جست‌وجوی استاد و پژوهشگر

James S. Kimمشاهده پروفایل

استاد

• Early Education
• Education Policy
• Evidence-Based Intervention
• +۷ مورد دیگر

James S. Kim is a Professor of Education at Harvard University's Graduate School of Education, where he conducts policy-relevant research focused on improving literacy outcomes for low-income students and struggling readers. With an Ed.D. from Harvard University (2002), he leads the READS Lab (Research Enhances Adaptations Designed for Scale in Literacy), a research team that partners with school districts to solve literacy challenges through evidence-based interventions. Dr. Kim's research centers on understanding how building students' domain knowledge and reading engagement can foster long-term improvements in reading comprehension. His work emphasizes experimental design and evidence-based interventions, with a particular focus on addressing educational inequality. His research interests include early education, education policy, evidence-based intervention, human development, inequality and education gaps, informal and out-of-school learning, language and literacy development, and teachers and teaching. Kim's most significant contribution is the Model of Reading Engagement (MORE), a spiraled and sustained content literacy intervention co-developed with schoolteachers that has been shown to improve first to third-grade students' reading comprehension in science, English language arts, and math. Notably, research on MORE meets WWC (What Works Clearinghouse) standards without reservation, and long-term follow-up suggests the intervention's impact persists through fourth grade. His publications reveal a consistent focus on content literacy, domain knowledge development, and transfer effects in reading comprehension. Research on MORE meets WWC standards without reservation Long-term effects of MORE persist through fourth grade Commitment to Open Science principles (open data, open materials, preregistration) As a servant leader, Kim builds long-term partnerships with school districts to implement literacy interventions at scale. His READS Lab promotes open science practices while developing practical solutions to literacy challenges. His research on summer reading interventions, parental text messaging, and classroom-based content literacy approaches demonstrates his commitment to translating research into practice. Kim's current work includes scaling the MORE intervention to improve reading comprehension for high-needs students in moderate to high poverty schools through a Department of Education-funded project (2024-2028).

Andrew Lanمشاهده پروفایل

دانشیار

• Artificial Intelligence in Education
• Educational Data Mining
• Personalized Learning
• +۵ مورد دیگر

Andrew Lan is an Associate Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst, where he also serves as the CS Undergraduate Program Director. He was granted tenure by the UMass Board of Trustees in June 2025 and is currently on leave through Spring 2026. His research focuses on developing human-in-the-loop machine learning methods to enable scalable, effective, and personalized learning experiences in education. Dr. Lan received his BS in Physics and Mathematics from the Hong Kong University of Science and Technology, followed by his MS (2014) and PhD (2016) in Electrical and Computer Engineering from Rice University. He completed postdoctoral research at Rice University (2016) and Princeton University's EDGE Lab (2017-2018). His research spans artificial intelligence for education, with particular expertise in educational data mining, knowledge tracing, personalized learning systems, and human-AI collaboration in educational contexts. Dr. Lan's work leverages massive and multimodal learner and content data collected from both traditional classrooms and online learning platforms to develop systems that deliver high-quality, affordable, and personalized learning experiences. He has made significant contributions to areas including computerized adaptive testing, math word problem generation, student affect detection, and automated grading systems. His recent work increasingly focuses on leveraging large language models for educational applications while maintaining rigorous scientific validation of these approaches. Best Student Paper Award at the 2024 AIED Conference (with Alexander Scarlatos) Best Paper Nominee at LAK 2021 Best Student Paper Award at IEEE Big Data 2020 NAEP Math Automated Scoring Challenge Grand Prize Winner Dr. Lan actively mentors graduate students and postdoctoral researchers, with several of his advisees receiving recognition for their work. He has secured substantial funding from the National Science Foundation, including a $90M grant for the SafeInsights project, a secure cyberinfrastructure for educational research. His research group collaborates with institutions including Worcester Polytechnic Institute, University of Pennsylvania, and Rice University. He teaches undergraduate and graduate courses including COMPSCI 240 (Reasoning under Uncertainty) and COMPSCI 590OP (Applied Numerical Optimization), with a focus on the practical application of theoretical concepts in machine learning and artificial intelligence. His educational philosophy emphasizes bridging the gap between theoretical foundations and real-world implementation in educational technology.

