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Juan YeView profile

Academic

• Adaptive Pervasive Systems
• Sensor-Based Human Activity Recognition
• Sensor Fusion
• +6 more

Dr Juan Ye is a Reader in the School of Computer Science at the University of St Andrews. He holds a PhD from University College Dublin and BSc/MSc degrees from Wuhan University. His research focuses on adaptive pervasive systems, sensor-based human activity recognition, and overcoming challenges in real-world sensor data analytics. He teaches modules on component technologies and machine learning, and supervises PhD students and research internships. His research addresses the limitations of classical machine learning by developing continual-learning frameworks for long-term, real-world applications. Key interests include sensor fusion, context awareness, ontologies, and uncertainty reasoning. He has contributed to projects on gaze tracking, animal density estimation, and robust gaze interaction methods. Dr Ye collaborates with interdisciplinary teams and has published extensively on topics like continual learning, gaze estimation, and ecological monitoring. His work aligns with UN Sustainable Development Goals through applications in environmental and assistive technologies.

Daniele PanozzoView profile

Assistant Professor

• Geometry Processing
• Computer Graphics
• Digital Fabrication
• +3 more

Daniele Panozzo is an Assistant Professor of Computer Science at the Courant Institute of Mathematical Sciences, New York University (NYU). Previously, he was a senior researcher at ETH Zurich's Interactive Geometry Lab. He holds a BSc and Ph.D. in Computer Science from the University of Genoa (2008, 2012). His research focuses on geometry processing, computer graphics, and digital fabrication, with notable contributions to meshing, finite element simulation, and computational geometry. Key achievements include the EUROGRAPHICS Annual Award for Best Ph.D. Thesis (2013), the Young Researcher Award (2015), an NSF CAREER Award (2017), and a Sloan Research Fellowship (2020). His work emphasizes robust algorithms for geometric computing, with applications in engineering, biology, and cultural heritage preservation. Publications span topics like microstructure optimization, contact dynamics, and 3D scanning, reflecting his interdisciplinary approach. He leads the Geometric Computing Lab at NYU, advancing open-source tools such as libigl for geometry processing. Ongoing projects include developing differentiable solvers and high-order meshing techniques for complex simulations.

Spyros KonstantopoulosView profile

Professor

• Multilevel Models
• Meta-Analysis
• Educational Policy
• +7 more

Spyros Konstantopoulos is a Professor in the Department of Counseling, Educational Psychology and Special Education at the College of Education, Michigan State University. He holds a Ph.D. in Research Methods in Education from the University of Chicago. Prior to joining MSU in 2009, he was faculty at Northwestern University and Boston College. His research focuses on methodological applications of multilevel models, experimental design, and meta-analysis, alongside substantive inquiries into teacher and school effectiveness, educational evaluation, and policy. His educational background includes advanced training in research methods, with a strong emphasis on quantitative techniques in education. Spyros teaches courses in educational statistics, including multivariate analysis, multilevel modeling, and meta-analysis. His research interests are deeply rooted in statistical methodologies for education data, particularly multilevel modeling and meta-analysis. He explores how class size, teacher characteristics, and policy interventions impact student achievement, with a focus on cognitive and non-cognitive outcomes in early education. His work often employs fixed effects approaches to isolate causal relationships in complex educational settings. Notably, he has contributed to understanding the persistence of teacher effects and the design of optimal experiments in education. While no specific scientific awards are mentioned, his publications span prestigious journals such as Educational and Psychological Measurement, the American Educational Research Journal, and Multivariate Behavioral Research. He has also authored the Konstantopoulos CV detailing his contributions to education research. In advising and grants, he has no listed advisees but has been involved in curriculum coordination and program design. His work includes collaborations with the CREATE for STEM Institute and the Office of International Studies in Education, though these affiliations are not explicitly detailed in the text. No specific labs or teams are mentioned in the provided texts.

Daisy PowellView profile

Associate Professor

• Literacy Development
• Educational Psychology
• Developmental Cognitive Neuroscience
• +4 more

Dr Daisy Powell is an Associate Professor at the University of Reading's Institute of Education, serving as Director of the University of Reading Statistics Community of Practice and Theme co-Lead for Neuroscience and Cognition at the Centre for Literacy and Multilingualism (CeLM). Her research focuses on reading/writing development, bilingual literacy, environmental factors influencing literacy acquisition, and the role of technology in education. She leads the Child Development in Education module for MA Education students and contributes to teacher training programs. Her funded projects include investigations into bilingualism's impact on literacy in socioeconomically disadvantaged children, school readiness interventions in deprived communities, and cross-linguistic literacy transfer in Malay-English bilinguals. Notable collaborations involve the Nuffield Foundation and British Academy. External roles: Series Editor for Routledge's "Early Literacy, Language and Communication"; Editorial Board member for British Journal of Developmental Psychology and Journal of Research in Reading Professional memberships: Chartered Member of the British Psychological Society, Society for the Scientific Study of Reading Dr Powell supervises 11 doctoral students exploring topics like mobile apps for literacy, error analysis in learning difficulties, and speech recognition software in schools. Her work bridges cognitive science and educational practice, emphasizing evidence-based interventions for diverse learner needs.

