جست‌وجوی استاد و پژوهشگر

• Computational Linguistics
• Educational Technology
• Mechanical Engineering Education
• +۵ مورد دیگر

Aneet D Narendranath is a Teaching Professor in the Department of Mechanical and Aerospace Engineering at Michigan Technological University. His work focuses on integrating computational linguistics, educational technology, and engineering pedagogy to enhance student learning and problem-solving skills. He develops tools and frameworks for analyzing student writing and reflection, leveraging AI and network analysis to quantify educational outcomes. His research interests span mechanical engineering education, fluid dynamics, thermodynamics, and finite element methods. Notable contributions include applying artificial neural networks to thermodynamics instruction and developing Raspberry Pi-based systems for educational resource optimization. Recent articles highlight his exploration of critical thinking assessment through directed graphs, metadiscourse analysis in student reflections, and AI-driven communication bridges in engineering education. These studies underscore his commitment to bridging technical and communication skills in engineering curricula. No scientific awards are explicitly mentioned in the provided texts. Aneet actively contributes to open-source projects like CompLing and ComputationalMechanicsPlayGround , emphasizing educational software development and computational tools for mechanical engineering students.

• Mobile Learning Technologies
• Contextualized Learning
• Immersive Media for Learning
• +۳ مورد دیگر

Prof. Marcus Specht is a Professor for Digital Education at Delft University of Technology and Director of the Leiden-Delft-Erasmus Center for Education and Learning. He also holds a Professorship for Learning Technologies at the Welten Institute of the Open Universiteit. His academic journey includes a Diploma in Psychology (1995) and a Dissertation in adaptive information technology (1998) from the University of Trier. Key roles include leading the 'Mobile Knowledge' department at Fraunhofer Institute (2001–2018) and heading the Learning Innovation Lab at Open Universiteit (2005–2018). He currently serves as Leader of the 4TU.Centre for Engineering Education (2020–2025). Research focuses on Mobile Learning Technologies, Contextualized Learning, and Immersive Media for Learning. Notable awards include being an Apple Distinguished Educator and former President of the International Association of Mobile Learning (2013–2015). Recent work explores algorithmic abstraction, goal-setting interventions, and cognitive processes in digital reading through hybrid intelligence approaches. Over 90 publications and datasets highlight contributions to educational technology and learning analytics. Education: Diploma in Psychology (University of Trier, 1995), Dissertation (1998) Labs/Teams: Leiden-Delft-Erasmus Center for Education and Learning, Learning Innovation Lab Grants & Activities: Leadership in 4TU.Centre, contributions to conferences like LAK 2025 and EDUCA 2025

Alexander Schüler-Meyerمشاهده پروفایل

دانشیار

• Mathematics Education
• Teacher Education
• Lesson Study
• +۴ مورد دیگر

Alexander Schüler-Meyer is an Associate Professor at the Eindhoven School of Education (ESoE) within Eindhoven University of Technology (TU/e) . His research focuses on pre-service and in-service teacher education, particularly through Lesson Study , and language-responsive teaching of mathematics. He explores algebra education and the transition from school to university mathematics, leading international projects like ML2 (multilingual mathematics learning) and LESSAM (Lesson Study in mathematics). He teaches master’s courses in mathematics education and conducts workshops for in-service teachers. Academic Background: PhD in mathematics education from the University of Oldenburg (Germany), postdoc at TU Dortmund University, and interim professor at the University of Bremen before joining TU/e in 2018. Research Interests: Schüler-Meyer emphasizes critical inquiry in teacher education and multilingual classroom strategies to enhance mathematical reasoning. His work bridges theory and practice, addressing challenges like student ownership in project-based learning and language barriers in STEM education. Key Projects: ML2 (Erasmus+), Hybrid Professional Learning Communities (Comenius fellowship), and LESSAM (Lesson Study implementation). His recent publications analyze CBL’s impact on student autonomy, bilingual teaching interventions, and algebraic thinking patterns. Awards: Comenius Teaching Fellow (2018). Teaching and Supervision: Leads master’s courses like Mathematics Teaching and supervises design-based research projects. His collaborative work spans institutions in Europe and beyond.

