جست‌وجوی استاد و پژوهشگر

Derek A. Kreagerمشاهده پروفایل

استاد

• Life Course
• Crime and Delinquency
• Incarceration
• +۳ مورد دیگر

Derek A. Kreager serves as Professor of Sociology and Criminology at Pennsylvania State University's College of the Liberal Arts, holding dual appointments in Sociology and Criminology and Demography. He concurrently serves as Associate Head of the Department of Sociology and Criminology and is a Co-Funded Faculty member at the Social Science Research Institute (SSRI). His educational background includes a Ph.D. and M.A. in Sociology from the University of Washington (2006, 2002) and a B.S. in Psychology from the United States Naval Academy (1993). Ph.D. Sociology, University of Washington, 2006 M.A. Sociology, University of Washington, 2002 B.S. Psychology, United States Naval Academy, 1993 Kreager's research centers on social network dynamics within criminological contexts, with dual specialization in prison sociology and adolescent development. His work examines how friendships and romantic relationships influence risk-taking behaviors across the life course, particularly focusing on incarceration experiences and community re-entry processes. He employs advanced network methodologies to study inmate social structures, family visitation impacts, community reintegration challenges, and prison substance use treatment efficacy. His adolescent research investigates how romantic relationships shape sexual development and health-risk behaviors through peer network mechanisms. Analysis of his recent publications reveals a consistent methodological emphasis on dynamic network modeling applied to criminological questions. His work bridges macro-level institutional analysis (prison systems) with micro-level interpersonal dynamics (romantic partnerships), demonstrating how social structures constrain and enable behavioral trajectories. Key thematic trends include the examination of gendered double standards in adolescent sexuality, the transformation of inmate social hierarchies during mass incarceration, and the cognitive mechanisms underlying desistance from crime. His scientific recognition includes prestigious awards such as the Ruth Shonle Cavan Young Scholars Award from the American Society of Criminology and the William T. Grant Foundation Scholars Award. Additional honors reflect his methodological innovation and theoretical contributions: Ruth Shonle Cavan Young Scholars Award, American Society of Criminology William T. Grant Foundation Scholars Award Roy C. Buck Award for Outstanding Article by a PSU Junior Faculty Member Outstanding Article Award for 2006, American Society of Criminology First Prize, American Society of Criminology Gene Carte Student Paper Award Kreager's research program is sustained through major external funding from the National Science Foundation, National Institute of Justice, and National Institutes of Health (including NIAAA). He collaborates extensively as part of the NIH-funded PROSPER Peers research team examining adolescent social networks. His grant portfolio demonstrates consistent success in securing competitive funding for longitudinal network studies in high-impact settings including correctional facilities and school systems. His laboratory work focuses on developing specialized methodologies for collecting social network data in constrained environments like prisons, with particular emphasis on ethical considerations and longitudinal tracking during community re-entry. Kreager actively participates in interdisciplinary research teams bridging criminology, public health, and network science to address complex societal challenges related to incarceration and adolescent development.

Martin Gibbsمشاهده پروفایل

استاد

• Historical Archaeology
• Maritime Archaeology
• Pacific Archaeology
• +۴ مورد دیگر

