جست‌وجوی استاد و پژوهشگر

• Interactive Design
• Music Technology
• Human-Computer Interaction
• +۳ مورد دیگر

Teodoro Dannemann is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. He contributes to undergraduate teaching through the Creating Interactive Objects module, focusing on microcontrollers, sensors, and Arduino-based prototyping. His research explores interactive design, music technology, and human-computer interaction, with a particular interest in augmented instruments and participatory design methods involving disabled musicians. He actively promotes Latin American perspectives in New Interfaces for Musical Expression (NIME) research, advocating for regional academic collaboration. His work spans interdisciplinary projects, including the Sabotaging Piano initiative, which investigates auditory and haptic feedback disruption in musical improvisation. Beyond technology, he has engaged in policy-oriented research on Latin American development, urban segregation, and ecological modeling. His teaching and research emphasize creativity, accessibility, and critical approaches to technology. Education details: Not explicitly stated in available texts. Research interests include designing interactive physical systems, exploring error as a creative material, and fostering inclusive musical practices. He has contributed to tools like SonicDraw, a web-based sound-sketching platform, and collaborates on global development initiatives. His work bridges artistic innovation with technical rigor, often challenging conventional approaches in instrument design and user interaction. Recent publications highlight themes of instrument modification, participatory methods, and cultural representation in technology. While no scientific awards are noted, his contributions to NIME community-building and Latin American research networks are notable.

• Biomedical Systems Biology
• Computational Biology
• Metabolic Networks Modeling
• +۴ مورد دیگر

Natal AW van Riel is a Professor of Biomedical Systems Biology at Eindhoven University of Technology (TU/e) and part-time Professor of Computational Modeling at Amsterdam University Medical Centers. His research focuses on computational modeling of metabolic networks, systems biology, and digital twins for cardiometabolic diseases like obesity and type 2 diabetes. He leads the Computational Biology group and the Systems Biology and Metabolic Diseases program at TU/e, emphasizing translational research integrating AI, physiology, and clinical data. Academically, van Riel holds a PhD from Utrecht University (2000) and has held positions from Assistant Professor (2003) to Associate Professor (2014) before becoming Full Professor (2019). He is also Head of the Computational Biology group (2023) and chairs TU/e's Biomedical Engineering Examination Committee. His work contributes to UN Sustainable Development Goals, particularly in health and nutrition. Research interests include bile acid metabolism, gut microbiome interactions, metabolic health indices (e.g., MHI), and digital twins for personalized medicine. Collaborations span clinicians, engineers, and AI specialists to translate models into clinical tools like the DiaGame diabetes management game. Notable achievements include the Dutch Royal Order of Orange-Nassau (2014) and leadership in projects like RESOLVE and ACACIA. He has supervised 104 research works and contributed to over 300 publications, emphasizing metabolic resilience, computational methods, and translational systems biology.

Silvia Liveraniمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Spatial Modelling
• Computational Statistics
• +۳ مورد دیگر

Silvia Liverani is a Reader in Statistics and Head of the Centre in Data Science, Statistics and Probability at Queen Mary University of London (School of Mathematical Sciences). Her research focuses on Bayesian methods, spatial-temporal modeling, and computational statistics, with applications in epidemiology, environmental health, and genetics. She leads projects on biodiversity analysis and spatial health data, collaborating with institutions like the Royal Botanical Gardens, Kew and the Alan Turing Institute. Education: PhD in Bayesian Clustering from the University of Warwick (2009). Grants & Initiatives: Awarded £1,000 (2025) for an LGBTQ+ Mathematics Open Day; led a Data Study Group at the Alan Turing Institute (2023). Labs/Teams: Director of the Centre in Data Science, developer of R packages PReMiuM and CARME for Bayesian clustering and spatial modeling. Key Research Themes: Dirichlet process mixture models, spatial misalignment correction, and interdisciplinary collaborations in health and environment. Current work includes predicting biodiversity loss under climate change and analyzing socio-economic health disparities through spatio-temporal frameworks.

