جست‌وجوی استاد و پژوهشگر

• Programming Language Theory
• Semantics of Programming Languages
• Domain Theory
• +۷ مورد دیگر

Gordon Plotkin is a Professor at the School of Informatics, University of Edinburgh, where he is affiliated with the Laboratory for Foundations of Computer Science (LFCS). His research lies at the intersection of theoretical computer science and programming language semantics, with a profound influence on the formal understanding of computation. His research interests include Programming Language Theory, Semantics of Programming Languages, Domain Theory, Operational Semantics, Lambda Calculus, Type Theory, Concurrency Theory, and Algebraic Effects. His seminal work on structural operational semantics and domain theory has laid the foundation for modern semantics of programming languages. His publications span over five decades, showing a sustained and evolving research trajectory from foundational work in lambda calculus and domain theory to recent contributions in algebraic effects, probabilistic computation, and biochemical systems modeling. The articles demonstrate a consistent focus on formal methods, mathematical rigor, and the algebraic structure of computational effects. He has collaborated with leading researchers including Martín Abadi, John Power, Glynn Winskel, and John Reynolds. His work continues to influence both theoretical and practical developments in programming languages and systems. Gordon Plotkin has made foundational contributions to computer science, particularly through his development of structural operational semantics and domain-theoretic models of computation. He has advised numerous researchers and supervised many influential PhD theses, though specific student names are not listed in the provided text. His work has been supported by long-standing affiliations with the Laboratory for Foundations of Computer Science and the University of Edinburgh, and he has contributed to major collaborative projects in programming language design and verification. He is associated with several research groups and labs, most notably the Laboratory for Foundations of Computer Science (LFCS), which serves as a hub for theoretical research in programming languages, semantics, and logic at the University of Edinburgh.

Kenneth Rossمشاهده پروفایل

استاد

• Database Systems
• Query Processing
• Query Language Design
• +۷ مورد دیگر

Kenneth Ross is a Professor in the Computer Science Department at Columbia University in New York City. His primary appointment is within the Department of Computer Science, with affiliations including the Foundations of Data Science Committee. His work bridges theoretical database research and practical system implementation. His research focuses on database systems with particular expertise in query processing, query language design, data warehousing, and architecture-sensitive database system design. Additional research spans computational biology, especially analysis of large genomic data sets. Current projects include Linear Algebra Operators in Databases for machine learning workloads and Repeats and Somatic Mutation analysis in genomics. His work consistently addresses the intersection of hardware capabilities and database system design. Ross leads the Database Research Lab at Columbia, which has produced significant work on query optimization, GPU database processing, and hardware-conscious database systems. His recent publications demonstrate strong focus on adapting database systems to modern hardware including GPUs, SIMD processors, and persistent memory. His scientific recognition includes: Packard Foundation Fellowship Sloan Foundation Fellowship NSF Young Investigator Award Distinguished Faculty Teaching Award (2008) Ross actively advises undergraduate engineering students (juniors with last names P-Z) and has taught foundational courses including Introduction to Databases and Programming and Problem Solving for over two decades. His teaching portfolio shows consistent engagement with both theoretical concepts and practical implementation challenges in computer science education.