Martin Gibbs is Professor of Australian Archaeology within the School of Humanities, Arts, and Social Sciences at the University of New England. Appointed in 2014, he has previously taught at the University of Western Australia, James Cook University and University of Sydney. His academic journey began with a BSc (Hons) from the University of Western Australia, followed by a PhD from the same institution in 2006. Gibbs' research focuses on historical and maritime archaeologies of the Australia-Pacific region, with particular emphasis on convict systems, Pacific archaeology, and maritime heritage. His work bridges academic research with public engagement through digital platforms like the Convict Landscapes interactive webmap (https://convictlandscapes.com.au/). Current major projects include 'Landscapes of Production and Punishment' exploring industrial aspects of the convict system, and research on WWII sites in the Solomon Islands conducted in collaboration with the Solomon Islands National Museum. His publication record demonstrates consistent scholarly output, with recent articles examining convict labor systems, maritime industries, and Pacific archaeology. The research trends show a growing integration of digital humanities approaches with traditional archaeological methods, particularly in mapping convict landscapes and life courses. His work increasingly incorporates interdisciplinary perspectives, bringing together archaeology, history, and digital technologies. Society of Antiquaries (London) membership Gibbs has supervised numerous PhD students to completion, with research topics spanning convict archaeology, maritime heritage, Pacific archaeology, and WWII sites. His supervision record demonstrates a commitment to developing the next generation of archaeologists. Current research includes ARC-funded projects examining convict labor management and industrial aspects of penal systems. He co-founded the Archaeology of Sydney Research Group and established NSW Archaeology Online, a digital archive preserving 40 years of archaeological reports. Gibbs maintains active research laboratories and field projects, particularly through the 'Landscapes of Production and Punishment' project which involves collaboration between UNE, UTas and the Port Arthur Historic Site Management Authority. His work with the Solomon Islands National Museum represents significant international engagement and capacity building in Pacific archaeology.

Ivan Bogicevicمشاهده پروفایل

پژوهشگر ارشد

• Software Engineering
• Agile Methodologies
• Documentation
• +۳ مورد دیگر

Ivan Bogicevic is a Research Fellow at the Department of Software Engineering Group, University of Stuttgart. His work bridges academic research and practical application in software development methodologies, focusing on agile practices and architectural documentation.

Manos Papagelisمشاهده پروفایل

دانشیار

• Data Mining
• Graph Mining
• Machine Learning
• +۳ مورد دیگر

Dr. Manos Papagelis is an Associate Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering. He serves as the Graduate Program Director for the MSc and MScAI programs. His research focuses on data science and machine learning, particularly in data mining, graph mining, big data analytics, mobility analytics, and knowledge discovery. Education: PhD in Computer Science (University of Toronto), MSc and BSc in Computer Science (University of Crete, Greece). Prior to York, he held postdoctoral and research roles at UC Berkeley, Yahoo! Labs Barcelona, and FORTH, Greece. Research emphasizes trajectory analysis, emotion recognition, and scalable systems. Recent work includes TrajLearn (2025) and Disease Outbreak Detection (2025), highlighting contributions to mobility and health informatics. He has filed three U.S. patents and designed systems like Confious (conference management) and Green2.0 (socio-technical building analysis). Honors include the Lassonde Educator of the Year (2021) and IEEE MDM Best Paper Awards (2020, 2018). His advising spans interdisciplinary teams in AI and data science, with active involvement in grants related to mobility analytics and healthcare technologies. Labs/Teams: Director of the Data Mining Lab, collaborating on projects like trajectory prediction and emotion-aware systems.

Simonas Saltenisمشاهده پروفایل

دانشیار

• Database Systems
• Spatial Data Management
• Data Engineering
• +۳ مورد دیگر

Simonas Saltenis is an Associate Professor in the Department of Computer Science at Aalborg University, affiliated with The Technical Faculty of IT and Design. He is part of the Data Engineering, Science and Systems group and the Daisy Center for Data-intensive Systems. His research focuses on database systems, spatial data management, and data engineering with a strong emphasis on temporal and spatial query optimization. He has been involved in key projects such as TimeCenter (active since 2000) and "Effective Shedding and Efficient Processing of Massive Sensor Update Loads" (2008–2011), both funded by Danish research councils. These projects explore database management systems, temporal data handling, and real-time sensor data processing. His research interests include indexing techniques for spatial and temporal data, points-of-interest retrieval, and probabilistic models for energy use prediction. He has also contributed to conference organization, serving as a general co-chair for MDM 2018. Saltenis has supervised at least one PhD student and has actively engaged in peer review for top journals including the VLDB Journal, IEEE Transactions on Knowledge and Data Engineering, and ACM Transactions on Information Systems.

