جست‌وجوی استاد و پژوهشگر

Peter M. Shearerمشاهده پروفایل

استاد

• Observational Seismology
• Earthquake Locations
• Earthquake Source Properties
• +۷ مورد دیگر

Peter M. Shearer is a Professor of Geophysics at the Scripps Institution of Oceanography (SIO), University of California, San Diego, where he is affiliated with the Institute of Geophysics and Planetary Physics (IGPP). He has been a faculty member since 1995, following progressive academic roles at SIO and postdoctoral work at the University of Cambridge. B.S. in Geology and Geophysics, Yale University, 1978 Ph.D. in Geophysics, University of California, San Diego, 1986 Dr. Shearer's research centers on observational seismology, employing large datasets to study earthquake locations, source properties, and Earth's internal structure. His work spans lithospheric and upper-mantle discontinuities , seismic scattering , anisotropic wave propagation , and back-projection techniques for earthquake analysis. He is also the author of the widely used textbook Introduction to Seismology . His recent research, inferred from ongoing projects and publications, emphasizes high-resolution imaging of earthquake sources and deep Earth structure using advanced seismic array methods and global datasets. Fellow, American Geophysical Union (1999) SIO Outstanding Graduate Teaching Award (2003) AGU Excellence in Refereeing (2003) AGU Gutenberg Lecturer (2005) Elected to the National Academy of Sciences (2009) Lehmann Medal, American Geophysical Union (2020) Dr. Shearer has made significant contributions to seismological education and research methodology. His career reflects sustained leadership in geophysics through mentorship, textbook authorship, and service to the community via involvement with SCEC, SCEDC, IRIS, USArray, USGS, AGU, and SSA. He has not been mentioned as leading a named lab, but his work is conducted within the research environment of IGPP and SIO. He is actively involved in the seismology community and continues to advance observational techniques for understanding earthquake processes and Earth structure.

Haihua Chenمشاهده پروفایل

استادیار

• Artificial Intelligence
• Data Science
• Data Quality
• +۷ مورد دیگر

Haihua Chen is an Assistant Professor of Data Science in the Department of Information Science at the University of North Texas (UNT), with a co-affiliation in Health Informatics. They lead the Intelligent Data Engineering and Analytics (IDEA) Lab, focusing on interdisciplinary research in artificial intelligence, data science, and informatics. Chen earned a Ph.D. in Information Science (concentrating in Data Science) from UNT in 2022, an M.S. in Information Science from Wuhan University, and dual B.S. degrees in Information Science and English Literature from Central China Normal University. Research interests span applied machine learning, data quality evaluation, NLP, and informatics applications in legal and healthcare domains. Notable work includes developing frameworks for measuring scientific novelty, constructing high-quality legal and biomedical datasets, and leveraging AI for precision medicine and disaster response. Chen has secured over $499K in external grants, including NSF REU and HSI projects, and $20K+ in internal grants. Their work has been published in top journals like Journal of Informetrics , IEEE Transactions on Reliability , and Scientometrics , with a strong focus on innovation measurement and data-centric AI. Teaching includes courses on computational methods, data analysis, and AI in healthcare. Professional leadership roles include chairing ASIS&T SIG-STI and editorial roles for Journal of the Association for Information Science and Technology , Knowledge and Information Systems , and others. Awards include UNT’s Great Grads Award and the Linda Schamber Writing Award.