• Biomedical Signal Processing
• Machine Learning
• Neonatal EEG Analysis
• +5 more

William Marnane is a Professor of Electrical and Electronic Engineering at University College Cork (UCC). He holds a B.E. from UCC (1984) and a D.Phil. from the University of Oxford (1989). His career includes roles as Lecturer (1989), Senior Lecturer (1999), Dean of Graduate Studies (2013–2016), and Head of the School of Engineering (2016–2019). He has led major research initiatives, including the SFI-funded INFANT Centre and the Claude Shannon Institute. His research focuses on biomedical signal processing, machine learning, and neonatal EEG analysis, with notable contributions to neonatal seizure detection algorithms and fetal health monitoring. He has been awarded the Giner de Los Ríos Visiting Research Fellowship (2007, 2020) and leads projects funded by Wellcome Trust, Science Foundation Ireland, and EU grants. His educational background includes significant contributions to curriculum development and graduate training in engineering and biomedical sciences. He has supervised numerous research projects, including advancements in cryptographic hardware, embedded systems, and low-power signal processing architectures. Research highlights include the ANSeR neonatal seizure detection algorithm (Wellcome Trust-funded), clinical trials on EEG-based seizure recognition, and development of secure TLS coprocessors. He has published over 200 peer-reviewed articles, with key contributions in neonatal EEG analysis, machine learning applications in healthcare, and cryptographic processor design. Current research explores AI for fetal heart rate monitoring and wearable health technologies. Grants and partnerships include leadership roles in EU-funded projects, SFI Strategic Research Clusters, and industry collaborations. He co-directs the INFANT Centre, bridging engineering, medicine, and data science for translational research in neonatal care.

Mi LuView profile

Professor

• Computer Arithmetic
• Parallel Computing
• Distributed Processing
• +6 more

Mi Lu is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, affiliated with the Dwight Look College of Engineering. She holds the Eugene E. Webb’43 Fellowship (2002-2003) and has been recognized in multiple Who's Who directories. Her research focuses on computer arithmetic, parallel computing, distributed systems, and VLSI algorithms, with over 200 publications and 4 authored books/chapters. Education: Ph.D. and M.S. in Electrical Engineering from Rice University (1987 and 1984). She has co-chaired 51 research advisory committees for graduate students and served on editorial boards of journals like IEEE Transactions on ITS and TNNLS. Her work bridges computational methods with biomedical applications, including stent biomechanics, vascular modeling, and coronary artery disease progression analysis. Research emphasizes computational fluid dynamics (CFD), arterial hemodynamics, and wall shear stress analysis. Key contributions include frameworks for longitudinal coronary artery assessment and co-registration of medical imaging data. Awards include the Innovative Award at CSCI 2019 and conference leadership roles. Awards: Innovative Award (CSCI 2019), Eugene E. Webb Fellowship, multiple Who's Who recognitions Grants/Advising: 51 graduate committees chaired, NSF review panel service Labs/Teams: Biomedical engineering and computational modeling collaborations

Edinah GnangView profile

Assistant Professor

• Discrete Mathematics
• Graph Theory
• Multilinear Algebra
• +3 more

Edinah Gnang is an Assistant Professor of Applied Mathematics and Statistics at Johns Hopkins University. His research focuses on discrete mathematics, graph theory, multilinear algebra, and experimental mathematics, with notable contributions to hypermatrix spectral decomposition and arithmetic formula complexity. He has been funded by APL and NSF, and recognized with the Spira Teaching Award and Joel Dean Award. Education: B.S. in Mathematics and Physics from University of Montreal (2005); Ph.D. in Computer Science from Rutgers University (2013). Postdoctoral positions included the Institute for Advanced Study and Princeton University's Center for Computational Intractability (2013-2014). Joined JHU in 2017 after serving as Golomb Visiting Assistant Professor of Mathematics at Purdue University. Research highlights include developing the first hypermatrix spectral decomposition theorem, optimal arithmetic formula algorithms, and bridging hypermatrix algebra with machine learning applications. His work integrates combinatorial, numerical, and algorithmic approaches to tackle challenges in signal analysis and medical imaging. Awards include teaching excellence recognitions and research grants. Current research explores extending linear algebra to hypermatrices and analyzing non-linear systems' solution complexity.

