جست‌وجوی استاد و پژوهشگر

• Digital Humanities
• Poetry
• Victorian Literature
• +۸ مورد دیگر

Meredith A. Martin is a Professor of English at Princeton University and serves as the Faculty Director of the Center for Digital Humanities (CDH), established under her leadership in 2014. She specializes in anglophone poetry, historical prosody, and the intersection of digital humanities with literary studies. Her work bridges traditional literary analysis with computational methodologies, as seen in projects like the Princeton Prosody Archive (1570–1923) and her forthcoming book Poetry’s Data (2025), which explores critical data studies in humanities contexts. Her research interests extend to Victorian literature, modernism, and interdisciplinary approaches to poetry. She holds leadership roles in multiple initiatives, including co-editing the Journal of Cultural Analytics and serving on committees for the Princeton AI Lab and Language and Intelligence Center. Martin is also affiliated with the Center for Statistics and Machine Learning and the Program in Media and Modernity. Recognized for her mentorship, she has received the Princeton University Graduate Mentoring Award (2021) and the Clio Award (2023). She advises graduate students on digital humanities, critical data studies, and comparative poetics, while overseeing collaborative projects like the Nineteenth-Century Data Collective and the Historical Poetics Reading Group. Her publications include The Rise and Fall of Meter (2012), which won multiple awards, and recent contributions to PMLA , Victorian Studies , and Paideuma . She actively promotes interdisciplinary dialogue through teaching courses on digital culture, poetry, and literature’s relationship with other disciplines.

Zheng (Tracy) Keمشاهده پروفایل

دانشیار

• High-dimensional statistics
• Machine learning
• Social network analysis
• +۳ مورد دیگر

Zheng (Tracy) Ke is an Associate Professor of Statistics at Harvard University. She holds a Ph.D. from Princeton University (2014) and a B.S. from Tsinghua University (2009). Her research focuses on high-dimensional statistics, machine learning, social network analysis, text mining, and bioinformatics. Notable contributions include advancements in network data analysis (e.g., SCORE normalization), text analysis methodologies, and statistical genetics pipelines. Dr. Ke has received prestigious awards such as the COPSS Emerging Leader Award (2024) and the Sloan Research Fellowship (2023). She has organized major conferences like the Workshop on Statistical Network Analysis and Beyond (2024) and contributed to the MADStat dataset analyzing statisticians' co-authorship networks. Her research interests span theoretical and applied domains, with a focus on developing scalable algorithms and rigorous statistical frameworks for complex data. Recent work emphasizes challenges like severe degree heterogeneity in networks and rare/weak signal detection in high-dimensional settings. Dr. Ke is an Associate Editor for the Journal of the American Statistical Association and actively collaborates on interdisciplinary projects.

Andrew Thomas Campbellمشاهده پروفایل

استاد

• Ubiquitous Computing
• Machine Learning
• Human-Computer Interaction
• +۳ مورد دیگر

Andrew Thomas Campbell is a Professor and Albert Bradley 1915 Third Century Professor in the Department of Computer Science at Dartmouth College. His research focuses on ubiquitous computing, machine learning, and mental health, particularly using mobile and wearable sensors to assess and manage mental illnesses. He leads the StudentLife project, which tracks college students' mental health over four years, and co-directs the HealthX Lab. Campbell's work has received prestigious awards, including the ACM UbiComp 10-Year Impact Award for pioneering mobile sensing in mental health. He previously held tenure as an Associate Professor at Columbia University and has industry experience at Google and Verily. His research spans $42M in grants from NIH, NSF, and corporate partners, emphasizing technology-driven solutions for mental health challenges. Education: B.Sc. from Aston University, M.Sc. from City University, Ph.D. from Lancaster University. He teaches CS 1 Introduction to Programming and mentors numerous students. Awards include the Dean of the Faculty Award for Mentoring (2025) and multiple ACM Test of Time Awards. His lab collaborations involve跨学科 teams addressing mental health through AI and sensing technologies.

