جست‌وجوی استاد و پژوهشگر

Dipendra Yadavمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Artificial Intelligence
• Transfer Learning
• +۵ مورد دیگر

Dipendra Yadav is a scientific researcher and PhD student at the Institute of Data Science, University of Greifswald, Germany, working under Prof. Dr.-Ing Kristina Yordanova since January 2023. His research focuses on natural language processing with expertise in transfer learning, multilinguality, and domain adaptation for low-resource languages, alongside innovative work on symbolic reasoning for explainability in large language models. Yadav holds an M.Sc. in Electrical Engineering from the University of Rostock (2020). Prior to his current role, he worked as a data science student assistant at Market Logic Software in Berlin and completed an NLP internship/Master's thesis at PlanetAI GmbH, where he continued as a software engineer. His research spans critical NLP challenges including cross-lingual transfer for languages like Nepali and Hindi, domain adaptation in resource-constrained environments, and enhancing LLM explainability through symbolic reasoning integration. He also investigates AI safety dimensions such as dangerous capability evaluation and situational awareness in large language models. Recent publications reveal strong trends in low-resource language processing through cross-lingual transfer techniques, domain-specific NLP applications (particularly in dementia research), and safety evaluations of large language models. His work consistently addresses real-world data challenges while advancing methodologies for underrepresented languages. No scientific awards are currently documented. Yadav has not taken formal advising roles for students. His research operates within the Institute of Data Science's framework, which emphasizes interdisciplinary applications in healthcare and language technologies through collaborative projects like ARDUOUS.

• Machine Learning
• Sentiment Analysis
• Hybrid Filtering Algorithms
• +۵ مورد دیگر

Hayder Murad is a researcher with expertise in machine learning, sentiment analysis, and hybrid filtering algorithms, particularly applied to intelligent tutoring systems and medical diagnostics. His work bridges computational methods with practical applications in education and healthcare. He earned his PhD from the University of Portsmouth in 2019 with a thesis titled An integrated approach to recommending online video materials through sentiment analysis and hybrid filtering algorithms . His recent publications focus on knowledge graph construction, large language model applications, and open science initiatives. Notable contributions include enhancing systematic literature reviews, improving dataset interoperability, and advancing FAIR data principles across disciplines. Research Interests Machine learning for healthcare diagnostics Hybrid recommendation systems Knowledge graph development Open science infrastructure Intelligent tutoring systems LLM-based research synthesis

• Database Systems
• Data-Driven Applications
• Declarative Programming
• +۷ مورد دیگر

Nitin Gupta is a researcher with affiliations spanning institutions like Google, Cornell University, and Carnegie Mellon University. His work bridges theoretical and applied computer science, focusing on database systems, data-driven applications, and knowledge graphs. Research interests include: Structured data on the web and semantic publishing Scalability in virtual environments and declarative programming Integration of large language models (LLMs) into scholarly workflows FAIR data principles and research data management Recent publications explore hybrid question answering datasets, LLM applications for literature reviews, and knowledge graph construction for software engineering. His contributions emphasize machine-actionable metadata, open science, and enhancing reproducibility through standardized research artifacts. Collaborations span institutions and disciplines, with coauthorships on topics like: Game development and data coordination Non-invasive brain stimulation analysis Neuro-symbolic approaches for digital libraries Ontology matching and FAIR digital objects

• Uncertain Data Management
• MapReduce Algorithms
• Social Choice Theory
• +۵ مورد دیگر

Anish Das Sarma is a researcher affiliated with Google, USA , specializing in uncertain data management, MapReduce algorithms, and knowledge graph systems. He earned a PhD from Stanford University in 2010 under the supervision of Jennifer Widom and Alon Halevy, with a dissertation on "Managing Uncertain Data." His career spans collaborations with leading institutions, focusing on scalable data integration, social choice theory, and machine learning applications in scholarly knowledge organization. PhD in Computer Science, Stanford University (2010) Key collaborations: Stanford, Google Research, NFDI4DataScience His research interests intersect uncertain data modeling , MapReduce optimization , and large language model applications for scientific synthesis. Recent work includes FAIR data frameworks, ontology learning, and clinical entity linking. Article trends highlight his evolution from foundational database systems (2004-2015) to modern applications of LLMs in scholarly communication (2023-2024). Key areas: scalable algorithms, research data management, and ethical AI.

