Fabrício de Oliveira Ourique is a Lecturer in Digital Signal Processing at Queen Mary University of London's School of Physical and Chemical Sciences. He holds a Ph.D. from the University of New Mexico, USA. His academic roles include former Associate Professor positions at Brazil's Federal University of Santa Catarina, where he supervised Master's and Ph.D. students. Research focuses on Digital Signal Processing (DSP), machine learning, and IoT applications. Key areas include image watermarking, time series forecasting, energy monitoring systems, and wearable cardiac signal acquisition devices. He has developed embedded systems for smart meters and semi-automatic music transcription techniques. Publications emphasize healthcare analytics (e.g., excess death estimation in Brazil) and optimization in cancer treatment pathways using perishable inventory models. His work on Markov chains introduced multi-cluster time aggregation methods published in Automatica. No scientific awards explicitly listed. Advised students during prior academic roles but no names provided. Active in interdisciplinary projects combining signal processing with healthcare and IoT.
جستوجوی استاد و پژوهشگر
استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۷۳۶-۷۵۰ از ۱٬۶۰۳ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
Michael Mapunduمشاهده پروفایل
پژوهشگر ارشد
Dr. Michael Mapundu is a Research Fellow at Northumbria University's Applied Sciences Department, specializing in AI-driven bioinformatics and public health informatics. He holds a PhD in Bioinformatics from Wits University, an MSc in Information Technology from Vaal University of Technology, and a BSc (Hons) in Information Systems from Midlands State University. His research focuses on leveraging AI/ML for genomic data analysis and public health systems, including NLP for verbal autopsy analysis and scalable cloud solutions (AWS). Key areas include healthcare decision-making, epidemiological insights from clinical narratives, and improving healthcare informatics efficiency. He has published extensively on machine learning applications in public health and contributed to UN Sustainable Development Goals through data-driven healthcare solutions. Mapundu has experience in curriculum development, research funding acquisition, and mentoring. His work emphasizes interdisciplinary approaches to address healthcare challenges, combining computational methods with real-world healthcare data.
Wolfgang Aigner is a Professor at TU Wien's Department of Visual Analytics, part of the Faculty of Computer Science. His main affiliation is with the Visual Analytics Research Department (E193-07). He focuses on advancing information visualization, time-oriented data representation, and knowledge-assisted visual analytics. His work emphasizes user onboarding methods for unfamiliar visualizations, integrating explicit domain knowledge into analytic workflows, and developing systems for malware analysis and healthcare informatics. Key research areas include: Design of intuitive visualization onboarding systems Temporal data visualization and pattern detection Integration of explicit knowledge in visual analytics Malware behavior visualization and forensic analysis Healthcare data exploration focusing on temporal patient trajectories Notable contributions include the KAMAS malware analysis system, task cube conceptual framework for visualization design evaluation, and methodologies for visually guided time series imputation. He has supervised 14+ graduate theses exploring topics like glyph editors, temporal data visualization, and bias detection in language models. Recent work emphasizes educational theories in visualization onboarding, with comparative studies on user guidance effectiveness and system scaffolding approaches. His research bridges cognitive science principles with practical visual analytics tools for domains like cybersecurity and healthcare.
- Applied machine learning and artificial intelligence
- Data science and eResearch
- Computational methods for physical sciences
- +۲ مورد دیگر
Professor Amanda Barnard is a Senior Professor of Computational Science at the ANU College of Engineering and Computer Science. She leads research in computational modeling, high-performance supercomputing, and AI applications in materials science. With a BSc (Hons) in applied physics (2000) and PhD in theoretical condensed matter physics (2003) from RMIT University, she has held prestigious roles including Distinguished Postdoctoral Fellow at Argonne National Lab (USA) and Violette & Samuel Glasstone Fellow at Oxford University (UK). Board member at BioViS (Garvan Institute), CTCMS (AIBN), Our Health in Our Hands (ANU), and NeSI (New Zealand eScience). Former Chair of the Australian National Computational Merit Allocation Scheme (NCMAS) and current Chair of the Australasian Leadership Computing Grants (ALCG). Research focuses on materials informatics, nanoinformatics, and AI-driven material discovery. Awards include the 2009 Malcolm McIntosh Physical Scientist of the Year, 2014 Feynman Prize in Nanotechnology, and 2019 AMMA Medal. Her work bridges computational science with real-world applications in energy storage, carbon removal, and hydrogen economy technologies. Collaborates with industry through ChoiceFlows Inc. and Data61 (CSIRO).
