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Connie KoView profile

Adjunct Professor

• LiDAR Data Processing
• 3D Object Detection
• Geographic Information Systems
• +4 more

Connie Ko is Adjunct Professor in the Faculty of Environmental and Urban Change at York University, where she serves as GIS/Remote Sensing Technician and Research Associate. Her research focuses on advancing LiDAR data processing techniques and developing AI applications for 3D object detection in environmental contexts. Education credentials: PhD in Earth and Space Science & Engineering (York University, 2014) MSc in Geography (York University, 2004) Bachelor in Environmental Science (York University, 2001) Research specializes in: AI-driven LiDAR analysis for environmental mapping 3D object detection methodologies Tree genus classification using geometric features Large-scale dataset creation for machine learning Publications demonstrate consistent focus on LiDAR applications (80% of works), particularly in forestry and hydrological contexts. Recent shift toward AI integration evident since 2018. Authored foundational datasets like YUTO Tree5000 and Yuto Semantic to support geospatial AI development. Teaches GIS/Remote Sensing techniques and mentors students in spatial analysis. Transformed GIS computer lab into collaborative learning hub supporting interdisciplinary environmental research.

Michael HunterView profile

Assistant Professor

• Human Development
• Mathematical Modeling
• Statistical Computing
• +7 more

Michael Hunter is an Assistant Professor in the Department of Human Development and Family Studies at Pennsylvania State University's College of Health and Human Development. His research integrates human development with mathematical and computational methodologies. Ph.D. in Psychology (2014), University of Oklahoma M.A. in Psychology (2011), University of Virginia B.S. in Mathematics and Psychology (2008), Michigan State University Hunter's research focuses on dynamical systems , structural equation modeling , and behavior genetics . His work emphasizes: Development of dynamical systems models for human behavior Creation of statistical software packages (OpenMx, dynr) Analysis of intensive longitudinal data and twin studies Applications to addiction relapse, cognitive development, and affect dynamics Recent publications highlight expertise in nonlinear modeling , state-space algorithms , and genetic-environmental interaction analysis. His methodological contributions bridge theoretical statistics with practical behavioral research implementations.

• Sustainable Tourism
• Regional Economics
• Sentiment Analysis
• +3 more

Shira Fano is an Assistant Professor at the Department of Economics and Management at the University of Trento. Her research focuses on sustainable tourism, regional economics, sentiment analysis, teaching evaluation methodologies, and labor economics. She has conducted studies on policy impacts, including waste management in Venice and the effects of dual labor markets. Her work leverages social media data (e.g., Twitter) to analyze regional economic trends and political campaigns, developing indices like RETI for economic monitoring. She also explores pedagogical strategies, applying statistical models like Partial Least Squares to improve teaching evaluations. Her interdisciplinary approach bridges economics with environmental science, digital sociology, and education policy. While no specific grants or lab affiliations are listed, her publications reflect active engagement with both academic and applied research questions.

Vass VerkhodanovaView profile

Lecturer

• Phonetics
• Phonology
• Speech Prosody
• +3 more

Vass Verkhodanova is a Lecturer at the University of Groningen's Campus Fryslân, affiliated with the Language Technology and Culture (LTC) department. Her research focuses on phonetics, speech prosody, and Parkinson's disease speech analysis. She completed her PhD in 2021 with the thesis titled More than words: Recognizing speech of people with Parkinson's disease . Her work spans cross-linguistic studies of speech healthiness, dysarthric speech recognition, and perceptual effects of expertise. Recent publications (2024-2017) emphasize Dutch dysarthric speech recognition strategies, prosodic changes in Parkinson's patients, and vowel articulation in multilingual contexts. Key contributions include developing machine learning approaches for speaker-specific speech recognition and analyzing acoustic correlates of Parkinson's-related speech disorders. Media engagements highlight her work on speech technology for Parkinson's detection.

• Urban Economics
• Housing Markets
• Econometrics
• +3 more

Greg Costello is an Adjunct Research Fellow at Curtin University's School of Accounting, Economics and Finance, within the Faculty of Business and Law. He is affiliated with the Office of the Provost and is based at the Curtin Perth Campus in Building Business and Physio, Room 3043. His research focuses on urban economics, housing markets, econometrics, and spatial analysis, with a particular emphasis on Australian housing dynamics and policy impacts. Costello has contributed extensively to understanding monetary policy influences, land price dynamics, and housing affordability in urban contexts. His work often intersects with macroeconomic trends and regional development challenges. His research interests include analyzing housing market sub-markets, evaluating policy effectiveness, and exploring spatial and temporal patterns in property values. Notable contributions include studies on house price indices, the impact of tax policies, and the implications of demographic changes on housing demand. Costello's interdisciplinary approach bridges econometric modeling with real-world policy applications, particularly in urban and regional planning. Publications highlight a sustained focus on Australian housing markets since the early 2000s, with recent work emphasizing spatial dynamics and submarket segmentation. His research trajectory reflects a commitment to addressing contemporary challenges in housing economics, including affordability and policy-driven market changes. Costello has collaborated with institutions and scholars on projects analyzing housing cycles, urban development, and property valuation methods. While no awards are explicitly listed, his prolific publication record underscores his academic influence in real estate and urban economics.