• Biomedical Ontologies
• Genomic Metadata Standards
• Disease Classification
• +۵ مورد دیگر

Dr. Lynn M. Schriml is a Professor at the University of Maryland School of Medicine, with primary appointments in the Department of Epidemiology and Public Health and the Institute for Genome Sciences. Her research develops bioinformatic tools, metadata standards, and ontologies to understand relationships between pathogens, genomics, and disease. She leads the Disease Ontology project and Built Environment MIxS-BE Metadata initiative, supported by the Alfred P. Sloan Foundation. Education: Bachelor of Arts in Biology, Wells College (1989) PhD in Biology, University of Ottawa (1997) IRTA/CRTA Postdoctoral Fellowship, National Cancer Institute (1996-1999) Her research integrates human disease classification, microbiome analysis, and genomic metadata standardization. She coordinates international consortia including the Genomic Standards Consortium (where she served as President) and MetaSUB, examining urban microbiomes. Key focus areas include ontological representation of cancer, rare diseases, and metadata frameworks for large-scale genomic databases. Publications emphasize genomic standards, disease ontology development, and microbiome research, with recent work advancing FAIR data principles, reproducibility frameworks, and cross-domain interoperability. Trends show deepening integration of ontology-based approaches with clinical genomics and environmental microbiomics. Awards & Leadership: Medical School Teaching Award, University of Maryland (2015) President, Genomic Standards Consortium (2015-2016) She directs collaborative projects with NASA, ClinGen Pediatric Cancer Taskforce, and the Omic Biodiversity Observation Network. Current initiatives include modeling complex disease etiology and enhancing metadata standards for planetary protection protocols.

David Ryan Glowackiمشاهده پروفایل

استاد پژوهشی

• Virtual Reality
• Molecular Dynamics
• Drug Discovery
• +۷ مورد دیگر

David Ryan Glowacki is a cross-disciplinary Research Professor at Universidad de Santiago de Compostela, specializing in the intersection of virtual reality, molecular dynamics, and computational chemistry. He is the founder of the Intangible Realities Laboratory (IRL), a research group working at the immersive frontiers of scientific, aesthetic, computational, and technological practice. His educational background includes a B.A. from the University of Pennsylvania (2003), an M.A. in cultural theory from Manchester University (2004), and a Ph.D. in molecular physics from Leeds University (2008). His diverse academic training spans chemistry, mathematics, philosophy, comparative literature, and religions, reflecting his interdisciplinary approach. Glowacki's research focuses on interactive virtual reality applications for scientific simulation and visualization, particularly in molecular dynamics and drug discovery. He has pioneered the development of interactive molecular dynamics in virtual reality (iMD-VR) as a tool for flexible substrate and inhibitor docking, reaction network exploration, and computational drug design. His work bridges computer science, nanoscience, aesthetics, and cultural theory, creating innovative approaches to scientific problems. An analysis of his recent publications reveals a strong trend toward applying virtual reality technologies to solve complex problems in computational chemistry and drug discovery. His work on iMD-VR has been particularly influential, demonstrating how immersive technologies can enhance molecular modeling, protein-ligand binding studies, and educational approaches in chemistry. He has made significant contributions to understanding reaction networks, SARS-CoV-2 protease inhibition, and the application of machine learning to molecular systems. Royal Society Research Fellowship Philip Leverhulme award ERC grant SIG-CHI best paper award Glowacki has secured substantial research funding through prestigious grants including an ERC grant and Royal Society Fellowship, enabling his innovative work at the intersection of science and technology. His Narupa framework provides an open-source, multi-person VR environment that has been applied across multiple research domains. While specific student advising isn't detailed in the provided information, his educational publications suggest active engagement in teaching computational chemistry through innovative VR approaches. As founder of the Intangible Realities Laboratory, Glowacki leads a team exploring how immersive technologies can transform scientific practice. The lab's work spans from fundamental molecular dynamics research to applications in drug discovery and mental health, demonstrating the broad impact potential of interactive VR technologies. Their citizen science approach to distributed VR experiments represents a novel methodology for conducting large-scale psychological research.