Richard Garnerمشاهده پروفایل

مدرس

• Category Theory
• Algebraic Structures
• Homotopy Type Theory
• +۳ مورد دیگر

Richard Garner is a lecturer at Macquarie University's School of Mathematical and Physical Sciences, Faculty of Science and Engineering. He specializes in teaching mathematics to engineering and computing students in units like MATH2055 and MATH1007, focusing on problem-solving and real-world applications. His teaching philosophy emphasizes authentic mathematical experiences, blending abstract concepts with practical examples, such as connecting multivariable calculus to AI technologies. School: School of Mathematical and Physical Sciences University: Macquarie University Teaching Areas: Mathematics for engineering and computing, convolution, multivariable calculus Richard won a Student Nominated Award in the 2023 Vice Chancellor’s Learning and Teaching Awards, reflecting his commitment to student-centered education. He prioritizes clarity in course design, using visual tools and accessible materials to enhance learning, and fosters a supportive environment where students feel comfortable asking questions. Key Teaching Strategies Organized iLearn layouts following Macquarie University's standards Multiple formats for lecture materials (diagrams, color-coded slides) Weekly task clarity and real-world problem framing Live worked examples and transparent success criteria His research spans category theory, computational effects, and homotopy theory, with publications on topics like comodels, monoidal bicategories, and enriched categories. Richard's work bridges abstract mathematics with applications in computer science and logic. Scientific Awards 2023 Vice Chancellor’s Learning and Teaching Award (Student Nominated) Students praise his ability to make complex concepts intuitive, his enthusiasm for mathematics, and his dedication to explaining the 'why' behind the subject. His teaching design, including time-sensitive banners and structured weekly content, has been highlighted as exemplary.

Matilde Marcolliمشاهده پروفایل

استاد

• Noncommutative Geometry
• Mathematical Physics
• Number Theory
• +۵ مورد دیگر

Matilde Marcolli is the Robert F. Christy Professor of Mathematics and Computing and Mathematical Sciences at the California Institute of Technology (Caltech). She holds joint appointments in the Division of Physics, Mathematics, and Astronomy (PMA) and the Division of Engineering and Applied Sciences (EAS). Her research spans noncommutative geometry, mathematical physics, number theory, and mathematical linguistics. She has been recognized with prestigious awards such as the Sofja Kovalevskaya Award (2001) and the Heinz Maier Leibnitz Prize (2001). Marcolli has advised numerous PhD students and contributed to over 300 publications. Her work bridges abstract mathematics with applications in cosmology, quantum field theory, and computational linguistics. Education : PhD in Mathematics, University of Chicago, 1997 M.Sc., University of Chicago, 1994 Laurea in Mathematics, University of Pavia, 1993 Research Interests : Marcolli’s research explores the intersection of geometry, number theory, and physics. Key areas include noncommutative geometry models of particle physics and cosmology, motives in quantum field theory, and algebraic models of generative linguistics. She applies advanced techniques such as Feynman integrals, spectral action principles, and Hopf algebras to interdisciplinary problems. Grants & Awards : NSF grants DMS-2104330, DMS-1707882, and others NSERC Discovery Grant RGPIN-2018-04937 FQXi grant FQXi-RFP-1804 Collaborations & Labs : Marcolli collaborates with institutions like the Perimeter Institute and Florida State University. She leads research groups on topics such as quantum statistical mechanics, holography, and neural information networks. Her work on syntax-semantics interfaces and quantum gravity models has been pivotal in interdisciplinary studies.

• Database Systems
• Graph Databases
• Data Management
• +۷ مورد دیگر

Amol Deshpande is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, College of Engineering. With over 160 publications spanning from 2000 to 2025, his research has significantly impacted the database systems community. His work bridges theoretical foundations with practical systems, evidenced by numerous publications in top-tier venues including SIGMOD, VLDB, and ICDE. Professor Deshpande's research focuses on database systems, with particular expertise in graph databases, data management, probabilistic databases, query optimization, and data provenance. His work addresses fundamental challenges in managing complex data, including efficient graph analytics, dataset versioning, streaming data processing, and privacy-preserving data management. Recent research directions include entity-relationship abstractions beyond traditional relations, standalone catalog engines for large data systems, and graph theoretical approaches to dataset versioning. His publication trends show a consistent focus on evolving database technologies, with early work on probabilistic databases and query optimization, transitioning to graph analytics and data provenance, and more recently addressing modern challenges in data cataloging, privacy-first data management, and serverless stream processing. His research spans both theoretical contributions (e.g., approximation algorithms for stochastic optimization) and practical systems building (e.g., RStore, TreeCat). Professor Deshpande has mentored numerous PhD students who have become active researchers in the database community, including Hui Miao, Souvik Bhattacherjee, and Konstantinos Xirogiannopoulos. His collaborative work spans across institutions, with frequent collaborations with researchers from MIT, University of Maryland, and other leading institutions. His research has been supported by major funding agencies and has influenced both academic research and industry practices in data management. The evolution of his work reflects the changing landscape of data management, from traditional relational systems to modern graph and streaming data challenges.

