جست‌وجوی استاد و پژوهشگر

• Educational Technology
• Informatics Education
• Programming Pedagogy
• +۳ مورد دیگر

Snježana Babić serves as Associate Professor and Head of the Graduate Study Program Informatics - Teaching Major at the Faculty of Informatics in Pula , University of Pula. With a PhD obtained in 2016 and university affiliation since 2017, she teaches across undergraduate and graduate programs focusing on ICT fundamentals, web design, digital competencies, and informatics education methodology. Her educational background includes graduation in 1993 followed by doctoral studies culminating in 2016. Though specific institutions aren't detailed in the text, her career trajectory shows progression from foundational ICT teaching to specialized informatics education leadership. Babić's research centers on educational technology adoption with particular emphasis on programming pedagogy for young learners through the MEMA method. Her work investigates digital competencies across multiple educational contexts, from primary schools to higher education. Recent publications demonstrate rapid adaptation to emerging technologies, with 2025 studies examining ChatGPT integration in Croatian educational settings and cross-cultural applications of programming methods. Analysis of her 15 most recent publications reveals consistent focus on technology acceptance models applied to Croatian educators and students. The research trajectory shows evolution from foundational e-learning systems (2016-2020) to cutting-edge AI and robotics applications (2022-2025), maintaining strong methodological focus on teacher motivation , implementation barriers , and culturally-specific adoption factors . Her teaching portfolio demonstrates deep integration with research interests: Undergraduate: ICT Fundamentals, Web Design and Application Graduate: Digital Competencies in Education, Informatics Education Methodology, Practicum for IT Educators Cross-disciplinary: ICT in Nursing, Healthcare Informatics Babić actively contributes to institutional leadership as program head while maintaining robust research productivity. Her work bridges theoretical technology adoption frameworks with practical implementation challenges in Croatian educational contexts, particularly examining how emerging technologies like humanoid robots and AI assistants can be effectively integrated into teaching practices.

Ajmal Mianمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +۸ مورد دیگر

Ajmal Mian is a Professor of Computer Science at the University of Western Australia (UWA), affiliated with the School of Physics, Maths and Computing. He holds an Australian Research Council Future Fellowship (2022) and leads research in Artificial Intelligence, Computer Vision, and Machine Learning. His work focuses on 3D computer vision, adversarial AI defense, and explainable AI. His research interests include 3D point cloud analysis, face recognition, human action recognition, and remote sensing. He has published over 300 papers and secured major grants from ARC, NHMRC, and DARPA, totaling millions in funding. He has supervised 29 PhD students and mentored 12 postdoctoral researchers. Key projects include 3D diffusion models for scene generation, robust 3D vision systems, and defense against AI deception attacks. He serves as a fellow of IAPR, an ACM Distinguished Speaker, and has editorial roles at IEEE Transactions on Neural Networks and Pattern Recognition. Research Awards: HBF Mid-Career Scientist of the Year, West Australian Early Career Scientist of the Year, IAPR Best Scientific Paper Award. Grants: ARC Discovery Projects, National Intelligence & Security Discovery grants, DARPA grants for AI security. His teaching spans computer vision, machine learning, and programming courses. Collaborations include defense, medical, and agricultural applications.

Vincent Alevenمشاهده پروفایل

استاد

• Intelligent Educational Systems
• Learning Sciences
• Human-Computer Interaction
• +۵ مورد دیگر

Vincent Aleven is a Professor in the Human-Computer Interaction Institute (HCII) at Carnegie Mellon University, where he leads the CATS Lab focused on developing 'smart classrooms' through human-AI collaboration in educational settings. His work bridges learning sciences, human-computer interaction, artificial intelligence, and education to create dynamic learning environments that leverage complementary strengths of learners, educators, and AI systems. His research centers on intelligent educational systems with core interests in cognitive modeling, metacognitive skill development, and case-based reasoning. Key initiatives include AI-based tutoring software for individual/collaborative learning (e.g., middle-school math equation solving), tools supporting self-regulated learning (help-seeking, self-assessment), teacher orchestration dashboards, and browser-based authoring tools like CTAT/Tutorshop that lower development barriers for intelligent tutoring systems. Current projects investigate smartphone-based homework solutions, gamified tutoring interfaces, and comparative effectiveness against platforms like Khan Academy. Scientific Awards: No specific awards mentioned in source materials. Professor Aleven advises graduate students including Kexin Bella Yang and Conrad Borchers. His research is supported by multiple grants, notably a $2M award for testing AI-based mobile tutoring software in underserved middle-school contexts. Major projects encompass: MEME Project : Boosting math outcomes through motivation and self-regulation Metacognition and Motivation : Pittsburgh Science of Learning Center initiative Rapid Tutor Development : Streamlining cognitive model creation Learning-Oriented Dialogue : Enhancing engagement via collaborative problem-solving The CATS Lab operates through deep collaboration with teachers and students in real classrooms, currently exploring how smartphone accessibility, targeted tutor redesign, and parental involvement optimize homework effectiveness. Future directions address AI support for tutors/parents, balancing self-regulatory development with domain learning, and democratizing tutor authoring for educators.