• Combinatorics
• Graph Theory
• Discrete Mathematics
• +3 more

Dr. Ku Cheng Yeaw is a Senior Lecturer at the Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University (NTU). He holds a PhD from Queen Mary, University of London (2005) and has held prior academic positions at the California Institute of Technology (Harry Bateman Research Instructor, 2005–2008) and the National University of Singapore (Visiting Fellow/Lecturer/Senior Lecturer, 2008–2016). His research focuses on combinatorics, graph theory, and discrete mathematics, with notable contributions to eigenvalue analysis, intersecting families, and extremal set theory. He has an extensive publication record spanning over two decades, addressing topics such as hypergraphs, permutation patterns, and spectral properties of graphs. Dr. Ku has not been explicitly noted for awards or student advisement in the provided text.

Antonio GuimarãesView profile

Researcher

• Fully Homomorphic Encryption (FHE)
• Verifiable Computation
• Privacy-preserving technologies
• +3 more

Antonio Guimarães is a postdoctoral researcher at the IMDEA Software Institute in Madrid, Spain. His research focuses on practical aspects of Fully Homomorphic Encryption (FHE), including verifiable FHE, fast bootstrapping algorithms, and efficient homomorphic evaluation of cryptographic primitives. He holds a Ph.D. in Computer Science from the University of Campinas (2019-2023), where he also completed his MSc (2017-2019) and Computer Engineering degree (2012-2016). During his Ph.D., he was a visiting student at Aarhus University (2022-2023). His work emphasizes privacy-preserving technologies and verifiable computation, with applications in secure cloud computing and encrypted machine learning. Education: Ph.D. in Computer Science, University of Campinas (2019-2023) Visiting Ph.D. Student, Aarhus University (2022-2023) MSc in Computer Science, University of Campinas (2017-2019) Computer Engineering, University of Campinas (2012-2016) His research interests include advancing FHE efficiency, developing verifiable computation frameworks for encrypted data, and exploring practical implementations of post-quantum cryptographic algorithms. He has presented work at venues such as CRYPTO, ASIACRYPT, and CHES, and contributed to open-source cryptographic libraries like MOSFHET and HELIOPOLIS. His recent projects focus on homomorphic evaluation of neural networks and optimizing FHE for real-world applications. His publications highlight contributions to bootstrapping algorithms, verifiable computation over approximate arithmetic, and privacy-preserving machine learning. These works emphasize balancing cryptographic security with computational efficiency. Grants and Collaborations: Collaborations include projects on secure transciphering for MPC and high-performance seismic data processing in cloud environments. His work often bridges theoretical cryptography with practical software implementations. Labs/Teams: His research is conducted within the IMDEA Software Institute’s cryptography group, focusing on applied cryptography and privacy-preserving technologies.

• Deductive Verification
• Interactive Theorem Proving
• Formal Methods
• +7 more

Yong Kiam Tan is a research scientist at the Institute for Infocomm Research (I²R), A*STAR, Singapore, and holds a joint appointment as a Nanyang Assistant Professor at the College of Computing and Data Science, Nanyang Technological University (NTU), Singapore. He earned his PhD in Computer Science (Pure and Applied Logic) from Carnegie Mellon University, advised by Prof. André Platzer, under the A*STAR National Science Scholarship. His research lies at the intersection of formal methods, interactive theorem proving, and automated reasoning, with applications in hybrid systems, compiler verification, and cybersecurity. He has made significant contributions to the CakeML and KeYmaera X projects, focusing on verified compilation, deductive verification of differential equations, and formalized mathematics. The 15 most recent publications reflect a strong trend in formally verified automated reasoning, certified algorithms, and applications in both classical and emerging domains such as neural networks and cryptography. His work consistently appears in top-tier venues like POPL, PLDI, CAV, and ITP, emphasizing end-to-end verification from logic to executable code. Distinguished Paper Award at CAV 2024 Best Paper and Best Repeatability Evaluation Award at HSCC 2022 Best Tool Paper Award at FM 2019 Peter Landin Prize at IFL 2015 CMU SCS Distinguished Dissertation Award 2022 Singapore NRF Fellowship Class of 2024 He actively advises students and researchers at NTU and A*STAR, collaborates internationally with experts such as Magnus Myreen, André Platzer, and Jakob Nordström, and has secured competitive funding including the NRF Fellowship. He has served on program committees for CPP, ITP, TACAS, and PLDI Artifact Evaluation, demonstrating active engagement in the research community. He leads a research team working on verified tools for automated reasoning and hybrid systems, with current members including Wei-Lin Wu, Joe Watt, Shuhan He, and several student researchers. His group maintains verified proof checkers in CakeML and contributes extensively to the Archive of Formal Proofs (AFP) in Isabelle/HOL.