• Bayesian Statistics
• Environmental Science
• Climate Change
• +۵ مورد دیگر

Jonathan Rougier is a Visiting Professor at Imperial College London's Faculty of Natural Sciences (Department of Mathematics) with an honorary appointment at the University of Bristol. He specializes in statistics, focusing on applications in environmental science, climate change, and geohazards. His work bridges academia and industry, emphasizing probabilistic modeling, uncertainty quantification, and decision-support systems for natural hazards. Education & Affiliations: Honorary staff at Imperial's Mathematics and Statistics departments. Collaborates with institutions on global geophysical and climate projects. Research Interests: Bayesian methods, spatio-temporal modeling, volcanic eruption prediction, climate change impacts, and risk assessment. His research addresses challenges in environmental data analysis, including unreliable historical records and complex simulation outputs. Articles Trends: Recent work focuses on volcanic hazard modeling (e.g., Aso volcano likelihoods), Antarctic ice mass evolution, and Kalman filter applications in environmental monitoring. Themes include uncertainty quantification, multi-scale computational methods, and interdisciplinary data integration. Advising & Grants: No formal advisees listed. Contributions include methodological advancements for climate projections and hazard analysis, supported by collaborative research networks. Labs/Teams: Engages in global initiatives like the VOLCORE database and Antarctic mass balance studies, emphasizing interdisciplinary teams for geophysical and environmental challenges.

Vincent Lostanlenمشاهده پروفایل

پژوهشگر

• Machine Learning
• Audio Content Analysis
• Computer Music
• +۱۱ مورد دیگر

Vincent Lostanlen is a Researcher (chargé de recherche) at CNRS, affiliated with the LS2N laboratory located at Centrale Nantes engineering school. He also serves as a visiting scholar at New York University. His research focuses on developing artificial intelligence for audio analysis, emphasizing machine listening systems, bioacoustic monitoring, and computer music. Key projects include the BirdVox platform for avian migration monitoring and the Kymatio Python library for wavelet scattering transforms. Education: Ph.D. in Mathematics from École Normale Supérieure (2017), supervised by Stéphane Mallat; MSc in Acoustics & Music Informatics from Université Pierre-et-Marie-Curie (2013); Dipl. Ing. in Signal Processing from Télécom ParisTech (2013). Research interests span audio representation learning, environmental sensor networks, and generative models for sound. Notable contributions include the SONYC-UST urban sound dataset and the PCEN signal frontend for robust acoustic sensing. Scientific awards include the 2019 AFIM Young Researcher Award, 2016 SFA Best Poster Prize, and 2015 IEEE MLSP Best Paper Award. He advises graduate students across computer science and music technology domains, with former advisees working in academia and industry. Current activities include developing green AI for audio processing and collaborating on bioacoustic sensor networks for biodiversity monitoring. His work bridges signal processing theory with practical applications in music, ecology, and biomedical engineering.

Tim Hansenمشاهده پروفایل

دانشیار

• Electric Power Systems
• Optimization
• High-Performance Computing
• +۵ مورد دیگر

Tim Hansen is the Harold C. Hohbach Endowed Associate Professor at the Department of Electrical Engineering and Computer Science at South Dakota State University. He holds a B.S. in Computer Engineering from Milwaukee School of Engineering (2011) and a Ph.D. in Electrical Engineering from Colorado State University (2015). B.S. in Computer Engineering, Milwaukee School of Engineering, 2011 Ph.D. in Electrical Engineering, Colorado State University, 2015 Dr. Hansen's research focuses on electric power systems and markets , with a specialization in optimization, high-performance computing, and machine learning applied to cyber-physical-social systems. His work addresses critical challenges in grid stability, including virtual inertia implementation , multi-area inertia estimation , and low-inertia microgrid control , leveraging advanced computational techniques for energy management systems. He also investigates data-driven approaches for secure electric grids in communities impacted by climate change. Recent research trends include federated learning for grid analysis, model predictive control for voltage and frequency regulation, and computationally efficient inverter modeling. His publications emphasize converter-dominated power systems , energy storage applications , and smart grid technologies across multiple spatiotemporal scales. Scientific awards include: 2024 Outstanding Researcher Award, South Dakota State University Jerome J. Lohr College of Engineering 2022 Best Paper Award for AC Optimal Power Flow research 2020 Milwaukee School of Engineering Graduate of the Last Decade 2019 IEEE-HKN Teaching Award Multiple IEEE best paper awards (2017, 2016) Dr. Hansen directs grants totaling over $2 million, including: $357,467 (DOE SETO, 2024): Transmission/Distribution Systems with Flexible Coordination $750,000 (NSF, 2023): Secure Grids for Climate-Impacted Communities $978,708 (NSF, 2022): Heterogeneous HPC Cluster Development $630,897 (DOE, 2019-2021): Dynamic Response Modeling of Converter-Dominated Systems He chairs IEEE committees including Region 4 Siouxland Section Executive Committee and co-chairs the IEEE PES Research Committee, while advising the IEEE-HKN Honor Society.

