جست‌وجوی استاد و پژوهشگر

K. Selçuk Candanمشاهده پروفایل

استاد

• Data Science
• Database Systems
• Machine Learning
• +۵ مورد دیگر

K. Selçuk Candan is a Professor of Computer Science and Engineering at Arizona State University (ASU) and the Director of the Center for Assured and Scalable Data Engineering (CASCADE). He holds affiliations with the Global Institute of Sustainability and the Center for Cybersecurity and Digital Forensics. His academic roles include interim co-director of the School of Computing and Augmented Intelligence (2022) and coordinator of the Big Data Systems concentration. He earned his Ph.D. from the University of Maryland and his B.S. from Bilkent University. Education: Ph.D. Computer Science (University of Maryland, 1997), B.S. Computer Engineering (Bilkent University, 1993) Affiliations: CASCADE Director, Global Futures Scientist, NSF APPEX Center His research focuses on data management, analysis of heterogeneous data, and applications in healthcare, energy, and cybersecurity. He has authored over 250 peer-reviewed publications and secured $34M in grants. Key projects include pandemic modeling, building energy optimization, and cybersecurity for smart infrastructure. Recipient of the 2023 Daniel Jankowski Legacy Award and ACM SIGMOD Contributions Award, he emphasizes interdisciplinary collaboration and mentorship. He has advised 16 doctoral students and 35+ graduate students, contributing to teaching excellence awards. His work bridges academia and industry, with grants from NSF, DOE, and DoD. Current efforts include causal learning frameworks, spatio-temporal modeling, and cyber-infrastructure for pandemic response.

K. Selcuk Candanمشاهده پروفایل

استاد

• Data Management
• Multimedia Databases
• Distributed Systems
• +۵ مورد دیگر

K. Selcuk Candan is a Professor of Computer Science and Engineering at Arizona State University (ASU) and Director of the Center for Assured and Scalable Data Engineering (CASCADE). He holds affiliations with multiple institutes, including the Global Futures Scientists and Scholars and the Center for Cybersecurity and Trusted Foundations. Candan has been at ASU since 1997, following his PhD from the University of Maryland. His research focuses on managing and analyzing non-traditional data types like multimedia, web, and scientific data. Key interests include scalable data processing, sensor data integration, and accessibility technologies for visually impaired individuals. He has led numerous grants from NSF, DoD, and others, resulting in over 250 peer-reviewed publications and 9 patents. Notable contributions include the DataStorm framework for coupled simulations and the OASIS system for accessible digital content. Candan has served as program chair for top conferences like SIGMOD and MM, and is an ACM Distinguished Scientist. His awards include the SIGMOD Contributions Award and Dan Jankowski Legacy Award. He has advised numerous students and led interdisciplinary projects in areas like pandemic modeling (APPEX Center) and building automation security. His work bridges theory and application, emphasizing real-world impact in healthcare, urban systems, and educational accessibility.

Yuan Zhangمشاهده پروفایل

دانشیار

• Statistical Network Analysis
• Nonparametric Inference
• U-statistics
• +۴ مورد دیگر

Yuan Zhang is an Associate Professor of Statistics at The Ohio State University, affiliated with the Department of Statistics and Translational Data Analytics (TDA) initiative. He holds a BSc in Statistics from the University of Science and Technology of China (2011) and a PhD in Statistics from the University of Michigan (2016). His research focuses on statistical network analysis, nonparametric inference, U-statistics, and conformal prediction. He advises several PhD students, including Meijia Shao (recipient of the 2023–2024 Whitney Research Award), Torey Hilbert, Kiljae Lee, Fangyi Wang, and Qian Zhou. Key research contributions include advancements in network-based statistical methods, robust distribution-free testing, and conformal prediction frameworks. His work bridges theoretical statistics with practical applications in neuroscience, genomics, and high-dimensional data analysis. Notable publications include work on U-statistic reduction (Journal of the American Statistical Association, 2025), stable conformal prediction (ICLR 2025), and neuroimaging connectivity analysis (Biometrics, 2025). Zhang collaborates extensively with researchers in biostatistics and computational science, contributing to projects in voxel-wise genome-wide association studies and brain connectivity analysis. His research is supported by affiliations with TDA and interdisciplinary collaborations at Ohio State.