Michael Bloodgood is an Associate Professor of Computer Science in the Department of Computer Science at The College of New Jersey (TCNJ), part of the School of Science. He has been actively contributing to the academic community at TCNJ since joining as one of six new full-time faculty members in 2016, as noted in a TCNJ Science news article from August 2016. Dr. Bloodgood earned his Ph.D. in Computer Science from the University of Delaware in 2009. His academic journey has led him to become a respected researcher and educator specializing in data-intensive computational fields. Dr. Bloodgood's research primarily focuses on data science, machine learning, and natural language processing . His work bridges theoretical computer science with practical applications, particularly in text classification, active learning methodologies, and computational linguistics. He has developed innovative approaches to stopping criteria in active learning systems, translation lexicon acquisition for low-resource languages, and methods for improving cognates detection through global constraints. His research often involves undergraduate student collaborators, reflecting TCNJ's emphasis on mentored research experiences. Dr. Bloodgood has received multiple Mentored Undergraduate Student Experience (MUSE) Awards (2024, 2022) and a Support of Scholarly Activities (SOSA) Award (2023) from TCNJ, recognizing his commitment to undergraduate research mentorship. His papers have been finalists for Best Paper at the IEEE International Conference on Semantic Computing (ICSC) in 2022 and 2020, demonstrating the quality and impact of his scholarly work. As an educator, Dr. Bloodgood teaches a range of courses including Machine Learning (CS 426), Natural Language Processing (CS 427), and Mentored Research. He serves on multiple departmental and college committees, including the Computer Science Promotion and Reappointment Committee (as Chair for 2024-2025) and the Mentored Research and Internships Council. His dedication to student research is evident through his active recruitment of TCNJ students for mentored research projects, with dedicated web pages for research projects, mentees, and funding opportunities.
Xingche Guoمشاهده پروفایل
استادیار
Xingche Guo serves as an Assistant Professor in the Department of Statistics at the University of Connecticut, Storrs Campus. His research program develops advanced statistical methodologies with critical applications in behavioral decision-making and neuroscientific data analysis. His primary research domains include: Reinforcement Learning and Inverse Reinforcement Learning Functional Data Analysis in high-dimensional contexts Variable Selection techniques for complex models Markov Chain Monte Carlo (MCMC) methods State-space modeling for dynamic systems Statistical applications in mental health and neuroscience Analysis of recent publications reveals concentrated innovation in modeling decision dynamics through reinforcement learning frameworks applied to mental disorders and behavioral experiments. His methodological contributions frequently integrate hierarchical modeling and semiparametric approaches to address complex data structures in neuroscience, particularly for EEG analysis and biomarker-guided treatment development. The publication pattern demonstrates consistent output in top-tier statistical journals and growing interdisciplinary impact.
Guansong PANGمشاهده پروفایل
استادیار
- Machine Learning
- Data Mining
- Computer Vision
- +۷ مورد دیگر
Guansong PANG is a full-time Assistant Professor at the School of Computing and Information Systems , Singapore Management University (SMU) , where he leads the Machine Learning & Applications (MaLA) Lab . He holds a PhD from University of Technology Sydney (2019) and has previously served as a Research Fellow at the Australian Institute for Machine Learning , University of Adelaide. Research Interests : His work centers on machine learning and data mining , particularly focusing on anomaly detection , open-world learning , and foundation models for graphs, time series, and tabular data. He investigates security and safety in foundation models , including hallucination mitigation and AI-generated content detection , with applications spanning network intrusion detection , fraud detection , industrial defect detection , and biometric anti-spoofing . Recent Awards : Lee Kong Chian Fellowship (2024-2026) World's Top 2% Scientists (2022-2024) DSAA 2023 Best Paper Award (Applications Track) Most Influential KDD 2023 Paper Professional Roles : Area Chair for NeurIPS, ICLR, ICML, CVPR, KDD, PAKDD, IJCAI, and AAAI; Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and Pattern Recognition ; Editorial Board member for IEEE Intelligent Systems and International Journal of Data Science and Analytics .