Elizabeth Maroonمشاهده پروفایل

استادیار

• Climate Science
• Oceanography
• Climate Change
• +۳ مورد دیگر

Elizabeth Maroon is an Assistant Professor in the Department of Atmospheric and Oceanic Sciences at the University of Wisconsin-Madison (College of Letters and Science). Her research focuses on ocean-climate interactions, particularly how ocean dynamics influence climate variability and predictions. She holds a PhD from the University of Washington. Research interests include large-scale ocean dynamics, climate change, and decadal climate predictability. Her work uses climate models, observational data, and statistical methods to improve understanding of coupled ocean-atmosphere processes. Key areas include North Atlantic circulation, marine heatwaves, and Antarctic sea ice responses to extreme events. Her recent publications analyze North Atlantic climate trajectories, ocean carbon storage variability, and nuclear war impacts on sea ice. She leads the Oceans and Climate Group, mentoring PhD students and a research scientist addressing topics like thermohaline processes and tropical-extratropical interactions. She also collaborates with the Mead Witter School of Music on the Earth Signals project, translating ocean data into music for public outreach. Labs/Teams: Oceans and Climate Group at UW-Madison. NSF CAREER Award supports transdisciplinary work like the 2025 Earth Signals concert on El Niño, blending science with jazz-classical performances.

C. Jessica E. Metcalfمشاهده پروفایل

استاد

• Demography
• Infectious Disease Dynamics
• Vaccine Policy
• +۶ مورد دیگر

C. Jessica E. Metcalf is a Professor of Ecology and Evolutionary Biology and Public Affairs at Princeton University, with an affiliation to the Princeton School of Public and International Affairs. She serves as Co-Director of the Program in Global Health and Health Policy and is part of the Climate Change and Infectious Disease Initiative and Grand Challenges Program. Her research integrates demography, infectious disease dynamics, and policy, with a focus on vaccine equity, immune function, and disease elimination. She holds a PhD from Imperial College London, where she studied evolutionary demography, followed by postdoctoral research at the Max Planck Institute for Demographic Research, Duke University, and Penn State University. Her work spans theoretical and applied aspects of epidemiology, including climate-driven disease shifts, pathogen evolution, and public health policy. Her recent articles emphasize climate impacts on disease seasonality, vaccine trial design, and microbial decline in Africa. She collaborates globally, addressing challenges in Madagascar’s health systems and advancing tools like the RSero R package for pathogen circulation analysis. Her research bridges ecological, immunological, and policy dimensions to combat global health inequities.

