جست‌وجوی استاد و پژوهشگر

Bruno Lemaitreمشاهده پروفایل

استاد

• Immunology
• Innate Immunity
• Toll-like Receptors (TLRs)
• +۷ مورد دیگر

Bruno Lemaitre is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Life Sciences and the Global Health Institute (UPLEM). He also contributes to teaching in the Doctoral Program in Molecular and Cellular Life Sciences (EDMS), the School of Humanities (SHS), and the School of Basic Sciences (SV-SSV). He leads the Lemaitre Lab, focusing on immunology and host-pathogen interactions. Full Professor, EPFL – Global Health Institute (UPLEM) Full Professor, EPFL – School of Life Sciences (SV-SSV) Full Professor, EPFL – Doctoral Program (EDMS) Full Professor, EPFL – School of Humanities (SHS) Member, EPFL Committee of Academic Evaluation (CEAE) His research spans immunology, evolutionary psychology, and the sociology of science. Key areas include innate immunity, Toll-like receptors, Drosophila host defense, and the influence of narcissism on scientific success. He has published extensively on how personality traits shape research culture and ethics. The most recent 15 articles reflect a strong interdisciplinary trend, combining molecular immunology with psychological and sociological themes. While early works focus on TLRs and Drosophila immunity, recent publications explore narcissism, obesity, scientific discovery myths, and the cultural impact of personality. This indicates a broadening research scope from molecular biology to the human dimensions of science. Scientific awards and recognitions include: EMBO Member (2007) Bruno Lemaitre supervises a dynamic group of PhD students, both current and past, and is involved in significant educational initiatives, including MOOCs on immunology and authored textbooks. He has also contributed to public discourse through interviews and media appearances. His laboratory, the Lemaitre Lab, is a hub for research on infection and immunity, and he plays a role in academic governance through committee memberships.

• Architectures for fault-tolerant quantum computing
• Topological error correcting codes
• Magic states
• +۵ مورد دیگر

Professor Earl T. Campbell is a Professor of Quantum Computation at the School of Mathematical and Physical Sciences, University of Sheffield, and serves as VP of Quantum Science at Riverlane. His research focuses on practical quantum technologies with capabilities beyond classical physics, particularly in fault-tolerant quantum computing. His educational background includes: Physics & Philosophy MSc (2001-2005, Bristol University) Quantum computing PhD (2005-2008, Oxford University) Professor Campbell's research centers on the central question 'What are the optimal blue-prints for large-scale, reliable quantum technologies?' His work spans architectures for fault-tolerant quantum computing, topological error correcting codes, magic states, gate synthesis, and Majorana fermions. His team designs protocols and software to operate future quantum computers reliably despite noise and decoherence, including development of error correction codes, magic state factories (which determine quantum computer processing speed), and algorithm compilation into machine-level commands. His recent publications demonstrate a strong focus on practical aspects of quantum error correction, with significant contributions to surface codes, homological product codes, and efficient decoding algorithms. His work bridges theoretical quantum information science with practical implementation challenges, aiming to reduce resource requirements for fault-tolerant quantum computing. His notable scientific achievements include: Royal Commission of Great Exhibition of 1851 fellowship (2008-2010) EPSRC early career Research Fellow (2015-present) Professor Campbell supervises PhD student Luke Heyfron and collaborates with postdoc Mark Howard. He has received significant grant funding including an EPSRC early career fellow project 'Towards fault-tolerant quantum computing with minimal resources' (EP/M024261/1, £675,867) and Royal Commission fellowship funding (~£100,000). He is an active member of the EPSRC Peer Review College and the Institute of Physics QQQ group Committee, and has organized the FTQT2016 workshop in Benasque, Spain.

