جست‌وجوی استاد و پژوهشگر

• Semantic Modeling
• Geographic Information Systems
• Spatio-temporal Databases
• +۳ مورد دیگر

Stefano Spaccapietra is a professor at the Swiss Federal Institute of Technology in Lausanne (EPFL) specializing in semantic modeling, geographic information systems, and spatio-temporal databases. A recipient of the 2012 Peter P. Chen Award, his research focuses on foundational aspects of data representation and knowledge engineering. His research explores: Semantic modeling for geospatial systems Multi-representation databases Trajectory data analysis and mobility patterns Ontology design for spatial information Temporal evolution in object-oriented systems Publication analysis reveals strong emphasis on: Fundamental theories of spatial and temporal representation Integration frameworks for heterogeneous systems Evolution of conceptual modeling approaches Spatio-temporal query processing techniques Awards & Honors: Peter P. Chen Award (2012) His work has significantly contributed to database integration methodologies and established foundations for modern geospatial data management systems through both theoretical frameworks and practical implementations.

• Complex Geometry
• Kähler Geometry
• Stability of Algebraic Varieties
• +۵ مورد دیگر

Chi Li is an Associate Professor in the Department of Mathematics at Rutgers, The State University of New Jersey. Their research focuses on Complex Geometry, including canonical metrics in Kähler geometry, stability of algebraic varieties, and pluripotential theory. They hold an office in HLL-511 and can be contacted at cl1412@rutgers.edu. Research interests emphasize foundational aspects of geometric analysis with applications to algebraic structures. Recent work explores interdisciplinary applications in AI, such as anomaly detection, cross-lingual NLP, and large language model optimization. Key contributions include frameworks for domain-oriented model compression and dynamic routing in mixed LLMs. Publications span 2020–2025, addressing topics from turbulence forecasting to spatio-temporal recommendation systems. Notable trends include integration of geometric principles with machine learning methodologies. Extensive contributions to log anomaly detection (GLAD, Cat), time series meta-contrastive learning, and graph-based cross-lingual transfer. Active in developing interpretable imitation learning and uncertainty quantification techniques for in-context learning.

Hanna-Mari Kupariمشاهده پروفایل

پژوهشگر

• Medieval Latin
• Corpus Linguistics
• Digital Humanities
• +۳ مورد دیگر

Hanna-Mari Kupari is a doctoral researcher in the Department of Digital Linguistics at the University of Turku, Finland. Her research focuses on medieval Latin using computational methods, particularly in the context of Vatican Penitentiary Documents. She is funded by the Emil Aaltonen Foundation and is an active member of the TurkuNLP and TUCEMEMS research groups. Her educational background includes: Master of Arts (Classical Philology, specialization in Latin) from University of Turku Kupari's research integrates digital humanities with medieval Latin studies, employing corpus linguistics and machine learning. She specializes in morpho-syntactic parsing of historical texts and advocates for open science, publishing her code and data openly. Her work addresses linguistic variation in medieval Latin documents, particularly apostolic penitentiary records. Her recent publications demonstrate a strong trend in applying NLP to historical languages, with a focus on Latin dependency parsing and corpus construction. She also actively contributes to science communication through blog posts on open science and academic life. Kupari has secured multiple research grants, including: Emil Aaltonen Foundation grant (2022–2024) Kordelin Foundation full-time working grant (2024) Otto A. Malm mobility grant (2024) Various travel grants from Turku University Foundation and others She is a member of the TurkuNLP research group, which focuses on natural language processing, and TUCEMEMS, a center for medieval and early modern studies. Her work involves international collaboration, with research visits to Harvard, the American Academy in Rome, and upcoming presentations in Zurich.

Jessica Kissingerمشاهده پروفایل

استاد پژوهشی

• Parasite Genomics
• Bioinformatics
• Evolutionary Genetics
• +۳ مورد دیگر

Jessica Kissinger is a Distinguished Research Professor in the Department of Genetics at the University of Georgia's Franklin College of Arts and Sciences. Her research focuses on parasite genomics, bioinformatics, and host-pathogen interactions, with emphasis on apicomplexan pathogens including Toxoplasma , Cryptosporidium , and malaria-causing Plasmodium species. Education: • A.B. cum laude, University of Chicago (1989) • Ph.D. in Molecular, Cellular & Developmental Biology, Indiana University (1995) • Postdoctoral training at NIH/NIAID, FIOCRUZ Brazil, and University of Pennsylvania Research Focus: Dr. Kissinger leads projects in comparative genomics, data integration, and systems biology of eukaryotic pathogens. Her lab develops computational tools for analyzing parasite genome evolution and host-pathogen dynamics, supported by NIH funding including the VEuPathDB resource center. Awards and Honors: University Professor, UGA (2025) Lamar Dodd Creative Research Award (2024) Fellow, AAAS (2021) Fulbright Scholar (2022) Distinguished Research Professor (2017) Current Projects: Her group combines wet-lab and computational approaches to study organellar genome evolution, gene regulation in Cryptosporidium , and multi-omics analysis of host-parasite systems through active NIH grants (R21AI180871, R01AI148667). She advises graduate students and collaborates internationally on parasitic disease research.