Gabriel A. Silvaمشاهده پروفایل

استاد

• Neuroscience
• Neural Computing
• Quantum Algorithms
• +۷ مورد دیگر

Gabriel A. Silva is a Professor in the Shu Chien-Gene Lay Department of Bioengineering at UC San Diego’s Jacobs School of Engineering, with a joint appointment as Assistant Professor in Ophthalmology. His research bridges neuroscience, theoretical physics, and applied mathematics to explore how the brain encodes and processes information, leveraging quantum logic and algorithms for advanced neural modeling. University: University of California, San Diego School: Jacobs School of Engineering Department: Shu Chien-Gene Lay Department of Bioengineering Academic Rank: Professor Joint Appointment: Assistant Professor in Ophthalmology Research Interests: Silva focuses on neural computation at cellular and network scales, aiming to abstract biological mechanisms into mathematical models that emulate brain-like processing. His work has implications for understanding neurological disorders, developing neural engineering nanotechnologies, and advancing AI systems through emergent complexity. Recent Article Trends: His publications span quantum-enhanced neural modeling, EEG-based disease detection, nonlinear dynamics in brain networks, and interdisciplinary applications of graph theory. Emerging themes include the integration of category theory for network analysis and AI optimization via emergence-promoting schemes. Labs & Teams: Affiliated with UC San Diego’s Institute of Engineering in Medicine, Silva leads research at the intersection of bioengineering, ophthalmology, and neural systems, fostering collaborations with neuroscience and quantum computing domains.

• 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

• GPU Computing
• Parallel Computing
• Domain-Specific Languages
• +۵ مورد دیگر

Prof. Dr.-Ing. Richard Membarth is a Research Professor for System-on-a-Chip and AI at the Edge Computing at Technische Hochschule Ingolstadt (THI). He is affiliated with the Hardware-Software Co-Design group and holds a secondary position at the German Research Center for Artificial Intelligence (DFKI) Saarbrücken. Co-creator of DSL frameworks like AnyDSL and Hipacc Key contributor to MetaDL (AI metaprogramming) and PRIME (predictive rendering) His research bridges GPU computing , domain-specific languages , and compiler technology , with recent work on Vulkan SPIR-V compilation and device-driven SpMV algorithms . Notable awards include the HiPEAC Paper Award (2018) and multiple Best Paper Awards for his compiler frameworks.

Alexandra Fedorovaمشاهده پروفایل

استاد

• Systems Software
• Memory and Storage Management
• Accelerator-Centric Computing
• +۴ مورد دیگر

Dr. Alexandra Fedorova is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), with an Associate Member role in the Computer Science department. She leads the Systopia systems research group, focusing on system software design, memory/storage management, and accelerator-centric computing. Her work emphasizes performance optimization, energy efficiency, and hardware-software co-design. She holds a PhD from Harvard University (2006), where she researched operating system scheduling under Margo Seltzer. Prior to UBC, she was an Associate Professor at Simon Fraser University (2006–2015). Fedorova is a recipient of the Alfred P. Sloan Research Fellowship and the Anita Borg Early Career Award. She consults for MongoDB's storage engine team and collaborates with industry on storage and performance challenges. Her research spans tools like Non-sequitur for program trace visualization, studies on storage-class memory (e.g., Optane), and frameworks for GPU acceleration. Recent efforts include Sunstone (spatial accelerator scheduling) and ExtMem (application-aware memory management). Her work bridges low-level systems with high-performance computing needs. Key contributions include optimizing NUMA systems, improving storage engine performance, and exploring processing-in-memory architectures. Fedorova’s projects often involve open-source collaboration, reflected in her GitHub repositories such as vividperf and perf-logging , which support performance analysis tools.