• Hydrological Modeling
• Climate Change Impact Analysis
• Statistical Climate Emulation
• +۳ مورد دیگر

Dr. David McInerney is an ARC Grant-Funded Researcher (B) in the School of Architecture and Civil Engineering at the University of Adelaide, affiliated with the Environmental Engineering department. His research focuses on improving the understanding and management of physical systems through mathematical and statistical models. Key areas include quantifying uncertainty in hydrological predictions, developing climate model emulators, robust decision-making under climate uncertainty, hydrodynamic modeling of lakes and floodplains, and numerical/statistical modeling of geological processes. His work integrates advanced statistical methodologies with environmental systems analysis, emphasizing practical applications for decision support in water resource management and climate adaptation. Notable contributions include the development of the MuTHRE model for seamless subseasonal streamflow forecasting and frameworks for evaluating probabilistic predictions in hydrological models. Dr. McInerney collaborates on interdisciplinary projects, such as the Virtual Hydrological Laboratories initiative, aimed at advancing conceptual models for decision-making under climate change. Recent publications (2021–2024) highlight his expertise in probabilistic forecasting, climate stress testing, and hydrological error modeling. His research addresses critical challenges in environmental science, including improving forecast accuracy for water resource systems and quantifying impacts of wildfires and climate variability on catchment dynamics.

• Vehicle Design
• Sustainable Engineering
• Aerodynamics
• +۵ مورد دیگر

Ciarán O'Reilly is an Associate Professor and Docent in Conceptual Vehicle Design at KTH Royal Institute of Technology, Sweden. He leads the Conceptual Vehicle Design group within the KTH Engineering Mechanics department and directs the Centre for ECO2 Vehicle Design. His research focuses on holistic methodologies to address functional conflicts in vehicle design, combining mechanical engineering principles with multidisciplinary approaches. Key areas include life cycle energy optimization, sustainable design, crosswind dynamics, and noise impact assessment. He teaches courses such as Sustainable Vehicle Design (course responsible), Vehicle Engineering, and supervises degree projects. His academic roles emphasize synergizing research with pedagogical activities. Notable contributions include advancing frameworks for energy-efficient vehicle design, aerodynamic analysis of heavy ground vehicles under crosswind conditions, and applying network theory to complex design challenges. Recent work highlights include agent-based noise impact assessment models, barrier analysis for circular product design in the automotive sector, and manuscripts exploring remanufacturing viability and energy consumption under extreme crosswinds. His research bridges engineering disciplines, addressing both technical and systemic sustainability challenges in transportation systems.

• Computational Complexity
• Randomness and Computation
• Algorithms and Optimization
• +۴ مورد دیگر

Avi Wigderson is the Herbert H. Maass Professor at the School of Mathematics, Institute for Advanced Study (IAS), Princeton. He leads the Computer Science and Discrete Mathematics (CSDM) activities at IAS, focusing on foundational questions in computational complexity, randomness, and their applications. His research bridges theoretical computer science, mathematics, and physics, addressing challenges such as P vs NP, quantum computing, and cryptographic security. Education: Ph.D. in Mathematics from Princeton University (1983), with prior roles including professorships at The Hebrew University of Jerusalem and visiting positions at Princeton University and UC Berkeley. Research interests span computational complexity theory, randomness in computation, cryptography, quantum computation, and pseudorandomness. He has pioneered work on derandomization, probabilistic proof systems, and the interplay between algebraic structures and computational efficiency. Awards include the 2023 ACM Turing Award, 2021 Abel Prize, Rolf Nevanlinna Prize (1994), and Gödel Prize (2009). His contributions to theoretical computer science and mathematics have profoundly impacted areas like algorithm design, cryptography, and computational geometry. Key contributions include work on pseudorandomness, proof complexity, and the role of randomness in computation. He has also explored applications of computational methods to mathematics, such as algebraic geometry and number theory.

Neil J. Hatfieldمشاهده پروفایل

عضو هیئت علمی

• Statistics Education
• Probability Education
• Stochastic Processes
• +۴ مورد دیگر