Florian Knollمشاهده پروفایل

استاد

• Medical Imaging
• Machine Learning
• Magnetic Resonance Imaging (MRI)
• +۳ مورد دیگر

Prof. Dr. Florian Knoll is a full professor in Computational Imaging at the Department of Artificial Intelligence in Biomedical Engineering (AIBE) at Friedrich-Alexander-Universität Erlangen-Nürnberg. He leads the Computational Imaging Lab, focusing on machine learning applications in medical imaging, particularly accelerating MRI through innovative reconstruction algorithms and translating them into clinical practice. His research emphasizes improving MRI speed, artifact robustness, and accessibility, alongside developing quantitative biomarkers for disease processes. Knoll's work is funded by NIH grants, including projects on machine learning for musculoskeletal imaging, MR fingerprinting, and deep learning frameworks for MRI reconstruction. He is a key figure in open science initiatives, co-creating the fastMRI dataset with Facebook AI, providing public access to over 1300 knee and 7000 brain MRI scans. He currently serves as deputy editor of Magnetic Resonance in Medicine and chairs the ISMRM Reproducible Research Study Group. His contributions extend to reproducible research, maintaining GitHub repositories with code for image reconstruction techniques (e.g., AGILE, gpuNUFFT) and educational materials. He teaches medical imaging fundamentals at FAU, integrating theoretical and practical insights for students and researchers. Grants: NIH R01EB024532, R21EB027241, P41EB017183, R01EB029957 Labs/Teams: Computational Imaging Lab, fastMRI initiative Software: GitHub repositories for MRI reconstruction (e.g., github.com/FlorianKnoll )

Bart Bonikowskiمشاهده پروفایل

دانشیار

• Politics
• Culture
• Nationalism
• +۶ مورد دیگر

Bart Bonikowski is an Associate Professor of Sociology and Politics at New York University, serving as Director of Graduate Studies in the Department of Sociology. He is affiliated with NYU’s Center for Data Science and leads initiatives such as the Political Polarization and Radicalization Working Group at the Institute for Public Knowledge. His research integrates cultural sociology with computational methods to study nationalism, populism, and automation’s political impacts in the U.S. and Europe. Education: Ph.D., Sociology, Princeton University (2011) M.A., Sociology, Princeton University (2008) M.A., Sociology, Duke University (2005) B.A., Sociology, Queen’s University (Canada, 2003) Research Interests: Bonikowski’s work focuses on nationalism, populism, radical-right politics, and the sociopolitical implications of technological change. He employs relational surveys, experiments, and computational text analysis to explore topics like AI-driven job displacement, anti-immigrant attitudes, and ideological polarization. Recent research bridges AI governance with globalization studies. Articles Trends: His 2020s publications emphasize automation’s electoral consequences, AI policy dynamics, and the intersection of geopolitics with anti-immigrant sentiment. Earlier work established frameworks for measuring populism and nationalism in political discourse. Awards: 2023–present: Fellow, Canadian Institute for Advanced Research’s Boundaries, Membership, and Belonging Program Advising & Grants: While specific student advisees are not listed, Bonikowski directs NYU’s Sociology graduate program and collaborates on projects funded by research initiatives like CIFAR. He edits Sociological Science and contributes to editorial boards of top journals. Labs/Teams: Co-organizes NYU’s Identities & Ideologies Project and participates in the Sociology of Culture Workshop. His working groups at the Institute for Public Knowledge address radicalization and polarization.