Tyler Lawsonمشاهده پروفایل

استاد

• Algebraic topology
• K-theory
• Homotopy theory
• +۳ مورد دیگر

Tyler Lawson is a Professor in the School of Mathematics at the University of Minnesota, holding the distinguished Albert and Dorothy Marden Professorship. His office is located in Vincent Hall 323 at 206 Church Street SE, Minneapolis, MN 55455, and he can be reached at tlawson@umn.edu or (612) 625-6802. Professor Lawson's research focuses on algebraic topology and K-theory, with significant contributions to homotopy theory, quandles, stable homotopy theory, and braided Hopf algebras. His work bridges abstract algebraic structures with topological applications, particularly exploring connections between category theory and homotopy-theoretic constructions. He has made notable advances in understanding filtrations and derived spaces, the generating hypothesis in stable homotopy theory, and the homotopy theory of quandles as they relate to knot invariants. Analysis of his recent publications reveals a strong emphasis on structural theorems in algebraic topology, with particular attention to equivariant phenomena, rational homotopy theory, and connections to arithmetic statistics. His research demonstrates sophisticated interplay between combinatorial structures and topological invariants, often revealing unexpected connections between seemingly disparate mathematical domains. Albert and Dorothy Marden Professor Professor Lawson actively advises graduate students and participates in the topology seminar series at the University of Minnesota, where he has presented on topics ranging from filtrations and derived spaces to the generating hypothesis. His research program includes collaborations with mathematicians across institutions, contributing to advancements in homotopy theory and its applications to other mathematical fields. He maintains an active research group focused on exploring the frontiers of algebraic topology and its connections to related disciplines.

Alexander Dieslمشاهده پروفایل

استاد

• Noncommutative Ring Theory
• Abstract Algebra
• Classification of Algebraic Structures
• +۳ مورد دیگر

Alexander Diesl is a Professor in the Department of Mathematics & Statistics at Wellesley College, where he integrates mathematics into a liberal arts framework. His research focuses on noncommutative ring theory, emphasizing classification and visualization of algebraic structures, and explores pedagogical approaches in mathematics education. Education: B.A., Johns Hopkins University M.A., Johns Hopkins University Ph.D., University of California-Berkeley Teaching and Research: Diesl teaches courses across all levels, including Abstract Algebra (MATH305) and Number Theory (MATH223) , which emphasizes proof-writing and applications in cryptography. His research investigates structural similarities in rings, generalizing number-theoretic concepts to abstract settings. Advising: In 2010, he mentored three Wellesley students in a summer research project on zero-divisor graphs of rings. He advocates for student engagement in mathematical exploration and its interdisciplinary connections.

Vivienne Szeمشاهده پروفایل

استاد

• Energy-Efficient Machine Learning
• Computer Vision
• Video Compression/Processing
• +۷ مورد دیگر

Vivienne Sze is a Professor at MIT's Department of Electrical Engineering and Computer Science (EECS), part of the School of Engineering. Her research focuses on energy-efficient computing systems for machine learning, computer vision, and video compression, with applications in autonomous systems, healthcare, and IoT. She leads projects integrating algorithmic innovations with hardware design to achieve low-power solutions for embedded and mobile devices. Her work has been recognized through prestigious awards, including the Primetime Engineering Emmy Award for co-developing the HEVC video compression standard and multiple faculty awards from tech giants like Google and Qualcomm. She co-authored the book *Efficient Processing of Deep Neural Networks*, emphasizing practical hardware-software co-design strategies. Research Interests: Energy-Efficient Machine Learning Accelerators Video Coding and Compression Standards Embedded Systems and Mobile Computing Processing-in-Memory (PIM) Architectures AI for Health Monitoring and Digital Health Sustainability in AI Infrastructure Publications highlight trends in: Optimizing DNNs for edge devices Innovations in entropy coding and CABAC Memory-efficient Gaussian-based algorithms Energy-aware design for photonic computing Awards include IEEE conference best paper awards and industry recognitions for her contributions to video coding and hardware acceleration. Her lab's collaborative efforts span academia and industry, aiming to bridge theoretical research with real-world deployable systems.

