جست‌وجوی استاد و پژوهشگر

Barbara Shinn-Cunninghamمشاهده پروفایل

استاد

• Auditory Neuroscience
• Cognitive Neuroscience
• Perception
• +۵ مورد دیگر

Barbara Shinn-Cunningham is the Glen de Vries Dean of the Mellon College of Science at Carnegie Mellon University (CMU) and holds professorships in Psychology, Biomedical Engineering, and Electrical and Computer Engineering. She is also the founding director of CMU's Neuroscience Institute. Her research focuses on auditory neuroscience, particularly auditory attention, binaural hearing, and multisensory integration, with applications to hearing disorders and assistive technologies. Shinn-Cunningham earned her B.S. from Brown University and her M.S. and Ph.D. from MIT in Electrical and Computer Engineering. Education: B.S., Electrical Engineering, Brown University (1986) M.S., Electrical & Computer Engineering, MIT (1988) Ph.D., Electrical & Computer Engineering, MIT (1994) Research Interests: She investigates how the brain processes sound in complex environments, including spatial hearing, auditory attention deficits in aging and clinical populations, and the neural mechanisms underlying cochlear synaptopathy. Her work integrates behavioral studies, neuroimaging (EEG, fMRI), and computational modeling to bridge basic science and translational research. Awards & Recognition: Fellow, Acoustical Society of America (2009) Alfred P. Sloan Research Fellow (2000) National Security Science and Engineering Faculty Fellow (2008) Helmholtz-Rayleigh Interdisciplinary Silver Medal (2019) Advising & Grants: She mentors a diverse team of graduate students and postdocs, focusing on training the next generation of auditory neuroscientists. Her grants include funding from NSF, NIH, and the Department of Defense. She leads the LiMN Lab, which explores neural mechanisms of sensory processing and attention. Labs & Teams: Director of the Lab in Multisensory Neuroscience (LiMN) at CMU, part of the Carnegie Mellon Neuroscience Institute. Collaborates with engineers, clinicians, and marine biologists to advance auditory technology and neuroimaging techniques.

John Guttagمشاهده پروفایل

استاد

• AI for Healthcare and Life Sciences
• Biological and Medical Devices and Systems
• Graphics and Vision
• +۳ مورد دیگر

John Guttag is the Dugald C. Jackson Professor in Electrical Engineering and Computer Science at MIT. His work focuses on AI-driven healthcare solutions, biomedical systems, and advanced computer vision applications. He leads research in medical image analysis, machine learning reliability, and healthcare equity. Guttag's contributions include innovative frameworks like MultiMorph and Scale-Space Hypernetworks, addressing challenges in medical imaging and clinical decision-making. Affiliations: MIT Electrical Engineering & Computer Science Department (EECS) Research emphasizes AI for healthcare, particularly in segmentation, predictive analytics, and ethical algorithm design. Notable projects include real-time fraud detection systems and studies on racial disparities in clinical risk scores. His work bridges computer science with clinical practice through tools like Voxelmorph for medical image registration and ScribblePrompt for interactive biomedical segmentation. Recent publications highlight advancements in uncertainty-aware AI, contrastive learning, and scalable medical data processing. Guttag’s methodologies prioritize practical clinical applications, aiming to improve diagnostics and healthcare workflows. His lab develops open-source tools and frameworks that enhance accessibility to advanced medical imaging technologies.

Wendy Johnsonمشاهده پروفایل

استاد

• Individual Differences
• Intelligence
• Personality Development
• +۳ مورد دیگر

Wendy Johnson is a Professor of Psychology at the School of Philosophy, Psychology and Language Sciences, University of Edinburgh. She holds a Personal Chair of Differential Development and is currently on research leave from July 2019 to December 2020. Her research focuses on individual differences in intelligence and personality, lifespan development, genetic and environmental influences on behavior, and aging. She has led multiple research grants, including projects on gene-environment interplay in aging outcomes, social causes of life chances, and the longitudinal study of cognitive function in the Scotland in Miniature cohort. Her current PhD students include Alicia Aranda Diaz, Emma Waterston, Alicia Forsberg, and Ross Stewart. Key research areas include personality stability and change, the role of environmental factors in cognitive aging, and interactions between genetics and social contexts. She has contributed to over 200 research outputs, with recent work exploring bidirectional associations between loneliness and health, and the replicability of personality-trait life outcomes across cultures. Grants leadership includes principal investigator roles in projects funded by MRC, EU, and non-EU grants totaling over £multi-million. Her work bridges behavioral genetics, developmental psychology, and public health, emphasizing the interplay of biological and social factors in human development.