Juan Carlos Pichel Camposمشاهده پروفایل

استاد

• parallel and distributed computing
• Big Data technologies
• programming models
• +۷ مورد دیگر

Juan Carlos Pichel Campos is a Full Professor at the University of Santiago de Compostela (USC) specializing in high performance computing and language technologies. His research spans quantum computing, distributed systems, and Big Data technologies with a focus on practical applications in health informatics and computational physics. He received his B.Sc. and M.Sc. in Physics from University of Santiago de Compostela (Spain) and completed his Ph.D. there in 2006. He conducted postdoctoral research at University Carlos III de Madrid and University of Illinois at Urbana-Champaign, and worked as a researcher and project manager at Galicia Supercomputing Center. Professor Pichel's research interests include parallel and distributed computing, Big Data technologies, programming models, and software optimization techniques for emerging architectures. His recent work has focused on bridging quantum computing with classical high performance computing systems, developing efficient algorithms for processing massive biological datasets, and creating tools for health-related information retrieval and misinformation detection. His interdisciplinary approach combines techniques from computer science, physics, and biomedical informatics to solve complex computational problems. Analysis of his recent publications reveals a strong trend toward quantum computing applications and integration with classical HPC systems (accounting for approximately 40% of his recent work), followed by health informatics and natural language processing (about 30%), and bioinformatics and computational physics (about 30%). His research demonstrates a consistent focus on developing practical tools and frameworks that address real-world computational challenges across multiple domains. rePowerSiC: High-Efficiency High-Power Laser Beaming In-Space Systems Based On Sic (2024-2028) C3HS: Content curation for consumer health search - Search and misinformation detection (2023-2026) Big-eRisk: Early Prediction of Personal Risks on Massive Data (2021-2024) eRISK: Technologies for the early prediction of signs related with psychological disorders (2019-2021) BigNLP: Approaching High Performance Computing to Big Data Technologies: Natural Language Processing as Case Study (2015-2018) Professor Pichel has established strong collaborations with research groups across Europe and the United States, particularly in the fields of quantum computing and biomedical informatics. He is actively involved with CiTIUS (Centro singular de investigación en tecnoloxías da información e da comunicación de USC), contributing to its mission of advancing information and communication technologies through interdisciplinary research.

Dallas Cardمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning
• Computational Social Science
• +۳ مورد دیگر

Dallas Card is an Assistant Professor in the School of Information at the University of Michigan. Prior to his current position, he was a postdoctoral researcher with the Stanford NLP Group and the Stanford Data Science Institute. His academic journey began with a Ph.D. from the Machine Learning Department at Carnegie Mellon University, where he was advised by Noah Smith. Dallas Card's research centers on making machine learning more reliable and responsible, and on using machine learning and natural language processing to learn about society, history, and culture. His work spans multiple domains including computational social science, digital humanities, and AI ethics. He investigates how language models can be made more trustworthy while also applying these models to understand societal phenomena, historical texts, and cultural dynamics. His publication record demonstrates significant contributions across several key areas. Card's research shows a clear trajectory from core NLP methodology development toward increasingly societal applications. His recent work focuses on semantic change detection across the lifespan, media ecosystem analysis through podcasts, linguistic coordination methodologies, and historical language analysis using corpora like the Corpus of Founding Era American English. His research consistently bridges technical NLP advances with societal impact, particularly examining bias, reliability, and the cultural implications of language technologies. Among his notable achievements, Card received a Distinguished Paper Award at the ACM Conference on Fairness, Accountability, and Transparency (FAccT) in 2022 for "The Values Encoded in Machine Learning Research." His work has also garnered media attention from major outlets including The New York Times, Washington Post, and NPR for research on political framing of immigration. Card currently advises several Ph.D. students including Ben Litterer (co-advised with David Jurgens), Lavinia Dunagan, and Meera Desai (co-advised with Abigail Jacobs). His research is supported by collaborations across institutions including Stanford University and Carnegie Mellon University. He actively contributes to the academic community through service on conference committees including as a member of the FAccT steering committee (2023-2025) and as ACL 2025 publicity chair.