Theodore J. Iwashynaمشاهده پروفایل

استاد

• Critical Care Medicine
• Health Services Research
• Sepsis
• +۱۳ مورد دیگر

Theodore J. Iwashyna is a Professor at the University of Michigan in the Department of Health Policy and Management, with a joint affiliation at the Johns Hopkins School of Medicine. He is a dual-trained physician-scientist (MD, PhD) specializing in critical care and health services research, focusing on the long-term impact of severe illness and systemic factors influencing recovery. MD, University of Chicago, 2002 PhD, University of Chicago, 2001 AB, Princeton University, 1994 Dr. Iwashyna's research centers on critical illness survivorship, particularly sepsis and ICU outcomes. He investigates how social, economic, and organizational contexts shape recovery, including cognitive and functional decline, caregiver burden, and financial toxicity. His work has redefined post-ICU care by emphasizing integration of social support and neighborhood factors into treatment planning. His recent publications highlight emerging concerns such as racial bias in pulse oximetry, center-level variation in neonatal outcomes, and tele-triage decision-making. Collectively, his work spans health policy, medical equity, and clinical innovation, with a strong emphasis on data-driven, patient-centered improvements in care delivery. Selected for the 2025 STATUS List, STAT News Member, Association of American Physicians Johns Hopkins Discovery Award Distinguished Mentor Award, University of Michigan Member, American Society for Clinical Investigation (ASCI) Sepsis Hero, Sepsis Alliance American Medical Women's Association Gender Equity Award Fellow, American College of Critical Care Medicine Pulmonary and Critical Care Fellows' Outstanding Educator Award Jerome W. Conn Award for Excellence in Research H. Marvin Pollard Award for Outstanding Teaching Dr. Iwashyna has led numerous federally funded studies using large administrative datasets like Medicare claims to evaluate long-term outcomes after critical illness. He mentors a broad network of trainees and collaborates internationally to improve sepsis care and survivorship. His work on family-centered recovery has influenced clinical guidelines and policy recommendations. He is actively involved in multiple research teams studying ICU recovery, health equity in medical devices, and organizational effectiveness. His lab integrates epidemiology, health services research, and policy analysis to generate actionable insights for clinicians, hospitals, and policymakers.

Zheng Zhangمشاهده پروفایل

پژوهشگر

• Human-AI Interaction
• Human-Computer Interaction
• Machine Learning
• +۳ مورد دیگر

Zheng Zhang is a Ph.D. candidate in the Department of Computer Science and Engineering at the University of Notre Dame, where he focuses on human-AI interaction. He also works as an Applied Scientist in Adobe's GenAI team, developing interactive AI systems for user experience enhancement. He has held internships at Apple, AWS AI, and Meta Reality Labs. Education: Ph.D. (in progress) at University of Notre Dame M.S. in Computer Science from University of Rochester and University of Minnesota B.Eng. in Software Engineering from Shaanxi Normal University His research explores the intersection of human-computer interaction (HCI) and machine learning (ML), emphasizing systems that provide adaptive, context-sensitive support for complex cognitive tasks. Key areas include collaborative tools for team ideation, incremental learning from user demonstrations, and multimodal context-aware interfaces. Recent publications demonstrate a focus on AI-augmented collaboration through large shared displays (LADICA), audio-visual annotation (PEANUT), and interactive qualitative coding (PaTAT). His work increasingly integrates generative AI with real-time human input, particularly for co-located teams and educational applications. He serves as a Program Committee member and reviewer for top HCI/ML conferences including CHI, UIST, IUI, and ACL. Teaching experience spans courses in human-AI systems, algorithms, and data structures.

Shrideep Pallickaraمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Large-Scale Systems
• +۱۲ مورد دیگر

Shrideep Pallickara is a Professor in the Department of Computer Science at Colorado State University, where he also directs the Center for eXascale Spatial Data Analytics and Computing (XSD) . His research is funded by the National Science Foundation, Department of Homeland Security, Environmental Protection Agency, Department of Agriculture, and the UK's e-Science program. Research Interests: His research lies at the intersection of machine learning and large-scale systems, focusing on: Spatiotemporal data management and analytics Extreme-scale storage systems Stream processing for IoT and cyber-physical systems Deep learning over petabyte-scale, high-dimensional datasets Model construction for forecasting natural and urban phenomena His work addresses challenges in computational tractability, resource utilization, and convergence in distributed environments. Systems developed in his lab are deployed in domains such as urban sustainability, agriculture, epidemiology, environmental monitoring, healthcare, and defense. Research Trends in Publications: His recent publications demonstrate a strong focus on scalable analytics for geospatial and environmental data. Key themes include deep learning for soil moisture and salinity prediction, efficient visualization of massive satellite datasets, spatiotemporal search and summarization, and model performance profiling across spatial domains. The work integrates scientific domain knowledge with machine learning and systems innovation. Scientific Awards: NSF CAREER Award Board of Governors Award for Excellence in Undergraduate Teaching OLIE Award N. Preston Davis Award Monfort Professorship Best Paper Award at IEEE/ACM CCGrid 2019 Best Paper Award at BDCAT 2023 Best Paper Award at IEEE Cluster 2012 Best Student Paper Award at IEEE CloudCom 2010 Shortlisted for ACM DEBS-2015 Grand Challenge Award One of the Six Best Papers at ACM/IEEE GRID 2005 Advising and Grants: He advises numerous graduate students, many of whom are co-authors on his publications. His research is supported by major grants from NSF, DHS, EPA, USDA, and UK e-Science, enabling the development of open-source systems such as Granules, NaradaBrokering, Galileo, Funnel, and Spindle. Labs and Teams: He leads the XSD Center, which develops and maintains large-scale open-source software systems involving over 2500 classes and a million lines of code. These systems are used in academic, commercial, and defense applications.