Miao Xuمشاهده پروفایل

پژوهشگر ارشد

• Data Science
• Machine Learning
• Time Series Analysis
• +۴ مورد دیگر

Dr Miao Xu is a Research Fellow at the University of Queensland (UQ), affiliated with the School of Electrical Engineering and Computer Science within the Faculty of Engineering, Architecture and Information Technology. She holds an Australian Research Council DECRA Fellowship (ARC DECRA), recognizing her early-career research excellence. Her research focuses on machine learning, data science, and time series analysis, with applications in healthcare, materials science, and algorithmic fairness. Dr Xu's work addresses challenges in noisy label handling, unlearning mechanisms, and adaptive modeling for irregular data. Education: She earned a Doctor of Philosophy (PhD) from Nanjing University. She is actively involved in supervising research and contributes to the Centre for Enterprise AI at UQ. Research Interests: Dr Xu’s expertise spans machine learning , time series analysis , deep learning , and unsupervised learning . Her recent work emphasizes robust learning with noisy or incomplete labels, model unlearning, and applications in alloy design and medical informatics. She explores methods like instance-attention GNNs for irregular time series and confidence-guided techniques for adversarial attack detection. Publications: Her recent work includes advancements in GNN-based time series modeling, bias mitigation in text classification, and active learning for alloy design. Key themes include improving generalization, reducing algorithmic bias, and enhancing model transparency. Awards: Her ARC DECRA fellowship (202X–202X) supports her research on data-driven methodologies. Supervision & Grants: Available for PhD supervision in machine learning and data science. Her grants include funding for projects in unlearning mechanisms and spatiotemporal modeling. Labs/Teams: Affiliated with the Centre for Enterprise AI at UQ, collaborating on enterprise-scale AI applications and interdisciplinary research.

Mattias Villaniمشاهده پروفایل

استاد

• Bayesian Statistics
• Machine Learning
• Computational Methods
• +۳ مورد دیگر

Mattias Villani is Professor of Statistics at Stockholm University, specializing in Bayesian statistics and machine learning. He obtained his PhD in Statistics from Stockholm University in 2000 and has held positions at Sveriges Riksbank and Linköping University. Villani develops computationally efficient Bayesian methods for inference, prediction and decision-making with flexible probabilistic models. Research Interests: His work spans Bayesian computation (MCMC, HMC, variational inference), machine learning (Gaussian processes, mixture models), and applications in neuroimaging, transportation, and econometrics. Research focuses on scalable Bayesian methods for large datasets and complex models. Publication Focus: Recent articles concentrate on Bayesian neuroimaging analysis, transportation network modeling, and efficient MCMC algorithms. Methodological innovations in subsampling techniques for large-scale Bayesian computation represent a significant research trend. Student Advising: Supervises PhD students in statistical methodology development and applications. Current research groups focus on spatiotemporal modeling, locally stationary processes, and neuroimaging statistics.

Samantha Curleمشاهده پروفایل

استاد

• English Medium Instruction (EMI)
• Integrating Content and Language in Higher Education (ICLHE)
• Advanced Quantitative Research Methods (AQM)
• +۴ مورد دیگر

Samantha Curle is a Reader in the Department of Education at the University of Bath, serving as Director of all MRes Programmes in the Humanities and Social Sciences Faculty. She also holds roles as Institutional Academic Lead for the South West Doctoral Training Partnership and Director of Studies for the MRes in Advanced Quantitative Methods. Her research focuses on English Medium Instruction (EMI) in global higher education, particularly its academic and linguistic outcomes, technology integration, and psychological dimensions. Career highlights include a PhD from the University of Oxford (2018) on stakeholders’ attitudes toward EMI in Japanese higher education. She has extensive editorial roles, including guest editorships at Frontiers in Educational Psychology and Data in Brief . Her expertise spans quantitative methods, applied linguistics, and bilingual education, contributing to UN Sustainable Development Goal 4 (Quality Education). Education: PhD (University of Oxford, 2018) MSc Research Methods (University of Oxford, 2014) Bachelor of Education (Open University of Hong Kong, 2010) Grants and Projects: Leading projects on EMI in Saudi Arabia, Brazil, and Hong Kong-UK collaboration Focus on educational inequalities in Spain using CART models Awards: Adjunct Professor (2024) Her research has yielded 88+ publications, emphasizing EMI’s role in STEM, teacher professional development, and sustainable education. She advises doctoral students on quantitative EMI studies and advocates for evidence-based policy in multilingual education.