Yi Hongمشاهده پروفایل

استادیار مدعو

• Medical Image Analysis
• Computer Vision
• Statistical Shape Analysis
• +۳ مورد دیگر

Yi Hong is an Adjunct Assistant Professor in the Department of Computer Science at the University of Georgia, Franklin College of Arts & Sciences. Her research focuses on medical image analysis, statistical shape analysis, computer vision, and interdisciplinary applications. She has led grants including an NSF-funded project on Human-Robot Collaboration and another on Image Metamorphosis Analysis. She holds a PhD from the University of North Carolina at Chapel Hill (2016), MSc from the Chinese Academy of Sciences (2011), and BSc from Wuhan University (2007). Research Interests: Medical Imaging, Computer Vision, Statistical Shape Analysis, Deep Learning, Biomedical Engineering Funding: NSF Grant: NRI: FND (2018-2021) NSF Grant: CRII: SCH (2018-2020) Smart Cyber-Physical Systems for Controlled-Environment Agriculture (2017-2018) Awards: NSF Early-Career Research Grants (2018) Her work bridges computational methodologies with clinical applications, including pediatric airway analysis, neurodegenerative disease diagnostics, and image registration techniques. She collaborates across disciplines to advance medical imaging technologies.

• Forest Genetics
• Reproductive Dynamics
• Gene Flow
• +۴ مورد دیگر

Camilla Avanzi is a Researcher III at the Institute of Biosciences and BioResources (IBBR-CNR) in Florence, Italy. Her work focuses on forest genetics, reproductive dynamics, and conservation of marginal tree populations. She holds a Ph.D. in Evolutionary Biology and Ecology (2019) from the University of Ferrara and a Master’s in Ecology and Conservation (2015) from the University of Parma. Her research integrates genetic data with dendrochronology to study tree resilience under environmental pressures. Key projects include EU-funded initiatives like B4EST and FORGENIUS, focusing on forest genetic resources and climate adaptation. Technical expertise spans field sampling (tree coring, environmental characterization), lab techniques (DNA extraction, PCR), and computational tools (R, genetic analysis software). Publications emphasize population genetics, gene flow dynamics, and conservation strategies for conifers like Picea and Abies species. She actively participates in national/international projects addressing forest sustainability and biodiversity preservation.

Cresten Mansfeldtمشاهده پروفایل

استادیار

• Environmental Engineering
• Environmental Microbiology
• Bioinformatics
• +۷ مورد دیگر

Cresten Mansfeldt is an Assistant Professor in Environmental Engineering at the University of Colorado Boulder, affiliated with the Civil, Environmental, and Architectural Engineering Department. He holds a B.C.E. from the University of Minnesota (2007) and a Ph.D. from Cornell University (2013). Previously, he worked as a Research Scientist at Eawag (Swiss Federal Institute of Aquatic Science and Technology). His research focuses on microbial processes interacting with human-engineered systems, including wastewater treatment, riverine ecosystems, and bioremediation. Key interests include microbial community dynamics, contaminant biotransformation, and reactor design for resource recovery. Mansfeldt’s lab actively recruits students at all levels, emphasizing interdisciplinary approaches combining computational modeling, molecular microbiology, and environmental analytics. Research highlights include developing frameworks for wastewater-based public health surveillance, analyzing wildfire impacts on water quality, and modeling microbial community responses to environmental perturbations. Awards include the 2025 '40 Under 40' recognition from the American Academy of Environmental Engineers and Scientists, and a 2008 NSF Graduate Research Fellowship. Recent work addresses ethical considerations in wastewater sampling and integrates ecological risk assessment with synthetic biology. His publications span topics like microbial enzyme biomarkers, micropollutant fate in activated sludge systems, and lipid-accumulating algae for biofuel production. Current projects aim to optimize wastewater treatment for trace organic contaminant removal while enhancing microbial community resilience.