Neil J. Hatfield is an Assistant Research Professor in the Department of Statistics at Pennsylvania State University's Eberly College of Science. He holds a PhD in Mathematics Education from Arizona State University (2019), an MS in Mathematics Education from Northwest Missouri State University (2011), and a BS in Mathematics from Doane University (2009). His research focuses on Stochastics Education, emphasizing student cognition in statistics and probability, the impact of web applets on learning, and STEM diversity initiatives. He currently coordinates the Statistics and Data Science Education Working Group for the Research in Undergraduate Mathematics Education SIG of the MAA. Education: PhD in Mathematics Education (ASU 2019), MS in Education (NWMSU 2011), BS in Mathematics (Doane 2009) Affiliations: Penn State (current), University of Northern Colorado (Visiting Assistant Professor), Arizona State University (Instructor) His research explores how students develop conceptual understandings of stochastic processes, distributions, and probability. He develops educational technologies like the StatsTools JMP add-in and web-based applets (e.g., Fish Sampling, Four Rooms) to enhance statistical learning. Hatfield has received multiple teaching awards, including the Robert G. Maule Excellence in Teaching Mathematics Award (2017) and the Graduate Teaching Excellence Award (2016-2017). Professional contributions include serving on the Board of the Bootstrap Organization and co-authoring influential papers on covariational reasoning across international teacher populations. His work bridges qualitative research methods with curriculum development, emphasizing interdisciplinary approaches to stochastic literacy.

• Statistics
• Data Science
• Bayesian Statistics
• +۵ مورد دیگر

Peter Kramlinger is affiliated with the University of California, Davis, specifically within the Department of Statistics under the College of Letters and Science. He is actively involved in teaching and developing course materials for undergraduate and graduate-level statistics and data science courses. University: University of California, Davis School: College of Letters and Science Department: Department of Statistics Role: Lecturer His research and instructional interests center on modern statistical computing, Bayesian methods, and data science education. He emphasizes computational tools and reproducible workflows in teaching. The recent course repositories and tutorials reflect a strong focus on practical data analysis, with an emphasis on MCMC methods, statistical programming, and large-scale data processing. These materials span topics in both foundational and advanced data science, indicating a commitment to evolving pedagogical practices in statistics. Although no formal scientific awards are listed, his open-source contributions to educational materials demonstrate scholarly engagement and a dedication to accessible learning resources. Peter Kramlinger advises no listed students, but his instructional role suggests mentorship of students through coursework. There is no mention of external grants or funded research projects in the available data. He maintains an active GitHub presence with publicly shared teaching materials, indicating participation in open academic communities and collaborative learning environments.

• Physics Education
• Didactics of Physics
• Science Teaching and Learning
• +۷ مورد دیگر

Bianca Watzka is a Professor of Physics and Technology Education at RWTH Aachen University, affiliated with the Department of Physics within the Faculty of Mathematics, Computer Science and Natural Sciences. She conducts research and teaching in the didactics of physics, focusing on innovative pedagogical methods and technology integration. Her research interests include: Physics Education and Didactics Context-Based and Phenomenon-Based Learning Cognitive Load Theory in Instruction Educational Technology and Multimedia Learning Teacher Education and Diagnostic Competence Use of Sensors and Digital Tools (Arduino, H5P) in Science Classrooms Eye-Tracking in Learning Environments Her recent publications (2023–2025) reflect a strong focus on enhancing physics instruction through multimedia cues, cognitive load optimization, interactive tasks, context-rich problems, and teacher training. She investigates how auditory and visual supports, VR annotations, force diagrams, and real-world contexts like wind and thermodynamics improve learning outcomes. Her work increasingly integrates technology for formative assessment and reflective teaching. Scientific contributions include: Development of context-oriented physics modules Application of eye-tracking in educational diagnostics Promotion of inquiry-based methods like the Mystery method Integration of Cornell Notes and H5P in teacher education Use of authentic contexts such as CO2 sensors, LEDs, and bionics in teaching She advises pre-service physics teachers and contributes to improving teaching quality through student feedback and structured learning tools. Her work bridges theoretical research with practical classroom applications, particularly in secondary and higher education settings. While no formal lab or team is mentioned, her research implies collaboration in educational technology and teacher training projects.