Douglas K. Hartmanمشاهده پروفایل

استاد

• Educational Technology
• Learning Technologies
• Human Learning
• +۳ مورد دیگر

Douglas K. Hartman is a Professor in the Department of Teacher Education at Michigan State University (MSU) , with a joint appointment in Educational Psychology and Educational Technology. He holds a Ph.D. from the University of Illinois Urbana-Champaign. His research focuses on the application of technologies to enhance human learning across diverse contexts, including schools, communities, workplaces, and sports. Hartman’s work bridges educational theory and practice, emphasizing innovation in teaching methodologies and digital literacy. His research interests span educational technology , new literacies , and technology integration in learning environments. He has contributed to understanding how digital tools impact early childhood education, teacher professional development, and global educational policies. His recent studies explore AI applications in literacy assessment and generative AI’s role in artistic disciplines. Key themes in his publications include cross-cultural educational collaboration (e.g., CLIL approaches in EMI contexts), early literacy development, and teacher roles in online learning. While no awards are explicitly noted, his extensive scholarly output reflects sustained contributions to educational innovation. Hartman’s affiliation with MSU’s College of Education positions him at the forefront of interdisciplinary research, though specific grants or labs are not detailed in the provided texts. He maintains an active research agenda addressing 21st-century challenges in education through technology-driven solutions.

Daniel Angusمشاهده پروفایل

استاد

• Artificial Intelligence
• Social Media Analysis
• Computational Social Science
• +۳ مورد دیگر

Professor Daniel Angus is a faculty member at Queensland University of Technology (QUT), holding the position of Professor of Digital Communication in the School of Communication and serving as Director of QUT's Digital Media Research Centre (DMRC). His research focuses on computational methods applied to communication and media studies, with a particular emphasis on AI, automation, misinformation, and digital societal impacts. He holds a PhD in computer science from Swinburne University of Technology and has extensive experience in interdisciplinary research across computer science, design, communication, linguistics, and journalism. Affiliations: ARC Centre of Excellence for Automated Decision Making & Society, ARC Centre of Excellence for the Dynamics of Language. Research Projects: Leads projects like 'Using Machine Vision to Explore Instagram’s Everyday Promotional Cultures' and 'Evaluating the Challenge of ‘Fake News’ and Other Malinformation'. Research Interests: Daniel’s work bridges technology and society, exploring AI ethics, algorithmic transparency, social media governance, and computational methodologies for analyzing communication patterns. He develops tools like Discursis and PauseCode to study discourse and conversational dynamics in healthcare, aged care, and media contexts. Grants & Awards: Principal Investigator on multiple ARC grants and collaborates with industry stakeholders to address challenges like unhealthy food advertising and platform accountability. His research has informed policy submissions to parliamentary committees on social media regulation and AI adoption. Supervision: Current PhD students focus on topics like algorithmic transparency, computational methods for meme analysis, and AI in publishing. Labs/Teams: Directs the Digital Media Research Centre, fostering interdisciplinary projects on digital culture and platform studies.

Joshua T. Vogelsteinمشاهده پروفایل

دانشیار

• Machine Learning
• Neuroscience
• Network Science
• +۴ مورد دیگر

Dr. Joshua T. Vogelstein is an Associate Professor in the Department of Biomedical Engineering at Johns Hopkins University, holding joint appointments in Biostatistics, Applied Mathematics & Statistics, Neuroscience, and Computer Science. He leads the NeuroData lab, focusing on big data science, machine learning, and connectomics. Education: PhD and MSE in Neuroscience and Applied Mathematics from Johns Hopkins (2009), BS in Biomedical Engineering from Washington University (2002). Notable achievements include co-founding the Open Connectome Project (acquired by APL) and Gigantum (acquired by NVIDIA). Recognized with the NSF CAREER Award (2020), F1000 Prime (2014), and multiple Johns Hopkins Discovery Awards. Research emphasizes statistical connectomics, network science, and applying AI to biomedical challenges. Key contributions include mapping the first insect brain connectome (Science 2023) and developing open-source tools like CloudReg and BrainLine. Collaborates with Microsoft Research and industry partners, co-founding ventures like Global Domain Partners and Mind-X. Advised over 60 trainees, teaches machine learning and data science. Promotes open science through NeuroData's ecosystem of tools and data. Current work explores organoid intelligence, prospective learning, and neural network dynamics.