Ivon Arroyoمشاهده پروفایل

استاد

• Learning Sciences
• Computer Science
• Data Science
• +۹ مورد دیگر

Ivon Arroyo is a Professor in the Department of Teacher Education & Curriculum Studies (TECS) at the University of Massachusetts Amherst. Her research focuses on integrating novel technologies into math and computational thinking education, emphasizing affective and metacognitive states. She develops intelligent tutoring systems, such as COVES, which personalize learning in real-time and utilize facial expression recognition to enhance engagement. Her work on WearableLearning explores embodied, physically active multiplayer games for K-12 classrooms, leveraging mobile devices and wearable technologies to create immersive learning experiences. Dr. Arroyo holds an Ed.D. (2003) and M.S. (2000) from UMass Amherst and a B.S. from Universidad Blas Pascal in Argentina (1995). She has been recognized with multiple awards, including Best Paper Awards at the 2009 International Conference on Artificial Intelligence in Education and the 2010 Educational Data Mining Conference, a Fulbright Fellowship (1996), and a 1994 undergraduate prize for computer vision research. Her research interests span interdisciplinary areas such as Learning Sciences , Computer Science , Data Science , and Psychology . She prioritizes culturally responsive pedagogical agents and cross-cultural studies in educational technology, particularly in Argentina, India, and the U.S. Her projects often address challenges in developing countries, including localization of tutoring systems to Spanish. Advising and grants are central to her work, with grants like the NSF CAREER Award (2020) supporting embodied math classrooms. She collaborates on teacher dashboard frameworks and explores ethical AI integration in education. Her labs focus on creating tools that merge computational innovation with theoretical learning science principles, emphasizing real-world applications like the WearableLearning Cloud Platform.

Chen Sunمشاهده پروفایل

استادیار

• Computer Vision
• Deep Learning
• Machine Learning
• +۸ مورد دیگر

Chen Sun is an Assistant Professor of Computer Science at Brown University and a part-time Staff Research Scientist at Google DeepMind . His research bridges computer vision, machine learning, and artificial intelligence , focusing on multimodal representation learning, visual commonsense, and controllable video generation . He directs the PALM🌴 research lab , which explores scalable models for robotic planning, video understanding, and human activity recognition . Chen Sun earned a Ph.D. in Computer Science from the University of Southern California (2016) , advised by Professor Ram Nevatia , and a Bachelor of Science in Computer Science from Tsinghua University (2011) . His lab's work has been supported by Adobe, Honda, Meta, NASA, and Samsung , and he is affiliated with the NSF AI Research Institute on Interaction for AI Assistants . His research spans multimodal transformers, embodied agents, and physics-informed video generation . Key trends include Learning from unlabeled videos for human activity recognition Developing controllable generation techniques using motion trajectories and physics-based signals Advancing scalable frameworks for video-language tasks Scientific awards include the Brown University Richard B. Salomon Faculty Research Award and Samsung Global Research Outreach Award . He has served as Workshop Chair (CVPR 2025) , Action Editor (TMLR) , and Area Chair for top conferences like ICLR, CVPR, and NeurIPS . Chen Sun mentors a dynamic team including Ph.D. students Apoorv Khandelwal (Presidential Fellow) Calvin Luo (Research Mobility Fellow) Nate Gillman (Math Department) Shijie Wang Tian Yun (co-advised with Ellie Pavlick) Yuan Zang Zilai Zeng Zitian Tang and alumni now pursuing Ph.D. programs at Princeton, Cornell, UBC, and UNC . His teaching portfolio includes graduate-level courses on Deep Learning (CSCI 2470) , Advanced Topics in Deep Learning (CSCI 2952N) , and a short course on Multimodal Transformers at ICASSP 2022 and AAAI 2023 .

Ziyu Yaoمشاهده پروفایل

استادیار

• Natural Language Processing
• Artificial Intelligence
• Knowledge Grounding
• +۱۴ مورد دیگر