Alex Townsendمشاهده پروفایل

دانشیار

• Applied Mathematics
• Numerical Analysis
• Scientific Computing
• +۶ مورد دیگر

Alex Townsend is an Associate Professor of Mathematics at Cornell University, affiliated with the College of Arts and Sciences. He holds the Stephen H. Weiss Junior Fellowship and has been recognized for both research and teaching excellence. His research focuses on numerical analysis, scientific computing, and theoretical aspects of deep learning, with contributions to spectral methods, low-rank techniques, and computational algebraic geometry. Education: Townsend earned a DPhil (PhD) in Mathematics from the University of Oxford in 2014. Research Interests: Townsend's work spans several areas: novel spectral methods for differential equations, low-rank matrix and tensor techniques, theoretical foundations of deep learning, and computational algebraic geometry. His research emphasizes developing fast, accurate, and robust numerical algorithms with applications in science and engineering. Teaching & Mentoring: Townsend is a dedicated educator, having taught courses at MIT and Cornell on topics ranging from linear algebra and numerical analysis to advanced graduate-level subjects like kernel-based learning and top-ten algorithms of the 20th century. He has mentored numerous PhD students and postdocs, many of whom now hold academic and industry positions. Awards & Honors: 2022 Stephen H. Weiss Teaching Award 2022 Simons Fellowship in Mathematics 2018 SIAG/LA Early Career Prize 2015 Leslie Fox Prize in Numerical Analysis Grants & Funding: Townsend has secured significant funding, including an NSF CAREER grant (2021), to support his work on operator learning and spectral methods. Labs & Collaborations: While not tied to a specific lab, his research frequently intersects with computational mathematics and machine learning communities. He collaborates widely, contributing to open-source tools like Chebfun and Diskfun.

Maryam Mehri Dehnaviمشاهده پروفایل

دانشیار

• High-Performance Computing
• Cloud Computing
• Machine Learning
• +۳ مورد دیگر

Maryam Mehri Dehnavi is an Associate Professor in the Department of Computer Science at the University of Toronto and a Principal Research Scientist at NVIDIA. She holds the Canada Research Chair in Parallel and Distributed Computing and leads the ParaMathics research group. Research focuses on high-performance computing , machine learning , sparse matrix optimizations , and compiler design for heterogeneous systems. Her work develops domain-specific languages , scalable numerical libraries , and auto-vectorization techniques for cloud and GPU platforms. Recent publications address LLM compression , sparse code translation , GPU kernel synchronization , and control flow optimization . Scientific recognition: Ontario Early Researcher Award (2021), NSF CRII Grant, NSERC New Frontiers in Research Fund. Current students: Mushegh Shahinyan , Martin Phan , Maryam Haghifam , and others. Former advisees: Kazem Cheshmi (NJIT), Zachary Blanco (MIT Lincoln Lab), Yuanxi Li (Amazon).

Jesse Thalerمشاهده پروفایل

استاد

• Theoretical Particle Physics
• Quantum Field Theory
• Machine Learning
• +۷ مورد دیگر

Jesse Thaler is a Professor of Physics at the Massachusetts Institute of Technology (MIT), where he has been a faculty member since 2010. He is affiliated with the MIT Center for Theoretical Physics - a Leinweber Institute (CTP-LI), the Laboratory for Nuclear Science (LNS), the Statistics and Data Science Center (SDSC), and the Institute for Data, Systems, and Society (IDSS). Since 2020, he has served as the inaugural Director of the NSF Institute for Artificial Intelligence and Fundamental Interactions (IAIFI). Thaler received his Sc.B. in Math/Physics from Brown University in 2002, followed by a Ph.D. in Physics from Harvard University in 2006. From 2006 to 2009, he was a Miller Research Fellow at the University of California, Berkeley. His research focuses on theoretical particle physics, particularly on fusing quantum field theory with machine learning techniques to address fundamental physics questions. His work spans three main areas: Data Science and AI/ML applications in particle physics, Collider Physics and QCD, and Beyond the Standard Model physics. He is an expert in jet physics, which involves studying collimated sprays of particles produced at the Large Hadron Collider (LHC), and he investigates jet substructure to enhance searches for new phenomena and understand gauge theory dynamics. His research also explores strategies for probing dark matter at the LHC and theoretical structures of supersymmetry. Data Science and AI/ML: Merging deep learning with physics principles for improved particle physics analysis Collider Physics and QCD: Developing new theoretical frameworks for analyzing collider data Beyond Standard Model: Exploring dark matter detection strategies and theoretical extensions to the Standard Model Thaler has received numerous prestigious awards including being named an American Physical Society (APS) Fellow in 2022 and receiving a Simons Investigator Award in the same year. Other notable honors include the Fermilab Distinguished Scholar (2018-2020), Simons Fellowship in Theoretical Physics (2018), Frank E. Perkins Award for Excellence in Graduate Advising (2017), and the Presidential Early Career Award for Scientists and Engineers (2012). As an advisor, Thaler has mentored numerous Ph.D. students, postdoctoral researchers, and undergraduate students. His research group has produced significant work in jet physics, machine learning applications for particle physics, and beyond the standard model physics. He has also secured substantial research funding through grants from the Department of Energy and other agencies. Thaler leads the Thaler Research Group at MIT and serves as Director of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions, which brings together researchers from MIT, Northeastern University, Harvard University, and Tufts University to advance AI research with applications to fundamental physics.

