جست‌وجوی استاد و پژوهشگر

Giovanni Colavizzaمشاهده پروفایل

استاد

• Open Science
• Scientometrics
• Digital Humanities
• +۴ مورد دیگر

Giovanni Colavizza holds a dual academic appointment as Professor in the Department of Communication at the University of Copenhagen and Associate Professor at the University of Bologna (since November 2023). His research bridges information science, digital humanities, and open science practices. His primary research areas include: Quantitative analysis of open research practices (data/code/preprint sharing) and citation impact Wikipedia's role in scientific knowledge dissemination and news source reliability assessment Digital tools for humanities including knowledge graphs and NLP applications for historical archives Recent publications (2024-2025) reveal strong interdisciplinary trends, combining scientometrics with natural language processing to analyze scientific communication patterns and colonial archival materials. His work appears in leading venues including PLoS ONE, Scientometrics, and IEEE Journal of Biomedical and Health Informatics, demonstrating methodological innovation across information science and humanities domains.

Gagan Agrawalمشاهده پروفایل

استاد

• Parallel Computing
• High-Performance Computing (HPC)
• Data Analytics
• +۵ مورد دیگر

Gagan Agrawal is the UGA Foundation Professorship in Computing and a Professor at the School of Computing, University of Georgia. He serves as Director of the School and is affiliated with the Franklin College of Arts & Sciences. Agrawal holds a PhD and MS in Computer Science from the University of Maryland (1994-1996). His research focuses on high-performance computing, parallel algorithms, compiler optimization for deep learning, GPU acceleration, and interdisciplinary applications in health informatics. Notable contributions include frameworks like ForensiBlock (blockchain for data forensics) and DELITE (tensorized instruction compilation). Agrawal has secured over $2.5M in NSF grants for projects addressing extreme-scale computing challenges. Recent grants include: SHF: Small: Memory Hierarchy Optimizations Meet Transformers (MITTEN) ($600K, 2024-2027) DELITE compilation system for deep learning models ($600K, 2023-2026) Publications span parallel computing methodologies, cybersecurity frameworks, and health outcomes analysis. His work on social determinants of health in cancer survival has been systematically reviewed in top-tier medical journals. Agrawal leads UGA's computing initiatives, emphasizing interdisciplinary research and student mentorship in HPC and AI domains.

Stefano Ferilliمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Data Mining
• +۶ مورد دیگر

Stefano Ferilli is a Professor of Computer Science at the University of Bari, Italy, where he leads the ARA (Apprendimento e Ragionamento Automatico) research lab within the Department of Computer Science. His academic roles include former Director of the Interdepartmental Center for Logic and Applications (CILA) and current head of the Artificial Intelligence & Intelligent Systems node in the CINI national laboratory. He holds a PhD in Computer Science and has been a key figure in advancing machine learning, logic programming, and digital library technologies. Education: Laurea (MSc equivalent) in Information Sciences (1996), Specialist Laurea in Computer Science (2003), and PhD in Computer Science (2001). His research focuses on foundational aspects of machine learning, multi-strategy reasoning, process mining, and applications in cultural heritage, bioinformatics, and smart environments. Research Contributions: Developed frameworks like INTHELEX (incremental theory learner), WoMan (process mining), and DoMInUS (document management). Over 370 publications, including a Springer monograph and multiple award-winning papers. Active in organizing conferences like ECML-PKDD, ICDM, and IRCDL, and serves on editorial boards of journals like Information Sciences . Projects: Led or participated in over 30 national and European projects, including EU-funded initiatives on digital libraries (COLLATE, DELOS) and AI applications. Collaborates with industries like Samsung and institutions like the Italian Police for traffic analysis and cultural heritage preservation. Awards: Recognized for outstanding peer review (MDPI, ECMLPKDD), best paper awards, and contributions to AI education and cultural heritage. Member of prestigious associations like AI*IA (Italian AI Society) and AICA (Italian Computing Society).