Ziyu Yao is an Assistant Professor in the Department of Computer Science at George Mason University , co-leading the George Mason NLP Group . He is affiliated with the C4I & Cyber Center , Center for Advancing Human-Machine Partnership , and Institute for Digital InnovAtion at GMU. PhD in Computer Science and Engineering from Ohio State University (2021) Internships: Microsoft Semantic Machines, Carnegie Mellon University, Microsoft Research, Fujitsu Lab of America, Tsinghua University Research Interests: Focus on Natural Language Processing (NLP) and Artificial Intelligence (AI) , particularly advancing LLM systems through knowledge grounding , reasoning , and planning . Key areas include: Mechanistic Interpretability for LLMs Interactive Semantic Parsing/Code Generation Responsible and Trustworthy NLP Interfaces Interdisciplinary Applications in Mathematics Education and Network Communication Recent Articles (2024-2025) explore trends in LLM cascading for cost efficiency, mechanistic interpretability surveys, vision-language model reasoning, and interdisciplinary educational technology. Collaborations span institutions like Microsoft Research , William & Mary , and University of Cambridge . Scientific Awards: Presidential Fellowship (OSU Graduate School, 2020) Graduate Student Research Award (OSU CSE, 2021) Top Reviewer at NeurIPS 2023 Advising & Grants: Mentors PhD students like Murong Yue , Hao Yan , and Mohamed Aghzal . Leads NSF projects on AI-driven Mathematics Education and LLM Interpretability , alongside grants from Commonwealth Cyber Initiative and Microsoft Accelerate Foundation Models Research . Organized workshops at COLM 2025 and ICML 2025 . Labs & Teams: Co-leads the NLP Lab at GMU and collaborates with the MathVC NSF Project team (w/ Jennifer Suh, William & Mary). Develops platforms like Gentopia for tool-augmented LLMs and IntelliExplain for non-professional programmers.

Daniel Frischemeierمشاهده پروفایل

استاد

• Mathematics Education
• Statistics Education
• Data Science Education
• +۵ مورد دیگر

Daniel Frischemeier is a Professor of Mathematics Didactics with a focus on Primary Education at the University of Münster's Faculty of Mathematics and Computer Science. He has established himself as a leading researcher in statistics and data science education for primary school students, with extensive contributions to educational methodology and teacher training. University of Münster (2021-present) TU Dortmund (2020-2021) University of Paderborn (2009-2020) Ludwig-Maximilians-Universität München (2017-2018) Dr. Frischemeier completed his doctoral studies at the University of Paderborn with a dissertation on statistical thinking and research using TinkerPlots software. His educational background includes graduate studies in Mathematics and undergraduate studies in Mathematics and Physics for teaching at various school levels. His research focuses on the design and testing of teaching-learning environments for primary mathematics education, particularly in the areas of data analysis, probability, and statistics. He conducts qualitative analysis of learners' cognitive processes related to the guiding principle of 'data and chance' in primary education. His work also includes the design and evaluation of teaching materials in data science and civil statistics, the use of learning videos to promote process-related skills, and the implementation of Fermi tasks and computer science education within primary mathematics lessons. Analysis of Dr. Frischemeier's recent publications reveals a strong emphasis on data literacy development in primary education, with increasing focus on the integration of digital tools and the conceptual understanding of data as models. His work bridges mathematics education with emerging fields of data science, addressing both theoretical frameworks and practical classroom applications. The research demonstrates a progression from basic statistical concepts toward more complex data modeling approaches suitable for young learners. Elected member of the International Statistical Institute (ISI) Chair of the Local Organizing Committees for IASE Satellite 2025 Conference Council-Member of the International Statistical Institute Special Edition Editor of the Statistics Education Research Journal Member of International Program Committees for major statistics education conferences Co-Leader of CERME Thematic Working Group 5 on Probability and Statistics Education Dr. Frischemeier serves in numerous editorial capacities and review roles for prominent journals in mathematics and statistics education. He leads significant research projects including 'Promoting Data Science Education for Teacher Education at the University level (DataSETUP)' and 'Data Science Education in STEAM for Civic Engagement and Social Justice from the Early Years (DataScEd4CiEn)'. His work has substantial impact on teacher education programs and curriculum development in statistics and data science for primary schools. He is actively involved in the development and leadership of the Math Center Münster (MaZ), which promotes mathematical potential for all students. His team includes numerous research assistants and doctoral candidates working on various aspects of mathematics education research, particularly focusing on data literacy and statistical reasoning in primary education contexts.