Darcy Birdمشاهده پروفایل

پژوهشگر

• Computational Archaeology
• Socio-Ecological Systems
• Paleodemography
• +۳ مورد دیگر

Darcy Bird is a Researcher at the University of Florida, affiliated with the Florida Museum of Natural History. He earned his PhD in Anthropology at Washington State University under Dr. Tim Kohler and collaborates with Dr. Nicolas Gauthier. Bird serves as Data Steward for the p3k14c global radiocarbon database, Liaison for the PEOPLE3000 Early Career Network, and a member of PAGES. His research integrates computational archaeology, socio-ecological systems, and paleodemography to study human population dynamics across millennia. Key themes include the Agricultural Demographic Transition, human-environment interactions in the American Southwest, and global analyses of population density. Bird's work employs large radiocarbon datasets, dendrochronology, and Bayesian modeling to evaluate stability, resilience, and collapse in prehistoric societies. Recent publications highlight Bird's interdisciplinary approach, spanning climate modeling with machine learning (2024) settlement persistence in the Mesa Verde region (2024) wealth inequality in prehispanic societies (2023) radiocarbon-database development (2022) landscape engineering impacts (2021) and global paleo-demographic trends (2020) . Bird's methodological contributions include synthesizing radiocarbon datasets, validating demographic proxies, and proposing frameworks for understanding population stability. His findings suggest agricultural societies, while stable under normal conditions, face vulnerabilities during rare environmental shocks, contrasting with the flexibility of hunter-gatherer systems. He also explores unconventional topics like space debris dynamics, reflecting broader interests in sustainability and collapse mechanisms.

Paul Ziemannمشاهده پروفایل

استاد

• Atmospheric Chemistry
• Organic Aerosol Formation
• Air Quality Modeling
• +۷ مورد دیگر

Paul Ziemann is a Professor in the Department of Chemistry at the University of Colorado Boulder and an Institute Fellow at the Cooperative Institute for Research in Environmental Sciences (CIRES). He received his Ph.D. from Penn State in 1991 and has since established himself as a leading researcher in atmospheric chemistry, particularly specializing in organic aerosol formation and atmospheric reaction mechanisms. Dr. Ziemann's research primarily focuses on elucidating mechanisms by which volatile organic compounds emitted from biogenic and anthropogenic sources react in the atmosphere to form submicron organic aerosol particles. His work involves conducting experiments in large-volume environmental chambers under simulated atmospheric conditions, followed by detailed chemical analysis of reaction products using advanced analytical techniques. The resulting data is used to develop quantitative chemical reaction mechanisms that improve air quality and climate models. His recent work has expanded to include indoor air chemistry, with studies conducted at locations such as the CU-Boulder Art Museum and athletic facilities. His publication record shows consistent high-impact research over the past three decades, with recent work focusing on secondary organic aerosol formation from various precursors including alkanes, alkenes, and terpenes. His research group has made significant contributions to understanding the effects of molecular structure on aerosol formation, gas-wall partitioning artifacts in chamber studies, and the chemical mechanisms of indoor air pollution. 2001: Kenneth T. Whitby Award, American Association for Aerosol Research 2017: Fellow, American Association for Aerosol Research 2018: Outstanding publication Award, Aerosol Science and Technology Dr. Ziemann's collaborative work spans multiple institutions, including Colorado State University and North Carolina State University, particularly in studies examining how chemical and physical properties of organic aerosol particles influence cloud formation. His research has practical applications for understanding climate impacts, visibility issues, and human health effects related to atmospheric particles.