Arjun Mukherjeeمشاهده پروفایل

مدرس

• Bayesian Inference
• Data Mining
• Natural Language Processing
• +۳ مورد دیگر

Arjun Mukherjee is a Lecturer at the Department of Computer Science , University of Houston , where he teaches courses in Machine Learning , Data Mining , Natural Language Processing , and Data Structures . His research focuses on Bayesian Inference , Data Mining , Natural Language Processing , Sentiment Analysis , Opinion Spam , and Web Mining , with a strong emphasis on deception detection and social media analysis. His recent publications explore advanced techniques in LLM-generated content detection synthetic data applications cross-domain deception modeling temporal user behavior analysis , reflecting his commitment to addressing modern challenges in digital content authenticity and machine learning robustness. Dr. Mukherjee has developed educational materials for graduate-level courses, including a well-structured Machine Learning course (COSC 6342) covering probabilistic inference, supervised/unsupervised learning, and neural networks. He earned his Ph.D. from the University of Illinois at Chicago in 2014, with a thesis titled Probabilistic Models for Fine-Grained Opinion Mining: Algorithms and Applications .

Xiaoli Fernمشاهده پروفایل

دانشیار

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

Xiaoli Fern is an Associate Professor in the School of Electrical Engineering and Computer Science at Oregon State University. She holds a Ph.D. in Computer Engineering from Purdue University (2005) and dual degrees (B.S. and M.S.) in Automation and Computer Science from Shanghai Jiao Tong University (2000). Her research focuses on applied machine learning , graph learning , and explainability in AI systems , with applications in microbiome analysis , ecological monitoring , and human-computer interaction . Research Expertise: Unsupervised learning, clustering, correlation analysis, outlier detection, and scientific data mining. Collaborations: Active involvement in the IGERT Ecosystem Informatics program and interdisciplinary projects with ecologists, roboticists, and biologists. Awards: 2011 NSF CAREER Award for early-career excellence in research. Her recent work includes applying deep learning to microbiome data and developing interactive systems that bridge theory with real-world applications in biology and materials science. She mentors students across all academic levels and emphasizes the importance of collaborative, real-world problem-solving in her research lab.

Mo El-Hajمشاهده پروفایل

دانشیار

• Natural Language Processing
• Financial NLP
• Arabic NLP
• +۹ مورد دیگر

Dr. Mo El-Haj is a Reader (Associate Professor) in Natural Language Processing (NLP) at the College of Engineering & Computer Science, VinUniversity, Hanoi, Vietnam, and holds a visiting role at Lancaster University. He specializes in NLP with a focus on Financial NLP, Arabic NLP, and multilingual systems for under-resourced languages. Education: PhD in Computer Science (University of Essex, 2012), MSc in Information Systems (University of Jordan, 2008), BSc in Computer Information Systems (University of Jordan, 2005). Awards include the FHEA Fellowship (2021) and the 2016 BBC NewsHack Best Tool award. Research interests include text summarization, financial narrative processing, biomedical NLP, and corpus linguistics. He leads the VinNLP research group and has supervised/co-supervised over 20 PhD students. Notable projects include the Welsh Automatic Text Summarisation tool (ACC) and the FreeTxt bilingual analysis toolkit, funded by the Welsh Government and AHRC. Publications span 92+ works in top journals/conferences like Computational Linguistics and LREC. Active in organizing workshops (e.g., WACL-4, FinNLP) and has served as external/internal PhD examiner at UK universities.

Vladimir Vlassovمشاهده پروفایل

استاد

• Machine Learning
• Data-intensive computing
• Cloud computing
• +۷ مورد دیگر

Dr. Vladimir Vlassov is a full Professor in Computer Systems at the Division of Software and Computer Systems (SCS) , Department of Computer Science (CS) , School of Electrical Engineering and Computer Science (EECS) , KTH Royal Institute of Technology , Stockholm, Sweden. He leads the AVA project in ALEC2, an AI-powered system for mental health care. He is a member of the Distributed Computing research group (DC@KTH) . Education & Roles: Holds a PhD and is a member of ACM and IEEE. Previously visited MIT (1998) and UMass Amherst (2004). Teaches courses on Data Mining , Distributed Systems , and Concurrent Programming . Research Interests: Focus on scalable AI, Cloud computing, distributed systems, and NLP for mental health. Projects include ExtremeEarth (Copernicus data analytics) and EMJD-DC (distributed computing PhD program). Grants & Projects: Principal Investigator in ALEC2 (adaptive mental health care) and ExtremeEarth (EU H2020). Led EU projects like ENCORE (manycore systems) and PaPP (embedded systems). Labs & Teams: Directs the Distributed Computing group, contributing to Hopsworks (machine learning feature store) and Maggy (hyperparameter optimization).