• Online Learning
• Instructional Design
• Augmented Reality in Education
• +۷ مورد دیگر

Dr. Wanju Huang is a Clinical Assistant Professor in the Learning Design and Technology program at Purdue University , where she focuses on enhancing online learning experiences through innovative instructional design and emerging technologies. Prior to joining Purdue in Fall 2016, she served as an instructional design manager at Teaching and Learning Technologies and spent six years as a lecturer and instructional designer at Eastern Kentucky University. Education: Ph.D. in Curriculum & Instruction (Technology concentration) from University of Illinois at Urbana-Champaign Her research interests center on technology-mediated online communities, instructor presence in digital learning, augmented reality applications, and faculty development programs. She has published extensively on these topics, including studies on multimedia interventions for teaching presence, immersive technologies in ESL education, and adaptive cyber training platforms. Dr. Huang's publications reflect a strong focus on EdTech innovation , with trends spanning AI integration in K-12 , model-based systems engineering , and microlearning strategies . Her work bridges theoretical frameworks like social constructivism with practical implementations in professional education. Professional memberships include the American Educational Research Association, Association for Educational Communications and Technology, and Quality Matters. She teaches courses on learning design foundations, e-learning systems, and professional competency demonstrations in LDT.

Ken Koedingerمشاهده پروفایل

استاد

• Cognitive Science
• Educational Technology
• Intelligent Tutoring Systems
• +۳ مورد دیگر

Ken Koedinger is a University Professor at Carnegie Mellon University (CMU), holding appointments in the School of Computer Science (Human-Computer Interaction Institute) and the Dietrich College of Humanities and Social Sciences (Department of Psychology). He is the co-founder of Carnegie Learning, Inc., and directs the LearnLab, now the scientific arm of CMU’s Simon Initiative. His research focuses on cognitive modeling, intelligent tutoring systems, and the science of learning. Dr. Koedinger has over 250 peer-reviewed publications and has been awarded prestigious titles such as the Hillman Professorship of Computer Science and Fellow of the Cognitive Science Society. Education: Ph.D. in Cognitive Psychology (CMU, 1990), M.S. in Computer Science (University of Wisconsin, 1986). Prior to academia, he taught in an urban high school, informing his interdisciplinary approach to education research. Research Interests: Cognitive modeling, educational technology, learning analytics, and the integration of AI into learning systems. Key projects include Cognitive Tutors, LearnLab, and the Pittsburgh Science of Learning Center. Awards: Over 30 grants from NSF, Department of Education, and private foundations, including major contributions to K-12 math education and AI-driven personalized learning. Advising: Supervised over 30 graduate students, many now leading roles in academia and industry. Current students focus on AI ethics, scalable education, and mixed-reality learning.

Eunjung Leeمشاهده پروفایل

استادیار

• Educational Technology
• Gifted Education
• Teacher Education
• +۳ مورد دیگر

Eunjung Lee serves as Assistant Professor of Business Analytics in the College of Business at Lewis University, bringing extensive industry experience from Samsung and LG alongside prior academic roles at Indiana State University. Her expertise bridges business analytics and educational technology with a pronounced focus on equity-driven research. Her educational foundation includes: Ph.D. in Information Technology Management, University of Wisconsin-Milwaukee M.B.A., Korea University B.S., Kwangwoon University Dr. Lee's research trajectory evolved from business analytics toward educational equity, with recent work emphasizing computational thinking in mathematics education, teacher preparation models, and machine learning applications for gifted identification. She investigates how digital tools transform pedagogy while addressing systemic barriers for underrepresented students. Analysis of her 2022-2025 publications reveals dominant themes in equitable gifted identification through cross-cultural validation of the HOPE rating scale, computational thinking integration in teacher training, and longitudinal studies of enrichment program impacts. Her methodology consistently combines quantitative analysis with equity-centered frameworks. No scientific awards were documented in available sources. While specific advising details remain unreported, her courses in Business Intelligence and Forecasting demonstrate applied analytics instruction. Research grants weren't specified in source materials. Collaborative structures like the Lowell Stahl Center for Entrepreneurship provide institutional context, though dedicated labs or research teams weren't explicitly referenced.