Florin Rusuمشاهده پروفایل

استاد

• Database systems
• Large-scale data management
• Query processing and optimization
• +۴ مورد دیگر

Florin Rusu is a Professor and Chair of the Department of Computer Science and Engineering at the University of California Merced, School of Engineering. He joined UC Merced in 2010 and has served in multiple administrative positions including as chair of the School of Engineering's Executive Committee and currently as chair of the Department of Computer Science and Engineering. His educational background includes a B.Eng. degree from the Technical University of Cluj-Napoca, Faculty of Automation and Computer Science (2004), and M.Sc. and Ph.D. degrees from the University of Florida in Computer Science (2008 and 2009). Rusu's research focuses on database systems and large-scale data management, with particular emphasis on designing infrastructure for Big Data analytics. His specific research areas include query processing and optimization, approximate and randomized algorithms, scalable machine learning, multi-dimensional array data management, and in-situ data processing. His work bridges theoretical aspects with practical system design issues. His research has been funded by multiple prestigious organizations including the US Department of Energy (DOE), National Science Foundation (NSF), California Department of Education, Hellman Foundation, LogicBlox, and TigerGraph. His recent publications show a continued focus on database query optimization, particularly around cardinality estimation, sketch-based methods, and innovative approaches to query plan generation. His work spans both theoretical contributions and practical implementations, with several projects transitioning into real-world database systems. Scientific Awards: DOE Early Career Award (2014) Hellman Faculty Fellowship (2013) Rusu has advised numerous graduate students through their Ph.D. and Master's programs, with many going on to successful careers at major tech companies (Google, Meta, TigerGraph) and academic positions. His research group has secured substantial funding from NSF (COMPASS project 2020-2025), DOE Early Career Award (2014-2021), TigerGraph, California Department of Education, and Hellman Foundation. His research group maintains active projects in Database Query Optimization, Scalable Gradient Descent Optimization, Array Databases, In-Situ Data Processing, GLADE, Online Aggregation, and Sketches, demonstrating a comprehensive research program spanning multiple aspects of database systems and large-scale data management.

Erin R Ottmarمشاهده پروفایل

دانشیار

• Mathematics Education
• Educational Psychology
• Perceptual Learning
• +۲ مورد دیگر

Dr. Erin R. Ottmar is an Associate Professor of Psychology and Learning Sciences at Worcester Polytechnic Institute (WPI), affiliated with the Department of Social Science & Policy Studies. She directs the MAPLE (Math Abstraction Play Learning Embodiment) Lab, focusing on educational technology and mathematics cognition. She holds a BA in Psychology and Elementary Education from the University of Richmond (2005) and a PhD in Educational Psychology: Applied Developmental Science from the University of Virginia (2011). Her postdoctoral training was completed at the University of Richmond. Her research integrates educational, cognitive, and developmental psychology to design interventions improving mathematics teaching/learning. Key projects include developing dynamic educational technologies like From Here to There and Graspable Math that embed perceptual learning and gesture-based interaction. Her current work examines how cognitive and non-cognitive pathways combine to enhance K-12 mathematics education, using classroom observations, longitudinal data, and multi-level modeling. Publications demonstrate consistent focus on: Perceptual learning mechanisms in mathematics Educational technology efficacy Classroom implementation studies Game-based learning analytics Cognitive factors in mathematical reasoning Awards include: NSF CAREER Award (2022) Sigma Xi Outstanding Junior Faculty Researcher Award (2022) AERA Early Career Fellow (2016) She actively mentors graduate/undergraduate students in the LST program and Psychology department. Major grants include NSF funding for projects on perceptual cues in algebra learning ($668K) and AI-supported teacher tools. She emphasizes interdisciplinary collaboration and real-world applications in her lab.