Isak Samstenمشاهده پروفایل

مدرس ارشد

• Data Science
• Machine Learning
• Time Series Analysis
• +۳ مورد دیگر

Isak Samsten is a Senior Lecturer at Stockholm University's Department of Computer and Systems Sciences (DSV), specializing in data science and machine learning. He leads research in temporal machine learning, counterfactual explanations, and interdisciplinary applications in healthcare and environmental science. His work includes developing the wildboar Python module for time series analysis. Current research projects focus on AI for insurance fraud detection and environmental remediation. Samsten is affiliated with the Data Science Research Group, which bridges algorithmic innovation with practical decision-making. He holds an ORCID identifier (0000-0002-3056-6801) and is active in publishing influential papers on topics like time series classification, ESG performance prediction, and clinical decision support systems. Education: Unspecified in text (assumed doctoral degree given academic rank) Affiliations: DSV, Stockholm University; Data Science Research Group Research Interests: Time series analysis, interpretable machine learning, healthcare informatics, environmental sustainability metrics, and AI ethics. Key contributions include shapelet-based classification methods (e.g., Castor algorithm) and counterfactual explanation frameworks (e.g., Glacier system). Grants & Awards: None explicitly listed in provided text. Labs/Teams: Leads the Data Science Research Group, collaborating on projects like AI to detect unclear insurance claims and Toxicity guided inverse design of materials .

Moses Kansangaمشاهده پروفایل

استادیار

• Agriculture and Sustainable Food Systems
• Political Ecology
• Natural Resource Management
• +۳ مورد دیگر

Moses Kansanga is an Assistant Professor in the Department of Geography at George Washington University, holding a PhD from the University of Western Ontario. His research focuses on agriculture and sustainable food systems, political ecology, natural resource management, environment and health, and Sub-Saharan Africa. He teaches GEOG 3195/6293: Agriculture and Sustainable Food Systems. Research Interests: Prof. Kansanga explores how climate change, policy frameworks, and socio-cultural dynamics intersect with food security, natural resource governance, and gender equity in African contexts. His work emphasizes community-led solutions, agroecological practices, and the political dimensions of environmental conservation. Recent studies investigate smallholder farmers' adaptation strategies, the impact of mechanization on gender roles, and the role of social capital in climate resilience. Key Contributions: His research spans Ghana, Malawi, and Canada, addressing topics like charcoal trade governance, migrant women's food insecurity, and solar-powered food preservation technologies. He critiques conventional conservation models and advocates for participatory approaches to agricultural policy. Affiliations: Full-time faculty member at GWU's Department of Geography. Engaged in interdisciplinary collaborations on climate resilience and food systems. Grants & Projects: No specific grants mentioned in provided texts. Active in publishing peer-reviewed articles focusing on Sub-Saharan Africa's environmental and socio-economic challenges.

Manuel Moralesمشاهده پروفایل

دانشیار

• Financial Mathematics
• Actuarial Science
• Machine Learning Applications in Finance
• +۵ مورد دیگر

Manuel Morales is an Associate Professor in the Department of Mathematics and Statistics at the University of Montreal since 2005. He holds a Ph.D. in Mathematics (2003) from Concordia University, an M.Sc. in Statistics (2000) from Concordia, and a B.Sc. in Mathematics (1996) from the National Autonomous University of Mexico. His research focuses on Financial and Actuarial Mathematics, particularly in Ruin Theory, Lévy processes, and High-Frequency Finance. He leads applied projects integrating Machine Learning in Banking and ESG Investment, and has pioneered AI governance frameworks at the National Bank of Canada as their Chief AI Scientist. Education: Ph.D. Mathematics, Concordia University, 2003 M.Sc. Statistics, Concordia University, 2000 B.Sc. Mathematics, National Autonomous University of Mexico, 1996 Research Interests: His work spans theoretical and applied directions, including non-Gaussian option pricing, regime-switching models, Limit Order Book dynamics simulation, and AI applications in finance. He emphasizes responsible investment and ESG factors through alternative data analysis. Advising & Partnerships: Supervises Master’s/Ph.D. students in Insurance and Financial Mathematics. Leads the FinML Network (since 2018) and collaborates with industry partners like the National Bank of Canada on AI-driven financial projects. His grants and contracts enable applied research in high-frequency market surveillance and model governance. Labs & Teams: Directs the FinML Network and oversees the National Bank’s AI initiatives, focusing on AI governance and algorithmic trading strategies.