Qing Cindy Changمشاهده پروفایل

استاد

• Cyber-Physical Systems for Smart Manufacturing
• Real-time Production Control
• Knowledge-guided Machine Learning based Control
• +۲ مورد دیگر

Qing (Cindy) Chang is a Professor in the Department of Mechanical and Aerospace Engineering at the University of Virginia, where she directs the Intelligent Systems Lab. She joined UVA in 2019 after serving as an associate professor at Stony Brook University. Prior to academia, she spent a decade at General Motors R&D, receiving their highest innovation awards. Education: M.S. from University of Wisconsin-Madison Ph.D. in Manufacturing from University of Michigan Research Focus: Chang's work integrates math-based modeling and data-driven methods to optimize manufacturing systems. Key areas include: Adaptive control and machine learning for production efficiency Human-robot collaboration frameworks Sustainable manufacturing through energy management Real-time control of cyber-physical production systems Reinforcement learning applications in industrial automation Research Trends: Her recent publications (2024-2025) demonstrate strong focus on AI-driven manufacturing optimization, with 80% leveraging reinforcement learning/LLMs for robotic control. Key themes include multi-agent coordination (67% of papers), energy efficiency (53%), and flexible production systems (47%). Awards & Recognition: Inducted as SME Scholar (2024) 20 Most Influential Professors in Smart Manufacturing - SME (2020) NSF CAREER Award (2014) Three-time GM Boss Kettering Award winner (2005,2006,2008) ASME and SME Fellow Leadership & Funding: Serves on NAMRI/SME Board of Directors with editorial roles across ASME/IEEE/SME journals. Research supported by NSF (including CAREER), Department of Energy, and multiple industry partners. Leads projects on human-robot collaboration and sustainable manufacturing. Lab & Collaboration: Directs the Intelligent Systems Lab at UVA, focusing on industrial AI applications. Collaborates with automotive and energy sectors to translate research into practical solutions for smart factories.

Douglas D. Readyمشاهده پروفایل

دانشیار

• Education Policy
• Social Policy
• Educational Equity
• +۳ مورد دیگر

Douglas D. Ready is an Associate Professor of Education and Public Policy at Teachers College, Columbia University , where he serves as Director of the Consortium for Policy Research in Education (CPRE-TC). His research focuses on the intersections of education policy, social policy, and educational equity, particularly how policies influence socio-demographic disparities in cognitive development. Research Interests: Ready’s work examines the structural determinants of educational equity, including school choice architecture, personalized learning, early childhood education, and the impacts of technology on instruction. He has published extensively in journals such as Educational Evaluation and Policy Analysis and Sociology of Education . Editorial Roles: Ready contributes to the academic community as a member of the editorial boards of Educational Evaluation and Policy Analysis and American Educational Research Journal . Key Projects: He has led analyses of programs like Teach to One: Math and Pre-K for All , exploring their effects on student outcomes and equity.

Marcia Barnesمشاهده پروفایل

استاد

• Cognitive Processes in Learning
• Learning Disabilities
• Reading Comprehension
• +۷ مورد دیگر