- Metabolomics
- Cancer Biology
- Computational Biology
- +۱۲ مورد دیگر
Gary Patti is a Professor of Chemistry and of Genetics and Medicine at Washington University in St. Louis, with appointments in the Department of Chemistry, Department of Genetics, and Department of Medicine (School of Medicine). He serves as Senior Director of the Center for Metabolomics and Isotope Tracing, Director of the Clinical Research Core in Medicine, and Director of Faculty Affairs in Chemistry. His research focuses on the intersection of cancer biology , metabolomics , and computational biology . The Patti laboratory investigates how cancer cells rewire metabolism to support proliferation, how tumors manipulate host metabolism across tissues, and develops cutting-edge tools in isotope tracing, mass spectrometry imaging, and metabolomics data analysis. His work spans cancer metabolism , single-cell metabolomics , spatial omics , and bioinformatics tool development . The recent publications highlight a strong trend in quantitative metabolic imaging , isotope-based flux analysis , and machine learning for metabolomics . His lab integrates stable isotope tracing with LC/MS , MALDI/DESI imaging , and spatial transcriptomics to resolve metabolic dynamics at the cellular and tissue level. A major focus is overcoming data annotation bottlenecks through tools like DecoID and PeakDetective . Selected scientific awards include: Alfred P. Sloan Award (2014) Pew Biomedical Scholars Award (2015) Camille Dreyfus Teacher-Scholar Award (2015) Mallinckrodt Scholar Award (2016) Inaugural NIEHS Award for Visionary Research (2017) Blavatnik National Finalist (2020) ACS Midwest Award (2023) Professor Patti advises a multidisciplinary research team and leads multiple institutional cores. His grants support technology development in metabolomics, cancer metabolism studies using zebrafish and mouse models, and software tools for omics data integration. He collaborates with instrument manufacturers to improve data acquisition strategies and promotes inclusive research practices. He leads the Center for Metabolomics and Isotope Tracing and the Clinical Research Core in Medicine , fostering collaboration across chemistry, genetics, and medicine. His lab is at the forefront of advancing single-cell metabolomics and in vivo metabolic imaging .
Jeffrey S. Rosenthal is a Professor in the Department of Statistical Sciences at the University of Toronto, Faculty of Arts and Science. He holds a PhD in Mathematics from Harvard University and a BSc from the University of Toronto. PhD, Mathematics, Harvard University BSc, University of Toronto His research centers on probability theory , stochastic processes , and statistical computation , with a particular focus on Markov chain Monte Carlo (MCMC) algorithms . His work spans theoretical foundations and practical applications, including random walks on groups and interdisciplinary modeling. He is also known for his public engagement in statistics through bestselling books and media appearances. The recent publications reflect a sustained focus on the theoretical underpinnings and convergence properties of MCMC methods, including adaptive and non-reversible algorithms. His work also extends into data science applications, such as analyzing streaks in online chess and the long-term impact of the COVID-19 pandemic on mortality. The keywords span probability, statistics, computational mathematics, and machine learning, indicating a blend of theoretical rigor and applied relevance. Scientific Awards and Honors: CRM-SSC Prize in Statistics COPSS Presidents' Award SSC Gold Medal Fellow of the Royal Society of Canada Fellow of the Institute of Mathematical Statistics Alumnus of Influence, University College Pierre Robillard Award SSC Student Research Presentation Award Savage Award Finalist Academic Supervision and Grants: Professor Rosenthal has supervised a large and diverse group of students, including numerous PhD candidates, MSc students, and post-doctoral fellows, many of whom have gone on to successful academic careers. His research is supported by ongoing publications and collaborations, indicating active grant funding and a vibrant research program. He maintains a well-documented research team and provides extensive resources for students and collaborators. Research Teams and Labs: He leads an active research group in probability and computational statistics, with a documented team of current and past post-doctoral fellows, PhD students, and research assistants. The group maintains a collaborative environment, as evidenced by joint publications and team photos, and focuses on advanced topics in MCMC theory and applications.
- Mixture Models
- Modal Clustering
- Graphical Models
- +۵ مورد دیگر
Alessandro Casa is an Associate Professor of Statistics at the Faculty of Economics and Management, Free University of Bozen-Bolzano (Italy). His research focuses on developing statistical methodologies for high-dimensional and complex data, including latent variable models, density-based clustering, and applications in spectroscopy. He holds a PhD in Statistics from the University of Padova (2016-2020), preceded by master's and bachelor's degrees in Statistical Sciences and Statistics, Economics, and Finance, both from the University of Padova. Education: PhD in Statistics, University of Padova (2016-2020) Master Degree in Statistical Sciences, University of Padova (2014-2016) Bachelor Degree in Statistics, Economics, and Finance, University of Padova (2011-2014) Research Interests: Mixture models, modal clustering, graphical models, variable selection, latent variable modeling, high-dimensional data inference, computational statistics, and chemometrics. His work often addresses challenges in spectroscopic data analysis and applications in agriculture and physics. Teaching: Statistical Methods for Business Analysis (MSc programs) Research Methods and Experimental Design (Tourism Management) Statistics for Tourism, Sport, and Event Management (BSc) Professional Experience: Associate Professor (2024–Present), Free University of Bozen-Bolzano Senior Assistant Professor (2024), University of Bergamo Postdoctoral Research Fellow (2019–2021), University College Dublin Labs/Teams: Collaborations with Vistamilk SFI Research Centre and Insight SFI Research Center during his postdoctoral tenure. Active in model-based clustering research communities, contributing to conferences and workshops globally.