• Organizational Technology Adoption
• Sentiment Analysis
• Knowledge Graphs
• +۲ مورد دیگر

Eu-Gene Siew is an Associate Professor at Panteion University, specializing in organizational technology adoption, knowledge graphs, and computational analysis methods. His research examines factors influencing technology implementation in professional settings, particularly audit systems and information extraction techniques. Research interests span organizational adoption of computer-assisted audit tools (CAATTs), sentiment analysis methods, knowledge graph construction, and event extraction from textual data. Recent work includes developing annotated corpora for commodity news analysis and exploring literature-based discovery approaches. Publications demonstrate consistent focus on knowledge extraction methodologies, including a 2020 study examining CAATTs adoption in Malaysian audit firms, and 2022 research on crude oil news event extraction. Earlier work explored bibliographic information networks and literature-based discovery techniques. Teaching includes postgraduate courses such as 'Violence and Politics' and undergraduate courses in political science and history methodologies.

• Data Science
• Machine Learning
• Distributed Computing
• +۴ مورد دیگر

Chris Mattmann is an academic affiliated with the University of Southern California, specializing in data science, machine learning, and high-performance computing. His work spans interdisciplinary applications in Earth sciences, climate modeling, and distributed systems. He has contributed to projects like the Earth System Grid Federation and Apache open-source software. His research emphasizes scalable data management, secure parser development, and automated machine learning systems. Education & Background: While specific educational details aren't listed, his extensive publication record in top-tier journals and conferences indicates advanced training in computer science and computational science. Research Interests: Focus areas include distributed computing infrastructure for scientific applications, machine learning for geosciences, and data-intensive systems. He explores challenges in data movement, scalable supercomputing, and open-source tools for scientific workflows. Publications: Over 150 peer-reviewed articles since 2003, with recent work on super-resolution satellite data, deep learning in geosciences, and secure parser systems. His contributions highlight advancements in both foundational computing and applied environmental science.

• Human-Computer Interaction
• Affective Computing
• Multimedia Systems
• +۲ مورد دیگر

Andrej Kosir is a researcher at the University of Ljubljana's Faculty of Electrical Engineering, specializing in multimedia systems and human-computer interaction. His core research examines physiological signal analysis for affective computing applications, including advertising engagement measurement and cognitive load prediction in automotive interfaces. Research Focus: His work spans multimedia ad exposure modeling, physiological computing, and recommender systems. Recent studies analyze biometric responses to advertising content and develop metrics for evaluating user engagement through physiological signals like heart rate and pupillary response. Publication Trends: Kosir's 15 most recent publications (2010-2025) demonstrate consistent focus on affective computing applications. Earlier work centered on recommender systems and affective labeling, while recent publications emphasize physiological signal processing in automotive and advertising contexts. Methodologically, his work combines machine learning with psychophysiological measurement techniques.

• Computational Pathology
• Artificial Intelligence in Medical Imaging
• Deep Learning
• +۲ مورد دیگر

B. Dislich is affiliated with the German Cancer Research Center (DKFZ), contributing to cutting-edge research in computational pathology and artificial intelligence applications for cancer diagnostics. As a Researcher, Dislich participates in developing deep learning methodologies for histopathological slide analysis. Dislich's research focuses on weakly-supervised learning pipelines for whole slide image classification, addressing critical challenges in computational pathology where ground truth annotations exist at the slide level rather than individual tiles. This work bridges AI innovation with clinical pathology needs, particularly in biomarker detection and cancer subtyping. The publications reveal a strong emphasis on methodological rigor in medical AI validation , with research spanning neural network architectures optimized for histopathological data. Dislich's contributions support the broader DKFZ mission of translating computational advances into clinical diagnostic tools. While specific advising roles or grants aren't documented in the available records, the collaborative nature of the publications (with multi-institutional co-authors including Loeffler, Muti, and Ghaffari Laleh) indicates participation in DKFZ's interdisciplinary cancer research ecosystem.

Efstathios Christodoulidesمشاهده پروفایل

استادیار

• Physical Literacy
• Physical Education
• Sport and Digital Technologies
• +۳ مورد دیگر

Dr. Efstathios Christodoulides is an Assistant Professor in Sport and Exercise Science at the University of Central Lancashire Cyprus (UCLan Cyprus), serving as Course Leader for the BSc (Hons) Sport and Exercise Science program. He holds a PhD from Semmelweis University (Budapest), an M.Ed. from the University of Physical Education (Budapest), and specialized coaching certifications. His research focuses on physical literacy, sport sociology, and the integration of digital technologies in physical education. He has led projects funded by the Erasmus+ program and the UNESCO Chair on Governance & Social Responsibility in Sport. Education: Ph.D. (Sport Sciences), M.Ed. (Physical Education), Coaching Certifications (Swimming/Recreation), and a Fellow of the Higher Education Academy. Research Interests: Physical literacy's role in socialization, sport's societal impact, digital tools in physical education, and physical activity's influence on mental health. He has published extensively on topics like adolescent mental health during the pandemic, fair-play promotion in sports, and physical literacy implementation across Europe. Awards: UNESCO Co-Chair, IPLA Country Lead (Cyprus), Fulbright Scholar, and Fellow of Advance HE. He has collaborated with institutions in Europe and North America through international research networks and Erasmus+ projects. Grants: Led €400,000 Erasmus+ project 'ePhyLi' (2022–2025), 'PAMeCy' (2022), and 'EUROPLIT' (2022). His work spans 15+ international publications and multiple conference presentations on global sport policy and education.

