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Paulo Félix LamasView profile

Professor

• Machine Learning
• Biomedical Signal Processing
• Probabilistic Reasoning
• +3 more

Paulo Félix Lamas is a Full Professor at the University of Santiago de Compostela (USC), specializing in Computer Science and Artificial Intelligence. He obtained his PhD in Physics from USC in 1999 and became an Associate Professor in 2002. As a foundational leader of CiTIUS (Research Centre in Intelligent Technologies), he served as Head (2010–2019) and Deputy Director (2019–2020). His research focuses on probabilistic learning , temporal abductive reasoning , and biomedical signal interpretation . Key applications include ECG analysis, glucose monitoring, and AI-driven healthcare solutions. His work bridges machine learning with clinical diagnostics, emphasizing interpretability and real-time systems. Recent publications (2015–2024) demonstrate interdisciplinary innovation, spanning: Healthcare AI : Personalized drug dosing, arrhythmia detection, and continuous glucose monitoring. Computational methods : Stochastic embeddings, kernel-based missing data handling, and adaptive clustering. Theoretical advances : Abductive reasoning frameworks for time-series interpretation. He leads major projects like: SOSFood (2024–2028): AI for sustainable food systems. XAI4SOC (2022–2025): Explainable AI for healthy aging. INSIDE (2022–2024): Predictive cardiac rehabilitation. At CiTIUS, he directs research on intelligent healthcare technologies, integrating sensor data with machine learning for chronic disease management.

Mohammad RastegariView profile

Adjunct Assistant Professor

• Computer Vision
• Efficient Machine Learning
• Model Compression
• +3 more

Mohammad Rastegari serves as a senior technical manager in Apple's AI/ML organization and holds an affiliate assistant professor position in the Computer Science and Engineering Department at the University of Washington. Previously, he was a research scientist at the Allen Institute for AI (AI2) where he contributed to the PRIOR team, and co-founded XNOR.ai as Chief Technology Officer. He completed his Ph.D. at the University of Maryland under the supervision of Professor Larry S. Davis, focusing on computer vision and machine learning. His research centers on efficient deep learning with major contributions in binary neural networks (notably XNOR-Networks for resource-constrained devices) and model compression . Current work extends to large language model efficiency through weight clustering, token pruning, and memory optimization while maintaining strong foundations in computer vision. Analysis of his recent publications reveals a strategic shift toward optimizing transformer-based architectures across vision and language domains, with consistent emphasis on deployable solutions for edge computing and real-world applications. He co-founded XNOR.ai (acquired by Apple) and contributed to AI2's PRIOR initiative, demonstrating a career trajectory bridging academic innovation and industrial implementation in efficient AI systems.

• Statistical Machine Learning
• Bayesian Nonparametrics
• Mixture Models
• +13 more

Keefe Murphy is a Lecturer in Statistics within the Department of Mathematics and Statistics at Maynooth University , Faculty of Science & Engineering, and is affiliated with the Hamilton Institute . He is an active researcher in statistical machine learning, Bayesian nonparametrics, and clustering/classification of complex, high-dimensional data. Education: PhD in Statistics, University College Dublin MSc in Statistics, University College Dublin BSc in Economics & Mathematics, University of Limerick Research Interests: His work centres on developing and extending statistical methodologies for supervised and unsupervised learning , with emphasis on mixture models, latent variable models, Bayesian nonparametrics, and computational statistics . He actively contributes novel algorithms and software implementations, including the R packages IMIFA , MoEClust , and MEDseq available on CRAN. Current projects include extensions to Bayesian Additive Regression Trees (BART) , handling missing data , modelling multivariate count data , and variable selection in model-based clustering. Publication Profile: His recent publications (2021–2025) demonstrate a clear trajectory in advancing Bayesian machine learning methods, with contributions to Gaussian process BART models , sparse factor analysis , and educational data mining . Collaborative work spans learning analytics and multi-omic prostate cancer biomarker discovery , illustrating broad interdisciplinary impact. Awards & Recognition: Distinguished Dissertation Award (The Classification Society, 2020) Service & Advising: He serves as Associate Editor for Statistical Analysis and Data Mining and on departmental committees (Course Committee, PR Committee). He has successfully supervised PhD student Mateus Maia (graduated 2024) and currently teaches modules such as Advanced R Programming , Introduction to Data Science , and Nonparametric Statistics . Labs & Collaborations: He is affiliated with the Hamilton Institute , which fosters interdisciplinary research in applied mathematics and statistics, providing a collaborative environment for advancing computational and methodological statistics.