Tianyi Liمشاهده پروفایل

استادیار

• Digital Twin
• Graph Neural Networks
• Energy Engineering
• +۸ مورد دیگر

Tianyi Li is a Tenure Track Assistant Professor at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design and the Daisy - Center for Data-intensive Systems. His research focuses on data engineering, spatiotemporal systems, and digital twins, with applications in energy, transportation, and vehicular networks. Key areas include trajectory data management, graph neural networks, compression techniques, and federated learning. He has contributed to over 37 publications, including works on digital twin frameworks for autonomous driving and smart grids, trajectory prediction systems, and efficient time series compression. His research integrates machine learning, distributed systems, and cybersecurity to address challenges in data-intensive environments. Key Projects: Digital twin-enabled voltage control, federated vehicular networks, trajectory simplification frameworks. Labs: Involved with the Daisy Center, focusing on data-centric systems and scalable solutions. Technical Expertise: Blockchain systems, energy systems optimization, and real-time data processing.

• Animal Cognition
• Spatial Memory
• Temporal Memory
• +۳ مورد دیگر

Christina Thorpe is a Professor in the Department of Psychology at Memorial University of Newfoundland, within the Faculty of Science. She holds a B.Sc. (Hons.) from Memorial University, an M.A., and a Ph.D. from the University of British Columbia. Her research focuses on animal cognition, particularly spatial and temporal memory processes in rats, with a specific emphasis on time-place learning (TPL). She also investigates spatiotemporal abilities in children, comparing human and non-human animal cognition. Dr. Thorpe completed a post-doctoral fellowship at University College London under the NSERC fellowship. She actively mentors undergraduate and graduate students, offering hands-on research experience in experimental design, data collection, and analysis. She recruits graduate students interested in human and animal spatial/temporal cognition. Her research highlights how animals encode spatial and temporal information, emphasizing the neural mechanisms underlying memory organization. Recent studies explore the impact of brain lesions on spatial learning and the role of start-point orientation in navigation tasks. Dr. Thorpe collaborates widely, with publications in journals like Behavioral Neuroscience and Animal Cognition . Her work bridges animal behavior and developmental psychology, addressing questions about cognitive evolution and learning mechanisms.

Farah Chancharyمشاهده پروفایل

مدرس

• Computer Science
• Cloud Computing
• Data Structures
• +۷ مورد دیگر

Farah Chanchary is a Lecturer and Lab/Course Coordinator at the School of Computer Science, Carleton University. She specializes in areas such as data structures, cloud computing, and educational technologies. Her work spans algorithm design, privacy in advertising, and challenges in e-learning, particularly in Saudi Arabia. She coordinates laboratory and course activities, supporting academic infrastructure. Research Interests: - Data Structures and Algorithms - Cloud Computing and Migration - Privacy-Preserving Models in Advertising - E-Learning Readiness and Challenges - Machine Learning Applications (Neural Networks, Genetic Algorithms) - Geometric Query Processing Publications Trends: Her recent work focuses on efficient data structures for window queries, privacy concerns in online advertising, and cloud computing challenges. Earlier research includes studies on e-learning adoption in Saudi Arabia and neural network applications in rainfall forecasting. No awards or grants are listed. She has no documented advisees. She manages laboratory facilities and course coordination in the School of Computer Science.

Lin Liuمشاهده پروفایل

استاد

• Geographic Information Systems
• Crime Mapping
• Spatial Analysis
• +۲ مورد دیگر

Lin Liu is Professor and Graduate Director in the Department of Geography at the University of Cincinnati's College of Arts and Science. He serves as Co-Director of the Joint Center for GIS and Spatial Analysis, overseeing research initiatives and graduate education. His research spans spatial interaction modeling, crime pattern analysis, geographic visualization, and environmental applications of GIS. Dr. Liu has secured over $1.5 million in research funding from agencies including the NSF and EPA, supporting projects on urban crime patterns, land use classification, and environmental health impacts. Recent publications demonstrate methodological innovations in spatial analysis, including machine learning approaches to land cover classification and crime prediction. Articles frequently appear in journals such as Remote Sensing, ISPRS Journal, and Applied Geography. Best Service Award, International Association of Chinese Professionals in GIS (2010) Guest Professorship at Sun Yat-sen University (2006-2009) Dr. Liu has advised over 20 MA and PhD students and maintains international collaborations across Asia and Europe.