Anna Keuneمشاهده پروفایل

استاد

• Learning Sciences
• STEM Education
• Educational Design Technologies
• +۴ مورد دیگر

Prof. Anna Keune is a Professor of Learning Sciences and Educational Design Technologies at the Technical University of Munich (TUM), affiliated with the TUM School of Social Sciences and Technology. Her research focuses on the intersection of learning sciences and design studies, particularly examining how design technologies and materials influence STEM learning, with an emphasis on gender equity and posthumanist methodologies. She holds a PhD in Learning Sciences from Indiana University (2020) and has conducted postdoctoral research at the University of California, Irvine and Ruhr University Bochum. Keune's work explores the materiality of learning through crafts like weaving and sewing, linking these practices to computational thinking and mathematical reasoning. She advocates for inclusive educational technologies that support diverse learners, leveraging participatory design approaches. Key theoretical frameworks in her research include constructivism and posthumanism. Education: PhD in Learning Sciences (Indiana University, 2020), MSc in New Media Arts and Design (Aalto University) Awards: Indiana University Distinguished PhD Dissertation Award (2020), ASEE PCEE Best Paper Award (2021) Her recent publications emphasize posthumanist methodologies, AI ethics in education, and the role of fiber crafts in fostering STEM engagement. She co-leads projects like the #quiltsforpulse initiative, which uses craftivism for socio-political activism, and explores material-based learning in makerspaces to enhance equity and accessibility.

Ziv Scullyمشاهده پروفایل

استادیار

• Decision-Making Under Uncertainty
• Queueing Theory
• Scheduling Algorithms
• +۵ مورد دیگر

Ziv Scully is an Assistant Professor in the Department of Operations Research and Information Engineering at Cornell University . His research focuses on decision-making under uncertainty, particularly in queueing systems and Markovian/Bayesian bandits. He has advised four PhD students and published extensively on scheduling theory, tail latency optimization, and stochastic control. Education: PhD in Computer Science from Carnegie Mellon University (2022), advised by Mor Harchol-Balter and Guy Blelloch Research Interests: Ziv explores stochastic control approaches to scheduling and resource allocation, with a focus on queueing theory and Gittins indices. His work bridges queueing systems and Pandora's box problems, aiming to achieve asymptotic optimality in tail latency and resource utilization. Recent efforts emphasize heavy-traffic analysis and cost-aware Bayesian optimization. Key Trends in Articles: His publications address tail probability analysis, Gittins index applications, and asymptotic optimality in queueing systems. He also investigates preemption overhead and workload diffusion approximations using Stein's method. Advising: Currently mentors four PhD students: Qian Xie, Amit Harlev, Shefali Ramakrishna, and George Yu. No listed grants, but active in the Simons Institute and Harvard/MIT collaborations. Teaching: Advocates for advanced mathematical rigor in performance modeling education, as highlighted in his 2023 paper on pedagogical approaches.

• Formal Methods
• Theoretical Computer Science
• Automata Theory
• +۳ مورد دیگر

Alexandra Silva is a Professor of Computer Science at Cornell University and holds an affiliation with University College London (UCL), where she was previously a Royal Society Wolfson Fellow and Professor of Algebra, Semantics, and Computation. Her research focuses on the modular development of specification languages and algorithms for computational models, leveraging coalgebraic methods and probabilistic systems. Education and Background: Silva's academic journey includes a Ph.D. in Theoretical Computer Science from Radboud University Nijmegen, supervised by Prof. Jan Rutten and Dr. Marcello Bonsangue. Her work bridges algebraic foundations with practical applications in programming languages and formal methods. Research Interests: Silva's work spans formal methods, automata theory, probabilistic programming, and concurrency. She has pioneered frameworks like NetKAT and Guarded Kleene Algebra with Tests (GKAT), advancing network verification and program analysis. Her research emphasizes coalgebraic semantics, automata learning, and categorical approaches to computation. Awards and Recognition: Silva is a Royal Society Wolfson Fellow and has received multiple best paper awards, including for contributions to NetKAT and GKAT. Her work on Outcome Logic and probabilistic systems has been widely cited in the field of program verification. Grants and Collaboration: Silva leads collaborative projects on formal methods and network verification, supported by grants from NSF and EU initiatives. She co-organizes conferences like OPLSS and serves on program committees for POPL, CAV, and CONCUR. Labs and Teams: Her research group includes students and postdocs working on projects in probabilistic automata, concurrency theory, and applied formal methods. Key collaborators include Nate Foster, Dexter Kozen, and Fabio Zanasi.

