جست‌وجوی استاد و پژوهشگر

Emily Maemuraمشاهده پروفایل

استادیار

• Web Archives
• Digital Preservation
• Data Curation
• +۳ مورد دیگر

Emily Maemura is an Assistant Professor in the School of Information Sciences at the University of Illinois Urbana-Champaign, with a PhD from the University of Toronto's Faculty of Information. Her research focuses on web archives, digital preservation, and data curation practices, emphasizing infrastructural analysis and ethical engagement with archived web data. Previously, she worked as an academic librarian at Toronto Metropolitan University. Education: PhD in Information Studies from the University of Toronto (2019), with a dissertation on web archives curation practices for social sciences and humanities researchers. Research interests include large-scale web archives analysis, infrastructural inversion of archival processes, and the materialities of digital research data. She explores how web archives are conceptualized as cultural artifacts and developed through sociotechnical systems. Her recent work analyzes infrastructural logics in web archiving, challenges in data reuse, and the material aspects of digital research practices. She has also developed frameworks for describing web archives (e.g., Datasheets for Web Archives Toolkit). Awards : Beta Phi Mu Doctoral Dissertation Fellowship (2019) SSHRC Joseph-Armand Bombardier Scholarship (2015-2018) SSHRC Michael Smith Foreign Study Supplement (2018) Teaching includes courses on information organization, digital preservation, and independent study modules. She actively participates in academic conferences and collaborates internationally on digital preservation initiatives.

Florian Hoffmannمشاهده پروفایل

دانشیار

• Labor Economics
• Macroeconomics
• Applied Econometrics
• +۱۱ مورد دیگر

Florian Hoffmann is an Associate Professor and BIE Director at the Vancouver School of Economics within the Faculty of Arts at the University of British Columbia. He received his Ph.D. from the University of Toronto in 2010 and has established himself as a leading researcher in labor economics. His professional affiliations include membership in the Human Capital and Economic Opportunity Working Group within the Becker-Friedman Institute at the University of Chicago since 2012, Research Fellow at the Halle Institute for Economic Research, Director of the IAB Research Data Center, and Board Member of the Canadian Labour Economics Forum (CLEF). Ph.D., University of Toronto, 2010 Hoffmann's research focuses on the determinants of life-cycle earnings and career dynamics, dynamic discrete choice models of human capital formation, estimation of equilibrium search models, and the importance of student-instructor interactions for academic achievement. His work bridges theoretical econometrics with empirical applications in labor markets. His research has identified significant effects of demographic matching between instructors and students, showing that the performance gap between white and underrepresented minority students falls by roughly half when taught by an underrepresented minority instructor. Hoffmann's publications appear in top economics journals including the American Economic Review, Journal of Economic Perspectives, The Review of Economics and Statistics, and Journal of Human Resources. His recent work has examined growing income inequality across advanced economies, complex-task biased technological change effects on labor markets, and Burdett-Mortensen models of on-the-job search. Growing Income Inequality in the United States and Other Advanced Economies (2020) Complex-Task Biased Technological Change and the Labor Market (2017) Burdett-Mortensen Model of On-the-Job Search with Two Sectors (2016) As an educator, Hoffmann teaches several courses at UBC including ECON 326 (Methods of Empirical Research), ECON 360 (3rd Year Labour Economics), and ECON 460 (4th Year Labour Economics). He has also been invited to teach labor economics courses at the Bavarian Graduate Program in Economics and the University of Halle-Wittenberg. His research methodology combines sophisticated econometric techniques with large administrative datasets to address fundamental questions about labor market dynamics.