Ovidiu Șerbanمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Natural Language Processing
• Interactive Systems
• +۶ مورد دیگر

Ovidiu Șerban is a Research Fellow at the Data Science Institute, Imperial College London, leading the Data Observatory group. His work focuses on real-time Natural Language Processing, Data Curation, and Large Scale Visualization Systems. PhD in Computer Science (2013) - Joint from INSA de Rouen Normandy and Babeș-Bolyai University MSc in Artificial Intelligence (2009) - Babeș-Bolyai University BSc in Computer Science (2008) - Babeș-Bolyai University Research interests span Artifical Intelligence, Natural Language Processing, Interactive Systems, Affective Computing, and Deep Learning. Recent publications emphasize knowledge graph completion, temporal graph analysis, and multimodal data processing frameworks. Contributed to development of OVE (Open Visualization Environment) for scalable data rendering Created TKGQA dataset for temporal knowledge graph validation Advanced conflict-aware multilingual knowledge graph techniques Projects include SENTINEL for real-time event detection, Watchme for workplace analytics, Intuitel for e-learning enhancement, and Agentslang for distributed interactive systems. Affiliations include Imperial College London, University of Cambridge, and University of Reading.

Daniel Hermanمشاهده پروفایل

استادیار

• Biomedical Informatics
• Machine Learning
• Cardiovascular Disease
• +۵ مورد دیگر

Daniel Herman serves as Assistant Professor of Pathology and Laboratory Medicine, leveraging dual MD/PhD training to advance cardiovascular diagnostics through computational approaches. His academic foundation includes medical and doctoral degrees, though specific institutions remain unreported in available materials. Research focuses on biomedical informatics and machine learning applications for cardiovascular disease prediction, with emphasis on hypertension phenotyping, endocrine disorder screening, and electronic health record optimization. Key interests span computable phenotypes for treatment-resistant hypertension, racial equity in prenatal neural tube defect screening, and AI oversight frameworks for clinical laboratories. Recent publications (2023-2025) reveal concentrated work on EHR-based diagnostic algorithms, particularly for primary aldosteronism and systemic mastocytosis, alongside critical analyses of race-based adjustments in medical screening. His PREDICT-SM project develops machine learning tools for rare disease detection while the CIRCE initiative establishes clinically validated research data infrastructure. No scientific awards were documented in source materials. While no student mentorship or grant activities were specified, his CIRCE project involvement indicates collaborative work in clinical data standardization. Current research trajectories suggest continued innovation in AI-driven diagnostic validation and health equity in laboratory medicine.

• Computational Social Science
• Misinformation Dynamics
• Political Communication
• +۷ مورد دیگر