Teo Paolettiمشاهده پروفایل

دانشیار

• Mathematics Education
• Student Understanding of Mathematical Concepts
• Design-Based Research
• +۳ مورد دیگر

Teo Paoletti is an Associate Professor in the School of Education at the University of Delaware, specializing in mathematics education. His research focuses on student understanding of mathematical concepts across K-12 and post-secondary levels, leveraging design-based methods and dynamic software tools like GeoGebra. He holds a Ph.D. from the University of Georgia and previously taught middle/high school mathematics in New Jersey. Education: Ph.D., Mathematics Education, University of Georgia (2015) M.S., Mathematics, Texas A&M University (2010) B.A., Mathematics with Teacher Certification, The College of New Jersey (2007) Research Interests: Dr. Paoletti investigates how students develop covariational reasoning and construct mathematical concepts through dynamic software. Key areas include: coordinate systems, graphical shape thinking, function classes, and inequalities. He emphasizes the role of reasoning about quantitative relationships in fostering deep mathematical understanding. Recent Publications Trends: His work explores covariational reasoning’s role in graph interpretation, task design for middle-schoolers, and cross-disciplinary applications of TIMSS data. Recent articles highlight innovations in graphing conventions, coordinate system meanings, and student reasoning trajectories. Awards: 2016 Honorable Mention, CRUME Best Paper Award 2016 AMTE STaR Fellow Grants & Collaborations: PI of the Spencer Foundation-funded Mid-CoS project (2017–present). Co-PI on NSF-funded initiatives to improve precalculus teaching. His research has been published in leading journals including Journal of Mathematical Behavior and For the Learning of Mathematics . Professional Contributions: Editorial board member of Journal of Mathematical Behavior . Active in teacher education, having transitioned from classroom teaching to university research.

John Franciscoمشاهده پروفایل

دانشیار

• Mathematics Education
• Science Education
• Teacher Education
• +۳ مورد دیگر

John Francisco is an Associate Professor in the Department of Teacher Education & Curriculum Studies (TECS) at the University of Massachusetts Amherst. His work focuses on mathematics and science education, particularly teacher education, classroom discourse, and student learning processes. He holds a B.A. from Pedagogical University in Maputo, Mozambique (1992), an M.Ed. (1999), and a Ph.D. (2004) from Rutgers University. His research emphasizes teacher support for argumentation, collaborative learning, and the role of technology in educational settings. Education: B.A., Pedagogical University, Maputo, Mozambique, 1992 M.Ed., Rutgers University, 1999 Ph.D., Rutgers University, 2004 His research explores how teachers foster mathematical reasoning and argumentation in classrooms, leveraging professional development programs and after-school interventions. Recent work highlights the interplay between digital tools and teacher guidance in enhancing student understanding. He has contributed to studies on nonlinear function reasoning, imaginative learning, and the legacy of educational theorists like Robert B. Davis. While no scientific awards are explicitly noted, his extensive publication record reflects ongoing contributions to mathematics education and pedagogical innovation. His advising and grants involve collaborations on problem-solving strategies and longitudinal studies tracking student mathematical development.

Charles Hohenseeمشاهده پروفایل

دانشیار

• Mathematics Education
• Educational Psychology
• Learning Theory
• +۳ مورد دیگر

Dr. Charles Hohensee is an Associate Professor in the School of Education at the University of Delaware, specializing in mathematics education. His research focuses on backward transfer—the influence of new learning on prior knowledge, particularly in middle/high school algebra. He has led an NSF CAREER grant examining backward transfer effects in quadratic/linear functions instruction and co-organized NSF-funded conferences for early career mathematics education researchers. His work bridges teacher practice and student learning, emphasizing quantitative reasoning and instructional design. Education: Ph.D. in Mathematics Education (University of California San Diego, 2011), M.A. in Mathematics Education (San Diego State University, 2007), B.Sc. in Physics (University of Alberta, 1985). Research Interests: Backward transfer, algebraic reasoning, teacher knowledge, and the integration of research on teaching and student learning. He explores how teaching practices shape students’ evolving understanding of mathematical concepts. Grants: NSF CAREER Award ($880,300, 2017–2023); Co-PI on an NSF DRK-12 conference grant ($99,971, 2019–2022). Awards: 2015 AERA SIG-RME Early Career Publication Award; 2012 NSF STaR Fellowship. Teaching: Courses include mathematics content courses for pre-service elementary/middle school teachers and doctoral seminars on mathematics learning, teaching, and curriculum. Notable courses: MATH 217, 252, 253; EDUC 833, 834, 835. Service: Former Associate Editor of the Journal for Research in Mathematics Education (2015–2020); contributor to cross-disciplinary teacher-coaching initiatives.