Marcia Barnes is the Dunn Family Chair in Psychoeducational Assessment and a Professor of Special Education at Vanderbilt University's Peabody College, Department of Special Education. Her research focuses on understanding and addressing learning difficulties in reading comprehension and mathematics among children with neurodevelopmental disorders and those at risk for learning disabilities. Her research interests include: Cognitive and linguistic processes in reading and math disabilities Development of assessment tools for inferential comprehension Design and evaluation of interventions for early mathematics and reading comprehension Co-occurring reading and math disabilities Technology-assisted and combined cognitive-academic interventions Her work employs longitudinal studies, randomized controlled trials, and meta-analyses, and has been supported by federal funding from the Institute of Education Sciences (IES) and the National Institutes of Health (NIH). Her publications appear in top journals such as Scientific Studies of Reading , Journal of Educational Psychology , and Neuropsychology . Marcia Barnes is the author of numerous peer-reviewed papers, book chapters, and a widely used book on learning disability identification and intervention. She leads the Barnes Lab at Vanderbilt, which is dedicated to improving outcomes for children with or at risk for learning disabilities through rigorous research. Ph.D. in Psychology, McMaster University, 1988 Postdoctoral Fellowship, Research Institute, The Toronto Hospital for Sick Children, 1988–1991 She advises students and leads research teams in the Barnes Lab, focusing on innovation in assessment and intervention for learning disabilities. While specific advisees are not listed, her federally funded projects suggest active mentorship and collaboration. Her lab explores future directions in technology-enhanced learning and integrated cognitive-academic supports. The Barnes Lab conducts cutting-edge research on: Risk factors for co-occurring math and reading disabilities Intervention efficacy through randomized trials Development of novel assessment instruments Technology-assisted learning tools Longitudinal trajectories of learning difficulties

• Educational Science
• Empirical Research in Education
• Student Motivation
• +۵ مورد دیگر

Rainer Watermann is a Full Professor for Empirical Research in Education at the Free University of Berlin since 2011, previously serving as Full Professor for Education and Empirical Research in Schools at Georg-August University of Göttingen (2005-2011). His academic career includes significant research positions at the Max Planck Institute for Human Development in Berlin (1997-2005). Watermann earned his Diploma in Educational Science from the University of Münster (1996), followed by a Dr. phil (2002) and Habilitation (2005), both from Freie Universität Berlin. His educational background established the foundation for his extensive research in educational transitions and disparities. His research focuses on educational transitions, particularly from primary to secondary school and into higher education, examining motivational factors, social background influences, and achievement goal development. Watermann's work consistently addresses how social disparities affect educational opportunities and outcomes, with particular attention to gender differences and longitudinal developmental patterns. His methodological expertise includes latent class analysis, structural equation modeling, and large-scale assessment design. Watermann's publication portfolio reveals consistent research trajectories examining motivational frameworks (particularly expectancy-value theory), educational transitions, social disparities in education, and political socialization. His work spans both theoretical development and practical applications for educational policy and practice, with increasing focus on intervention effectiveness in recent years. As an active member of the academic community, Watermann serves on multiple editorial boards including the Swiss Journal for Educational Sciences and Empirical Educational Science , and regularly reviews for major educational and psychological journals. He has also contributed to significant research centers, including serving as spokesman for the Center for Empirical Research on Teaching and Learning in Schools (ZeUS) at the University of Göttingen (2008-2010). Watermann maintains an active research program with numerous collaborations across Germany and internationally, evidenced by his consistent publication record through 2025. His work bridges educational psychology, sociology of education, and policy-relevant research, maintaining strong connections between theoretical frameworks and practical educational contexts.

Paul Taeleمشاهده پروفایل

عضو هیئت علمی

• Sketch Recognition
• Human-Computer Interaction (HCI)
• Artificial Intelligence
• +۵ مورد دیگر

Paul Taele is an Instructional Assistant Professor and Deputy Lab Director in the Sketch Recognition Lab at Texas A&M University's Department of Computer Science & Engineering. He holds a Ph.D. (2019), M.S. (2010), and dual B.S. degrees in Computer Science and Mathematics from the University of Texas at Austin (2006). His research focuses on sketch recognition, haptics, and intelligent interfaces for education and accessibility, with notable work in mid-air gesture recognition, educational sketching tools, and assistive technologies for disabilities. He has contributed to projects like Kanji Workbook , Hashigo , and HaptiMoto , and has published over 50 peer-reviewed articles across venues like CHI, IUI, and AAAI. Taele has received awards including the NSF Student Travel Grant (2014) and Ford Foundation Honorable Mention (2015). He teaches courses in capstone design, programming, and sketch recognition, and mentors students across all academic levels through strict eligibility criteria for research collaborations. Education : Ph.D. Computer Science, Texas A&M University (2019) M.S. Computer Science, Texas A&M University (2010) B.S. Computer Science & Mathematics, University of Texas at Austin (2006) Concentration in Mandarin Chinese, National Chengchi University (2007) Research Interests : Taele's work bridges HCI and AI to create accessible educational interfaces. His projects emphasize: Sketch Recognition : Developing algorithms for mid-air gestures, children's developmental assessments, and language learning Accessibility : Haptic systems for visually impaired learners and algebra education Educational Tech : Intelligent tutoring systems for music, math, and East Asian languages Awards & Grants : EAAI-20 Travel Grant (2020) Ford Foundation Dissertation Honorable Mention (2015) NSF East Asia-Pacific Summer Institutes (2013, 2012) Royce E. Wisenbaker Fellowship (2009) Lab & Teams : Director of the Sketch Recognition Lab (SRL) and collaborator with global institutions like Singapore Management University and National Taiwan University. Active in organizing workshops like SketchRec at IUI conferences.