Katherine DuncanView profile

Associate Professor

• Perception
• Cognition
• Cognitive Neuroscience
• +5 more

Katherine Duncan is an Associate Professor and Associate Chair, Undergraduate in the Department of Psychology at the University of Toronto's St. George campus. Her research focuses on the dynamic neurobiological mechanisms underlying memory formation and retrieval, particularly the interplay between hippocampal networks and neuromodulatory systems. Education: PhD in Psychology from New York University (hippocampal subfield specialization) Postdoctoral Research at Columbia University (hippocampus-dopamine interactions) Undergraduate Alumna of the University of Toronto Research Focus: The Duncan Lab investigates how slow neurochemical fluctuations and brain oscillations (particularly theta rhythms) temporally structure memory processes. Their work explores state-dependent learning, age-related changes in network specialization, and the clinical implications for attention disorders and neurodegenerative conditions. Scientific Recognition: Canada Research Chair renewal Glushko Senior Thesis Award (advised student Tien Yang) Banting Postdoctoral Fellowship (lab alumnus Alexandera Decker) NSERC and Ontario Graduate Scholarships (students Matt, Mila, Ethina Islam) Research Trends: Recent publications emphasize three key areas: 1) attentional modulation of memory encoding across developmental stages, 2) computational approaches to neural state segmentation in fMRI, and 3) rhythmic dynamics in memory acquisition across health and disease. These works bridge basic cognitive neuroscience with translational applications. Laboratory Team: The lab includes graduate students Mila Mac Neil, Thomas, Catalina, and Robyn, with alumni transitioning to prestigious positions like Tien Yang's PhD at the University of Michigan and Sanjivan Loganathan's Masters at Western University's Culham Lab. Dr. Connie Solidate currently contributes as visiting scholar.

José Javier Ramasco SukiaView profile

Research Professor

• Complex Networks
• Transport Systems
• Epidemic Modeling
• +5 more

José Javier Ramasco Sukia is a Research Professor at the Spanish National Research Council (CSIC), affiliated with the Institute for Cross-Disciplinary Physics and Complex Systems (IFISC) in Palma de Mallorca, Spain. He holds a PhD in Physics (2002) from Universidad de Cantabria and has held postdoctoral positions at the University of Oporto and Emory University. His research focuses on complexity science, particularly complex networks and urban transport systems, with applications to epidemic modeling, social dynamics, and air traffic analysis. Current affiliation: CSIC-IFISC Key projects: BigData4ATM, TREE, EUNOIA, GLEaMviz Research Interests include: Complex Networks (structure, dynamics, clustering) Transport Systems (air traffic, urban mobility) Epidemic Spread Modeling Data-Driven Urban Planning Social Network Analysis Scientific Awards Ramon y Cajal Fellowship (2011-2016) Teaching involves graduate courses in Computational Social Sciences and Data Analysis at the Master's Degree in Physics of Complex Systems. He has over 100 peer-reviewed publications and 50 conference presentations.

• Signal Processing
• Synthetic Aperture Radar (SAR)
• GNSS Reflectometry (GNSS-R)
• +4 more

Maurizio Di Bisceglie is an Associate Professor at the Department of Engineering (DING) of Università degli Studi del Sannio, Italy. He is an expert in signal processing for remote sensing applications, particularly in GNSS Reflectometry (GNSS-R) and Synthetic Aperture Radar (SAR) systems. His research focuses on: Advanced CFAR detection algorithms for extended targets in non-Gaussian clutter GNSS-R data analysis for ocean wind speed , altimetry , and inland water monitoring Statistical modeling of sea surface scattering and clutter characteristics Development of noise reduction and image enhancement techniques in SAR interferograms Applications of multi-spectral satellite data for environmental monitoring His recent publications show a trend toward spaceborne GNSS-R applications for coastal and flood monitoring , with a strong emphasis on machine learning and stochastic modeling . Key journals include IEEE Transactions on Geoscience and Remote Sensing and IEEE International Geoscience and Remote Sensing Symposium proceedings. He teaches Numerical Signal Processing and Sensors for Earth Observation at the University of Sannio. His work has been published in IEEE and other leading technical journals, with collaborations across Italy and international institutions.