• Compiler Optimization
• High Performance Computing
• Sparse Matrix Computations
• +۴ مورد دیگر

P. Sadayappan is a Professor at the School of Computing, University of Utah, specializing in high performance computing, compiler optimization, and scalable machine learning. His research focuses on developing efficient computational methods for scientific applications, particularly in the areas of sparse/dense matrix and tensor computations. His research interests include: Compiler Optimization for High Performance Computing Optimization of Sparse/Dense Matrix/Tensor Computations Scalable Machine Learning Algorithm-Architecture Co-Design Optimization Sadayappan's recent publications demonstrate a strong focus on tensor computations, GPU acceleration, and compiler optimizations for machine learning workloads. His work spans from fundamental compiler theory to practical implementations that improve performance across various architectures. A significant trend in his recent work involves the development of frameworks for efficient tensor operations, sparse matrix computations, and domain-specific code generation, with particular emphasis on performance portability across heterogeneous computing platforms. His notable scientific achievement includes receiving the ACM SIGPLAN Most Influential PLDI Paper Award in 2018 for his work on polyhedral compilation. Sadayappan has been principal investigator or co-investigator on numerous significant research grants, including: NSF award #2217154 (2022-2027): A Comprehensive Framework for Efficient, Scalable, and Performance-Portable Tensor Applications NSF award #2112606 (2021-2026): AI Institute for Intelligent CyberInfrastructure with Computational Learning in the Environment (ICICLE) NIH SBIR-Phase 2 (2023-2025): Enabling next generation machine learning for large scale image analysis NSF award #2009007 (2020-2024): Data Locality Optimization for Sparse Matrix/Tensor Computations DARPA SBIR-Phase 2 (2017-2022): Performance Portable Framework for Developing Graph Applications He teaches CS 4230/6230 (Parallel and High-Performance Computing) at the University of Utah and collaborates extensively with researchers across multiple institutions on projects involving computational chemistry, physics simulations, graph analytics, and machine learning.

Brent Nelsonمشاهده پروفایل

دانشیار

• Free Probability
• Operator Algebras
• Von Neumann Algebras
• +۳ مورد دیگر

Brent Nelson is an Associate Professor in the Department of Mathematics at Michigan State University (MSU). He holds a PhD in Mathematics from the University of California, Los Angeles (UCLA), under the supervision of Dimitri Shlyakhtenko, and a BS in Mathematics from the University of Illinois at Urbana-Champaign (UIUC). His research focuses on free probability, non-tracial von Neumann algebras, free transport, subfactor planar algebras, and bi-free probability. He has held postdoctoral positions at Vanderbilt University and the University of California, Berkeley, and has been recognized with awards including the Pacific Journal of Mathematics Dissertation Prize (2015) and the Distinguished Teaching Award (2018). Key research interests include the structure of von Neumann algebras, free monotone transport, and applications to quantum graphs and operator algebras. He has authored numerous publications in top-tier journals and presented at international conferences such as the Canadian Operator Symposium and the Banff International Research Station workshops. His teaching includes advanced courses on functional analysis, operator algebras, and real/complex analysis at MSU and other institutions. Grants include NSF funding for projects on non-tracial derivations and distributions, and he has organized conferences like the Groundwork for Operator Algebras Lecture Series (2020). His work bridges abstract operator algebra theory with applications in quantum probability and ergodic theory.