Neil Spencerمشاهده پروفایل

استادیار

• Network Analysis
• Bayesian Modeling
• Forensic Statistics
• +۲ مورد دیگر

Dr. Neil Spencer is an Assistant Professor in the Department of Statistics at the University of Connecticut. His research integrates Bayesian inference, network analysis, and computational statistics, with applications ranging from forensic science to neurological disorders. He earned a PhD in Statistics and Machine Learning from Carnegie Mellon University, MSc from University of British Columbia, and BScH from Acadia University. Research focuses on developing novel methods for network data analysis (latent position models, efficient MCMC), robust Bayesian inference, and forensic statistics. Publications demonstrate consistent innovation in computational techniques for complex data structures and interdisciplinary applications. Teaching includes STAT5410 (Statistical Computing) and STAT3345Q (Probability Models for Engineers). He co-advised PhD candidate Tolani Olarinre and participates in the New England Statistical Society's NextGen committee. Research publications emphasize methodological innovations in network modeling, Bayesian computation, and experimental design, with significant applications in neuroscience and forensic science.

• Cloud Computing
• Online Social Network Analysis
• Internet Computing
• +۲ مورد دیگر

Professor Marios Dikaiakos is a faculty member at the University of Cyprus Department of Computer Science, where he also served as Head of Department (2010-2014) and founded the Centre for Entrepreneurship (2014-2021). He holds a Ph.D. and M.A. in Computer Science from Princeton University and a Dipl.-Ing. in Electrical Engineering from the National Technical University of Athens. His research focuses on Cloud Computing systems performance and dependability, and Online Social Network analysis. Research Interests: Professor Dikaiakos leads the Laboratory for Internet Computing, directing numerous EU-funded projects. His work spans network-centric systems, cloud computing infrastructure, and social media analytics, with over 160 peer-reviewed publications. Recent work explores energy efficiency in edge computing and misinformation detection in polarized media environments. Publication Trends: His recent articles show a strong focus on sustainable computing, edge AI systems, and social media analysis, with emerging work on LLM ethics and industrial IoT. Methodologies include benchmarking frameworks, federated learning, and unsupervised NLP techniques applied to large-scale datasets. Collaborations & Labs: As Principal Investigator of the Laboratory for Internet Computing, he oversees multiple research projects and has held visiting positions at Rutgers University, Université de Paris Cité, and National Technical University of Athens. He serves as Associate Editor for Springer's Computing journal.

• Bayesian Statistics
• Biomedical Applications
• Healthcare Analytics
• +۳ مورد دیگر

Dr. Andrea Cremaschi is an Assistant Professor at IE University, specializing in Bayesian statistics with applications in biomedical research, public health, and data science. His academic journey includes roles at institutions such as the Singapore Institute for Clinical Sciences (SICS) and the National University of Singapore (NUS), where he contributed to interdisciplinary projects in biostatistics and clinical research. He holds a Ph.D. in Statistics from the University of Kent and degrees in Mathematical Engineering from Politecnico di Milano. His research focuses on developing novel Bayesian statistical methodologies for healthcare challenges, including drug sensitivity analysis in oncology, maternal and child health outcomes, and cost-effectiveness studies. He also explores applications in digital humanities and climate action, aligning with UN Sustainable Development Goals 3 (Good Health), 4 (Quality Education), and 13 (Climate Action). Key contributions include studies on postpartum diabetes screening, pediatric growth modeling, and ex vivo drug response analysis in leukemia. His work bridges computational statistics with real-world health challenges, emphasizing algorithmic innovation and interdisciplinary collaboration. Education: Ph.D. in Statistics (University of Kent), M.Sc./B.Sc. in Mathematical Engineering (Politecnico di Milano) Professional Affiliations: IE University, Singapore Institute for Clinical Sciences (SICS), National University of Singapore