Natalia Efremovaمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Deep Learning
• Computer Vision
• +۴ مورد دیگر

Dr. Natalia Efremova is a Senior Lecturer in Digital Economy at Queen Mary University of London (QMUL), School of Business and Management. She joined QMUL in November 2021 and is a member of the Centre for Globalisation Research (CGR) and a fellow of the Digital Environment Research Institute (DERI). Her research focuses on applying machine learning and deep neural networks to address sustainability challenges such as climate change, sustainable agriculture, and environmental monitoring. She holds a Ph.D. in Computer Science (Neural Networks for Computer Vision) from Kyoto University and an MBA from the University of Oxford, where she also worked as a Teradata Research Fellow at the Said Business School. Education: Ph.D. in Computer Science, Kyoto University, Japan (2012) MBA, University of Oxford, Said Business School (2021) Previous Roles: Associate Professor, Plekhanov University of Economics, Russia (2012–2016) Teradata Research Fellow, University of Oxford (2016–2021) Research Interests: Dr. Efremova’s work emphasizes developing transparent ML models for sustainable land-use and climate-related applications, as well as ethical AI frameworks for sustainable development goals. Her projects involve satellite data analysis (e.g., Sentinel imagery) for precision agriculture, soil moisture estimation, and crop monitoring. Teaching & Supervision: She teaches courses in Business Analytics (e.g., Group Projects in Business Analytics) and supervises PhD students in AI applications for sustainability. She is currently co-director of the MSc in Environmental Analytics program (2023). Key Themes in Publications: Her articles focus on AI-driven solutions for environmental challenges, including crop mapping, soil carbon estimation, and regenerative grazing monitoring. Methodologies include deep learning (e.g., Transformers, GANs) and remote sensing data fusion. Affiliations: Member of CGR and fellow of DERI, contributing to interdisciplinary research on globalization and environmental AI.

Anthony Constantinouمشاهده پروفایل

مدرس ارشد

• Causal Machine Learning
• Decision Systems
• Bayesian Networks
• +۶ مورد دیگر

Anthony Constantinou is a Senior Lecturer at Queen Mary University of London, part of the School of Electronic Engineering and Computer Science. He leads the MInDS Research Group and the Bayesian AI Lab, focusing on causal machine learning for decision-making under uncertainty. His research spans healthcare, defense, sports, economics, and gaming, with a strong emphasis on Bayesian networks and causal inference. Research Interests: Causal machine learning, decision systems, Bayesian networks, uncertainty quantification, and their applications in healthcare, military defense, sports analytics, and economics. He collaborates with academia and industry to advance AI-driven decision-making systems. Grants: Noted for securing funding such as the Engineering and Physical Sciences Research Council grant (EP/S001646/1) for Bayesian AI research. His work emphasizes practical applications in diverse fields through collaborative projects. Labs & Teams: Head of the MInDS Research Group and Bayesian AI Lab, fostering interdisciplinary research in AI and decision systems.

Dan S. Wallachمشاهده پروفایل

استاد

• Computer Security
• Cybersecurity
• Electronic Voting Systems
• +۳ مورد دیگر

Dan S. Wallach is a Professor of Computer Science and Electrical and Computer Engineering at Rice University, and a Program Manager at DARPA's Information Innovation Office since June 2023. He holds a PhD (1999) and MA (1995) from Princeton University, and a BS (1993) from UC Berkeley. His research focuses on cybersecurity, electronic voting systems, and mobile security. He directed the NSF-funded ACCURATE Center (2005-2011), led the STAR-Vote project, and advised U.S. election security policies including testifying before state and federal committees. He also served on the Air Force Science Advisory Board (2011-2015), USENIX Board (2011-2013), and IEEE Technical Guidelines Committee (2019-2023). Recent work includes developing ElectionGuard cryptographic tools for verifiable elections and analyzing cyber warfare in Ukraine. His 15+ years of teaching include courses like Introduction to Program Design and Election Systems Technologies. Publications span secure voting protocols, smartphone security, and election auditing. Collaborations include Microsoft and VotingWorks on cryptographic voting systems like ElectionGuard and Arlo-CVR-Encryption.