• Natural Language Generation
• Human Evaluation of NLP Systems
• Data-to-Text Generation
• +۶ مورد دیگر

Ehud Reiter is a Professor of Natural Language Generation at the University of Aberdeen's School of Natural and Computing Sciences, Department of Computing Science. With over three decades of research experience, he is recognized as one of the world's leading experts in Natural Language Generation (NLG), particularly in data-to-text systems, evaluation methodologies, and healthcare applications. Reiter's research primarily focuses on creating systems that generate accurate, useful, and understandable natural language from structured data. His work spans multiple domains including healthcare (medical note generation, patient-facing systems), sports reporting, and explainable AI. A significant portion of his recent research addresses the critical challenge of evaluating NLG systems, with particular emphasis on human evaluation methodologies, reproducibility of results, and factual accuracy in generated text. His work on Bayesian Networks and causal graph discovery represents his ongoing interest in knowledge representation and reasoning behind natural language explanations. His research has evolved from foundational work on reference generation and document planning to current projects addressing large language models, reproducibility crises in NLP evaluation, and human-AI collaboration frameworks. The SPHERE evaluation card framework he co-developed represents a systematic approach to evaluating human-AI interaction systems across five key dimensions. Reiter has been instrumental in organizing multiple shared tasks focused on reproducibility in NLG evaluations, demonstrating his commitment to improving research methodology in the field. His work on consultation checklists for medical note evaluation has introduced standardized protocols that increase objectivity in clinical text assessment. Through projects like BabyTalk (generating neonatal intensive care unit summaries) and DrivingBeacon (providing driving behavior feedback), Reiter has demonstrated the practical applications of NLG technology in critical domains. His research consistently bridges theoretical advances with real-world implementation challenges.

• Computational Linguistics
• Natural Language Processing
• Natural Language Generation
• +۵ مورد دیگر

Leo Wanner is a prominent Professor at Universitat Pompeu Fabra's Department of Information and Communication Technologies, specializing in Natural Language Processing. With a research career spanning over three decades, he has made significant contributions to computational linguistics, particularly in natural language generation, collocations, and hate speech detection. He has served as editor for multiple editions of the International Conference on Computational Linguistics (COLING) including the 2025 edition. Wanner's research interests encompass a wide range of topics in computational linguistics, with recent work focusing on hate speech detection, multilingual processing, and the capabilities of large language models. His work bridges theoretical linguistics with practical applications, addressing challenges in lexical semantics, syntax, and discourse analysis. Notably, he has pioneered research in collocation processing and has contributed to the development of frameworks for analyzing thematic progression in texts. His publication record demonstrates consistent productivity with significant contributions across multiple subfields. Recent work shows a strong focus on contemporary challenges in NLP, particularly hate speech detection and the capabilities of large language models. His research often takes a multilingual perspective, addressing challenges across different language families including Romance and Slavic languages. Wanner has led significant research projects including the development of FORGe, a multilingual deep sentence generator based on the Meaning-Text Theory, which achieved top performance in the WebNLG challenge. His work on multilingual surface realization has established important benchmarks in the field through shared tasks that have engaged researchers worldwide. As an academic leader, Wanner has mentored numerous researchers and contributed to building research infrastructure through corpus development and annotation schema design. His work on collocation resources, thematic progression analysis, and hate speech detection frameworks has provided valuable resources for the broader NLP community.