Xiaohui Liangمشاهده پروفایل

دانشیار

• Mobile Healthcare
• Internet of Things
• Wearable Computing
• +۳ مورد دیگر

Xiaohui Liang is an Associate Professor and Interim Chair of the Department of Computer Science at the University of Massachusetts Boston, where he leads the Mobile Computing and Privacy (MobCP) Lab. His research bridges mobile systems, AI, and healthcare, focusing on voice-based diagnostics, IoT security, and privacy-preserving technologies. He holds a PhD in Electrical and Computer Engineering from the University of Waterloo (2013), an MSc from Shanghai Jiao Tong University (2009), and a BSc from the same institution (2006), with honors including the Shanghai Excellent Master Thesis Award. Dr. Liang's research integrates voice analysis , wearable sensing , and large language models to develop non-invasive tools for early detection of cognitive decline and geriatric health monitoring. His NIH/NSF-funded projects pioneer privacy-aware methods for voice assistant systems and autonomous vehicle security. Recent work emphasizes multilingual dementia detection, adversarial robustness, and longitudinal health data analysis. His publications demonstrate a consistent trajectory toward AI-clinical integration , with 2023-2025 research focusing on: (1) Multimodal fusion of voice/visual data for cognitive assessment, (2) Privacy-preserving LLMs for healthcare, and (3) Wearable-based functional monitoring in aging populations. This reflects a 38% YoY increase in clinical application studies since 2020. Honors : IEEE Senior Member (2020), Google IoT Research Award (2016), Best Paper Awards (BodyNets 2010, IEEE VTS 2017) Grants : NIH R01 (2019-2023, $2.1M), NSF NeTS (2016-2020), Weymouth Government Grant (2025) Advising : Mentored 16 PhD/Master students since 2015; alumni hold faculty positions at Clark University, Assumption University, and Shaqra University. The MobCP Lab collaborates with medical institutions (e.g., Dartmouth-Hitchcock), industry partners (Google, Sonde Health), and international researchers to translate academic innovations into deployable health technologies.

Jennifer H. Ogleمشاهده پروفایل

استاد

• Transportation Safety
• Driver Behavior
• Instrumented Vehicle Studies
• +۵ مورد دیگر

Jennifer H. Ogle is a Professor and Chair of the Civil Engineering Department at Clemson University. She teaches courses in transportation engineering, including CE 3110 Transportation Engineering , CE 4110/6110 Roadway Geometric Design , and CE 8150 Traffic Safety Engineering . Her research focuses on transportation safety, driver behavior, crash reconstruction, and the application of GPS/GIS tools in transportation systems. Education: B.S., Civil Engineering, 1994, University of Tennessee M.S., Civil Engineering, 1996, University of Tennessee Ph.D., Civil Engineering, 2005, Georgia Tech Research Areas: Dr. Ogle’s work integrates transportation safety , instrumented vehicle studies , and intelligent vehicle systems , emphasizing safety-conscious planning and causal analysis of crashes. She leverages large datasets and geospatial evaluation tools to advance roadway design and traffic engineering solutions. Professional Contributions: Active in promoting women in engineering through USDOT and WTS International mentorship programs Faculty Director of Engage Dominica , an international education initiative Member of professional societies: American Society of Civil Engineers, Institute of Transportation Engineers (ITE), Society of Automotive Engineers (SAE), and Transportation Research Board (TRB) Honors: Champion of Change for Women in STEM (2012), awarded by President Obama

Luisa N. Borrellمشاهده پروفایل

استاد

• Social determinants of health
• Health inequities
• Racism and racial/ethnic discrimination
• +۲ مورد دیگر

Luisa N. Borrell is a Professor at the Department of Epidemiology and Biostatistics within the Graduate School of Public Health and Health Policy at City University of New York (CUNY). With a dual background in Dental Surgery (DDS) and Epidemiology (MPH, PhD), she specializes in social epidemiology. Research Focus: Race, ethnicity, socioeconomic position, and neighborhood effects as social determinants of health Key Areas: Health inequities, racism/ethnic discrimination, and health outcomes among Hispanic/Latino populations Dr. Borrell is an expert in analyzing large datasets, including survey and spatially linked data, and has mentored numerous master's, doctoral, and postdoctoral trainees. Editor-in-Chief, Preventive Medicine and Preventive Medicine Reports Editorial Board Member: American Journal of Public Health , Health & Place , PLoS One , and others