Milosh Jovanovikjمشاهده پروفایل

دانشیار

• Knowledge Graphs
• Data Engineering
• Semantic Web
• +۸ مورد دیگر

Dr. Milosh Jovanovikj holds the role of Associate Professor at the Faculty of Computer Science and Engineering in Skopje, North Macedonia, and concurrently serves as a PostDoc Researcher at TU Wien in Vienna, Austria. He is also a Senior R&D Knowledge Graphs Engineer at OpenLink Software in London. His primary research focuses on Knowledge Graphs (KGs), including their lifecycle processes such as modeling, enrichment, and analytics-driven AI applications. He has contributed to over 10 international and 25 national research projects, authored 60+ publications, and co-authored 3 books. His expertise spans Data Engineering, Semantic Web technologies, Linked Data, and Applied AI. Significant contributions include developing frameworks like RDFGraphGen for RDF graph generation and PharmKE for pharmaceutical text analysis. He has led initiatives in geospatial semantic web standards (GeoSPARQL) and healthcare informatics, including predictive analytics for nephrology data. Education: PhD in Computer Science and Engineering His work bridges academia and industry, with projects like the 3DFed federation engine and Semantic Sky (a Gmail plugin). He has extensive experience in ontology development (e.g., SEOntology, VEO for CO2 emissions) and open data initiatives, including LinkedDrugs for global drug data consolidation. Active in dissemination, his articles span knowledge graph applications in music recommendation, finance sentiment analysis, and drug-disease relation discovery. He has collaborated on benchmarks like the GeoSPARQL Compliance Benchmark and Mighty Storage Challenge (MOCHA). Grants/Projects: HOBBIT, SAGE, 3DFed, LinkedDrugs He leads teams in advancing semantic web integration, cloud service platforms (Semantic Sky), and educational materials like Web Programming Basics . His labs and teams focus on AI-driven solutions for healthcare, geospatial analytics, and enterprise systems.

• Computational Biology
• Structural Biology
• Protein Structure Prediction
• +۳ مورد دیگر

Andrej Sali, PhD, is a Professor of Bioengineering at the University of California, San Francisco (UCSF), affiliated with the School of Pharmacy. His research focuses on computational methods to study protein structure and function, including predicting protein structures, analyzing macromolecular assemblies, and annotating protein functions using structural data. This work bridges genomics and medicine, enhancing the impact of structural and functional genomics. Research Interests: Dr. Sali integrates physics-based computational models and evolutionary principles to address fundamental questions in structural biology. His lab develops methods for protein structure prediction, structural genomics, and modeling dynamic biological processes. Key areas include nuclear pore complex assembly, molecular mechanisms of transporters, and integrative modeling platforms like the Integrative Modeling Platform (IMP). Grants & Funding: He has led/co-investigated numerous NIH-funded projects, including: NIH S10OD021596 (2016-2017) for high-performance computing infrastructure NIH P01GM111126 (2015-2020) on transmembrane transporter conformational cycles NIH P41RR001081 (1976-2012) for Biocomputing resources Awards & Honors: Recognized with prestigious awards such as the Zois Award (2000-2003), Irma T. Hirschl Career Award (1998-2000), and election to the National Academy of Sciences (2018). His work has been supported by grants from the Jane Coffin Childs Memorial Fund and Merck Sharp & Dohm. Labs & Teams: Leads the Sali Lab at UCSF, collaborating on cutting-edge projects such as nuclear pore complex dynamics, drug resistance mechanisms, and structural modeling of biomolecular systems.

• Epidemiology
• Cancer Research
• Infectious Diseases
• +۳ مورد دیگر

Eric Kawaguchi is an Assistant Professor of Clinical Population and Public Health Sciences at the Keck School of Medicine, University of Southern California. His research focuses on epidemiology, cancer research, infectious diseases, and public health, with emphasis on statistical modeling of disease risks. His research investigates gene-environment interactions in cancer development, epidemiological patterns of infectious diseases like COVID-19, and clinical outcomes in surgical oncology. He utilizes advanced statistical methods to analyze large-scale genomic and clinical datasets. Recent publications demonstrate diverse methodological approaches including survival analysis, hierarchical modeling, and genomic risk assessment applied to oncology, infectious diseases, and public health interventions.