Jeffrey Heinzمشاهده پروفایل

استاد

• Phonology
• Linguistic typology
• Grammatical inference
• +۵ مورد دیگر

Jeffrey Heinz is a Professor at Stony Brook University , holding a joint appointment in the Department of Linguistics and the Institute for Advanced Computational Science . He has been at Stony Brook since 2017, following a decade at the University of Delaware. His research focuses on computational linguistics, formal language theory, grammatical inference, and phonology, with applications to robotics and artificial intelligence. He earned his Ph.D. in Linguistics from UCLA in 2007. Heinz’s work bridges theoretical linguistics and computational methods, emphasizing the learnability of linguistic patterns through formal models. He has contributed to understanding phonological typology, reduplication, and the mathematical foundations of language learning. His research has been published in Science , Phonology , and Machine Learning , among others. He was honored with the 2017 Early Career Award from the Linguistic Society of America for his contributions to computational learning theory in linguistics. He teaches advanced courses in computational phonology and linguistics, including a course at the LSA Summer Institute. He actively organizes academic sessions and serves on steering committees for conferences like ICGI. His interdisciplinary approach integrates linguistics with computer science, robotics, and mathematical logic. Award highlights include: 2017 Early Career Award (Linguistic Society of America) He advises students in linguistics and computational fields, though specific names are not listed here. His research labs and collaborations involve computational linguistics and robotics projects, such as stress pattern databases and grammatical inference benchmarks.

Rebecca Seahمشاهده پروفایل

مدرس

• Mathematics Education
• Special Education
• Curriculum and Pedagogy
• +۵ مورد دیگر

Rebecca Seah is a Lecturer in the School of Education at RMIT University. She holds an ORCID identifier (0000-0001-7307-2775) and is open to supervising Masters Research or PhD students. Her work focuses on Mathematics Education and Special Education, particularly in curriculum design, teacher professional learning, and supporting students with learning difficulties/disabilities. Rebecca has a doctorate investigating teacher professional knowledge and mathematics instruction for students with learning challenges. Prior to academia, she worked in special schools, early intervention programs for children with autism, and inclusive high school settings, where she developed integrated curricula for middle school students. She served as Acting Head of Special Education Services at Griffith University in 2009. Her research interests include Mathematical Reasoning, Learning Progressions, Design-Based Research, and STEM creativity frameworks. She contributed to the Reframing Mathematical Futures II project (2014–2018), developing geometric reasoning tasks for Years 7–10. This work earned her the RMIT Research Impact Award (2016) and the MERGA Beth Southwell Practical Implications Award (2018). Rebecca is a reviewer for journals like Mathematical Thinking and Learning and a regular presenter at conferences. She collaborates with organizations such as the Mathematics Education Research Group of Australasia and the Mathematics Associations of Victoria. Her industry experience includes roles in early childhood education and training workshops for people with special needs.

Tina Maltiمشاهده پروفایل

استاد

• Developmental Psychology
• Social-Emotional Development
• Child Mental Health
• +۵ مورد دیگر

Tina Malti is a Professor of Psychology at the University of Toronto Mississauga and Founding Director of the Laboratory for Social-Emotional Development and Intervention ( www.tinamalti.com ). She also established and leads the multi-disciplinary Centre for Child Development, Mental Health, and Policy at UTM and the Humboldt Science Center for Child Development at Leipzig University. As a registered clinical psychologist in Ontario and President of the International Society for the Study of Behavioural Development (ISSBD), she bridges research, intervention design, and policy advocacy. Key Affiliations: University of Toronto Mississauga (Professor), Leipzig University (Alexander von Humboldt Professorship), ISSBD (President) Research Themes: Social-emotional development, moral emotions, prosocial behavior promotion, and trauma-informed interventions for children facing adversity. Scientific Trends: Recent work spans developmental science, clinical psychology, and Edtech, with a focus on kindness cultivation, refugee mental health, digital well-being, and physiological correlates of emotional responses. She emphasizes interdisciplinary collaboration and policy translation. Scientific Leadership: Alexander von Humboldt Professorship, ISSBD Presidency, and founding roles in two research centers.