• Database Systems
• Healthcare Information Systems
• Artificial Intelligence
• +۵ مورد دیگر

Carlo Combi is a Full Professor at the Department of Computer Science, University of Verona. He serves as Department Director and holds roles in various academic committees, including the Faculty Board of PhD in Computer Science and the Computer Science Teaching Committee. His research focuses on databases, healthcare information systems, temporal data mining, and business process management. He leads the STARS laboratory and has contributed to projects like PREPARE (prostate cancer evaluation) and EDIPO (neuroimaging genetics). His teaching spans courses on biomedical databases, software engineering, and Python programming. Combi's work integrates ethical considerations into process models and addresses challenges in healthcare workflows, temporal analytics, and AI applications in medicine. Education: Not explicitly listed in provided texts. Research Projects: Includes 30+ projects since 2008, emphasizing healthcare informatics, temporal systems, and data warehousing. Labs/Teams: STARS (Semistructured Temporal clinical Geographical Systems) and collaborations with CBMC (Computational Biomedicine Center). Grants: Multiple national/international grants, including funding for temporal data mining and healthcare process modeling. Awards: None explicitly mentioned, though his leadership roles and extensive publications highlight academic recognition. His research bridges clinical processes, temporal databases, and AI, with applications in acute kidney injury prediction, BPMN extensions for healthcare, and explainable AI in medical systems. Combi actively contributes to open-source solutions for clinical data analysis and integrates ethical frameworks into process management.

Hanan Sametمشاهده پروفایل

استاد

• Spatial Data Structures
• Geographic Information Systems (GIS)
• Computer Vision
• +۳ مورد دیگر

Hanan Samet is a Distinguished University Professor in the Computer Science Department at the University of Maryland, College Park. He holds affiliations with the Center for Automation Research and the Institute for Advanced Computer Studies (UMIACS). His academic journey includes a PhD from Stanford University (1975) in Computer Science, following degrees in Engineering (UCLA) and Operations Research/Computer Science (Stanford). Affiliations: University of Maryland, College Park (since 1975) Roles: Professor, Founding Editor-in-Chief of ACM Transactions on Spatial Algorithms and Systems, Founder of ACM SIGSPATIAL Samet's research focuses on spatial data structures, spatial databases, GIS, computer vision, and information retrieval. His seminal work includes the Foundations of Multidimensional and Metric Data Structures , an award-winning book addressing spatial indexing and query optimization. He pioneered frameworks like NewsStand for map-based news exploration and Coronaviz for pandemic visualization. Key contributions span spatial synonyms for approximate search, SAND spatial browser for digital government, and trajectory analysis systems for aviation safety and urban mobility. His work bridges theory and practice, influencing databases, graphics, and geographic systems. Education: B.S. Engineering, UCLA M.S. Operations Research, Stanford M.S./Ph.D. Computer Science, Stanford Samet has advised numerous students and led NSF-funded projects on spatio-textual data, similarity search, and spreadsheet analysis. His honors include the ACM Paris Kanellakis Award (2011), IEEE Wallace McDowell Award (2014), and UCGIS Research Award (2009). His labs and teams focus on spatial algorithms, visualization, and GIS applications. Notable projects include VASCO (spatial index demo), MARCO (image databases), and CHOLERA (disease tracking).