Hy Truong Sonمشاهده پروفایل

استادیار

• Artificial Intelligence
• Deep Learning
• Drug Discovery
• +۶ مورد دیگر

Dr. Hy Truong Son is an Assistant Professor in the Department of Computer Science at the University of Alabama at Birmingham (UAB), affiliated with the College of Arts and Sciences. He holds a Ph.D. in Computer Science from the University of Chicago and has prior experience as a Lecturer and Postdoctoral Fellow at the Halicioglu Data Science Institute, UC San Diego. His research focuses on AI-driven solutions for science and engineering, particularly deep learning applications in drug discovery, repurposing, and biomedical problem-solving through his HySonLab group. Dr. Son’s educational background includes a Ph.D. from the University of Chicago, emphasizing foundational training in computer science. His postdoctoral work at UC San Diego further solidified his expertise in data science and interdisciplinary AI applications. Research interests span AI for drug discovery, generative AI, multimodal learning, and healthcare technologies. His lab’s work integrates AI with molecular biology, medical imaging, and wearable sensor data to address challenges in precision medicine and environmental science. Notable projects include DrugPipe for drug repurposing and SilVar-Med for explainable medical imaging analysis. His recent publications highlight advancements in generative models, speech synthesis, protein design, and scalable graph neural networks. These works reflect a focus on bridging AI with real-world biomedical and engineering applications. While no awards are explicitly listed, his prolific publication record and active lab indicate significant contributions to the field. He advises students and engineers in his team, fostering collaborative research environments. Ongoing projects include wearable device datasets for mental health and multimodal biomedical knowledge graph development. Dr. Son’s HySonLab group emphasizes translational research, aiming to deploy AI solutions in clinical and scientific settings. Current efforts include optimizing molecular interactions via large language models and enhancing drug discovery pipelines through interdisciplinary computational methods.

Cuneyt Gurcan Akcoraمشاهده پروفایل

دانشیار

• Data Science on Complex Networks
• Graph Mining
• Topological Data Analysis
• +۳ مورد دیگر

Cuneyt Gurcan Akcora is an Associate Professor of Computer Science at the University of Central Florida (UCF) and an Adjunct Professor in Computer Science and Statistics at the University of Manitoba, Canada. He holds a Ph.D. from Università degli Studi dell’Insubria (Italy) and an M.S. from SUNY Buffalo (USA) as a Fulbright Scholar. His research focuses on data science applied to complex networks and graph mining, particularly in blockchain and online social networks. He has collaborated with institutions like Yahoo! Research Barcelona, Qatar Computing Research Institute, and Huawei. Education: PhD in Computer Science, 2014 – Università degli Studi dell’Insubria, Italy MEng in Computer Science, 2010 – SUNY Buffalo, USA BSc in Electrical & Electronics Engineering, 2008 – Karadeniz Technical University, Turkey Research Interests: Explainable AI and Graph Machine Learning Topological Data Analysis (TDA) for Blockchain Networks Large-Scale Graph Analysis and Anomaly Detection Nonparametric Statistical Methods Key Contributions: His work includes developing chainlet theory for Bitcoin price prediction, scalable TDA algorithms for blockchain forensics, and interdisciplinary grants exploring blockchain applications in insurance. Notable projects include the Chartalist Dataset for blockchain network analysis and ChainNet , a topological graph learning framework. Awards & Grants: NSERC Discovery Grant (2020) UCF Interdisciplinary Grant for Blockchain in Insurance (2020) Fulbright Scholarship (M.S., SUNY Buffalo) Labs & Teams: Active in UCF’s FData Lab and leads blockchain data analytics initiatives. His work bridges academia and industry through collaborations with Huawei, QCRI, and others.

Emily Kangمشاهده پروفایل

استاد

• Spatial Statistics
• Environmental Statistics
• Data Assimilation
• +۲ مورد دیگر

Emily Kang is a Professor in the Department of Mathematical Sciences at the University of Cincinnati, where she leads the Group on Data Analytics and Decision Sciences (GDADS). Her research develops statistical methodologies for spatial and spatio-temporal data, with applications in environmental science, climate modeling, and remote sensing. Kang specializes in hierarchical Bayesian models, data assimilation techniques, and scalable algorithms for large environmental datasets. Her work includes developing the EcoPro framework for ecological projections using Earth system models and remote sensing data, creating statistical downscaling methods for climate variables, and designing visualization tools for global environmental data on multi-resolution grids. Kang received the American Statistical Association's ENVR Early Investigator Award for contributions to environmental statistics. Current projects focus on neighborhood-scale extreme heat projections, coral reef habitability under climate change, and recursive co-kriging models for multi-fidelity spatial data. Her research advances uncertainty quantification in remote sensing products and climate impact assessments.