• Mathematics Education
• Cognitive Development in Early Mathematics
• Integer Understanding
• +۷ مورد دیگر

Laura Bofferding is a Professor of Mathematics Education in the Department of Curriculum and Instruction at Purdue University's College of Education. Her career spans over a decade of progressive academic appointments, including Assistant Professor (2011-2017), Associate Professor (2017-2024), and current Professor (2024-present). She maintains active research and teaching roles focused on early mathematics cognition. Her educational background includes: Ph.D. in Curriculum Studies and Teacher Education from Stanford University (2011) M.A. in Learning, Design, and Technology in Education from Stanford University (2007) B.S. in Elementary Education from University of Wisconsin, River Falls (2002) Bofferding's research centers on cognitive development in early mathematics, particularly children's understanding of negative numbers, spatial reasoning through tangram puzzles, and the application of contrasting cases in instruction. Her work bridges theoretical cognitive science with practical classroom applications, emphasizing game-based learning and emergent bilingual education. Recent projects explore AI-generated word problems and programming debugging in elementary contexts, demonstrating her commitment to innovative pedagogical approaches. Her publication trends reveal sustained focus on integer conceptualization (2014-2023), expanding into spatial reasoning (2022-2023) and AI integration (2024). Key thematic clusters include measurement misconceptions, tangram-based shape transformation, and debugging strategies in computational thinking. Notable awards include: Purdue Faculty Engagement Scholar (2021) Purdue Faculty Scholar (2019) AMTE STaR Fellow (2012) Bofferding secures significant grant funding including an NSF CAREER award ($680,504) for integer understanding research and a Purdue Launch the Future Grant ($25,000) investigating AI-generated word problems. Her advising focuses on mathematics education doctoral students through courses like EDCI 62000 (Developing as a Mathematics Researcher), with grant collaborations spanning computational thinking (NSF ITEST) and emergent bilingual education (Purdue Small Research Grant).

Luis Barbaمشاهده پروفایل

پژوهشگر ارشد

• Computational Geometry
• Algorithms
• Graph Theory
• +۴ مورد دیگر

Luis Barba is a Research Fellow in the Machine Learning and Optimization group at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, working under Professor Martin Jaggi. He completed his PhD through a cotutelle program between Carleton University, Ottawa and Université Libre de Bruxelles, Brussels, supervised by Professors Stefan Langerman, Jit Bose, Pat Morin and Vida Dujmović. Prior to that, he earned his master's degree at Universidad Nacional Autónoma de México (UNAM) under Professor Jorge Urrutia. Dr. Barba's research spans computational geometry, algorithms, graph theory, and more recently, machine learning and optimization. His work addresses fundamental problems in geometric data structures, Voronoi diagrams, graph coloring, and distributed learning. He has made significant contributions to understanding time-space trade-offs in geometric algorithms and developing efficient methods for problems like geodesic Voronoi diagrams and dynamic graph coloring. Dr. Barba's publication record demonstrates a clear evolution from theoretical computational geometry to practical applications in machine learning. Early in his career, he focused on fundamental geometric problems including linear-time algorithms for geodesic Voronoi diagrams and efficient convex hull computation in polygonal domains with obstacles. More recently, his work has shifted toward machine learning, where he has developed novel optimization techniques for distributed and federated learning settings, including implicit gradient alignment methods and multilayer lookahead approaches. Dr. Barba has published extensively in top-tier conferences and journals including Symposium on Computational Geometry (SoCG), Canadian Conference on Computational Geometry (CCCG), Algorithmica, and Discrete and Computational Geometry. His collaborative work demonstrates strong connections across the computational geometry and algorithms communities, with frequent co-authorship with leading researchers in these fields. Throughout his career, Dr. Barba has maintained a consistent focus on algorithmic efficiency and computational complexity, whether addressing theoretical geometric problems or practical machine learning challenges. His work exemplifies how deep theoretical insights can inform practical computational approaches across different domains of computer science.