Jessica V. Richمشاهده پروفایل

استادیار مدعو

• Postsecondary Education
• Competency-Based Education
• Assessment and Evaluation
• +۴ مورد دیگر

Dr. Jessica V. Rich is an Adjunct Assistant Professor at Queen’s University’s Faculty of Education and a member of the Assessment and Evaluation Group (AEG). She holds a PhD in Measurement, Assessment, Policy, Leadership and Evaluation, complemented by extensive teaching qualifications in Biology, Chemistry, and Education. Her work focuses on competency-based education (CBE), programmatic assessment frameworks, and inclusive evaluation practices across postsecondary and professional education contexts. She actively contributes to the Scholarship of Teaching and Learning (SoTL) through research on feedback mechanisms and performance improvement strategies. Dr. Rich teaches courses such as PME 896 (Capstone), PME 894 (Educational Fieldwork), and GDE 828 (Program Evaluation), emphasizing practical application of assessment methodologies. She has pioneered educational development initiatives in simulated learning environments and workplace-based assessments. Her research examines the intersection between competency frameworks and real-world application, with particular attention to healthcare and engineering education reform. Current projects include evaluating transformative change models in medical education and exploring growth-oriented assessment strategies. She has collaborated with institutions to design competency frameworks for healthcare professions and contributed to knowledge translation tools for programmatic assessment implementation. Her publications span journals like Academic Medicine , Canadian Journal of Educational Administration , and Frontiers in Medicine , reflecting interdisciplinary engagement with assessment challenges in higher education. She maintains active Twitter and LinkedIn profiles to share insights on educational development and assessment innovation.

Gaja Jaroszمشاهده پروفایل

استاد

• Phonological Theory
• Computational Linguistics
• Language Acquisition
• +۵ مورد دیگر

Gaja Jarosz is a Professor & Chair of Graduate Admissions in the Department of Linguistics at the University of Massachusetts Amherst, located in the Integrative Learning Center (ILC N410). She holds a PhD in Cognitive Science from Johns Hopkins University and a BA in Mathematics and Social Thought from Washington University in St. Louis. Her research focuses on phonological theory, computational linguistics, and language acquisition, particularly how sound systems and their acquisition can be modeled computationally. She develops formal systems informed by phonological theory and grounded in statistical learning, exploring hidden phonological structure, lexical-grammatical interactions, and learning biases. Since 2015, her work integrates experimental methods with computational modeling. Her publications span computational phonological learning, parametric stress systems, and experimental studies of phonological acquisition. Notable contributions include analyzing Polish phonotactics, stress learning mechanisms, and the role of distributional cues in word segmentation. She co-founded the Society for Computation in Linguistics (SCiL) and organizes conferences like ACL and SCiL workshops. Her research is supported by grants (not explicitly listed) and involves collaborations with scholars like Aleksei Nazarov, Joe Pater, and J. Alex Johnson. She contributes to software tools (e.g., NECPhon) and has taught courses on phonology and computational linguistics.

Do-Hong Kimمشاهده پروفایل

استاد

• Research Design
• Program Evaluation
• Psychometric Methods
• +۴ مورد دیگر

Do-Hong Kim is a Professor in the Department of Educational Evaluation and Research at Wayne State University. His expertise spans research design, program evaluation, psychometric methods, and quantitative analysis in education. He focuses on assessment for culturally and linguistically diverse students, educational measurement validity, and bilingual research. Education: Ph.D. in Educational Psychology and Research from the College of Education, University of South Carolina. Research Interests: Kim specializes in educational and psychological assessment, validity issues in measurement, and program evaluation. His work often addresses equity in assessment, psychometric properties of scales (e.g., self-efficacy in language learning, mathematical problem-solving), and evaluation systems for early childhood education. Grant Leadership: He has led multiple funded projects, including a subaward from the U.S. Department of Education (2022-2027) for gifted education equity in Detroit. Other grants focus on data analytics, pre-K literacy, and formative assessment implementation. Scientific Recognition: Kim has received the NCARE Distinguished Paper Award (2013) and the College of Education Award for Excellence in Research at UNC Charlotte (2008).

