۳ مورد دیگر

Dr. Philip Ferguson is an Associate Professor of Mechanical Engineering and Magellan Industrial Research Chair at the University of Manitoba's Price Faculty of Engineering. He leads the Space Technology and Advanced Research Laboratory (STARLab) and collaborates with the Centre for Earth Observation Science (CEOS). His work focuses on making aerospace technology accessible through low-cost, reliable solutions for Arctic communities and search-and-rescue operations. Dr. Ferguson holds a PhD from MIT and has industry experience with Magellan Aerospace and PrecisionHawk Inc. Education: PhD in Aerospace Engineering, MIT (2006) SM in Aerospace Engineering, MIT (2003) B.A.Sc. in Engineering Science (Aerospace Option), University of Toronto (2000) Dr. Ferguson's research spans satellite guidance, drone navigation, Arctic remote sensing, and machine learning. His projects include CubeSats for climate change monitoring, drones for search-and-rescue, and airships for Arctic communications. He emphasizes interdisciplinary collaboration to address challenges in Northern Canada and Indigenous communities. His recent articles explore fault detection in spacecraft systems, drone-based payload control, and innovative navigation algorithms. He actively recruits graduate students interested in advancing aerospace accessibility. Labs/Teams: STARLab (founded 2014), CEOS collaborations, and industry partnerships with Magellan Aerospace.

• Condition Monitoring
• Predictive Maintenance
• Robotics and Control
• +۳ مورد دیگر

Dr. Xihui (Larry) Liang is an Associate Professor in the Department of Mechanical Engineering at the University of Manitoba, Canada. He holds a PhD in Mechanical Engineering from the University of Alberta (2016) and completed a postdoctoral fellowship there. His research focuses on condition monitoring, predictive maintenance, robotics, and reliability engineering, with applications in machinery and infrastructure systems. He is a recipient of the Terry G. Falconer Memorial Award for his work in developing intelligent monitoring systems. Education: PhD in Mechanical Engineering, University of Alberta, 2016 Research Interests: Dr. Liang's work spans advanced condition monitoring techniques for mechanical systems, robotics and control for manufacturing, and reliability modeling. Key areas include: Embedded systems and smart sensors for structural health monitoring Machine learning for fault diagnosis and RUL prediction Maintenance optimization strategies for wind turbines and gas engines Smart bearing design with self-sensing capabilities Recent Work Trends: His publications emphasize innovative sensor technologies (e.g., FBG and PVDF transducers) and machine learning approaches for predictive analytics. Recent studies address challenges in non-stationary signal processing and cross-domain fault detection. Awards: Terry G. Falconer Memorial Rh Institute Foundation Emerging Researcher Award (2024) Advising & Labs: Dr. Liang mentors graduate students in embedded systems, robotics, and reliability engineering. His lab develops advanced monitoring tools for industrial applications, including smart bearings and additive manufacturing quality control.