Roman Feimanمشاهده پروفایل

استادیار

• Cognitive Development
• Cognitive Psychology
• Cognitive Science
• +۳ مورد دیگر

Roman Feiman is the Thomas J. and Alice M. Tisch Assistant Professor of Cognitive, Linguistic, and Psychological Sciences and an Assistant Professor of Linguistics at Brown University. He directs the Brown Language and Thought Lab, focusing on how humans combine words into meaningful sentences and develop logical reasoning abilities. His research integrates methods from cognitive developmental psychology, psycholinguistics, and formal semantics. Feiman holds a PhD in Psychology from Harvard University (2015), followed by postdoctoral training at Harvard and UC San Diego. His work explores the cognitive systems underlying language and thought, including negation comprehension, quantifier scope, and the development of exact equality concepts. He teaches courses such as Language Processing in Humans and Machines and Logic in Language and Thought . His research interests span cognitive development, linguistic pragmatics, and the language of thought hypothesis. Notable findings include studies on children’s understanding of negation and how logical principles shape early language acquisition. Feiman has been recognized with the 2023 Henry Merritt Wriston Fellowship. His lab investigates topics like word referencing, semantic development, and the interplay between language and nonverbal reasoning. Recent work examines how neural networks might model human cognitive processes, bridging AI and psychological theory.

Hamsa Bastaniمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Healthcare Operations
• +۵ مورد دیگر

Hamsa Bastani is an Associate Professor of Operations, Information and Decisions at the Wharton School, University of Pennsylvania, with a secondary appointment in Statistics and Data Science. She co-directs the Wharton Healthcare Analytics Lab and serves as an Associate Editor for Operations Research, M&SOM and OR Letters. Her academic journey began with summa cum laude graduation from Harvard in 2012 with an A.M. in physics and A.B. in physics and mathematics. She completed her PhD in Stanford's Electrical Engineering department under Mohsen Bayati, followed by a Herman Goldstine postdoctoral fellowship at IBM Research. Professor Bastani's research focuses on developing novel machine learning algorithms for data-driven decision-making, with applications spanning healthcare operations, social good, and revenue management. Her work demonstrates particular expertise in sequential decision-making (bandits, reinforcement learning), learning from auxiliary data sources (transfer learning, meta-learning), and designing effective human-AI interfaces (interpretability, fairness). She has made significant contributions to understanding how AI systems affect and augment human behavior, with the goal of designing AI tools that help humans thrive. Her publications reveal a strong trend toward high-impact applications of machine learning in critical societal domains. A significant portion of her recent work focuses on healthcare applications, including optimizing health supply chains in low- and middle-income countries, designing clinical trial protocols, and creating targeted public health interventions. Another major theme examines the complex relationship between humans and AI systems, particularly how AI affects learning outcomes and decision-making processes. Her work frequently bridges theoretical advances with practical implementation, as evidenced by country-scale deployments in Greece and Sierra Leone. Wagner Prize for Excellence in Operations Research Practice (2021) Pierskalla Award for Best Paper in Healthcare (2021, 2019, 2016) Behavioral OM Best Paper Award (2021) Public Sector in OR Best Paper Award (2024) INFORMS Data Mining Best Paper Award (2022) Wharton Teaching Excellence Award (2019, 2020, 2021) Professor Bastani has advised numerous PhD students who have gone on to prominent positions, including Pia Ramchandani (Director of Responsible AI at PwC), Arielle Anderer (Assistant Professor at Cornell Johnson), and Kan Xu (Assistant Professor at ASU Carey). Her research has been supported by collaborations with national governments, including the Greek government where she co-designed Eva, the national-scale reinforcement learning system for targeted COVID-19 testing, and the Government of Sierra Leone where she improved patient access to essential medicines by nearly 20% via decision-aware learning. She has also conducted the first large field study deploying generative AI tutors in high school math classes. She leads the Wharton Healthcare Analytics Lab and serves on the Steering Committee for the Penn Center for Health Incentives and Behavioral Economics and on the statistics advisory committee for the AHA Food is Medicine Initiative. Outside academia, she serves on the Workday AI Advisory Board, demonstrating her commitment to translating academic research into practical applications.