David Lazer is a University Distinguished Professor in the Department of Political Science and College of Computer and Information Science at Northeastern University. He directs the Lazer Lab at Northeastern's Network Science Institute and is a co-leader of the COVID States Project. His work bridges political science and computational social science, focusing on how technology-mediated environments shape group learning, consensus formation, and opinion dynamics in political contexts. Dr. Lazer's research interests span computational social science, misinformation dynamics, democratic deliberation, collective intelligence, and algorithmic auditing. His work examines complex social dynamics in both physical and online spaces, developing network-based models for predicting complex systems and supporting effective decision-making. He investigates how information spreads through social networks, how consensus forms in groups, and how technology platforms influence political discourse and behavior. Lazer's recent publications reveal strong trends in studying social media's impact on mental health, political polarization, and information quality. His work frequently employs large-scale data analysis from social media platforms, survey research across multiple states, and experimental approaches to understand how people form beliefs and make decisions in networked environments. His research increasingly focuses on the intersection of mental health, social media usage, and political attitudes. Fellow, National Academy of Public Administration (2019) CHIP50 Warren J. Mitofsky Innovators Award (2025) Top 2% Scientists Worldwide by Stanford University Annual Assessment (2024) Dr. Lazer has served as Principal Investigator on more than $30 million in research grants. He advises numerous PhD students in Network Science at Northeastern University, including Hanyu Chwe, Burak Özturan, Hong Qu, Alexi Quintana Mathé, Alyssa Smith, Ata Aydin Uslu, and Allison Wan. His lab, the Lazer Lab, is part of Northeastern's Network Science Institute and focuses on computational social science research. The lab has attracted significant external funding and has produced influential research on misinformation, social networks, and political behavior. The Lazer Lab at Northeastern University's Network Science Institute conducts cutting-edge research at the intersection of political science and computer science. The lab includes postdoctoral researchers, staff members, and PhD students working collaboratively on projects related to social media analysis, misinformation detection, network dynamics, and computational social science methodologies. The lab has developed influential tools and datasets used by researchers worldwide and maintains active collaborations with other institutions through initiatives like the National Internet Observatory.

Lauramarie Popeمشاهده پروفایل

استادیار

• Augmentative and Alternative Communication (AAC)
• Autism Spectrum Disorders
• Naturalistic Developmental Behavioral Interventions (NDBIs)
• +۲ مورد دیگر

Lauramarie Pope is an Assistant Professor in the Department of Speech, Language and Hearing Sciences at Auburn University's College of Liberal Arts. Her work focuses on augmentative and alternative communication (AAC) systems design, with a particular emphasis on accessibility for individuals on the autism spectrum and those with complex communication needs. PhD, Penn State University MS, Penn State University BA, UCLA Research interests include: Augmentative and Alternative Communication (AAC) system design AAC for autism spectrum individuals Naturalistic Developmental Behavioral Interventions (NDBIs) Literacy development for AAC users Equitable access to communication technologies Notable Publication Trends : Recent work examines racial disparities in AAC access (2022), literacy integration in AAC systems (2024), and visual scene display implementation for autism spectrum users (2023-2025). Her research spans both clinical applications and computational linguistics, including large language model bias analysis (2024). Teaching : Courses include SLHS 3100 (Linguistics in SLHS), SLHS 7570 (Evaluation of Research in Speech Pathology), and SLHS 7840 (Augmentative and Alternative Communication).

Debayan Banerjeeمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Explainability
• Machine Learning
• +۵ مورد دیگر

Dr. Debayan Banerjee is a Researcher at the Institute for Business Information Systems (IIS) and part of the Professorship for Business Informatics, especially Artificial Intelligence and Explainability at Leuphana University Lüneburg. His work bridges academic research with practical applications in knowledge management, network science, and AI systems. Institute: Institute for Business Information Systems (IIS) Professorship: Business Informatics, Artificial Intelligence and Explainability Location: Universitätsallee 1, C4.308b, Lüneburg (21335) Email: debayan.banerjee@leuphana.de Research interests focus on knowledge graph integration, hybrid intelligence systems, and explainable AI. His projects include USIN5G, ARDIAS, and INSTANT, emphasizing human-AI collaboration and scholarly data accessibility. Recent publications highlight SPARQL translation automation, hybrid question answering frameworks, and environmental impact analysis of language models. Collaborative work spans DBpedia-Wikidata interoperability, DBLP knowledge graph applications, and graph embeddings for QA systems. Education and advising : Mentored students include Mathias Gross, Fatemeh Ghoochani, and Soham Majumder, focusing on final theses related to AI-driven data extraction and knowledge graph development.