Jiaxiang Zhangمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Cognitive Neuroscience
• +۲ مورد دیگر

Jiaxiang Zhang is Professor of Artificial Intelligence in the Department of Computer Science at Swansea University's Faculty of Science and Engineering. He holds a PhD in Computational Neuroscience from the University of Bristol and previously held positions at the University of Birmingham, MRC Cognition and Brain Sciences Unit (Cambridge), and Cardiff University where he founded the Cognition and Computational Brain Lab. Zhang's research integrates computational modeling, machine learning, brain imaging (MEG/EEG/fMRI), and experimental approaches to study human cognition, aging, and neurological disorders. Key focus areas include: Neural mechanisms of decision-making and problem-solving Computational models of cognitive processes AI applications in healthcare diagnostics and neuroimaging Brain network dynamics in neurological conditions Recent publications emphasize deep learning models for neural data, multimodal brain connectivity, decision-making impairments in Parkinson's disease, and neuroinformatics tools. His work shows strong clinical translation through epilepsy biomarker development and emergency department outcome prediction. Zhang has led research grants from ERC, MRC, BBSRC, and Wellcome Trust. As primary investigator for multiple projects, he oversees significant computational neuroscience initiatives. He is available for postgraduate supervision.

Xianyang Zhangمشاهده پروفایل

استاد

• High-dimensional statistics
• Functional data analysis
• Time series analysis
• +۸ مورد دیگر

Xianyang Zhang is a Professor in the Department of Statistics at Texas A&M University, affiliated with the College of Arts & Sciences. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (2013) and a B.S. from the University of Science & Technology of China (2008). His research focuses on high-dimensional statistics, functional data analysis, kernel methods, and genomics, supported by grants from NIH, NSF, and Texas A&M. Education: Ph.D., Statistics, University of Illinois at Urbana-Champaign, 2013 B.S., Statistics, University of Science & Technology of China, 2008 Research Interests: Xianyang Zhang develops statistical theories and methodologies for complex data structures, including high-dimensional inference, kernel-based testing, change-point detection, and microbiome analysis. His work bridges computational and theoretical statistics, addressing challenges in genomics, omics-wide studies, and spatial statistics. Key Contributions: Developed KDist , a package for kernel and distance-based statistical inference Authored fastcpd for efficient change-point detection Advanced covariate-adaptive FDR control methods for omics studies Contributed to microbiome analysis tools like MicrobiomeStat and LinDA Advising & Grants: Advises multiple Ph.D. students in statistics and interdisciplinary projects Recipient of NIH and NSF grants for high-dimensional statistical research Collaborates with institutions like Mayo Clinic and Chinese University of Hong Kong Labs/Teams: Leads research groups focused on statistical methodology development, software implementation, and applications in computational biology and genomics.

Wenwen Liمشاهده پروفایل

استاد

• Geographic Information Science
• Cyberinfrastructure
• Big Data
• +۸ مورد دیگر

Wenwen Li is a Professor at Arizona State University (ASU), holding roles as Director of the CyberInfrastructure and Computation Intelligence (CICI) Lab and Research Director at the Spatial Analysis Research Center (SPARC). She specializes in geographic information science, cyberinfrastructure, and geospatial big data. Her work focuses on developing intelligent cyberinfrastructure for environmental and urban studies, leveraging AI and semantic technologies. Li's research has been supported by NSF, USGS, and Google.org, among others. Education: Ph.D. in Earth System and Geoinformation Science (George Mason University, 2010), M.S. in Signal and Information Processing (Chinese Academy of Sciences, 2007), and B.S. in Computer Science (Beijing Normal University, 2004). Research Interests : Cyberinfrastructure, spatial-temporal data mining, semantic interoperability, GeoAI applications in climate science, and urban studies. Her lab, CICI, pioneers projects like the Arctic Permafrost Thaw analysis and disaster response systems. Awards : 2023 AAG and UCGIS Fellowships, 2021 NSF Mid-Career Award, 2015 NSF CAREER Award, and the 2024 Greg Leptoukh Lecture Award (AGU). She chairs AAG's Cyberinfrastructure Specialty Group and serves on editorial boards of key journals. Grants & Service : Leads NSF-funded projects on GeoAI, climate modeling, and Arctic science. Active in AAG leadership roles and global initiatives like the Polar Cyberinfrastructure Portal. Recruits Ph.D. students in Geography and Computer Science. Labs & Teams : Directs the CICI Lab, advancing interdisciplinary GeoAI research and training. Collaborates with global partners on environmental and computational geography projects.