Leonard Van Wykمشاهده پروفایل

استاد

• Geometric Group Theory
• Knot Theory
• Algebra
• +۲ مورد دیگر

Leonard Van Wyk is a Professor in the Department of Mathematics & Statistics at James Madison University (JMU), holding roles as Assistant AUH and C&I Chair. His research focuses on Geometric Group Theory and Knot Theory, with notable contributions to subgroup analysis and knot classification. He has advised multiple undergraduate researchers through JMU's REU programs. Education: PhD in Mathematics (Geometric Group Theory), SUNY Binghamton (1993) MA in Algebra, University of California, Berkeley (1983) BA in Physics and Mathematics, SUNY Potsdam (1980) Research Interests: Explores geometric and algebraic structures within groups, knot invariants, and collaborative research with students. His work bridges abstract algebra with topological visualization, emphasizing spiral knots and subgroup dynamics. Publications: Focus on advancing knot theory through REU collaborations (e.g., spiral knots classification) and geometric group theory (e.g., Σ-invariants in graph groups). Recent work emphasizes interdisciplinary applications of knot structures. Awards: None explicitly listed. Grants/Advising: Supervised student projects resulting in peer-reviewed publications; no grants mentioned.

• Developmental Psychology
• Cognitive Neuroscience
• Language Acquisition
• +۴ مورد دیگر

Judit Gervain is a Full Professor at the Department of Developmental and Social Psychology at the University of Padua, Italy, and a CNRS Senior Research Scientist (Directeur de Recherche) at the Integartive Neuroscience and Cognition Center (CNRS & Université Paris Descartes), Paris, France. Her research focuses on early speech perception, language acquisition in monolingual and bilingual infants, and the neural mechanisms underlying language learning. She pioneered studies on newborn speech perception using near-infrared spectroscopy (NIRS), revealing prenatal influences on perceptual abilities and the emergence of grammatical structures in preverbal infants. Education: PhD in Cognitive Neuroscience (2002, SISSA, Trieste), postdoctoral research at the University of British Columbia (2007–2009), and CNRS researcher since 2009. She has authored over 100 peer-reviewed articles in journals like Science Advances , Nature Communications , and Developmental Science . Research Interests: Infant language processing, bilingualism, neuroimaging techniques (fNIRS), and comparing infant learning trajectories to artificial intelligence systems. Her work bridges developmental psychology, neuroscience, and computational linguistics, emphasizing the role of innate biases and environmental input in language acquisition. Her recent studies explore how infants’ learning mechanisms differ from Large Language Models (LLMs), focusing on input requirements, critical periods, and the role of multimodal integration. She also investigates the impact of prenatal auditory experience on neonatal speech perception and the neural foundations of linguistic structure detection. Labs/Teams: Affiliated with the CNRS’s Integartive Neuroscience and Cognition Center and the University of Padua’s developmental psychology group. Serves as associate editor for Developmental Science and Neurophotonics .

• Control Theory
• Applied Mathematics
• Computational Mechanics
• +۷ مورد دیگر

Goong Chen is a Professor at Texas A&M University (TAMU) within the College of Arts & Sciences. His research focuses on Control Theory, Applied Mathematics, and interdisciplinary applications in computational mechanics, fluid dynamics, and quantum systems. He holds a Ph.D. from the University of Wisconsin (1977) and a B.S. from National Tsing-Hua University (1972). His research interests span computational biomechanics, forensic modeling, nanofluidics, and mathematical physics. Recent work includes modal analysis of animal motion, crash mechanics of aircraft, and forensic reconstruction of disasters. He has contributed to theoretical advancements in PDEs, nonlinear dynamics, and quantum computing. Chen’s articles address cutting-edge topics like thermoelastic plates, Volterra equations, and DFIM systems. He has pioneered numerical methods for complex systems using OpenFOAM and finite element techniques. His computational models have been applied to real-world scenarios such as submarine implosions and chemical warfare forensics. No academic awards are explicitly listed, but his extensive publication record reflects significant contributions to applied mathematics and engineering. He leads research teams focused on interdisciplinary challenges in computational science and engineering.