Aseem RastogiView profile

Researcher

• Programming languages
• Type systems
• Program verification
• +4 more

Aseem Rastogi is a Senior Principal Researcher at Microsoft Research India specializing in programming languages, type systems, program verification, and software security. He is a core designer and developer of F*, a language for program verification, and has made significant contributions to Project Everest, which builds verified secure communications components including cryptographic libraries and parsers. Dr. Rastogi earned his PhD from the University of Maryland, College Park under Michael Hicks and completed his M.S. at Stony Brook University with Rob Johnson. His research spans formal methods, programming language theory, and practical security applications, with recent work focusing on integrating Large Language Models with formal verification techniques. His publication record demonstrates expertise across multiple domains including concurrent separation logic, secure multi-party computation, and verified systems programming. Recent work shows a clear trend toward applying LLMs to traditional verification challenges, with publications on memory safety, Rust compilation error fixing, and loop invariant generation. Dr. Rastogi has served on numerous conference program committees including as PC co-chair for ISEC 2024 and Diversity, Equity, and Inclusion co-chair for POPL 2024. He has organized workshops such as VeriCrypt and taught F* at multiple summer schools, demonstrating strong commitment to academic community building. As part of Project Everest, he collaborates across Microsoft Research labs to develop verified security-critical components. His work on EverParse has hardened attack surfaces through formally proven parsers, while CrypTFlow enables secure medical image analysis through privacy-preserving machine learning techniques.

Aymeric FromherzView profile

Researcher

• Formal Methods
• Programming Languages
• Computer Security
• +9 more

Aymeric Fromherz is a researcher at Inria Paris, affiliated with the Prosecco Team, focusing on formal methods for secure systems. His work bridges programming languages, computer security, and computational law, with significant contributions in verified systems and cryptographic software. Research Interests: His research spans formal verification, programming languages (especially Rust), dependent types, separation logic, and the application of formal methods to legal computation via the Catala language. He develops high-assurance software with a focus on memory safety, concurrency, and correctness. Publications Trends: His recent publications (2020–2025) reflect a strong trend toward verified systems: from memory allocators (StarMalloc) and cryptographic primitives (HACL×N, EverCrypt) to legal verification (CUTECat, Catala). These works emphasize correctness, performance, and practical deployment, often in top-tier venues like POPL, ICFP, OOPSLA, and S&P. Scientific Awards: ACM SIGSAC Dissertation Award A.G. Milnes Dissertation Award Distinguished Artifact Award (ESOP 2025) Best Tool Paper Award (ESOP 2024) Spotlight Paper at ICLR 2021 Advising and Grants: He collaborates closely with researchers across institutions, mentoring students and co-developing tools and frameworks. His involvement in the Everest Project indicates long-term, large-scale funding for high-assurance software. He has contributed to grant-funded efforts in verified cryptography and formal legal systems. Labs and Teams: He is a core member of the Prosecco team at Inria, which specializes in computer security and formal methods. He was previously part of the Everest Project at Carnegie Mellon University, a multi-institution effort to build verified, industrial-grade cryptographic software.

• Neuroscience
• Developmental Dyslexia
• Brain Imaging
• +7 more

Guinevere F Eden is a tenured Professor in the Department of Pediatrics at Georgetown University and serves as Director of the Center for the Study of Learning (CSL) . Her research focuses on the neuroscience of developmental dyslexia , using behavioral measures and brain imaging techniques like functional/structural MRI to study reading disabilities and remediation strategies. Primary appointment: School of Medicine - Pediatrics Collaborations: Wake Forest School of Medicine and Gallaudet University Professional roles: Past-President of the International Dyslexia Association , editorial board member for journals including Annals of Dyslexia , Dyslexia , and Human Brain Mapping Her work explores neural correlates of learning disabilities , bilingual reading differences, and neurobiological impacts of instruction modes. Supported by NIH and NSF grants, she investigates brain plasticity during reading remediation and cross-linguistic neural representations. Recent publications highlight cerebellar activation patterns in combined reading/math disabilities, gray matter differences in bilingual populations, and functional neuroanatomy of arithmetic in mono- and bilingual cohorts. Her research bridges pediatric neuroscience and educational interventions . As a leader in Interdisciplinary Program for Neuroscience (IPN) , she mentors students through neuroimaging studies on language processing, skill acquisition, and sensory experience effects on brain development. CSL teams collaborate with institutions to advance understanding of developmental cognitive neuroscience and translational clinical applications.