Patrick Munkمشاهده پروفایل

دانشیار

• Antimicrobial Resistance
• Genomic Epidemiology
• Metagenomics
• +۱۲ مورد دیگر

Patrick Munk is an Associate Professor at the National Food Institute, Technical University of Denmark (DTU), where he leads research in the Research Group for Genomic Epidemiology and contributes to the DTU Microbes Initiative. His work integrates genomics, metagenomics, and bioinformatics to study antimicrobial resistance (AMR) across human, animal, and environmental systems. He is actively involved in global surveillance efforts and is currently accepting PhD students. PhD, MSc Member, The Danish Young Academy (2024–2029) Patrick Munk’s research focuses on antimicrobial resistance, genomic epidemiology, metagenomics, and bioinformatics . His work leverages large-scale sequencing of sewage, livestock, and environmental samples to understand the global spread of resistance genes. He has led and contributed to major international studies, including the global sewage surveillance project across 101 countries, and has developed key genomic resources such as the PanRes and MetalResistance databases. His recent publications span high-impact journals like Nature Communications and Nature Microbiology , with themes centered on sewage-based surveillance, fecal resistomes in livestock, source attribution of microbial communities, and novel sequencing methodologies . His research has been widely covered in media and policy documents, reflecting its societal impact. Genomic analysis of sewage from 101 countries (Nature Communications, 2022) Time-series metagenomics for source-attributed surveillance (Nature Communications, 2024) High-throughput single-cell metagenomic sequencing (Frontiers in Microbiology, 2025) Patrick Munk has received recognition through significant media coverage, policy citations, and widespread academic engagement. His work has been highlighted in over 40 news outlets, referenced in policy sources and patents, and discussed across social media and academic platforms like Mendeley and Wikipedia. He supervises multiple PhD students and is a Principal Investigator or Supervisor on several active and completed projects, including Bioinformatic methods for multi-metagenome integration and Host range and epidemiology of antibiotic resistance . He is also a co-creator of key datasets that support open science in AMR research. His lab is part of a broad collaborative network involving DTU, international consortia, and public health agencies.

Jaein Leeمشاهده پروفایل

استادیار

• Sociology
• Social Inequality
• Health Disparities
• +۷ مورد دیگر

Dr. Jaein Lee is an Assistant Professor of Sociology in the Department of Criminology, Sociology, and Geography at Arkansas State University, under the College of Liberal Arts and Communication. He holds a Ph.D. in Sociology from the University of Maryland, College Park, and previously served as a Visiting Assistant Professor at Gettysburg College. His research centers on social and individual factors influencing inequality, particularly in health behaviors and mortality. He investigates how structural and demographic variables shape disparities across populations, with a focus on gender, marital status, age, and religion. His work extends to cross-national studies, especially in South Korea, examining evolving social attitudes. The trends in his recent publications reflect a strong focus on demography, family dynamics, and social attitudes, often utilizing large-scale and restricted datasets. His work appears in leading sociology journals such as Journal of Marriage and Family and Social Forces , highlighting the impact and rigor of his research. Dr. Lee is actively engaged in collaborative research and grant development, including a current proposal with Dr. Lim (Utah State University) on family formation using CPS data. He collaborates with scholars such as Dr. Caudillo (University of Maryland) and Dr. Lin (University of Texas at San Antonio), demonstrating a robust network in the sociological research community. He advises students in sociology and criminology, though no current advisees are listed. His research team includes collaborators across institutions, contributing to interdisciplinary and policy-relevant studies on inequality and social change.