Wen Huaمشاهده پروفایل

پژوهشگر ارشد

• Data Science
• Machine Learning
• Data Mining
• +۵ مورد دیگر

Dr. Wen Hua is an Honorary Senior Research Fellow at the University of Queensland's School of Electrical Engineering & Computer Science. His research focuses on Data Science, Machine Learning, and Knowledge Graphs, with significant contributions to privacy-preserving techniques, neural network architectures, and trajectory analysis. He has co-authored over 80 peer-reviewed publications, including influential works on entity linking, meta-learning, and spatiotemporal data privacy. His work bridges theoretical advancements with practical applications in areas like recommendation systems, sentiment analysis, and information resilience. Key research interests include: Machine Learning and Deep Learning Privacy-Preserving Data Publishing Knowledge Graph Construction Text Mining and Natural Language Processing Spatiotemporal Trajectory Analysis Information Retrieval Systems Recent publications highlight trends in: Neural architectures for meta-learning and transformer-based systems Efficient algorithms for relation extraction and entity disambiguation Temporal and spatial data anonymization techniques Integration of knowledge graphs with text corpus analysis

• Metacognition AI
• Reinforcement Learning
• Robotics
• +۳ مورد دیگر

An Nguyen An Nguyen is a Researcher in the Department of Computer Science at the University of Oxford. Their work focuses on advancing AI systems through metacognition principles, reinforcement learning, and robotics applications. Key research themes include human-AI collaboration in robotic teleoperation, decision-making frameworks, and spatiotemporal task coordination. Nguyen's recent studies explore confidence calibration mechanisms in joint human-AI systems and their implications for autonomous decision support. Research interests span: Metacognition AI architectures for self-aware systems Reinforcement learning in dynamic environments Robotic teleoperation interfaces Human-robot trust dynamics Publications from 2020-2025 reflect a progression from transportation modeling to cutting-edge AI-robotics integration. Work combines unsupervised learning techniques with real-world robotics applications, demonstrating expertise in both theoretical and applied computer science domains.

Megan Cattauمشاهده پروفایل

استادیار

• Wildfire Ecology
• Geospatial Analysis
• Remote Sensing
• +۳ مورد دیگر

Megan Cattau is an Assistant Professor in the Department of Human-Environment Systems at Boise State University, with affiliations as a Research Scientist at the Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder. Her work focuses on understanding how anthropogenic and biophysical factors alter disturbance regimes, particularly wildfires, and evaluates interventions to enhance social-ecological resilience. She employs geospatial techniques on large datasets, including drone, satellite, and field-based data to study spatial/temporal patterns of disturbance and recovery. Education: Ph.D., Columbia University M.E.M., Duke University B.F.A., University of Georgia Research Interests: Megan’s research integrates remote sensing and ecological modeling to address wildfire management, climate change impacts, and land use dynamics. Her projects emphasize scalable solutions using open-source tools and UAS (drones) for high-resolution data collection. Key themes include wildfire vulnerability assessment, post-fire hydrological responses, and socio-environmental systems analysis. Publications Trends: Her articles (2023–2025) highlight interdisciplinary approaches to wildfire ecology, leveraging UAS and machine learning for precision monitoring. Recent work examines fire severity’s hydrological impacts, climate-driven shifts in plant species, and policy-relevant insights on fire regimes and conservation easements. Awards: NASA New Investigators Program Fellow Fulbright Scholar – Indonesia Scholarship Grants & Collaboration: Funded by NASA’s New Investigators Program, her research engages with institutions like Earth Lab (CU Boulder) and the National Ecological Observatory Network (NEON). She leads projects bridging ecological data science and community resilience strategies. Labs & Teams: Active in interdisciplinary teams at Earth Lab and NEON, focusing on cyberinfrastructure for environmental data science education and open-access ecological monitoring.

Deborah Wonمشاهده پروفایل

استاد

• Biomedical Engineering
• Neural Engineering
• Mobile Health Technology
• +۳ مورد دیگر