Marco Seeberمشاهده پروفایل

استاد

• Public Policy
• Higher Education Governance
• Research Evaluation
• +۵ مورد دیگر

Marco Seeber is a Professor at the University of Agder's Department of Political Science and Management. He holds a PhD from the University of Chieti Pescara (Italy) and has previously worked at the University of Lugano (Switzerland) and Ghent University (Belgium). His research focuses on public policy, governance, and management with specialization in higher education systems, research evaluation, and science policy. Professor Seeber's research examines policy implementation, research metrics, peer review systems, academic careers, internationalization, and interdisciplinarity in higher education. He employs comparative and quantitative methodologies to analyze governance structures and policy impacts across different educational systems. He has received significant recognition including the Swiss Prize for Research in Education (2019) and fellowships from IEEE, AIUM, and AIMBE. As Co-Editor-in-Chief of the European Journal of Higher Education, he shapes scholarly discourse in his field. He currently coordinates the Horizon CSA project IANUS (2025-) and leads courses in organizational theory, research methodology, and leadership.

• Digital Humanities
• Art History
• Urban History
• +۱۱ مورد دیگر

Isabella Di Lenardo is a Lecturer and Scientist at the Digital Humanities Institute (DHI) at École Polytechnique Fédérale de Lausanne (EPFL), where she also serves as the coordinator of the EPFL Time Machine Unit and the European Local Time Machines. She holds affiliations across multiple departments, including DHI-GE, SAR-ENS, SHS-ENS, and EDDH-ENS, reflecting her interdisciplinary role in teaching and research. Her educational background includes a PhD in Theories and Art History, with postdoctoral and faculty experience at institutions such as INHA (Paris), EPFL, and IUAV (Venice). Her research spans Digital Humanities, Art History, Urban History, and GIS , with a focus on digital urban reconstruction, historical cadastres, and AI applications in cultural heritage. She employs advanced computational methods including machine learning, 4D modeling, and semantic segmentation to analyze historical maps, cadastral records, and art archives. Her work bridges humanities scholarship with computer science, particularly in reconstructing urban evolution and analyzing visual patterns. The recent publications reveal a consistent trend in AI-powered historical data analysis , especially in processing non-standardized historical documents, reconstructing urban spaces, and developing open-source tools for digital heritage. Her work frequently involves large-scale datasets from Venice, Lausanne, Paris, and Jerusalem, demonstrating a transnational and interdisciplinary approach. She has contributed to significant collaborative projects such as the Venice Time Machine , Parcels of Venice , and Time Machine Organization , often acting as a principal investigator or project leader. Her role involves coordinating diverse teams of researchers, engineers, and cultural institutions. Scientific contributions include: Development of the Morphograph tool for visual pattern recognition in art archives Automatic vectorization and analysis of Napoleonic cadastres Creation of 4D models for historical cities AI-driven text and pattern extraction from historical maps Building discovery engines for digital art history She actively teaches ex cathedra courses in Digital Urban History and Art History at EPFL and internationally. Her work in grants and projects emphasizes open data, reproducibility, and interdisciplinary collaboration. She has led research funded by organizations supporting digital heritage innovation. She is a key member of the Digital Humanities Laboratory at EPFL and the Time Machine Organization , where she fosters collaboration between computer scientists, historians, and cultural institutions. Her work in the Replica Project and ARCHiVe center highlights her leadership in digitizing and making accessible large art historical archives.