Rob DeardonView profile

Professor

• Bayesian statistics
• computational statistics
• infectious disease epidemiology
• +5 more

Rob Deardon is a Professor jointly appointed in the Faculty of Veterinary Medicine and the Department of Mathematics and Statistics at the University of Calgary. His research spans Bayesian statistics, infectious disease epidemiology, and spatial modeling, with applications in human and animal health. PhD in Applied Statistics, University of Reading (2001) MSc in Medical Statistics, University of Southampton (1997) BSc in Pure Mathematics & Mathematical Statistics, University of Exeter (1996) Rob Deardon's work focuses on computational statistics, infectious disease modeling (including foot-and-mouth disease and influenza), and spatio-temporal analysis. His methodological interests include Monte Carlo methods, approximate Bayesian computation, and statistical learning. Recent publications emphasize spatial epidemic models, behavioral change analysis, and computational methods for disease surveillance. He leads a research group of 10 graduate students. He teaches graduate courses in infectious disease modeling and maintains collaborations across biostatistics, veterinary medicine, and public health.

Gangbing SongView profile

Professor

• Structural health monitoring
• Non-destructive testing
• Wind energy
• +7 more

Dr. Gangbing Song is a Moores Professor in the Department of Mechanical and Aerospace Engineering at the University of Houston , where he has been a tenured Full Professor since 2008. He previously served as an Assistant Professor (1998–2002) and Associate Professor with Tenure (2002–2008) at the University of Houston. Ph.D. , Mechanical Engineering, Columbia University, 1995 M.S. , Mechanical Engineering, Columbia University, 1991 B.S. , Energy Engineering, Zhejiang University, 1989 His research spans structural health monitoring , machine learning , and smart materials , focusing on non-destructive testing , wind energy , and structural vibration control . Recent work includes entropy-based fault diagnosis , underwater bolt monitoring , and upcycling of wind turbine blades in a circular economy framework. Dr. Song has secured over $3.2 million in external funding (including 13 NSF awards) and established the Smart Materials and Structures Laboratory . He has published 96 peer-reviewed journal papers , 191 conference papers , and holds 3 U.S. patents . Outstanding Technical Contribution Award (ASCE, 2008) NSF CAREER Award (2001) Best Paper Award (Earth and Space’08) General Chair, ASCE Earth and Space Conference (2010) He has advised 13 Ph.D. and 31 M.S. students , delivered 10 keynote speeches , and developed educational programs including a remote laboratory and REU initiative . His work integrates robotics , smart sensors , and data-driven algorithms for structural integrity and sustainability applications.