Philip Bellمشاهده پروفایل

استاد

• Equity Studies
• Learning Sciences
• Human Development
• +۴ مورد دیگر

Philip Bell is a Professor in the Learning Sciences & Human Development at the University of Washington College of Education . He holds the Shauna C. Larson Endowed Chair in Learning Sciences and serves as Director of the Institute for Science + Math Education . Additionally, he co-directs the LIFE Science of Learning Center , focusing on equity-driven innovation in STEM education. Education : PhD and MEd in Human Development & Cognition, Science/Math Education from UC Berkeley (1998, 1996); BS in Electrical Engineering & Computer Science from University of Colorado Boulder (1989). Bell’s research emphasizes cognitive and cultural approaches to learning across diverse environments, including everyday science expertise , learning technology design , culturally expansive instruction , and large-scale educational reform . His work bridges formal and informal learning contexts , addressing equity through participatory design research. Recent research trends include culturally sustaining pedagogies , climate justice science education , and systemic equity in STEM . His team develops STEM Teaching Tools and collaborates with national networks to improve science standards and instructional coherence. Scientific Awards : Shauna C. Larson Endowed Chair in Learning Sciences. Bell mentors a diverse group of doctoral students and has advised numerous PhD/MEd graduates now in academic and research roles. His projects span K-12 STEM innovation , science communication , and equity-focused policy , including contributions to the Next Generation Science Standards . He leads the Institute for Science + Math Education and collaborates with the Research+Practice Collaboratory , Seattle/Renton school districts, and national teams to scale equitable science education.

Angeline Lillardمشاهده پروفایل

استاد

• Developmental Psychology
• Montessori Education
• Child Social and Cognitive Development
• +۳ مورد دیگر

Dr. Angeline Lillard is Commonwealth Professor of Psychology and Director of the Montessori Science Program at the University of Virginia. She leads the Early Development Lab, focusing on children's social and cognitive development, particularly Montessori education's impact on learning and wellbeing. A Fellow of AAAS, APA, and APS, she earned her BA in English Literature from Smith College and PhD in Psychology from Stanford University. Research Interests: Dr. Lillard's work bridges Montessori pedagogy with developmental psychology, analyzing how play, educational environments, and culturally responsive teaching shape child outcomes. She explores standardized testing disparities, discipline equity, and the neurobiological underpinnings of pretend play. Key themes in her 15 most recent publications include Montessori's role in reducing educational inequality, the cognitive effects of fantasy in media, and the use of multilevel modeling to assess school discipline patterns. Her research spans preschool to adult wellbeing, emphasizing self-determination theory and longitudinal data. Scientific Recognition: Awarded the Nancy Staub Award for Puppetry Research (2024) Recognized for her book with the Cognitive Development Society Book Award (2006) James McKeen Cattell Sabbatical Fellow (2005-06) Albert Bandura Graduate Research Award (2016-17) Advising and Grants: Mentored 15+ graduate students including Lee LeBoeuf and Christina Carroll. Secured IES funding for a 600-child study on public Montessori preschools and Arnold Foundation support for kindergarten data collection.

Svitlana Rogovchenkoمشاهده پروفایل

استاد

• Differential Equations
• Impulsive Differential Equations
• Asymptotic Methods in Nonlinear Mechanics
• +۳ مورد دیگر

Svitlana Rogovchenko is a Professor at the Department of Engineering Sciences, University of Agder, Norway. Her academic work bridges pure mathematical research in differential equations with innovative pedagogical approaches in engineering and interdisciplinary education. Educational Background: Doctor of Philosophy in Differential Equations (1988), Institute of Mathematics, National Academy of Sciences, Kyiv, Ukraine Master of Science in Mathematics (1983), Kyiv State University, Ukraine Research Interests: Qualitative theory of ordinary and impulsive differential equations, asymptotic methods in nonlinear mechanics, mathematical modeling, and the intersection of mathematics education with engineering and biology curricula. She focuses on conceptual understanding, pedagogical challenges, and commognitive conflicts in learning differential equations. Recent Publications Trends: Her work spans mathematical modeling of vehicle crashworthiness, interdisciplinary education for biologists and engineers, and pedagogical innovations in ordinary differential equations. Articles emphasize assessment design, group work tensions, and bridging mathematical theory with applied contexts.