Robert Ullmannمشاهده پروفایل

استاد

• International Corporate Taxation
• Tax Planning
• Transfer Pricing
• +۵ مورد دیگر

Prof. Dr. Robert Ullmann serves as Full Professor and Chair of Business Taxation at the University of Augsburg's Faculty of Business Administration and Economics. His position places him at the forefront of tax research and education within the Institute of Business Administration, where he leads a substantial research team including numerous research assistants and doctoral candidates. Professor Ullmann's research spans several critical areas in contemporary taxation: international corporate taxation, tax planning strategies, transfer pricing mechanisms, accounting quality and policy frameworks, tax evasion detection methods, and advanced audit technologies. His methodological approach combines empirical analysis using Tax Data Analytics with normative-analytical examination of tax law norms, current legislation, and case law. This dual approach allows his research to bridge theoretical frameworks with practical applications in the taxation field. His recent scholarly output demonstrates a strong focus on timely topics including corporate tax disclosure practices, nuclear fuel taxation impacts on capital markets, renewable energy tax policy, and the relationship between tax complexity and venture capital investment. These publications reflect his engagement with both traditional tax research areas and emerging intersections with technology and sustainability. Professor Ullmann actively mentors doctoral candidates, with numerous completed doctorates including Dominik Greif (2024/2025), Simone Mögele (2024), Christine Schuster (2023), and others dating back to 2020. His supervisory role extends beyond dissertation guidance, as his team members regularly present research at international conferences and collaborate on publications. The Chair of Business Taxation under his leadership maintains strong connections with industry partners including DATEV, Sonntag & Partner, FGS Munich, and SWMP, facilitating knowledge transfer between academic research and professional practice. These partnerships support student benefits programs, specialized seminars, and practical learning opportunities that enhance the educational experience for business administration students at the University of Augsburg.

Seongjin Choiمشاهده پروفایل

استادیار

• Urban Mobility Data Analytics
• Spatiotemporal Data Modeling
• Deep Learning & Artificial Intelligence
• +۳ مورد دیگر

Seongjin Choi is an Assistant Professor in the Department of Civil, Environmental, and Geo-Engineering at the University of Minnesota, Twin Cities , where he began his role in January 2024. His research bridges Urban Mobility Data Analytics , Spatiotemporal Modeling , and Deep Learning to advance transportation systems. Affiliated with the Center for Transportation Studies , Minnesota Robotics Institute , and Data Science Initiative , he leads the Choi Research Group . Education: Ph.D., Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), 2021 M.S., Civil and Environmental Engineering, KAIST, 2017 B.S., Civil and Environmental Engineering, KAIST, 2015 His research focuses on Urban Mobility Data Analytics and Deep Learning to optimize transportation systems. Key areas include: Spatiotemporal Data Modeling for forecasting and imputation Generative AI applications in transportation data Reinforcement Learning for Connected Automated Vehicles (CAV) Cooperative Intelligent Transport Systems (C-ITS) Recent publications in Transportation Science and Transportation Research Part C highlight his work on probabilistic traffic forecasting , deep generative models , and vision-language-action frameworks for autonomous systems. His methodologies often combine AI-driven analytics with real-time mobility optimization . Dr. Choi serves as: Associate Editor of The Journal of the Korean Society of Transportation (JKST) , 2023–Present Guest Editor for Journal of Advanced Transportation special issue on "Advanced Data Intelligence Theory and Practice in Transport 2023", 2023–2024 He actively seeks PhD students/postdocs for 2025 cohorts focused on machine learning for transportation challenges. Current projects include AI-enhanced traffic forecasting, CAV control, and urban air mobility (UAM) integration studies.