جست‌وجوی استاد و پژوهشگر

• Mobile and Wireless Networking
• Information and Communication Technologies for Development (ICTD)
• Network Performance Analysis
• +۳ مورد دیگر

Elizabeth M. Belding is a Professor in the Department of Computer Science at the University of California, Santa Barbara (UCSB) and Associate Director of the Center for Information Technology and Society. She holds a Ph.D. (2000) and M.S. (1997) in Electrical and Computer Engineering from UCSB. Co-developer of AODV routing protocol (basis for IEEE 802.11s and Zigbee) Director of the Mobility Management and Networking (MOMENT) Laboratory Associate Dean and Faculty Equity Advisor, College of Engineering Her research focuses on mobile and wireless communication networks , including network performance analysis , digital equity , and information and communication technologies for development (ICTD) . She specializes in improving internet access for marginalized communities including Native American reservations, refugee camps, and rural areas in Zambia, South Africa, and Mongolia. Recent research trends include: Accurate broadband measurement in the U.S. using crowdsourced data Development of wireless solutions for underserved regions Analysis of 5G performance variability and GEO satellite latency Mapping cellular network evolution and infrastructure criticality Evaluating federal broadband funding programs (CAF, BEAD, NSF) Quantifying video streaming quality of experience (QoE) Scientific honors include: ACM Fellow (2018) IEEE Fellow (2014) AAAS Fellow ACM SIGMOBILE Test of Time Award (2018) NCWIT Harrold and Notkin Award (2015) UCSB Outstanding Graduate Mentor Award (2012) IRTF Applied Networking Research Prize (2024) She has advised 22 Ph.D. graduates from the MOMENT Lab, including Jiamo Liu (2024) and Udit Paul (2023). Current research receives funding from NSF, Bill & Melinda Gates Foundation, and industry partners like ViaSat, with emphasis on broadband deployment analysis and network solutions for challenged environments.

Sung Kyung Hongمشاهده پروفایل

استاد

• Autonomous Flight Control Systems
• Unmanned Aerial Vehicles (UAVs)
• Sensor Applications
• +۵ مورد دیگر

Sung Kyung Hong is a Professor in the Department of Intelligent Drone Convergence at Sejong University , specializing in autonomous flight control systems, UAV technology, and sensor applications. He has held leadership roles including Director of the Autonomous Unmanned Vehicle Research Center since 2017 and served as an Advisory Member of South Korea's Presidential Advisory Council on Science and Technology (2019-2021). Education : Ph.D. (1998) from Texas A&M University, M.S. (1989) and B.S. (1987) from Yonsei University. His research focuses on autonomous flight control , inertial sensor integration , and HILS testing for UAVs, with notable achievements in robust fault diagnosis, collision avoidance algorithms, and adaptive filtering in high-vibration environments. Recent publications highlight applications of deep reinforcement learning, fixed-time attitude control, and drone-view image dehazing techniques. The scientific awards section includes: Advisory Member, Presidential Advisory Council on Science and Technology (2019-2021) As advisor, he has mentored researchers in UAV dynamics and simulation, while leading the Autonomous Unmanned Vehicle Research Center to develop advanced drone technologies and hardware-in-the-loop testing frameworks.

• Computational hydroacoustics
• Vibroacoustics
• Flow-induced noise and vibrations
• +۱۰ مورد دیگر

Mahmoud Karimi is a Senior Lecturer at the School of Mechanical and Mechatronic Engineering , University of Technology Sydney (UTS), leading the Vibroacoustics Research Group within the Centre for Audio, Acoustics and Vibration. He holds a PhD in Mechanical Engineering from UNSW with specialization in vibration and acoustics, and has conducted visiting research at University of Cambridge, Technical University of Munich, and INSA Lyon. His research focuses on computational hydroacoustics, vibroacoustics, and uncertainty quantification in noise/vibration problems. Academic Leadership : Editor-in-Chief of Acoustics Australia since 2025 Research Income : Attracted $6M in competitive grants ($2M as Chief Investigator) since 2017 Technical Expertise : Specializes in acoustic black hole structures, flow-induced vibration modeling, and leak detection in buried pipelines Scientific Awards : Recipient of ARC DECRA Fellowship (DE190101412) 2019-2022 Research Trends : His 91+ publications demonstrate expertise in hybrid acoustic modeling techniques, sustainable hempcrete development, and vibration energy harvesting solutions with applications in mining, rail systems, and water infrastructure. International Collaborations: University of Cambridge (UK), Technical University of Munich (Germany), INSA Lyon (France) Teaching Portfolio: Advanced numerical methods, dynamics & control, and computational modeling at UTS