Hayley Stannardمشاهده پروفایل
مدرس ارشد
Dr Hayley Stannard is a Senior Lecturer in Animal Anatomy and Physiology at Charles Sturt University, within the School of Agricultural, Environmental and Veterinary Sciences. She specializes in wildlife ecology, marsupial physiology, and conservation, with a strong focus on Australian native species such as the bare-nosed wombat, kultarr, and Tasmanian devil. Her work integrates teaching and research, contributing significantly to both academic and practical outcomes in animal health and conservation. Her educational background includes a BAnimSc, BSc(Hons), PhD, and a Graduate Certificate in Learning and Teaching in Higher Education (GCLTHE). She completed her PhD in 2012 on carnivorous marsupial nutrition and served as a Postdoctoral Research Associate at the University of Sydney from 2015 to 2018 under an ARC Linkage project with Taronga Zoo. Dr Stannard's research focuses on animal nutrition, health, disease treatment (especially sarcoptic mange), and the physiological adaptations of wildlife to environmental challenges. She investigates how animals utilize scarce resources for survival and reproduction, and how pathogens impact animal health. Her research has led to improved disease treatments, nutritional guidelines for captive populations, and better health assessment tools for wildlife. Her recent publications span topics including wombat mange treatment, roadkill data, kultarr behavior, and marsupial nutrition. These works reflect a strong trend in applied conservation science, integrating field data, captive studies, and translational outcomes for wildlife management. Executive Dean’s Teaching Awards (2024) Fellowship, Higher Education Academy (2023) Vice-Chancellor's Research Excellence Award (2024) Grant: Investigating veterinary nanotechnology to treat sarcoptic mange in wombats (2024) Dr Stannard actively supervises higher-degree research students and collaborates with external organizations including zoos, conservation groups, and government bodies. She has been involved in grant-funded research and public engagement, including media contributions on wombat conservation and reproduction. While no formal lab name is mentioned, her research activities suggest leadership in a wildlife physiology and conservation research group at CSU.
Seokhyun Chungمشاهده پروفایل
استادیار
Seokhyun Chung is an Assistant Professor at the University of Virginia, located in Lab 257 of Olsson Hall. His research focuses on IoT-enabled systems, leveraging data-driven methods such as federated learning, multi-task learning, and Bayesian probabilistic modeling to address challenges in statistical heterogeneity, scalability, and personalization in connected systems. Applications span smart healthcare and manufacturing systems, emphasizing reliability and efficiency. Research Interests: Chung's work integrates IoT, machine learning, and systems engineering to develop collaborative analytics frameworks. He explores federated learning architectures for distributed data fusion, probabilistic modeling for uncertainty-aware predictions, and optimization techniques for resource allocation in edge computing environments. His healthcare focus includes gait analysis and load estimation for wearable devices, while manufacturing applications involve job scheduling and additive manufacturing quality control. Key Contributions: Recent work highlights include federated multi-output Gaussian processes for heterogeneous systems, real-time adaptation of time-series predictions, and fairness-aware machine learning for load carriage tasks. His interdisciplinary projects address both technical and operational challenges, such as emergency department layout optimization and toxic gas monitoring networks. Labs & Infrastructure: Chung's research is conducted in Olsson Hall's advanced labs, leveraging IoT sensor networks, edge computing setups, and collaborative simulation environments to prototype innovative smart systems solutions.
- Signal Processing
- Image Processing
- Remote Sensing
- +۹ مورد دیگر
JAVIER CALPE MARAVILLA is an Associate Professor in the Department of Electronic Engineering at the School of Engineering, University of Valencia, Spain. He is a key member of the Image and Signal Processing (ISP) research group and affiliated with the ERI Image Processing Laboratory (IPL). His work bridges academic research and industrial applications in electronics and signal processing. Research Interests: His research spans signal and image processing, remote sensing, hyperspectral imaging, machine learning for classification, biophysical parameter estimation, analog microelectronics, and sensor systems. He focuses on developing algorithms for cloud screening, vegetation stress detection, ECG signal analysis, and industrial sensing applications. The recent publications (2009–2021) highlight a strong trend in applying kernel methods, neural networks, and adaptive filters to remote sensing and biomedical signals. His work emphasizes practical implementations in industrial and environmental monitoring, including touchscreens, AOTF-based imaging, and low-power circuit design. Education: Ph.D. in Electronics, Universitat de València (1994), with thesis on spectral estimation techniques. Scientific Awards: Advising and Grants: While no explicit list of students or grants is provided, his academic rank and publication record suggest experience in supervising postgraduate research. He has contributed to educational curriculum development, particularly in analog microelectronics. No funding sources are mentioned in the text. Labs and Teams: He is actively involved in the ISP (Image and Signal Processing) research group and the ERI Image Processing Laboratory (IPL) at the University of Valencia, focusing on both theoretical and applied research in electronic systems and data analysis.