Deborah Won is a Professor in the Department of Electrical & Computer Engineering at California State University, Los Angeles, within the College of Engineering, Computer Science, and Technology. With a background in biomedical engineering and electrical engineering, she has focused on integrating research, teaching, and community engagement since 2009. Education: Ph.D. in Biomedical Engineering (Duke University, 2007), B.S. in Electrical Engineering with a Biomedical Engineering minor (MIT, 2000). Research Interests span neural engineering, rehabilitation technology, and mobile health. Her Neural Engineering Technology Laboratory (NETlab) investigates: Electrophysiological biomarkers for Parkinson's disease to improve deep brain stimulation (DBS) algorithms. Mobile health solutions like the WOW-Mobile fitness app for wheelchair users. Neural signatures of spreading depression in stroke progression. Collaborative rodent studies on spinal cord injury rehabilitation. Publications highlight her work in closed-loop DBS systems, exergaming technology, and educational program design. She received the 2020 BEST ZONE IV PAPER and BEST OVERALL ZONE WINNER award for her service-learning research. Leadership: Faculty advisor for the Biomedical Engineering Society (BME Society). Labs: Founder and director of the Neural Engineering Technology Laboratory (NETlab). Service: Co-developer of the BOOST program, which enhances engineering identity through community-focused projects.

Julio Cesar Ignacio Espinozaمشاهده پروفایل

استادیار

• Bioinformatics
• Data Science
• Genomics
• +۷ مورد دیگر

Julio Cesar Ignacio Espinoza is an Assistant Professor of Bioinformatics and Data Analytics at the Riggs School of Life Sciences, Keck Graduate Institute (KGI), part of the Claremont Colleges. He holds a PhD in Molecular and Cellular Biology from the University of Arizona, where he was supported by a Fulbright Fellowship. His research focuses on microbial and viral genomics, leveraging 'omics technologies and data science to study virus-host interactions in marine and environmental contexts. Before academia, he worked in startups, including Curative (establishing bioinformatics infrastructure for SARS-CoV-2 sequencing) and FormBio (developing NGS analysis software). His postdoctoral training at USC utilized the USC-Provost Fellowship to study marine viral evolution. His work spans bioinformatics, genomics, and virology, with notable contributions to viral discovery, evolutionary dynamics, and cloud computing applications in biology. Key awards include the Fulbright Fellowship and USC-Provost Fellowship. His lab at KGI integrates metagenomics, proteomics, and deep learning to model viral ecosystems.

José María Conejeroمشاهده پروفایل

دانشیار

• Model Driven Software Development
• Big Data
• Machine Learning
• +۳ مورد دیگر

José María Conejero is an Associate Professor in the Department of Systems Informatics and Telematics at the University of Extremadura, Spain. He holds a PhD in Computer Science (2010) from the same institution. His research focuses on Model-Driven Software Development, Big Data, Machine Learning, Web Engineering, and Educational Technology. He has published over 50 articles in journals and conferences, and actively participates in program committees. Research interests include software modernization, reproducible data science workflows, legacy system migration (e.g., MigraSOA project), and AI applications in education. He co-founded MetrikaMedia, a university spin-off startup. Notable contributions include model-driven approaches for service-oriented architectures, aspect-oriented software metrics, and smart city applications using unmanned systems. His educational contributions involve developing recommender systems for student enrollment decisions and analyzing ChatGPT's impact on engineering education assessment methods. He has received the 'Best Paper' award at JENUI 2023. Key projects include semantic alignment of business processes with web services, aspect mining methodologies, and DSL development for smart home automation (HAAIS-DSL). He has also explored crosscutting concerns in software product lines and technical debt impact analysis.

• Machine Learning
• Computer Vision
• Artificial Intelligence
• +۴ مورد دیگر

Manuel Felipe Muciéntes Molina is a Professor of Computer Science and Artificial Intelligence at the Research Center on Intelligent Technologies (CiTIUS), University of Santiago de Compostela (USC). He holds a PhD from USC (2002) focusing on fuzzy logic-based control systems for autonomous robots. His research emphasizes machine learning applications in computer vision, process mining, robotics, and healthcare. He teaches courses like Statistical Learning, Supervised Machine Learning, and Deep Learning. Key Projects: RAI4P: Responsible AI for Process Mining 2.0 QC4SUSTEX: Authentication and security of sustainable textiles CAREBOT: Intelligent robotic units for hospital care Research Interests: Machine learning, computer vision (object detection, tracking), process mining, robotics navigation, and healthcare AI applications. Labs/Teams: Leading the Grupo GSI (Systems Intelligence) within CiTIUS, focusing on intelligent systems and AI-driven solutions.