Stefan Hagelمشاهده پروفایل

پژوهشگر ارشد

• Ancient Greek Music
• Music Archaeology
• Musical Instrument Reconstruction
• +۳ مورد دیگر

Stefan Hagel is a Research Fellow at the Austrian Academy of Sciences and Privatdozent at the University of Vienna, specializing in ancient Greek and Roman music. His work bridges archaeology, philology, and digital reconstruction to analyze ancient musical instruments, notation systems, and performance practices. His research focuses on: Reconstruction of auloi (double-pipes) and hydraulis (water organs) Computational analysis of instrument acoustics Cross-cultural transmission of musical technologies Ancient harmonic theory and notation decipherment He leads major projects including Ancient Music Beyond Hellenisation (ERC Advanced Grant), Digitising Aspects of Graphical Representation in Ancient Music (FWF), and the European Music Archaeology Project . Hagel has developed specialized software for instrument analysis and serves as editor for the Journal of Music Archaeology . His publications demonstrate consistent focus on technical aspects of ancient music through interdisciplinary methodologies.

• Digital signal processing
• Smart grids
• Measurement systems
• +۵ مورد دیگر

David Macii is Associate Professor at the Department of Industrial Engineering, University of Trento, Italy, where he teaches "Digital Signal Processing for Mechatronics" and co-leads the "Laboratory of Internet of Things." His core expertise lies in digital signal processing, measurement science, smart-grid instrumentation, indoor positioning and industrial IoT applications. Research interests revolve around four pillars: (i) advanced estimation algorithms for frequency, ROCOF and synchrophasors to enhance power-quality monitoring in future smart-grids with high PV and EV penetration; (ii) design and metrological characterisation of low-cost PMU and smart-meter solutions; (iii) radar- and RFID-based indoor localisation and tracking for robotics and assisted-living scenarios; and (iv) embedded, IoT-enabled measurement systems bridging DSP, mechatronics and industrial electronics. Recent publications (2023-2025) reveal a clear methodological trend: development of fast, uncertainty-aware DSP algorithms (interpolated DFT, Kalman filtering, harmonic whitening) validated against real-world noise, interference and contingency conditions, followed by their embedding into resource-constrained hardware platforms for EV charging coordination, grid-support converters and robotic navigation. Although the supplied text does not list specific grants or doctoral students, the steady stream of joint publications with European colleagues and his leading teaching role in two inter-departmental master courses indicate an active, well-integrated research and educational profile within the University of Trento.

Marco L. Della Vedovaمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence and Machine Learning
• Interpretable and Explainable AI
• Geospatial AI
• +۷ مورد دیگر

Marco L. Della Vedova is a Senior Lecturer in Applied Artificial Intelligence at Chalmers University of Technology, Sweden. He works in the Vehicle Engineering and Autonomous Systems division within the Department of Mechanics and Maritime Sciences, as part of Prof. Mattias Wahde's research group. Since 2025, he has served as Director of the Data Science and AI master's programme (MPDSC) at Chalmers, where he teaches courses including Introduction to Artificial Intelligence and Digitalization in Sports. Dr. Della Vedova earned his academic foundation at the University of Pavia, Italy, where he completed his BSc (2006), MSc (2009), and PhD (2013) in Computer Engineering. His doctoral research focused on "Real-Time Physical Systems and Electric Load Scheduling" under Prof. Tullio Facchinetti. During his PhD studies, he spent a year at U.C. Berkeley hosted by Prof. Francesco Borrelli at the Model Based Predictive and Distributed Control Lab. His research spans multiple AI domains with a strong emphasis on interpretability. Dr. Della Vedova develops interpretable methods for conversational AI, naturalness evaluation of forests using canopy height models, and geospatial applications. His work bridges theoretical AI with practical societal benefits, particularly in environmental monitoring, transportation systems, and orienteering. He has previously contributed to cloud computing, hate speech detection, and cyber-physical energy systems, demonstrating his interdisciplinary approach to AI research. Dr. Della Vedova's publication record reveals a consistent trajectory of impactful research across multiple domains of artificial intelligence. His recent work shows a strong focus on interpretability in AI systems, with significant contributions to natural language processing, geospatial analysis, and causal inference. The research demonstrates both theoretical depth and practical applications, particularly in environmental monitoring and social media analysis. His methodology often combines traditional machine learning approaches with novel interpretability techniques, creating bridges between complex AI systems and human understanding. Dr. Della Vedova has received several prestigious recognitions for his work: Best PhD thesis award from the Order of the Engineers of Bergamo (2013) Italian champion of Il Cervellone (2012) Top Italian performer in IEEEXtreme 6.0 programming competition (148th overall globally, 2012) Premio Arturo Schena award from Fondazione Credito Valtellinese (2010) With over 50 students supervised through bachelor's and master's theses, Dr. Della Vedova has established himself as a dedicated mentor in the AI community. His current PhD students include Minerva Suvanto working on interpretable NLP and Vivien Lacorre developing AI for railway infrastructure inspection. His supervision spans diverse topics from forest naturalness evaluation to hate speech detection and transportation optimization. Beyond formal supervision, he actively contributes to educational initiatives including serving as Director of Chalmers' Data Science and AI master's program and developing innovative teaching methods that connect theoretical concepts with real-world applications. Dr. Della Vedova is deeply embedded in both academic and professional communities. He leads the Applied Artificial Intelligence research group at Chalmers while maintaining strong connections with European research networks through projects like the ERASMUS+ EUrienteering initiative. His interdisciplinary approach is reflected in collaborations across computer science, environmental science, and social sciences. Notably, he applies his AI expertise to orienteering both as a researcher developing localization methods and as a licensed Event Advisor for the International Orienteering Federation, demonstrating how his professional and personal interests converge in innovative ways.