• Transmission of texts from Arab world to West
• Arabic-Latin translations
• Medieval astronomy and astrology
• +5 more

Charles Burnett is Professor of the History of Islamic Influences in Europe at the Warburg Institute, University of London. His distinguished academic career has focused on documenting, editing, and translating scientific and philosophical texts that were transmitted from Arabic into Latin during the Middle Ages. Professor Burnett received his BA in Classics from Cambridge University in 1972 and completed his PhD in Modern and Medieval Languages at the same institution in 1976. His academic journey included positions as Junior Research Fellow at St John's College, Cambridge (1976-79), Senior Research Fellow at the Warburg Institute (1979-82), and Leverhulme Research Fellow at the University of Sheffield (1982-84, 1985), with an intervening year as Member of the Institute for Advanced Study, Princeton (1984-85). He was appointed Lecturer at the Warburg Institute in 1985 and promoted to Professor in 1999. Burnett's research encompasses several interconnected domains: Transmission of Knowledge : The transfer of scientific and philosophical texts from the Arab world to medieval Europe, particularly through Arabic-Latin translations Medieval Astronomy and Astrology : Critical editions of works by Abu Ma'shar and other key figures, examining both theoretical frameworks and practical applications History of Numerals and Arithmetic : Studies on the introduction and adoption of Hindu-Arabic numerals in European mathematical practice Palaeography : Analysis of manuscript traditions, including the writing of numerals and magical texts Magical Practices : Investigation of divinatory techniques such as scapulimancy and geomancy across cultural boundaries Jesuit Cultural Exchange : Examination of cultural transmission between Europe and Japan during the "Christian Century" His scholarly output includes numerous monographs, critical editions, and over 200 articles. Major publications include "Magic and Divination in the Middle Ages" (1996), "Arabic into Latin in the Middle Ages" (2009), and "Numerals and Arithmetic in the Middle Ages" (2010). Burnett's work reveals how medieval translators adapted Arabic scientific knowledge for European contexts, transforming both the content and the institutional frameworks for scientific learning. Professor Burnett has supervised numerous doctoral students whose research extends his scholarly legacy. His current supervisees include Beatrice Bottomley, Arianna Dalla Costa, and Dimitrios Roussos. Former students such as Helena Avelar and Juan Acevedo have established themselves as significant scholars in medieval intellectual history. Beyond traditional academic venues, Burnett has shared his expertise through media appearances including BBC's "In Our Time" and documentaries for Arte and the Japanese Broadcasting Corporation. His research continues to illuminate the complex processes through which scientific knowledge traversed cultural and linguistic boundaries during the medieval period.

• Fuzzy Logic
• Computational Intelligence
• Multiple-Criteria Decision Making (MCDM)
• +7 more

Pasi Luukka is a Full Professor (tenured) at the LUT Business School, Lappeenranta University of Technology (LUT University), Finland, where he is affiliated with the Department of Business Studies. His research bridges computational intelligence, fuzzy systems, and business decision-making, with a strong emphasis on practical applications in finance, innovation, and industrial performance. His research interests include: Fuzzy Logic and Soft Computing Multiple-Criteria Decision Making (MCDM) Machine Learning and Feature Selection Real Option Valuation under Uncertainty Financial Forecasting and Trading Strategies Innovation and Knowledge Management His recent publications (2022–2025) show a strong trend in enhancing fuzzy classification methods (e.g., fuzzy k-NN), developing aggregation operators (OWA, Bonferroni), and applying fuzzy logic to real-world problems such as stock market prediction, R&D investment analysis, and water-energy nexus modeling. His work frequently appears in top-tier journals like Expert Systems with Applications , Information Sciences , and Fuzzy Sets and Systems . He has received recognition through a substantial body of peer-reviewed publications and active collaboration with leading researchers in fuzzy systems and decision science. Pasi Luukka advises several graduate students, including Mahinda Mailagaha Kumbure and Christoph Lohrmann, and has contributed to numerous research projects involving real options, innovation evaluation, and intelligent systems. He has served as a peer reviewer for journals such as Decisions in Economics and Finance , Group Decision and Negotiation , and Soft Computing . He is a key member of a research group focused on intelligent decision support systems, fuzzy logic applications, and computational methods in business and finance. The team actively publishes and collaborates on interdisciplinary projects involving energy, finance, and industrial systems.