Feng Fuمشاهده پروفایل

دانشیار

• Evolutionary Game Theory
• Computational Social Science
• Biomedical Data Science
• +۷ مورد دیگر

Feng Fu is an Associate Professor of Mathematics at Dartmouth College, with an adjunct appointment in Biomedical Data Science. He leads the Fu Lab, focusing on interdisciplinary research at the intersection of evolutionary game theory, computational social science, and biomedical data science. His academic roles include teaching courses such as Evolutionary Game Theory, Stochastic Processes, and Game Theory and Artificial Intelligence. Education: Senior Postdoc, ETH Zurich (2012-2015); Postdoc, Harvard University (2010-2012); PhD, Peking University (2010); B.S., Fudan University (2004). Research interests span evolutionary dynamics of cooperation, computational models of human behavior and social networks, cancer evolution, and behavioral epidemiology. Notable work includes studies on vaccine hesitancy, misinformation dynamics, and the hysteresis effect in vaccination uptake. His lab has received prestigious funding, including a Bill & Melinda Gates Foundation Grant (2019). Teaching and mentoring: Advised numerous graduate and undergraduate researchers, many of whom have received awards and advanced to academic or industry roles. Courses taught include QSS/MATH 30.04 (Evolutionary Game Theory) and MATH 146 (Game Theory and AI). Labs/Teams: Fu Lab at Dartmouth collaborates across disciplines, with projects in cancer immunotherapy modeling, network-based interventions, and computational social science. Recent lab highlights include advancements in understanding polarization and the development of targeted public health strategies.

Ann Rivetمشاهده پروفایل

دانشیار

• Science Education
• Earth and Environmental Science Teaching and Learning
• Inquiry and Project-Based Science Learning
• +۴ مورد دیگر

Ann Rivet is an Associate Professor of Science Education at Teachers College, Columbia University, and serves as the Associate Director of the Center for Sustainable Futures. She previously held the role of Program Officer in the Directorate for STEM Education at the National Science Foundation from 2016 to 2019. Her expertise lies in science education, particularly in Earth and environmental science, curriculum design, and urban school reform. Education: Ph.D. in Science Education, University of Michigan M.S. in Science Education (Earth Science), University of Michigan Sc.B. in Physics, Brown University Research Interests: Dr. Rivet's research focuses on how students develop deep understandings of Earth and environmental science concepts. She examines the intersections of scientific reasoning, instructional design, and assessment, primarily at the middle and secondary school levels. Her current work investigates how crosscutting scientific concepts influence students’ understanding of core disciplinary ideas, and how phenomena-based instructional approaches can support learning about large-scale Earth systems. She is actively involved in supporting the adoption and implementation of the Next Generation Science Standards (NGSS) through policy and practitioner-level initiatives, including the development of free, open-source curriculum materials for middle and high school science. Publications & Trends: Her work has been published in leading journals such as Science , the Journal of Research in Science Teaching , and the American Educational Research Journal . Her publications span topics from learning progressions in science to contextualizing instruction for urban students, reflecting a deep commitment to both theoretical and applied aspects of science education. Grants & Funding: Principal Investigator, NSF: "Collaborative Research: Bridging the Gap Between Tabletop Models and the Earth System" ($982,080) Science Team Member, GE Foundation: "Harlem Schools Partnership for Science and Math Education" ($5,000,000) Co-Principal Investigator, Carnegie Foundation: "Enhancing Teacher Preparation for Adolescent Literacy" ($100,000) Co-Principal Investigator, NSF: "GSE/RES Girls’ Science Practices in Urban High Poverty Communities" ($499,334) Principal Investigator, NSF (subaward): "Developing the Next Generation of Middle School Science Materials" ($137,198) Leadership & Outreach: Dr. Rivet has led multiple federally funded projects aimed at improving science education in urban schools. She has also contributed to national policy discussions through her role at the NSF and continues to shape science education standards and practices through her research and curriculum development efforts.