Azza Abouziedمشاهده پروفایل

دانشیار

• Human-Data Interaction
• Database Systems
• Human-Computer Interaction
• +۷ مورد دیگر

Azza Abouzied is Associate Professor of Computer Science at New York University Abu Dhabi and Global Network Associate Professor at the Tandon School of Engineering. She serves as Vice Provost for Faculty Advancement and Engagement at NYUAD starting September 2024. Her research bridges database systems and human-computer interaction, focusing on intuitive tools for data querying and decision-making in uncertain, collaborative environments. PhD, Yale University (2013) MPhil, Yale University MSc, Dalhousie University BSc, Dalhousie University Her research centers on human-data interaction, designing systems that make data accessible to non-experts. She combines techniques from UI design, machine learning, and databases to build tools that simplify complex data tasks. Her earlier work focused on example-driven querying and synthetic data generation, while her recent work explores in-database prescriptive analytics and decision support in domains like disinformation mitigation and epidemic planning. Her publications span database and HCI venues, with a recurring theme of enhancing usability without sacrificing scalability. She co-founded Hadapt, a Big Data analytics platform, and has led interdisciplinary research through the Human-Data Interaction Lab and the Center for Interacting Urban Networks. Her teaching includes foundational courses such as Database Systems, Operating Systems, and Data, as well as the critical thinking course Techruption. VLDB Test of Time Award (2019) Best Paper Award in Database Systems Honorable Mention in HCI Publications Azza mentors undergraduate capstone students and advises prospective PhDs, research assistants, and postdocs. She is actively involved in academic leadership, having chaired NYUAD’s faculty council in 2024 and co-chaired the SIGMOD 2025 program. Her work emphasizes empowering users to critically engage with data and AI, both in research and education.

• Network Science
• Machine Learning
• Theoretical Computer Science
• +۵ مورد دیگر

Jennifer Chayes is Dean of the College of Computing, Data Science, and Society and a Professor at the University of California, Berkeley, with appointments in Electrical Engineering and Computer Sciences, Mathematics, Statistics, and Information. She co-founded Microsoft Research New England, New York City, and Montreal, leading interdisciplinary research for 23 years before joining Berkeley in 2020. Previously, she was a Professor of Mathematics at UCLA, where she received the Distinguished Teaching Award. Education PhD in Mathematical Physics (1983), Princeton University BA in Biology and Physics (1979), Wesleyan University Her research spans network science , machine learning , and theoretical computer science , focusing on phase transitions in networks, graphons for large-scale network modeling, and applications in cancer immunotherapy , ethical AI , and climate change . Her work on graph limits and exchangeable graphs has foundational implications for network analysis. Recent publications highlight trends in sparse graph theory , privacy-preserving algorithms , and fairness in AI . Awards include the Anita Borg Institute Women of Vision Leadership Award (2012), SIAM John von Neumann Lecture Prize (2015), and ACM Distinguished Service Award (2020). She is a member of the National Academy of Sciences and a Fellow of multiple academic societies. Chayes actively promotes Diversity in STEM and serves on advisory boards for institutions like MIT’s Schwarzman College of Computing, the Howard Hughes Medical Institute, and the National Science Foundation’s Institute for AI and Fundamental Interactions.

Stéphanie Cailliesمشاهده پروفایل

استاد

• Cognitive Psychology
• Social Cognition
• Theory of Mind
• +۴ مورد دیگر

Stéphanie Caillies is a Professor of Cognitive Psychology at the University of Reims Champagne-Ardenne and serves as Deputy Director of the C2S Laboratory (Cognition, Santé, Socialisation EA 6291). Her research focuses on social cognition, language processing, and theory of mind from developmental, neuropsychological, and psychopathological perspectives. Research Interests: Pragmatic language comprehension and figurative language processing Neural correlates of social cognition in clinical populations (schizophrenia, bipolar disorder) Developmental trajectories of theory of mind in neurotypical and at-risk children Neurocognitive mechanisms of irony and metaphor understanding Parent-child interactions in premature infants Her recent publications demonstrate strong emphasis on neuroimaging approaches (fMRI, ERP) to study social cognition, with recurring themes in clinical populations and developmental disorders. Research frequently bridges psycholinguistics, clinical psychology, and cognitive neuroscience. Current Research Projects: POSTURE (2023-2027): Psychologie et réalité virtuelle - ANR-funded project on dynamic posture recognition EDIRE (2022-2025): Horizon Europe project on educational development PHRC CALIN: Hospital clinical research on sensory-tonic stimulation for premature infants Teaching Activities: Regularly teaches courses in Statistics, Language Development, Academic Learning, and Social Cognition Disorders. Organized multiple scientific conferences including the 59th Congress of the French Society of Psychology. Doctoral Supervision: Currently supervising theses on executive functions in premature children and parent-child interactions. Previously supervised dissertations on pragmatic language in reading comprehension and neurofunctional correlates of theory of mind in schizophrenia.