• Sports Economics
• Tourism Management
• Economic Impact Analysis
• +3 more

Jesyca Maria Salgado Barandela is an Assistant Professor at the Department of Business Organization and Marketing under the Faculty of Business Sciences and Tourism at University of Vigo, based in the Ourense campus. Her research focuses on economic impact analysis of sports events, tourism management, and regional development, with particular emphasis on forgotten spaces and cross-border economic dynamics between Portugal and Galicia. Her academic background includes a PhD from University of Vigo, where her dissertation titled "Repercusión del turismo de eventos deportivos desde la óptica de los patrones de gasto y el impacto económico: aplicación a una liga profesional" laid the foundation for his career in sports economics and tourism analysis. He has collaborated with Dr. Ángel Barajas Alonso and Dr. Patricio Sánchez Fernández. Research interests span sports economics, tourism management, and event impact analysis. His work explores how sports events influence regional economies, with a focus on forgotten spaces, geographical distribution of economic impacts, and strategic leveraging of event portfolios. He investigates tourist expenditure behaviors across different sports categories and develops innovative tools for quantifying spatial economic impacts. His publication trends show consistent focus on sports event economics (2012–2025), with increasing attention to geographical impact distribution, uncertainty modeling in impact estimation, and cross-border regional development. Key areas include football analytics, event portfolio management, and rural revitalization through tourism. While no specific scientific awards are documented in the provided texts, his work has contributed significantly to the academic understanding of sports economics and tourism management. He has not explicitly mentioned advising students or securing grants in the available information. His methodological approaches incorporate cluster analysis, geospatial modeling, and portfolio optimization frameworks to assess economic impacts and tourist behaviors. The research often bridges theoretical economic models with practical event management applications.

Kazimierz ChorośView profile

Professor

• digital image and video processing
• content-based video indexing and retrieval
• computer animations
• +4 more

Kazimierz Choroś is a Professor at the Department of Applied Informatics , Faculty of Information and Communication Technology , Wrocław University of Science and Technology. He held leadership roles as Deputy Director of the Institute of Informatics (2008-2014) and Deputy Dean of the Faculty of Computer Science and Management (2016-2020). Research interests: digital image/video processing, content-based video indexing, computer animations, multimedia systems, web systems analysis, and information systems design. Organizer and Chair of the International Conference on Multimedia & Network Information Systems (MISSI 2022) Chair of Special Session WebSys 2020 at ICCCI 2020 Other activities: Since 1993, member and former President (1993-2002) of the SAGE Association (Polish Graduates of French Grandes Ecoles). Since 1982, member of the Polish Numismatic Society, author of dozens of numismatic publications, and Editor-in-Chief of Wrocławskie Zapiski Numizmatyczne (2003-present). Contact: Email: kazimierz.choros@pwr.edu.pl Phone: +48-71.320.3799 Office: Building D2, Room 201/1 Address: Wrocław University of Science and Technology, Wyb. Wyspiańskiego 27, 50-370 Wrocław, Poland

Michael Y. WangView profile

Professor

• Neurosurgery
• Spine Surgery
• Cervical Spondylotic Myelopathy
• +5 more

Michael Y. Wang serves as Professor in the Department of Neurological Surgery at the University of Miami Miller School of Medicine. His research focuses on evidence-based neurosurgical outcomes with particular emphasis on cervical spondylotic myelopathy (CSM) and lumbar spine disorders. Dr. Wang actively contributes to the Quality Outcomes Database (QOD) Spine Collaborative Research Study Group, leading multiple longitudinal studies examining surgical efficacy across diverse patient populations. His research interests span surgical outcomes measurement, comorbidity impact analysis, geriatric neurosurgery, and predictive modeling. Dr. Wang investigates how factors including age, ASA physical status classification, pain catastrophizing, and care dependency influence surgical results. Recent work integrates machine learning algorithms to predict long-term outcomes, demonstrating innovation in neurosurgical research methodology. Analysis of Dr. Wang's recent publications reveals consistent focus on patient-centered outcomes using large-scale prospective databases. His work establishes that surgical intervention provides significant benefit across diverse populations including elderly patients, those with multimorbidity, and individuals with high pain catastrophizing scores. The research demonstrates similar minimal clinically important difference achievement rates across demographic and clinical subgroups. Dr. Wang's scholarly contributions include numerous publications in Journal of Neurosurgery Spine , Neurosurgical Focus , and Clinical Spine Surgery . His work has established important findings regarding surgical indications and outcome expectations for complex spinal conditions. As an active investigator in the Quality Outcomes Database consortium, Dr. Wang contributes to large-scale multicenter research that shapes contemporary neurosurgical practice. His work provides evidence supporting surgical intervention across diverse patient populations, challenging previous assumptions about contraindications based on age or comorbidities. The research demonstrates that careful patient selection and expectation management can optimize outcomes even in traditionally challenging cases.