George Chenمشاهده پروفایل

استادیار

• Machine Learning
• Healthcare
• Information Systems
• +۳ مورد دیگر

George Chen is an Assistant Professor of Information Systems at Carnegie Mellon University's Heinz College and an affiliated faculty member of the Machine Learning Department. His research focuses on machine learning applications in healthcare and developing countries, particularly in time series analysis and forecasting. He holds a PhD in Electrical Engineering and Computer Science from MIT, where he received the George Sprowls Award for his thesis on nonparametric methods. He also advises the AgriTech startup CoolCrop, providing farmers in India with market forecasts and cold storage solutions. Education: PhD, Electrical Engineering and Computer Science, MIT (2015) SM, Electrical Engineering and Computer Science, MIT (2012) BS, Electrical Engineering and Computer Sciences & Engineering Mathematics and Statistics, UC Berkeley (2010) Research: Chen develops nonparametric machine learning methods for healthcare (e.g., predicting patient outcomes) and agricultural forecasting (e.g., crop pricing for farmers). His work emphasizes interpretable models and statistical guarantees. He recently authored a monograph on deep learning for survival analysis and co-organized the 2023 AAAI Survival Analysis Symposium. Awards: NSF Fellowship, NDSEG Fellowship, Siebel Scholarship (PhD) MIT Goodwin Medal (2015, top teaching award) George Sprowls Award for Best Computer Science Thesis (MIT) Teaching: Teaches courses on unstructured data analytics at CMU, including graduate-level machine learning and time series analysis. Previously taught at MIT and UC Berkeley, winning teaching awards at both institutions. Professional Service: Area chair for ICML, NeurIPS, and MLHC (2025). Active in organizing conferences and workshops related to survival analysis and healthcare ML. Labs/Teams: Leads research collaborations in healthcare analytics and developing-world technology through the Heinz College and CMU's Machine Learning Department.

Vincent Wadeمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۸ مورد دیگر

Professor Vincent Wade is a prominent academic and co-founder of the ADAPT SFI Research Centre, holding the Professorial Chair of Computer Science (established 1990) and a Personal Chair in Artificial Intelligence at Trinity College Dublin's School of Computer Science and Statistics. He co-directs the DREAL Centre for Research Training and leads ADAPT, a globally recognized centre for digital media technology and AI research. His work spans intelligent systems, personalisation, machine learning, and ethical AI applications in healthcare and education. Research interests include AI-driven personalisation, multimodal interaction, knowledge graphs, and ethical considerations in digital technologies. He has published over 350 peer-reviewed papers, earned the prestigious Provost Innovation Award (2018), and holds patents in personalisation technologies. He co-founded EmpowerTheUser, a TCD spin-out focused on simulation-based learning analytics. Major Achievements: 2018 Provost Innovation Award (Trinity College Dublin) 2010 European Language Label Award Fellow of Trinity College Dublin Over 350 scientific publications Key Contributions: Developed the ADELE corpus for social conversation analysis Pioneered cross-site personalisation frameworks Advanced adaptive e-learning systems through platforms like Slicepedia and AMASE His research bridges technical innovation with societal impact, addressing challenges in healthcare, education, and digital ethics.

Giorgio Querمشاهده پروفایل

استادیار

• Digital Medicine
• Artificial Intelligence in Healthcare
• Wearable Sensor Technology
• +۳ مورد دیگر

Giorgio Quer, PhD, is an Assistant Professor of Digital Medicine in the Department of Integrative Structural and Computational Biology at Scripps Research. He leads the Artificial Intelligence division for large-scale digital medicine studies, including the DETECT initiative tracking 40,000 participants using wearable devices. His work focuses on developing AI-driven methods for analyzing big health data from wearables like smartwatches and ECG patches. Education includes a PhD in Information Engineering from the University of Padova (2008–2011), a MicroMBA from UC San Diego's Rady School of Management (2018), and earlier degrees in Information Engineering and Telecommunications Engineering from Padova (2002–2007). He has held roles as a Research Scientist at Scripps Research Translational Institute (2017–present) and Postdoctoral Researcher at UC San Diego (2011–2016). Research interests span AI in healthcare, wearable sensor analytics, and public health surveillance. Notable contributions include predictive models for atrial fibrillation using ECG data, frameworks for AI transparency (MI-CLAIM/GEN checklists), and studies on vaccine reactogenicity monitoring through wearables. Awards : IEEE Distinguished Lecturer (2018, 2020, 2022), IEEE Senior Member (2016), and multiple academic fellowships. Labs/Teams : Directs AI initiatives in Scripps' digital medicine programs, coordinating interdisciplinary teams for large-scale health data analysis. Key projects include the DETECT study for real-time pandemic tracking and the PowerMom platform for maternal health monitoring. His work emphasizes ethical data use and advancing clinical AI standards.