Janet Walkoeمشاهده پروفایل

دانشیار

• Mathematics Education
• Algebra Teaching & Learning
• Teacher Noticing
• +۳ مورد دیگر

Dr. Janet Walkoe is an Associate Professor in the Department of Teaching and Learning, Policy and Leadership at the University of Maryland. Her work focuses on mathematics education, particularly algebra teaching and learning, and the role of teacher noticing in responsive instruction. She holds a Ph.D. in Learning Sciences from Northwestern University (2013), an MS in Mathematics from the University of Illinois at Chicago, and a BA in Mathematics from the University of Chicago. Before academia, she taught high school mathematics for a decade, earning National Board Certification. Her research emphasizes how teachers attend to student algebraic thinking during instruction and how pre-service teachers can develop noticing skills through video analysis. Key areas include teacher responsiveness, multimodal algebraic thinking, and computational thinking integration in mathematics education. She has led the NSF-funded CAREER grant (2020) exploring teacher noticing of student thinking, and co-authored influential works like Lenses for examining students’ mathematical thinking (2014), which earned an NCTM award. Notable Grants: NSF CAREER DRL 17-537 ($736,914), INFACT Subaward ($250k), Spencer Foundation Grant ($44k) Teaching: Courses include Interdisciplinary Teaching in Middle Grades (TLPL 423/424), Elementary Mathematics Education (EDCI 652), and Foundations of Mathematics Education (EDCI 751) Professional Development: Develops video-based tools for teacher reflection and computational thinking integration Walkoe advocates for culturally sustaining pedagogy and equity in teacher education. Her recent work bridges computational thinking with mathematics instruction, emphasizing interdisciplinary approaches to foster student engagement.

• Computational Linguistics
• Natural Language Processing
• Multilingual Models
• +۷ مورد دیگر

Malvina Nissim is a leading researcher in computational linguistics and NLP at the University of Groningen's Department of Artificial Intelligence, with a focus on multilingual modeling, bias mitigation, and human evaluation frameworks. Key Contributions : Developed CALAMITA (Italian LLM benchmark), IT5 models for Italian language processing, and ReproHum framework for NLP evaluation reproducibility Research Pillars : Multilingual reasoning consistency, perspective-based text analysis, and figurative language modeling Her work spans activation steering techniques, cross-lingual transfer learning, and the creation of specialized language resources like the EurekaRebus dataset and MAGPIE idiom corpus. She pioneered methods for gender bias measurement in BERT and developed the SocioFillmore tool for perspective visualization. Recent publications explore model uncertainty as MCQ difficulty proxy, Italian headline generation benchmarks, and multilingual multi-figurative language detection. She actively participates in teaching initiatives like the "NLP with Bracelets" workshop for Italian high school students. Scientific Awards : ACL Best Paper Award (2025) EMNLP Outstanding Reviewer (2023) EVALITA Leadership Recognition (2024) She advises PhD students in model bias analysis and has contributed to the development of the Dutch Abusive Language Corpus (DALC) and the ReproNLP reproducibility framework. Her collaborations span institutions in Italy, Netherlands, and international NLP communities.

Anton Petruninمشاهده پروفایل

استاد

• Geometry
• Alexandrov Geometry
• Metric Geometry
• +۶ مورد دیگر

Anton Petrunin is a Professor in the Department of Mathematics at The Pennsylvania State University, affiliated with the Eberly College of Science. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (1995). His research focuses on Geometry, particularly Alexandrov geometry, metric geometry, differential geometry, and their applications. He is known for contributions to curvature bounds, isometric embeddings, and geometric analysis. Teaching: Petrunin instructs courses such as MATH 471 (Geometry for Teachers) and actively engages in mathematical outreach, including organizing the Tournament of Towns, an international mathematics competition for school students. He emphasizes problem-solving and rigorous foundations in his pedagogy. Publications: Petrunin has authored influential books, including Alexandrov Geometry: Foundations , Pure Metric Geometry , and Euclidean Plane and Its Relatives . His work bridges theoretical advancements with accessible educational materials. Contributions: Beyond academia, he advocates for open-access research and self-publishing in mathematics. His GitHub repository reflects his commitment to sharing educational resources and collaborative projects in metric geometry and topology.

• Mathematics Education
• Geometry
• Pedagogy
• +۲ مورد دیگر

Michael Weiss is a Lecturer in the Department of Mathematics at the University of Michigan. He teaches foundational courses such as Linear Algebra (MATH 217) and Explorations in Euclidean Geometry (MATH 431). His office is located in East Hall (Room 1868) at 530 Church Street, Ann Arbor, MI. Current Courses: MATH 217-004 (Linear Algebra), MATH 217-011 (Linear Algebra), MATH 431-001 (Explorations in Euclidean Geometry) Weiss’s research focuses on mathematics education, particularly on pedagogical strategies for teaching geometry and mathematical reasoning. His work explores the use of visual representations, interactive technologies, and narrative tools (e.g., comics) to enhance learning and teacher development. He emphasizes the role of theory-building in secondary geometry instruction and investigates how students translate abstract mathematical concepts into concrete reasoning. His publications highlight interdisciplinary approaches to education, integrating technology (e.g., animated stories, voice assistants) with traditional teaching methods. Keywords across his work include Mathematics Education , Geometry , Interactive Media , Visual Learning , and Teacher Training . Contact: mweiss@umich.edu