• Automatic Control
• Systems Engineering
• Electrical Engineering
• +۳ مورد دیگر

Inès Miloud serves as an ATER (temporary teaching and research assistant) in Automatic Control and Systems at the University Institute of Technology (IUT) of the University of Poitiers, France. She is actively affiliated with the LIAS laboratory, which maintains dual sites at ENSIP in Poitiers and ISAE-ENSMA in Chasseneuil, focusing on sensor applications and control engineering within industrial contexts. Her research spans Automatic Control, Systems Engineering, and Electrical Engineering with specialized expertise in induction motor dynamics, real-time estimation algorithms, and sensor-based control systems. She employs advanced methodologies including Extended Kalman Filters, parameter identification techniques, and Marquardt optimization algorithms to address challenges in motor control performance and system reliability. Recent publications demonstrate her focus on practical industrial implementations, particularly in automated tuning of estimation algorithms and comparative analysis of parameter identification methods for squirrel cage induction motors. Her work bridges theoretical control systems with real-world motor drive applications, emphasizing sensor fusion and computational efficiency. As a core member of the Automatic Control Team within LIAS, Dr. Miloud collaborates on interdisciplinary projects involving sensor networks and real-time control systems, contributing to the laboratory's mission of advancing engineering solutions through applied research.

Ian Rennerمشاهده پروفایل

مدرس ارشد

• Ecological Statistics
• Species Distribution Models
• Statistical Theory
• +۳ مورد دیگر

Dr. Ian Renner is a Senior Lecturer in the School of Information and Physical Sciences at the University of Newcastle, specializing in Data Science and Statistics. He holds a PhD in Statistics from the University of New South Wales, complemented by a Master of Statistics from the University of Utah and a Bachelor of Science in Mathematics from Valparaiso University. His research focuses on species distribution models (SDMs), particularly leveraging presence-only data and point process models. He developed the PPM-LASSO approach and maintains the R package 'ppmlasso' for model implementation. Education: PhD (Statistics), University of New South Wales Master of Statistics, University of Utah Bachelor of Science (Mathematics), Valparaiso University Research Interests: Dr. Renner's work bridges statistics and ecology, emphasizing the development of robust SDMs. Key areas include: Unifying MAXENT and Poisson point process models Observer bias correction in ecological data Integration of regularization techniques (e.g., LASSO) for predictive accuracy Application of citizen science data in conservation His methodologies address challenges like taxonomy changes and sampling biases in species distribution studies. Publications: His recent work highlights advancements in SDM stability, citizen science applications, and regularization methods. Key themes include improving model reliability through penalized likelihoods and addressing ecological data complexities. Awards: JB Douglas Award (2011) Runner-up for Best Student Talk (2011) EJG Pitman Prize (2010) Grants & Supervision: He has secured $12,838 in internal grants, including a visiting fellowship at CNRS (France) and conference funding. He currently co-supervises a PhD on deep learning for speech depression recognition and has guided two other students in statistical ecology and methodology. Labs/Teams: Leads the development of the 'ppmlasso' R package, collaborating with researchers like Olivier Gimenez and Eric Beh to advance ecological statistics.