Silvia MikschView profile

Professor

• Visual Analytics
• Information Visualization
• Time-Oriented Data Analysis
• +3 more

Prof. Silvia Miksch is a full professor in the Department of Visual Analytics at TU Wien. Her research focuses on visual analytics, time-oriented data visualization, and human-computer interaction. She leads projects in cultural heritage analysis, fraud detection, and pandemic data visualization. Her work bridges computer science with digital humanities, emphasizing user-centric design and uncertainty modeling. Key contributions include guidance systems for VA environments and network visualization frameworks for art history. Affiliations: TU Wien (since 2000+) Research Labs: Network Lab, Visual Analytics Research Group Recent projects explore temporal patterns in artist exhibitions, parameter space exploration, and pandemic data communication. She has advised over 20 PhD/Master’s students, many contributing to VA systems like COVIS and NEVA. Awarded the VGTC Visualization Technical Achievement Award (2024) for foundational work in temporal visualization. Active in IEEE VAST and serves on journal editorial boards. Her labs develop open-source tools for interactive data exploration.

Brendan WrightView profile

Researcher

• Photovoltaics
• Machine Learning
• Computational Modelling
• +2 more

Dr. Brendan Wright is a researcher at the University of New South Wales , affiliated with the Faculty of Engineering and the School of Photovoltaic and Renewable Energy Engineering . His work bridges photovoltaics and machine learning , focusing on electrochemical defect mechanisms in silicon solar cells and generative representation learning for complex physical systems. With a PhD in Physics (2018) and a B. Nanotechnology Honours (2011) from the University of Wollongong, his research spans charge carrier dynamics in organic PV systems to computational chemistry. Research Interests: Machine learning applications for utility-scale photovoltaic systems State-space dynamics modelling using generative algorithms End-of-life classification of solar panels Deep learning for electroluminescence image analysis Surface polarization effects in silicon solar cells Grants: Australian Renewable Energy Agency (ARENA) R&D Project Grant (RG220681, 2022) Australian Centre for Advanced Photovoltaics (ACAP) Industrial Collaboration Grant (2022) Scientific Contributions: Developed polarizable force field models for polymer electrolytes Created deep learning frameworks for photovoltaic defect analysis Innovated perspective correction algorithms for solar module imaging Applied latent ODE networks to predict PV degradation trends Advising: Currently supervises Chukwuka Madumelu in photovoltaic engineering and provides secondary guidance to doctoral candidates. His mentorship extends to computational material science and AI-driven energy systems research.

Yasutaka FurukawaView profile

Associate Professor

• Computer Vision
• 3D Reconstruction
• Neural Networks
• +5 more

Yasutaka Furukawa is a Principal Scientist at Wayve and an Associate Professor of Computing Science at Simon Fraser University (SFU). He leads the SFU Visual Computing Group, ranked 12th globally in Computer Vision and Graphics. His research focuses on 3D reconstruction, neural networks, and computer graphics, with notable contributions in multi-view stereo, CAD modeling, and diffusion models. Furukawa holds a Ph.D. from the University of Illinois at Urbana-Champaign (2008) and has held roles at Washington University in St. Louis, Google, and the University of Washington. Education: Ph.D., Computer Science, UIUC, 2008 Postdoctoral Research, University of Washington (with Prof. Steven Seitz and Brian Curless) Research Interests: Furukawa’s work bridges computer vision and graphics, emphasizing robust 3D reconstruction techniques. His projects include PuzzleFusion++ (diffusion-based fracture assembly), BrepGen (B-rep CAD generation), and HouseDiffusion (floorplan synthesis). He explores applications in autonomous systems, AR/VR, and robotics. Key Projects: Generative Adversarial Networks for architectural design (House-GAN series) Neural networks for inertial navigation (RoNIN) Diffusion models for spatial puzzles and CAD modeling Awards: Recipient of the PAMI Longuet-Higgins Prize (2020), NSF CAREER Award (2015), and multiple Google Faculty Research Awards. Teaching: Courses include CMPT412 Computer Vision and CMPT762 Advanced Computer Vision at SFU. Active in supervising over 30 graduate students and postdocs.