Angela Boatmanمشاهده پروفایل

دانشیار

• Higher education policy
• college access and student success
• remedial and developmental education
• +۵ مورد دیگر

Angela Boatman is an Associate Professor in the Department of Educational Leadership & Higher Education at Boston College specializing in higher education policy evaluation, with emphasis on improving access and success for underrepresented student populations through evidence-based interventions. Her educational credentials include: Ed.D. in Higher Education from Harvard University M.P.P. and M.A. in Higher Education from the University of Michigan B.A. from the University of Minnesota Dr. Boatman's research program systematically examines college access barriers and success pathways, focusing on remedial education reform (particularly mathematics), financial aid design, community college transfer policies, and behavioral interventions to reduce loan aversion. She employs rigorous quasi-experimental and experimental methodologies to evaluate policy impacts on student outcomes, with particular attention to equity implications for traditionally marginalized groups. Her work bridges academic research and practical policy implementation through sustained engagement with federal and state education agencies. Analysis of her recent publications (2019-2025) reveals consistent thematic focus on remediation models (corequisite vs. traditional, high school vs. college timing), behavioral economics applications in financial aid, and cross-institutional transfer policies. Her research spans diverse contexts including U.S. community colleges, technical institutions in Chile, and rural-serving universities, demonstrating methodological versatility through large-scale administrative data analysis, randomized trials, and comparative international studies. Dr. Boatman has secured substantial external research funding from the Institute for Education Sciences, Bill & Melinda Gates Foundation, Lumina Foundation, and Peabody College. She actively contributes to national discourse as a faculty affiliate of the Center for the Analysis of Postsecondary Readiness (CAPR) and the Center for Education Policy Research (CEPR) at Harvard University, frequently presenting findings to policymakers and practitioners through congressional briefings, Federal Reserve conferences, and national education forums.

Margaret Greenwaldمشاهده پروفایل

دانشیار

• Adult neurogenic communication disorders
• Cognitive neuropsychology
• Stroke rehabilitation
• +۳ مورد دیگر

Margaret Greenwald serves as Associate Professor and Ph.D. Coordinator in the Department of Communication Sciences and Disorders (CSD) within Wayne State University's College of Liberal Arts and Sciences. Previously department chair (2014-2020), she is a Licensed and Certified Speech-Language Pathologist (CCC-SLP) recognized in 2022 as Wayne State's Outstanding Graduate Mentor in the health sciences. Her academic credentials include: M.A. in Speech-Language Pathology (University of Florida) Ph.D. in Communicative Sciences and Disorders (University of Florida) Post Doctoral Research Training in Cognitive Neuropsychology (University of Maryland, Department of Neurology) Dr. Greenwald's research centers on adult neurogenic communication disorders , specializing in aphasia, traumatic brain injury, and stroke rehabilitation through cognitive neuropsychology. Her work employs Magnetoencephalography (MEG) to map brain-behavior relationships during language and musical processing tasks, revealing critical insights for rehabilitation. She actively bridges clinical practice with neuroscience to develop evidence-based interventions. Analysis of her recent publications shows consistent focus on neuroimaging applications for understanding language disorders, with growing emphasis on telepractice implementation and the cognitive effects of musical training. Her work demonstrates strong translational value, connecting basic neuroscience to clinical rehabilitation protocols while maintaining rigorous methodological standards. Her scientific recognition includes: Outstanding Graduate Mentor in the Health Sciences, Wayne State University (2022) As an educator, Dr. Greenwald teaches graduate courses in aphasia, neuromuscular speech disorders, research methods, and doctoral seminars, plus undergraduate speech-language pathology instruction. She supervises doctoral research as Ph.D. coordinator and mentors students through clinical research projects. Her teaching integrates current neuroscience findings with clinical practice standards. She maintains active professional engagement through memberships in the American Speech-Language Hearing Association (ASHA), Academy of Aphasia, and International Neuropsychological Society, contributing to both clinical guidelines development and research community advancement.