Alexander Aksenovمشاهده پروفایل

استادیار

• Metabolomics
• Mass Spectrometry
• Molecular Diversity
• +۴ مورد دیگر

Alexander Aksenov serves as Assistant Professor in the Department of Chemistry at the University of Connecticut, Storrs Campus, specializing in metabolomics and molecular diversity research. His work centers on advancing mass spectrometry techniques and data analysis methods to explore the molecular 'dark matter' within living systems. His academic foundation includes a Ph.D. from the University of Florida (2005), a Postdoctoral Fellowship at Carnegie Mellon University (2006-2008), and a B.S. from Moscow State University (1999). This training established his expertise in analytical chemistry and biological systems. Aksenov's research investigates metabolic processes through 'global' metabolomics approaches, studying living systems in situ to capture native complexity. He develops advanced mass spectrometry workflows and computational tools to extract chemical knowledge from complex biological samples, with particular focus on gut-bone axis interactions, plant-derived compounds like anthocyanins, and microbial metabolite networks. His recent publications demonstrate strong interdisciplinary integration of chemistry, biology, and data science. Analysis of his 2025 publications reveals dominant research themes: metabolomics methodology development (including AI-driven data analysis), gut microbiome-bone health interactions, plant-pathogen chemical ecology (particularly citrus-Huanglongbing systems), and nutritional impacts on metabolic profiles. His work consistently bridges fundamental molecular discovery with translational applications in human health and agricultural science. Contact: Email aaksenov@uconn.edu , Phone (860) 486-6722, Office CHEM A-421, Department of Chemistry, University of Connecticut, 55 N. Eagleville Rd, Storrs, CT 06269.

Zhe Shandianمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning (Theory and Modeling)
• Physics-informed Machine Learning
• +۷ مورد دیگر

Zhe Shandian is an Associate Professor at the Kahlert School of Computing, University of Utah. His research focuses on advancing machine learning methodologies, particularly in physics-informed machine learning, operator learning, and Bayesian methods. He explores applications in differential equations, multi-omics data, and engineering simulations. His work emphasizes scalable algorithms and data efficiency in complex systems. Education details are not explicitly provided in the text, but his professional trajectory aligns with a strong computational and mathematical background. Research interests include developing novel frameworks for neural operators, symbolic regression, and functional Bayesian decomposition. Key research trends from his articles highlight advancements in physics-guided AI models, multi-fidelity simulation, and scalable kernel-based methods. His contributions span domains like material science, cancer prediction, and traffic flow modeling. No scientific awards are mentioned in the provided text. Zhe Shandian has not listed any advisees, and specific grants or labs are not detailed here. His work often involves collaboration across disciplines, leveraging both theoretical and applied machine learning techniques.

Markus Ådahlمشاهده پروفایل

دانشیار

• Spatial Statistics
• Ambulance Call Forecasting
• Prehospital Care Optimization
• +۴ مورد دیگر

Markus Ådahl serves as Associate Professor in the Department of Mathematics and Mathematical Statistics at Umeå University, Sweden. His research focuses on developing advanced statistical methodologies for emergency medical services optimization, with primary office location at MIT-huset, plan 3, Matematik och matematisk statistik, MIT.B.349, Umeå. His work specializes in spatial statistics and machine learning applications for prehospital care systems. Key research areas include spatio-temporal modeling of ambulance demand, composite likelihood intensity estimation, and large-scale forecasting of emergency calls using log-Gaussian Cox processes. This research directly addresses operational challenges in northern Sweden's emergency response infrastructure through data-driven approaches. Recent publications demonstrate consistent focus on ambulance call pattern analysis, with methodological innovations in regularized semi-parametric modeling (2023) and large-scale spatio-temporal forecasting (2020). These studies establish critical frameworks for optimizing ambulance resource allocation in geographically complex regions. Ådahl currently leads the long-term research initiative Data-driven optimization of prehospital care via statistics and machine learning (2018-2028), which integrates statistical modeling with machine learning to enhance emergency medical service efficiency. While no formal advisees are listed in available materials, this project coordinates cross-disciplinary collaboration between statisticians, healthcare providers, and emergency response authorities.