Xiang LinView profile

Senior Lecturer

• Macroeconomics
• Tourism Economics
• Entrepreneurship
• +3 more

Xiang Lin is a Senior Lecturer in Economics and Associate Professor at the Department of Social Sciences, Stockholm University. He serves as a Subject Coordinator and teaches financial economics and econometrics. His research focuses on macroeconomic policy, tourism economics, and entrepreneurship, with particular attention to monetary policy dynamics, pandemic impacts, and regional tourism resilience. Key research interests include the credibility of dual-mandate monetary policies (e.g., US Federal Reserve), transmission mechanisms in the Eurozone, and the interplay between land characteristics and tourism demand. He explores long-term economic phenomena such as bankruptcy fraud in Sweden's small business sector and the role of property rights in entrepreneurship. Recent publications analyze pandemic-driven shifts in tourism demand, stock market responses to crises, and spatial factors influencing recreational activities. His work bridges theoretical frameworks with empirical evidence, emphasizing policy relevance and cross-regional comparisons. No current grants or lab affiliations are mentioned. His advising and grant history remain unspecified in the provided texts.

Elahe GhalebiView profile

Researcher

• Network Science
• Machine Learning
• Data Analysis
• +3 more

Elahe Ghalebi is a researcher affiliated with TU Wien's Cyber-Physical Systems Research Area (E191-01). She holds a PhD from TU Wien, awarded in 2020 for her thesis on time-evolving sparse networks. Her work focuses on advanced statistical techniques and machine learning applied to network dynamics, temporal graphs, and distributed systems. She collaborates internationally with experts like Prof. Radu Grosu and Jure Leskovec. Research interests include modeling sparse networks, Bayesian nonparametric methods, compressive sensing for network analysis, and dynamic network reconstruction from partial data. Her contributions bridge theoretical network science with practical applications in cyber-physical systems and social networks. Publications span 2018–2020, emphasizing temporal multigraph analysis, edge clustering, and wafermap pattern recognition via variational autoencoders. Her work often combines computational modeling with signal processing techniques to address challenges in distributed systems and network topology inference. Affiliated with TU Wien's Network Lab, her research aims to develop scalable algorithms for analyzing evolving networks under resource constraints. No notable awards are listed, though her active collaboration network suggests strong academic engagement.

Kaveh JahanshahiView profile

Research Fellow

• Transport Modelling
• Urban Analysis
• Machine Learning
• +5 more

Kaveh Jahanshahi is a Visiting Fellow and Research Fellow in urban analysis and modelling at the University of Cambridge’s Department of Architecture. He concurrently serves as Transport Modelling Team Leader at the UK Department for Transport/DEFRA’s Joint Air Quality Unit. His work integrates advanced statistical and machine learning models to address urban challenges, with a focus on transport policy impacts and environmental sustainability. He holds a PhD in Transport Modelling from the University of Cambridge. Education : PhD in Transport Modelling, University of Cambridge Research Interests : His research emphasizes digital urban modelling, transport policy evaluation, and spatial analysis of public health challenges. He explores how built environments influence travel behaviors and develops methodologies combining structural equation models with latent class analysis. Recent work addresses SARS-CoV-2 transmission dynamics and long-term rail travel trends in Britain. Key Contributions : He leads strategic transport models like the UK National Transport Model and has pioneered frameworks for quantifying built-form impacts on mobility. His interdisciplinary approach bridges academia and policy-making, particularly in air quality and pandemic response. Labs/Teams : He manages the Joint Air Quality Unit’s transport modelling team and collaborates with DEFRA on environmental policy implementations. His academic work is anchored in the University of Cambridge’s Department of Architecture urban analysis group.

Richard ThackwayView profile

Adjunct Associate Professor

• Landscape Transformation
• Sustainable Land Management
• Ecological Monitoring
• +3 more

Richard Thackway is an Adjunct Associate Professor at the Fenner School of Environment & Society, Australian National University (ANU). With a Master of Science (Environmental Studies, ANU, 1985) and Bachelor of Science (Resource and Ecology, University of Canberra, 1980), he focuses on landscape transformation driven by land use and management practices. His work integrates remote sensing and ecological monitoring to assess ecosystem function, structure, and composition. Key research areas include regenerative landscape management, sustainable rangeland/forest use, and tracking impacts of invasive species (e.g., feral animals in NSW) or agricultural practices (e.g., herbicide spray drift). He has led national initiatives such as Australia's biogeographic regions framework, National Reserve Systems, and National Vegetation Information System. Recent research emphasizes using AI (e.g., U-Net neural networks for vegetation mapping) and remote sensing to analyze land cover dynamics. His work supports decision-makers in developing systems to monitor ecological outcomes through response indicators and soil-landscape chronologies. Collaborations span government agencies, NGOs, and universities, with projects in Kosciuszko National Park, the Lower Snowy River area, and defense estates. Outputs include frameworks like VAST-2 for vegetation condition tracking and protocols for national environmental accounting.