Maryam Fouad Abdelaty is a researcher in the Department of Medical Engineering at Ruhr University Bochum, where she completed her PhD in September 2024. Her work focuses on applying deep learning techniques to medical ultrasound imaging, including harmonic imaging, synthetic data generation, and contrast-enhanced imaging. She holds a bachelor's degree in electronics engineering from the German University in Cairo (2015), a master's degree in bio-impedance spectroscopy for non-invasive glucose monitoring (2017), and a Dr.-Ing. degree from Ruhr University Bochum (2024). Bachelor's: German University in Cairo (2015) Master's: Thesis on joint NIR and bio-impedance spectroscopy for glucose monitoring (2017) PhD: Focused on deep learning in medical ultrasound (2024) Her research emphasizes improving imaging resolution, reducing invasiveness, and enhancing diagnostic accuracy through AI-driven methodologies. Recent work includes comparative studies of deep learning vs. traditional amplitude modulation in contrast-enhanced ultrasound, as well as semi-supervised approaches for needle localization. Her publications span conferences like IEEE Ultrasonics Symposium and IEEE ISBI, with a focus on deep learning applications in biomedical imaging. Notable contributions include synthetic ultrasound signal generation using GANs and single-shot harmonic imaging techniques. While no formal advising or grants are explicitly listed, her research demonstrates significant contributions to medical imaging innovation. No lab affiliations or teams are mentioned in the provided text.
Ross Turner is a Lecturer in Mathematics and Physics at the University of Tasmania, working within the School of Natural Sciences, Department of Physics. He leads the analytical modeling research stream in the University's Theoretical and Computational Astrophysics Group (Active Galactic Nuclei; AGN) and collaborates with the Australian Centre for Excellence in Antarctic Science (ACEAS). His research spans computational physics with applications in astronomy, theoretical astrophysics, applied mathematics, and computational earth sciences, aligning with the University's themes of Data, Knowledge and Decisions, and Marine, Antarctic and Maritime. Ross received his Doctor of Philosophy in Astrophysics from the University of Tasmania in 2018 and a Bachelor of Science (First Class Honours) in Physics from the same institution in 2013. His research primarily focuses on developing the statistical tools to analyze radio astronomy observations from next-generation large sky surveys, including by the Square Kilometre Array. He created the highly-cited RAiSE (Radio AGN in Semi-Analytic Environments) software package to simulate the physics of jet-inflated lobes emanating from active supermassive black holes. His work in computational earth sciences relates to interpreting large datasets from active glaciers using complex dynamical models and machine learning techniques. Turner's publication record demonstrates strong expertise in both astrophysics and computational geophysics, with recent work spanning AGN jet dynamics, radio galaxy evolution, and innovative applications of machine learning to cryoseismology. His research bridges theoretical modeling with observational astronomy and geophysical applications, showing a consistent trajectory of increasingly sophisticated computational approaches to complex physical systems. Vice Chancellor's Award 2022, Early Career Researcher Award College of Science and Engineering Early Career Researcher Award 2022 Eureka Prize Finalist, Australian Museum, 2022 The Bok Prize, Astronomical Society of Australia, 2014 Multiple Teaching Commendations and Unit Commendations Student-nominated Teaching Appreciation Certificate Ross has been awarded over $2 million (equivalent value) in competitive research funding, including more than 2000 hours of observing time to study AGNs using terrestrial and satellite telescopes. He supports the learning of Honours and higher degree by research students in both applied mathematics and theoretical astrophysics; 70% of his primary supervised Honours students have received a University Medal. He serves as primary or co-supervisor for six current PhD candidates at the University of Tasmania and the International Centre for Radio Astronomy Research (ICRAR). Turner leads the analytical modeling research stream within the University's Theoretical and Computational Astrophysics Group and works with the Compute Antarctic Group as part of ACEAS. His work involves developing computational frameworks that bridge theoretical astrophysics with observational data analysis, creating tools that enable more precise interpretation of complex astronomical phenomena and geophysical processes.
فقط ۷۵۰ نتیجه اول نمایش داده میشود. برای رسیدن به نتایج دقیقتر، جستوجو را با فیلترها محدودتر کنید.