• High-performance computing
• Graph algorithms
• Graph machine learning
• +۱ مورد دیگر

Ariful Azad serves as an Associate Professor in the Department of Computer Science & Engineering at Texas A&M University, where he leads research at the intersection of high-performance computing and graph analytics. His work focuses on developing scalable algorithms for graph machine learning with applications in bioinformatics and security informatics. Educational Background: Ph.D. in Computer Science, Purdue University (2014) B.S. in Computer Science and Engineering, Bangladesh University of Engineering and Technology (2006) Research Focus: Dr. Azad specializes in high-performance graph algorithms , particularly for distributed-memory systems. His pioneering work includes the Combinatorial BLAS library and novel approaches for graph neural networks (GNNs), with emphasis on explainability through Shapley values and optimization of sparse matrix operations. His bioinformatics research tackles large-scale metagenomics challenges through projects like Exabiome. Publication Trends: Recent publications (2023-2025) reveal three dominant themes: (1) Scalable GNN explanation frameworks using distributed Shapley values, (2) High-performance sparse linear algebra for graph embeddings and knowledge graphs, and (3) Bioinformatics applications in metagenomics and network alignment. His work consistently bridges theoretical algorithm development with practical implementations for exascale systems. Scientific Recognition: NSF CAREER Award (2024) for foundational contributions to scalable graph algorithms Indiana University Trustee's Teaching Award (2024) U.S. Department of Energy Early Career Award (2021) Research Leadership: As principal investigator for multiple federal grants, Dr. Azad directs projects advancing graph analytics at extreme scales. His work on Weapons of Mass Destruction knowledge graphs demonstrates applied security research, while Exabiome represents significant contributions to computational biology. He actively develops open-source tools like PLANETALIGN for network analysis benchmarking, fostering reproducibility in computational science.

• Computer Science
• Artificial Intelligence
• Numerical Analysis
• +۳ مورد دیگر

Erkan Günerhan is a full-time Lecturer in the Department of Computer Technologies at Kağızman Vocational School, Kafkas University, Turkey, holding this position continuously since 2012. He has also served in significant administrative roles including Department Head (2017-2020) and Center Deputy Director (2013-2017) at the same institution. His academic credentials include: BSc in Computer Engineering from Pamukkale University (2003-2009) MSc in Computer Engineering from Atatürk University (2009) Master's Degree in Computer Engineering (Thesis) from Karadeniz Technical University (2019) Günerhan's research spans computational mathematics and artificial intelligence, with early work on numerical methods for differential equations and fractional calculus evolving toward machine learning applications. His expertise demonstrates a clear transition from theoretical mathematical modeling to practical AI implementations, particularly in speech recognition systems. Publication trends reveal a methodological progression: initial focus (2014-2015) on differential transform methods for population models and fractional PDEs shifted toward deep learning techniques by 2022, evidenced by his LSTM-based speaker recognition research. This trajectory highlights his adaptation to emerging computational paradigms while maintaining strong mathematical foundations. He has not received any notable scientific awards. Günerhan has not supervised thesis students nor participated in research projects or intellectual property development. His collaborations include Khatereh Tabatabaei (4 joint publications), Cemal Köse (1 publication), and Ercan Çelik (1 publication), primarily focused on numerical analysis and AI applications.

Nadia Benakliمشاهده پروفایل

استاد

• Geometric Group Theory
• Graph Theory
• Mathematics Education
• +۳ مورد دیگر

Dr. Nadia Benakli is a Professor of Mathematics at New York City College of Technology (City Tech), part of the City University of New York system, within the School of Arts & Sciences. She coordinates the Computer Science and Applied Mathematics internship programs, bridging academic theory with industry practice. Her career spans decades of contributions to both pure mathematics and educational innovation. Her educational foundation includes a Ph.D. in Mathematics from Paris-Sud University, France. Research interests center on Geometric Group Theory (hyperbolic groups, Coxeter groups, polygonal complexes), Graph Theory , and Mathematics Education innovations. She integrates computational tools like R and Maple into calculus and data analysis instruction, emphasizing visualization and hands-on projects to enhance student comprehension in foundational mathematics courses. Publication trends reveal an evolution from theoretical work (1991-2002) on geometric structures to applied educational research (2011-2017). Early papers established frameworks for hyperbolic geometry and group boundaries, while recent work focuses on computational thinking, faculty mentoring models, and internship program design—demonstrating her commitment to translating abstract mathematics into practical educational strategies. As internship coordinator, she mentors students through industry placements and co-developed SIAM News-published frameworks for applied mathematics internships. Her teaching portfolio spans foundational courses (MAT065, MAT1175) to advanced topics (MAT440, MAT4901), reflecting deep engagement with curriculum development across all undergraduate levels.