• Cybersecurity
• Artificial Intelligence
• Machine Learning
• +3 more

Mahdieh Zabihimayvan is an Assistant Professor in the Computer Science Department at Central Connecticut State University , specializing in Cybersecurity and Artificial Intelligence applications. Her research focuses on enhancing proactive cyber threat intelligence and privacy protection using applied machine learning and complex systems analysis . Areas of Expertise: Machine learning for Cybersecurity, Complex systems analysis Memberships: European Society for Fuzzy Logic and Technology, ACM-W Women in Network Science, Women in Machine Learning, WomenTech Network Courses Taught: Computer Security, Secure Software Development, Big Data Programming, Systems Programming, Secure Software Designs, Introduction to Internet Programming Research Interests include cybersecurity , machine learning , and complex systems . She has published extensively on topics such as darknet traffic classification , phishing attack detection , and web robot identification , often leveraging fuzzy logic and deep learning techniques. Scientific Awards include a nomination for the Undergraduate Research Competition in 2022. Her work intersects cybersecurity , machine learning , and privacy protection , particularly in analyzing Tor networks , dark web systems, and biomedical data .

Suzana LoshkovskaView profile

Professor

• Programming
• Visualization
• Human-Computer Interaction
• +3 more

Suzana Loshkovska, PhD , is a Full Professor at the Faculty of Computer Science and Engineering of Ss. Cyril and Methodius University in Skopje , Macedonia. Since 1992 she has held continuous academic appointments, interrupted only by a visiting-researcher stay at the Academy of Sciences of Lower Austria (1993–1994) where she designed a medical information system for Krems Hospital. Education : BSc (1989) and MSc (1992) in Computer Science and Automation, Faculty of Electrical Engineering, Skopje PhD (1995) in Technical Sciences, Technical University of Wien, Austria Research Interests span programming, visualization, human–computer interaction, virtual reality, medical imaging, and modelling/visualization of medical processes. Her work integrates advanced algorithmic techniques—such as hierarchical multi-label classification, support-vector machines, predictive clustering trees, and Bayesian networks—into practical medical and multimedia applications. Across more than one hundred peer-reviewed publications, her recent articles concentrate on content-based medical image retrieval , annotation of medical images , visual concept detection , and interactive medical systems . These contributions collectively advance computer-aided diagnosis, clinical decision support, and efficient management of large-scale medical data sets. Scientific Awards & Recognition : Member, Editorial Board, INFORMATICA (international journal, Slovenia) Projects & Funding : Principal investigator or participant in bilateral and EU-funded projects including SEE-ERA.NET Plus, TEMPUS DL@WEB, TEMPUS M.Sc. E-Learning, and multiple Macedonian Ministry of Science grants covering medical imaging, environmental modelling, distance learning, and electromagnetic-effects visualisation. While no specific research group or laboratory name is provided, her sustained involvement in national and international projects indicates active leadership within the university’s medical-informatics and multimedia-research community.