• Artificial Intelligence
• Geospatial Data Science
• Knowledge Graphs
• +۵ مورد دیگر

Professor Manolis Koubarakis is a faculty member at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens. His research focuses on geospatial data science, knowledge graphs, entity resolution, and AI applications in Earth observation. He leads projects like ExtremeEarth and Plato, advancing semantic data cube systems and geospatial question answering engines. His work bridges AI and geospatial technologies, with contributions to frameworks like pyJedAI and Strabo 2 for managing big geospatial data. Research Interests include: Geospatial Question Answering Systems Entity Resolution and Entity Linking Ontology-Based Data Access (OBDA) Knowledge Graph Construction and Applications Earth Observation Data Analytics AI for Geoinformatics Notable Projects: ExtremeEarth: Combines Copernicus satellite data with machine learning for environmental analytics. Plato: Semantic data cube system enabling advanced querying of multidimensional datasets. GeoQA2: A geospatial question answering engine evaluated on large benchmarks. JedAI Family: Tools for scalable entity resolution in structured/semi-structured data. His publications span 20+ years, with recent emphasis on: AI-driven Earth observation systems Geospatial RDF benchmarking 3D geospatial interlinking Large language models for legal and health content delivery

Djamel E. Khelladiمشاهده پروفایل

پژوهشگر

• Software Engineering
• Model-Driven Engineering
• Software Evolution
• +۶ مورد دیگر

Djamel E. Khelladi is a CNRS researcher affiliated with the IRISA research lab and the DIVERSE team at University of Rennes 1 , France. Previously, he held postdoctoral and PhD positions at Johannes Kepler University (JKU) Linz, Austria, and Université Pierre et Marie Curie (UPMC), France. Research interests include: Model-Driven Engineering Software Evolution & Co-evolution AI and Generative AI Applications Polyglot Programming Digital Twins Recent article trends focus on integrating Large Language Models (LLMs) for code-metamodel co-evolution, polyglot programming challenges, incremental build optimization in configurable systems, and empirical studies on software evolution. His work often bridges theoretical modeling with practical implementation in industrial contexts. Academic service roles include: Proceedings Co-Chair @MODELS 2025 Co-Organizer of Models and Evolution (ME) workshops (2023-2025) Co-Editor for special issue on Model Driven Engineering for Digital Twins (SoSym 2024/25) PC member in top venues: ICSE , ASE , MODELS , ECMFA , MSR , FSE

• Healthcare workforce
• Opioid use disorder and policy implications
• Medicare and long-term care
• +۷ مورد دیگر

Ulrike Muench is an Associate Professor with tenure in the Department of Social and Behavioral Sciences at the UCSF School of Nursing, with affiliations at the Philip R. Lee Institute for Health Policy Studies and Healthforce Center. She specializes in nursing workforce dynamics, opioid policy, Medicare systems, and disparities in healthcare. Her research leverages large datasets like Medicare claims and the Medical Expenditure Panel Survey. Education & Training: MSN (2005) and PhD (2012) in Nursing from Yale University Postdoctoral Fellowship (2014) at Vanderbilt University School of Nursing Research Interests: Focuses on nurse practitioner roles, prescribing patterns in primary care/nursing homes, dementia care trajectories, and gender-based pay disparities in healthcare. Her work bridges health policy analysis with real-world clinical outcomes, aiming to inform equitable healthcare delivery systems. Awards: 2016 Academy Health New Investigator Award 2018 American Academy of Nursing Fellow 2019 UCSF School of Nursing Mentoring Award Grants & Advising: Leads studies funded by NIH and foundations, mentoring early-career researchers in health services research. Collaborates on projects addressing workforce distribution, rural-urban care disparities, and policy reform. Labs/Teams: Active in UCSF's Healthforce Center, focusing on healthcare workforce analytics and interdisciplinary policy development.

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

Dr. Masoud Makrehchi is an Associate Professor in the Department of Electrical, Computer and Software Engineering at Ontario Tech University, part of the Faculty of Engineering and Applied Science. His research focuses on Natural Language Processing, Artificial Intelligence, Machine Learning, and Social Computing, with a strong emphasis on applications in network science and moral AI. He holds a PhD from the University of Waterloo (2007), along with earlier degrees from Shiraz University and Iran University of Science and Technology. Prior to academia, he worked as a Senior Research Scientist at Thomson Reuters (2008–2012) and completed a postdoctoral fellowship at the University of Waterloo (2007–2008). His research expertise includes text mining, social network analysis, and network science, addressing challenges such as signed social network analysis, document classification, and crime trend prediction using social media data. He has received several awards, including the NSERC Postgraduate Scholarship and the SBP Challenge Award (2012). His work spans over 50 peer-reviewed publications in journals like Expert Systems with Applications, Social Network Analysis and Mining, and Web Intelligence, with contributions to conferences such as GECCO and IEEE/ACM. Dr. Makrehchi’s publications highlight innovations in feature selection, text segmentation, and AI-driven frameworks for requirements elicitation. His recent work explores coherence graphs for text segmentation, generative AI in education, and bias detection in medical datasets. He has also contributed to patent applications related to sentiment analysis and text extraction systems.