Heidi Baselerمشاهده پروفایل

مدرس ارشد

• Visual Neuroscience
• Neuroimaging
• Sensory Processing
• +۴ مورد دیگر

Dr. Heidi Baseler is a Senior Lecturer in Imaging Sciences at the Hull York Medical School and the Department of Psychology , University of York . She serves as INSPIRE Programme Lead for undergraduate research and contributes to research governance and public/patient involvement in research through committees like the York Neuroimaging Centre Research Governance Committee and Involvement@York . Education : AB in Biology/Psychology from Dartmouth College (1986), PhD in Vision Science from University of California, Berkeley (1995) Career : Research Fellow at Stanford, Smith-Kettlewell Eye Research Institute, and University of York; Lecturer in Imaging Sciences at Hull York Medical School (2012-2021) Her research focuses on neural mechanisms for central/peripheral vision processing, cortical responses to sensory loss (e.g., AMD, deafness), and neuroprotective strategies like photobiomodulation. Techniques include structural and functional MRI , magnetic resonance spectroscopy , multifocal EEG , and electroretinography . Key findings include cortical reorganization in macular degeneration and superior visual performance in deaf adults. Recent article trends highlight: Neuroimaging biomarkers for AMD progression Cross-modal plasticity in deaf individuals Long-term effects of sensory deprivation on brain structure Photobiomodulation for neurodegeneration Functional connectivity in face-selective regions Contrast perception dynamics across aging Scientific awards include: Exceptional Contribution to Student Experience Finalist , Hull York Medical School (2024) Pursuing Excellence Award , Hull York Medical School (2023) Vice-Chancellor's Teaching Award , University of York (2020) Multiple Teacher of Excellence Finalist recognitions (2016-2018) She teaches undergraduate medical students in Foundations of Medicine and Neurological Diseases , and supervises MSc Cognitive Neuroscience projects. Collaborations span institutions like University of Hull , University of Lille , and University of Regensburg . Current students include Sharyfah Alasiri (PhD, Biomedical Sciences) and Erin English (PhD, Psychology) at the University of York.

Dian Zhouمشاهده پروفایل

استاد

• VLSI Design
• Analog Circuit Design
• Signal Processing
• +۳ مورد دیگر

Dr. Dian Zhou is a Professor of Electrical Engineering at the University of Texas at Dallas (UTD), affiliated with the Erik Jonsson School of Engineering and Computer Science. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (1990), and M.S. and B.S. degrees from Fudan University, Shanghai (1985 and 1982, respectively). His research focuses on VLSI design, CAD tools, analog circuit performance analysis, and nanotechnology applications. Notably, he served as Changjiang Honored Professor and Dean of the School of Microelectronics at Fudan University (2003–2006) on part-time leave from UTD. Key honors include the Changjiang Scholar Award (2002), NSF Young Investigator Award (1994), and IEEE Darlington Award (1993). His work has advanced VLSI optimization methods, analog circuit reliability, and nanowire biosensor technology. He has advised graduate students at UTD while managing research grants and collaborating internationally. He contributed to the development of Fudan’s Zhang-Jiang Institute to boost microelectronics innovation in Shanghai.

• Psychology
• Cognitive Neuroscience
• Sleep Studies
• +۵ مورد دیگر

Nicola Cellini is an Associate Professor at the Department of General Psychology of the University of Padova. His research focuses on cognitive neuroscience, sleep science, and psychological mechanisms underlying memory, emotion, and decision-making. He has conducted extensive studies on sleep deprivation effects, emotional memory consolidation, and the application of wearable health technology for sleep monitoring. His work bridges clinical psychology, neurophysiology, and behavioral science, often involving interdisciplinary collaborations. Key research areas include: Neurophysiological correlates of time perception Cognitive and emotional consequences of sleep fragmentation Role of REM sleep in emotional processing Technology validation for sleep tracking (e.g., Xiaomi Mi Band 5) Psychosocial factors in pandemic mental health (e.g., time perspective theory) His recent publications highlight trends in: Clinical applications of rTMS for addiction treatment Impact of biophilic environments on psychophysiological recovery Interplay between sleep quality and false memory formation Age-related differences in sleep patterns during pandemics Notable contributions include methodological advancements in sleep monitoring technology validation and cross-cultural analyses of psychological resilience during the COVID-19 pandemic.

Mads Røge Eldrupمشاهده پروفایل

استادیار

• Coastal Engineering
• Fluid Dynamics
• Numerical Modeling
• +۴ مورد دیگر

Mads Røge Eldrup is an Assistant Professor at the Department of the Built Environment, Aalborg University, within the Faculty of Engineering and Science. His research focuses on coastal engineering, wave dynamics, and structural resilience of marine infrastructure. He leads the Ocean and Coastal Engineering Research Group and contributes to the BLUE – Marine & Maritime Research initiative. Research interests include breakwater stability, nonlinear wave modeling, physical model testing, and numerical simulation techniques. Key projects involve the RESCUER initiative (2024-2028) addressing resilient coastal solutions. He has authored/co-authored 46 publications since 2014, with recent work on NL-SORS wave decomposition, submerged bar dynamics, and rock armour stability. He actively participates in international conferences and workshops, including sessions on Smoothed Particle Hydrodynamics and nonlinear wavemaker theory. Collaborations span institutions globally, focusing on coastal resilience and hydraulic engineering challenges.