• Multivariate Statistics
• Data Analysis
• Clustering Algorithms
• +4 more

Laura Bocci has been an Associate Professor of Statistics at Sapienza University of Rome since 2022. Previously, she worked as a researcher at Sapienza University from 1998–2000 in the Department of Communication and Social Research, later moving to the Department of Social and Economic Sciences in 2020. She earned her PhD in Methodological Statistics (XII Cycle, 2000) from Sapienza University, where she also graduated with honors in Statistical and Economic Sciences. Current Department: Department of Social and Economic Sciences Faculty: Faculty of Political Science, Sociology, Communication Office Location: Room 107, Building CU002 (Political Sciences), Sapienza University Campus Teaching: Statistics (A-K) and Data Science for Social Research Her research focuses on Multivariate Statistics , Data Analysis , and Statistical Methods with applications in social sciences, economics, and data clustering. She has developed novel techniques in dimensional reduction , model-based clustering , unsupervised classification , multidimensional scaling , and genetic algorithms . Her work often addresses complex data structures , including three-way data and multilevel models. Publications highlight trends in regional competitiveness , well-being analysis , clustering methodologies , and genetic algorithm applications . Key subfields include three-way data analysis , spatial clustering , and categorical variable optimization . Office hours are held Tuesdays 14:00–16:00 in person or via Google Meet (by appointment). She teaches courses in Statistics and Data Science for Social Research using Sapienza’s e-learning platform.

Augusto CerquaView profile

Associate Professor

• Regional and Industrial Policy
• Education and Labour Economics
• Political Economy
• +2 more

Augusto Cerqua serves as Associate Professor of Economic Statistics at Sapienza University of Rome's Department of Social and Economic Sciences. His research focuses on industrial/regional policy impacts and counterfactual evaluation methods, with publications spanning top journals including Journal of Regional Science, Regional Studies, and Journal of Environmental Economics and Management. He teaches advanced courses in empirical modeling, policy evaluation, and statistical methods for public policy. Cerqua's research program centers on Regional and Industrial Policy, Education and Labour Economics, Political Economy, and Tourism and Environmental Economics. He employs rigorous quantitative techniques—particularly regression discontinuity designs and non-parametric methods—to evaluate policy effectiveness. His work bridges theoretical frameworks with real-world applications in public administration, emphasizing causal inference in contexts like local governance, migration flows, and environmental policy implementation. The ERC SH1_13 and SH2_9 classifications reflect his expertise in institutional dynamics and regional development. Analysis of his recent publications reveals a dominant focus on policy evaluation methodologies applied to pressing socio-economic challenges. Key thematic clusters include political economy (examining electoral behavior and institutional incentives), regional development (assessing spatial disparities and place-based interventions), and crisis response (quantifying pandemic impacts and recovery trajectories). His methodological signature involves innovative causal inference approaches for contexts lacking clear control groups, frequently leveraging administrative data and spatial regression techniques. Cerqua actively leads research projects including 'The impact of place-based policies when public transfers fade out,' demonstrating sustained engagement with EU structural funds evaluation. His teaching portfolio—covering empirical models, computational policy methods, and quantitative public policy analysis—directly translates research expertise into graduate education for programs in International Economic Relations, Environmental Sustainability, and Global Economics.

Sergey Vasilyevich GolubView profile

Professor

• Agent programming
• Intelligent monitoring
• Medical informatics
• +4 more

Sergey Vasilyevich Golub is a Professor at the Department of Software for Automated Systems at Cherkasy State Technological University. He holds a Doctor of Technical Sciences degree and has been part-time faculty since 2018. His career spans multiple roles, including dean at CHNU and head of departments in intelligent systems and software engineering. His research focuses on agent programming, intelligent monitoring, medical informatics, and machine learning. He has contributed to interdisciplinary projects linking software engineering with environmental monitoring and healthcare diagnostics. Recent publications emphasize multi-agent systems, social network analysis, and data-driven disease prediction. Key article trends include integrating satellite data for climate monitoring, authorship classification in social media, and agent-based modeling for pandemic forecasting. He collaborates with researchers in Ukraine and abroad, with works in Springer LNNS and CEUR-WS proceedings. Thanks and honorary diplomas from Cherkasy National University (2000, 2003, 2015) Honorary diplomas from Cherkasy Regional State Administration (2001, 2014) Honorary diploma from Ukraine's Ministry of Education and Science (2011) He has authored monographs on data analysis technologies and cross-platform decision support systems. His work bridges academic research and practical applications in intellectual monitoring across healthcare, ecology, and cybersecurity domains.