• High-Performance Computing
• Parallel Programming
• Domain-Specific Languages
• +۳ مورد دیگر

Wenguang Chen is a researcher affiliated with Tsinghua University and Pengcheng Laboratory , specializing in computer science and high-performance computing . His work bridges theoretical advancements with practical applications in domain-specific languages , parallel programming , and machine learning . Research Interests include: Development of modular DSLs for numerical methods (e.g., Mat2Stencil) Performance optimization in distributed and parallel systems Compiler frameworks for privacy-preserving AI (e.g., FHE-based neural network inference) Graph algorithms scaling to trillion-edge datasets Applications of Rust in memory-safe pointer analysis Recent Publications span 2014–2025, focusing on: Parallelization strategies for supercomputing Compiler automation tools Extreme-scale data processing Performance variance diagnosis in production environments

• Machine Translation
• Natural Language Processing
• Multilingual Models
• +۵ مورد دیگر

Barry Haddow is a Senior Research Fellow at the Institute for Language, Cognition and Computation (ILCC) within the School of Informatics at The University of Edinburgh. With over 15 years of research experience, he coordinates major international projects in machine translation and natural language processing, including HPLT (Horizon Europe) and Utter (Horizon Europe). His work spans both theoretical and applied aspects of language technology with significant impact on multilingual systems. Haddow's research focuses on machine translation, particularly addressing challenges in low-resource languages, multilingual models, and speech translation. His recent work demonstrates a strong shift toward leveraging large language models for translation tasks while investigating their limitations in cross-lingual settings. He has made significant contributions to understanding how to adapt general language models for specialized translation scenarios and evaluating their performance across diverse language pairs. Analysis of Haddow's recent publications (2023-2025) reveals a clear research trajectory focusing on the intersection of large language models and machine translation. His work examines how to effectively adapt LLMs for translation tasks, investigates evaluation methodologies for multilingual systems, and develops techniques for improving translation quality across diverse language families. A notable trend is his focus on practical applications of these technologies in real-world settings, including financial language processing and speech translation systems. Haddow has coordinated multiple major European research projects including HPLT (2022-2025), Utter (2022-2025), GOURMET (2019-2022), ELITR (2019-2022), and HIML (2015-2018), securing substantial research funding from Horizon Europe and other sources. His work has established him as a leading figure in the machine translation community, regularly contributing to major shared tasks like WMT (Conference on Machine Translation). As a core member of the Institute for Language, Cognition and Computation at Edinburgh, Haddow collaborates closely with the Language Technology Group. His research directly contributes to the institute's mission of advancing computational approaches to human language processing, with particular emphasis on developing technologies that can operate effectively across the world's diverse languages.

Shurui Zhouمشاهده پروفایل

استادیار

• Software Engineering
• Open-Source Collaboration
• Fork-Based Development
• +۴ مورد دیگر

Shurui Zhou is an Assistant Professor at the University of Toronto , with primary appointment in the Department of Electrical & Computer Engineering and cross-appointments in the Department of Computer Science and the Department of Mechanical & Industrial Engineering . She is also affiliated with the Schwartz Reisman Institute and a member of the Data Sciences Institute . Zhou directs the FORCOLAB , focusing on enhancing collaboration in distributed software teams, especially in open-source and AI-enabled systems development. Her research integrates software engineering principles with human collaboration insights from organizational behavior, aiming to improve collaboration efficiency through tooling and interdisciplinary methods. Recent publications (2023-2025) explore LLM integration in software development , sustainability in scientific open-source communities , and collaborative challenges in computer-aided design . Education: PhD (2020) from Carnegie Mellon University's Software and Societal Systems Department , MSc from Peking University , BSc from Xi'an Jiaotong University Scientific Awards: Gordon Slemon Teaching of Design Award (2022) Grants: Collaborator on NSERC Alliance-Mitacs Accelerate Grant (2025) for LLM analysis in political data Her lab, FORCOLAB, develops tools like forks-insight.com to analyze GitHub forks and Redundant PR detection bots . Zhou organizes events like the Responsible LLM-Human Collaboration Hackathon (2024) and actively serves on conference committees including ICSE 2025 and ICSME 2024 .