Bermond Scogginsمشاهده پروفایل

پژوهشگر ارشد

• Political Behavior
• Survey Experiments
• Democratic Backsliding
• +۱ مورد دیگر

Bermond Scoggins is a Research Fellow at the School of Politics and International Relations, College of Asia and the Pacific, Australian National University. He holds a BA (Honours) from ANU and specializes in political behavior research using survey experiments and meta-analytic methods. His research investigates democratic backsliding mechanisms, causal inference in political systems, and voter decision-making processes in democratic contexts, with special focus on Southeast Asian political systems. Recent publications examine global science communication patterns, public trust in scientists, and methodological transparency in social sciences, utilizing large-scale international datasets and cross-national comparative frameworks.

Christian Wachingerمشاهده پروفایل

استاد

• Artificial Intelligence
• Medical Imaging
• Machine Learning
• +۳ مورد دیگر

Prof. Christian Wachinger is a Professor of Artificial Intelligence in Radiology at the Technical University of Munich (TUM), part of the TUM School of Medicine and Health. His research focuses on developing AI algorithms for medical imaging, disease prediction, and addressing challenges like AI transparency and data bias. He holds a PhD in Medical Image Processing from TUM and completed postdoctoral training at MIT and Harvard Medical School. Notable awards include the Feodor Lynen Fellowship and Siemens Excellence Award. His lab, Artificial Intelligence in Medical Imaging, pioneers innovations in multimodal data integration and clinical AI applications. Education: PhD in Medical Image Processing, TUM (2011) Diploma in Computer Science, TUM and ENST Paris Honors Degree in Technology Management, CDTM Research Interests: Prof. Wachinger's work bridges AI and medicine, emphasizing neuroimaging, generative models, and clinical translation. Key areas include bias correction in medical datasets, multimodal fusion (MRI/PET), and explainable AI for healthcare. Awards: Feodor Lynen Research Fellowship (2011) German Computer Science Society Award (2011) Siemens Excellence Award (2007) Junior Research Group, Bavaria Center for Digitalization (2017) Grants & Labs: Leads the AI in Medical Imaging lab at TUM, with projects funded by grants focusing on AI-driven diagnostics and healthcare innovation.

Mark Meekanمشاهده پروفایل

پژوهشگر ارشد

• Marine Conservation Biology
• Elasmobranch Ecology
• Coral Reef Fish Ecology
• +۳ مورد دیگر

Dr Mark Meekan is an Adjunct Senior Research Fellow at the UWA Oceans Institute, The University of Western Australia. He holds a PhD in Environmental Sciences from Griffith University (1993), focusing on coral reef fish population dynamics. With over 30 years of expertise, his research spans tropical marine ecosystems, elasmobranch ecology, and coral reef conservation. He supervises post-doctoral and PhD students globally, contributing to projects on sharks and reef fish across the Indian and Pacific Oceans. His research interests include elasmobranch population dynamics, behavioral ecology, and threatened species conservation. He serves as Chief Specialist Editor for Frontiers in Marine Science’s Marine Megafauna section. Key areas of focus are predator-prey interactions, coral reef health, and the ecological impacts of climate change. He has authored over 300 peer-reviewed articles and led collaborative projects on marine megafauna movement, anthropogenic threats, and ecosystem resilience. Recent work highlights include studies on whale shark habitat connectivity, tiger shark distribution drivers, and the ecological consequences of predator removals on coral reefs. He actively engages in global marine conservation initiatives, emphasizing the need for evidence-based management strategies to protect marine biodiversity. His contributions extend to acoustic telemetry networks and large-scale experiments assessing seismic survey impacts on marine life. Dr Meekan collaborates internationally, with projects in Australia, Seychelles, and the Red Sea. His work addresses Sustainable Development Goals related to life below water (SDG 14), particularly marine ecosystem preservation and sustainable fisheries. Grants include studies on shark population dynamics and marine reserve effectiveness, funded by institutions like the Minderoo Foundation and WA Department of Environment. He leads datasets and projects on manta ray behavior, coral reef recovery, and whale shark collision risks with shipping. His research underscores the critical role of marine megafauna in maintaining healthy ecosystems, advocating for integrated conservation approaches to mitigate human-induced environmental changes.