Ushenko Yuriy OleksandrovychView profile

Professor

• Information systems design
• Data mining
• Pattern recognition
• +7 more

Yuriy Ushenko is Professor and Head of the Department of Computer Science at Chernivtsi National University named after Yuriy Fedkovych. An Academician of the Academy of Engineering Sciences of Ukraine (2021), he holds a Doctor of Physical and Mathematical Sciences degree with specialization in optics and laser physics. His interdisciplinary work bridges computer science, biomedical engineering, and optical diagnostics. His educational background includes: Master of Telecommunications from Chernivtsi National University (2003, Diploma with honors) Doctor of Physical and Mathematical Sciences (2015) in specialty 01.04.05 – Optics, Laser Physics Professor title in Computer Science (2017) Professor Ushenko's research focuses on information systems design , data mining , pattern recognition , and digital image processing with specialization in laser polarimetry and interferometry . His work pioneers Mueller-matrix tomography and polarization-correlation microscopy for biomedical applications, developing computational methods to analyze biological tissues and fluids for diagnostic purposes. Key innovations include 3D digital polarization-holographic histology for myocardial damage assessment and intelligent systems for disease identification from medical images. His publication trends reveal a strong emphasis on applying machine learning to biomedical imaging and geospatial analysis, with recent work spanning transformer-based lyrics generation, polarization-singular tissue processing, and migration forecasting systems. The research demonstrates consistent translation of theoretical optics into practical diagnostic tools. Scientific recognition includes: Academician of the Academy of Engineering Sciences of Ukraine (2021) As Director of the Bukovinian Innovation Technologies Cluster (since 2019), he leads regional IT initiatives while serving as Associate Editor for the SCOPUS Q3 journal International Journal of Image, Graphics and Signal Processing . His mentorship has guided students to international awards including ICYS silver medals and EUCYS special prizes. Current projects involve developing automated intelligent systems for LLC 'KM Trade' and advancing Mueller-matrix diagnostic techniques through international collaborations with Springer and SPIE conferences. His laboratory work centers on creating computational frameworks for polarization-correlation microscopy, with active teams developing laser autofluorescence polarimetry techniques and digital image processing pipelines for biomedical diagnostics. The research group maintains strong ties with international partners in optical engineering and medical diagnostics.

Igor KononenkoView profile

Professor

• Artificial Intelligence
• Machine Learning
• Neural Networks
• +3 more

Prof. Igor Kononenko serves as a Professor at the Faculty of Computer and Information Science, University of Ljubljana, where he heads the Laboratory for Cognitive Modeling and teaches core courses including Algorithms and Data Structures 1, Artificial Intelligence, Intelligent Systems, and Machine Learning. Education: Ph.D. in Computer Science, University of Ljubljana (1990) Research Focus: His work centers on Artificial Intelligence , Machine Learning , and Cognitive Modeling , with recent emphasis on explainable AI and prediction reliability . He has pioneered techniques in feature contribution explanation, graph-based data mining, and archetypal analysis for complex datasets, resulting in approximately 210 publications and 10 textbooks . Publication Trends: Analysis of his 15 most recent articles reveals a decisive shift toward interpretable machine learning—particularly reliability estimation in data streams (2012-2014), graph mining for oceanographic/spatial data (2013-2019), and medical applications of explanation methods (2011-2018). His 2013-2016 work on archetypal analysis for multi-document summarization remains highly influential in NLP. Research Leadership: As principal investigator, he secured major funding including: Two ARRS programmes on Artificial Intelligence (2009-2014, 2015-2020) EU's AGROIT project for farming efficiency (2014-2016) Bilateral projects on imbalanced data learning (2010-2011), bioinformatics for cancer classification (2014-2015), and disease dataset analysis (2020-2021) Laboratory: The Laboratory for Cognitive Modeling under his direction drives innovation in AI theory and applications, with recent work spanning basketball analytics, coronary artery disease diagnostics, and hemodynamic simulation modeling.