Geoffrey Bartonمشاهده پروفایل

استاد

• Bioinformatics
• Computational Biology
• Protein Structure Prediction
• +۴ مورد دیگر

Professor Geoffrey Barton is the Chair of Bioinformatics at the Computational Biology department within the School of Life Sciences at the University of Dundee . With a career spanning decades, he has been pivotal in advancing bioinformatics tools and methodologies for analyzing biological data, particularly in protein structure prediction and RNA sequencing. Core Research Interests : Protein structure and function prediction, computational methods for large biological datasets, small RNA analysis, and integration with wet-lab collaborations across plants, model organisms, and human disease. Awards : Fellow of the Royal Society of Edinburgh (2019) Fellow of the Royal Society of Biology (2011) Brian Cox Prize for Excellence in Public Engagement with Research (2023) Publications and Software : He leads the development of widely used tools like JPred (25,000+ predictions/month) and Jalview (55,000+ installations). His group's work spans proteomics, transcriptomics, and structural genomics, addressing questions in basic science and clinical applications. Media and Collaboration : Available for media commentary on bioinformatics and computational biology, he collaborates extensively with international institutions, including the University of Cambridge and Oxford.

Selim Sametogluمشاهده پروفایل

پژوهشگر

• Developmental Psychology
• Behavioral Genetics
• Language Development
• +۷ مورد دیگر

Selim Sametoglu is a Postdoctoral Researcher in the Department of Language Development at the Max Planck Institute. His work focuses on the interplay between genetic and environmental factors in children's development, with a particular emphasis on language acquisition and psychological wellbeing. He utilizes advanced methodologies including genetically informed designs and digital behavioral assessment through social media text mining. His educational background includes a Research Master’s in Individual Differences and Assessment from Tilburg University (2020) and a PhD from Vrije Universiteit Amsterdam (2025), where his research investigated heritable influences on social media use and wellbeing, pioneering the use of digital language as a proxy for mental health indicators. His research interests lie at the intersection of developmental psychology, behavioral genetics, and computational social science. He specializes in unobtrusive, large-scale behavioral assessment methods that enable longitudinal tracking of psychological traits using naturalistic digital data. This approach allows for cost-effective, population-level insights into human development and wellbeing. The available text does not list specific publications, but his methodological focus suggests a strong trajectory in interdisciplinary research combining psychometrics, behavioral genetics, and natural language processing for psychological assessment. His work bridges traditional survey-based methods with innovative digital tools to enrich understanding of individual differences. Awards: No scientific awards are mentioned in the provided text. Selim Sametoglu is actively engaged in research and mentoring within the Max Planck Institute. While no formal advisees are listed, his role as a postdoctoral researcher in a leading developmental science department implies involvement in collaborative projects and potential supervision of junior researchers. His work is supported by institutional research infrastructure and likely involves grant-funded projects related to language development and digital phenotyping. He is part of the research team in the Department of Language Development, contributing to cutting-edge studies on how genetic and environmental factors shape early cognitive and linguistic growth. The department leverages large-scale datasets and innovative analytical techniques, positioning Selim at the forefront of data-driven developmental science.

Kajsa Hansen Yangمشاهده پروفایل

استاد

• School Equity
• Achievement Gaps
• Educational Sociology
• +۷ مورد دیگر

Kajsa Hansen Yang is a Professor at the Department of Education and Special Education, University of Gothenburg. Her work focuses on school equity, achievement gaps, and the impact of social background on academic performance, with a particular emphasis on changes in the Swedish educational system over time. She specializes in quantitative research methods, including multilevel analysis, latent variable modeling, and structural equation modeling. Her research examines how educational policies, teacher practices, and socioeconomic factors influence equity in mathematics and science education across Nordic and international contexts. She has led projects like Research on Educational Outcomes (FUR) , School Segregation in Sweden (SSIS) , and Quantitative Research Methods in Education (QRM) , often analyzing large-scale datasets (TIMSS, PISA, PIRLS) to explore global trends in educational inequality. Recent publications highlight her focus on academic resilience, teacher quality, and methodological rigor in international comparative studies. Her work appears in journals like European Journal of Psychology of Education , Studies in Educational Evaluation , and ZDM - the International Journal on Mathematics Education , with key themes spanning from classroom dynamics to systemic policy impacts.