Rachel Beanمشاهده پروفایل

استاد

• Cosmology
• Dark Energy
• Gravitational Physics
• +۲ مورد دیگر

Rachel Bean is Jacob Gould Schurman Professor of Astronomy at Cornell University and Senior Associate Dean for Math and Science. Her cosmology research focuses on dark energy properties, gravitational physics, and the early universe using cosmic microwave background and galaxy survey data. As co-recipient of the Gruber and Breakthrough prizes, she contributed to precision cosmology through the WMAP mission. Research develops methods to extract cosmological information from large astrophysical datasets, including cross-correlation techniques between CMB experiments (Atacama Cosmology Telescope, Simons Observatory) and galaxy surveys (DESI, Rubin LSST). Current projects investigate modified gravity constraints using cluster abundances and novel statistical approaches to kSZ velocity reconstruction. Publication themes include precision cosmology, gravity tests, and multi-messenger astrophysics. Recent work advances machine learning applications for cosmological inference, while earlier research established foundations in semiconductor device physics. Articles consistently demonstrate innovative approaches to cosmological parameter estimation and physical theory testing. Awards: Gruber Prize (2012), Breakthrough Prize (2018), Presidential Early Career Award, and Cottrell Scholar Award. Leadership includes former chair of LSST Dark Energy Science Collaboration and service on the Astronomy and Astrophysics Advisory Committee.

Daniel M. Liberzonمشاهده پروفایل

استاد

• Switched and Hybrid Systems
• Nonlinear Control Theory
• Control with Limited Information
• +۴ مورد دیگر

Daniel M. Liberzon is the Richard T. Cheng Professor in the Department of Electrical and Computer Engineering and a Professor at the Coordinated Science Laboratory at the University of Illinois Urbana-Champaign . He is also an affiliate professor in the Department of Mathematics . His career spans theoretical and applied research in control systems, with a focus on hybrid control, nonlinear systems, and communication constraints. Education : Ph.D. in Mathematics (Brandeis University, 1998), advised by Roger W. Brockett (Harvard). Undergraduate studies in Mathematics at Moscow State University (1989-1993). Research Interests include: Switched and Hybrid Systems with stability criteria and control design. Nonlinear Control Theory covering Lyapunov functions, ISS, and synchronization. Control with Limited Information focusing on quantized control and entropy-based methods. Uncertain/Stochastic Systems with applications in power grid synchronization and networked control. Article Trends show a focus on stability analysis, entropy metrics, and hybrid control algorithms across nonlinear and switched systems. Key themes include robust observer design, synchronization under disturbances, and quantized feedback. Scientific Awards : ACM SIGBED HSCC Best Paper (2019) IFAC Fellow (2016) IEEE Fellow (2013) AACC Donald P. Eckman Award (2007) NSF CAREER Award (2002) Advising and Grants : Collaborates with students and researchers like Sayan Mitra, Hyungbo Shim, and others. Leads NSF projects on Nonlinear Systems with Fast/Slow Dynamics and AFOSR MURI on Hybrid Dynamics . Labs and Teams : Directs the Decision and Control group at the Coordinated Science Lab, contributing to interdisciplinary projects in control theory and power systems.

Chris Geogaمشاهده پروفایل

استادیار

• Spatial Statistics
• Gaussian Processes
• Computational Statistics
• +۲ مورد دیگر

Chris Geoga is Assistant Professor of Statistics at the University of Wisconsin-Madison's School of Computer, Data & Information Sciences. His research develops computational methods for spatial statistics, focusing on scalable Gaussian process models, spectral analysis techniques, and high-performance statistical computing. Geoga's work enables efficient analysis of large spatial datasets through innovations in covariance approximation, automatic differentiation, and numerical algorithms. Research areas include: Scalable inference for nonstationary spatial processes Machine-precision spectral likelihood computation Automatic differentiation for covariance functions Hierarchical matrix methods for spatial statistics Irregular time series analysis He develops open-source Julia libraries including Vecchia.jl for Gaussian likelihood approximations, GPMaxlik.jl for statistical inference, and BesselK.jl for specialized mathematical functions. Applications span environmental monitoring, fluid dynamics, and large-scale spatiotemporal modeling.