• Functional Analysis
• Probability Theory
• Harmonic Analysis
• +۴ مورد دیگر

Gilles Pisier is the A.G. and M.E. Owen Chair and Distinguished Professor in the Department of Mathematics at Texas A&M University, affiliated with the College of Arts & Sciences. His research focuses on functional analysis, probability theory, harmonic analysis, and operator theory, with significant contributions to C*-algebras and operator spaces. Education: Ph.D. in Mathematics, Université Paris VII (1977) DEA de Mathématiques Pures, Université Pierre et Marie Curie (Paris VI, 1972) École Polytechnique (1969–1972) Research Interests: Pisier’s work bridges functional analysis with probability and operator algebras. He has pioneered studies on operator spaces, non-commutative L p spaces, and the Grothendieck inequality. His research often intersects quantum probability and applications to quantum information theory. Recent work includes investigations into the lifting properties of C*-algebras and the weak expectation property (WEP). Publications: His articles explore topics such as von Neumann algebra injectivity, tensor products of operator spaces, and Sidon sets in harmonic analysis. These contributions highlight his expertise in abstract operator theory and its applications. Academic Service: He has served on editorial boards for prestigious journals and institutions, including the Académie des Sciences. His textbook Introduction to Operator Space Theory (2003) remains a foundational resource in the field. Future Work: Current projects include advancing the Connes-Kirchberg conjecture and exploring metric entropy in Banach-Mazur compacta.

• Machine Learning
• Neural Networks
• Quantum Computing
• +۳ مورد دیگر

Zheng Zhang is a Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. His research focuses on neural networks, quantum computing, uncertainty quantification, and optimization, with particular emphasis on tensor networks, low-rank compression methods, and hardware-efficient machine learning systems. He leads efforts in developing memory-efficient training algorithms for large language models (LLMs), tensorized optical networks, and physics-informed neural PDE solvers. Key contributions include FLAT-LLM for LLM compression, FETTA hardware accelerators, and DeepOHeat for thermal simulation in 3D-IC design. His work spans cross-disciplinary areas such as quantum-inspired algorithms, stochastic control, and yield-aware optimization of photonic ICs. He holds a faculty position in the College of Engineering and is affiliated with the ECE department. Research trends in his 2025 publications emphasize scalable training techniques for transformers, zeroth-order optimization methods, and optical computing integration. His work consistently addresses computational efficiency, memory constraints, and hardware acceleration across domains like AI, quantum computing, and electronic design automation. Notable grants and lab affiliations include projects on FPGA-based neural training, quantum circuit simulation, and tensor-compressed PDE solvers. He advises on edge computing, neuromorphic systems, and uncertainty-aware design tools for integrated circuits.

James Chapmanمشاهده پروفایل

استادیار

• Computational Materials Informatics
• Machine Learning
• Materials Science
• +۵ مورد دیگر

James Chapman is an Assistant Professor at Boston University, with a dual affiliation in the Departments of Mechanical Engineering and Materials Science & Engineering. His primary appointment is in Mechanical Engineering. He holds a PhD in Materials Science and Engineering from the Georgia Institute of Technology (2020). His research focuses on computational materials informatics, integrating machine learning with atomistic simulations to design novel catalysts and corrosion-resistant materials. Current work emphasizes high-entropy alloys for hydrogen production and pollution mitigation. Key honors include the Junior Faculty Fellow from the Hariri Institute for Computing (2024), the Trusted Reviewer Award from the Institute of Physics (2023), and the Lamar H. Franklin Fellowship (2020). His research lab, the Materials Informatics Lab, explores interdisciplinary approaches at the intersection of machine learning and materials science. Notable contributions include advancements in graph neural networks for material characterization and predictive modeling of atomic structures using diffusion models. Publications highlight themes such as topological message-passing algorithms, stratified data analysis, and multiscale modeling of defects in materials. His work bridges fundamental computational methods with practical applications in energy storage and environmental sustainability.

Ed Corriganمشاهده پروفایل

استاد

• Mathematical Physics
• Quantum Field Theory
• Integrable Systems
• +۳ مورد دیگر

Ed Corrigan is a Professor of Mathematics at the University of York, holding this position since 2011. Previously, he served as Principal of Collingwood College, Durham University (2008–2011), and held roles including Head of the Department of Mathematical Sciences at Durham University (1999–2004, 2005–2007, 2011–2015). His academic journey includes a BA (1968) and PhD (1972) from the University of Cambridge, followed by fellowships at Durham University and CERN. His research focuses on Mathematical Physics, particularly classical and quantum integrable systems, integrable defects, and two-dimensional field theories with boundaries. Notable contributions include work on affine Toda field theories, soliton solutions, and defects in field theory. He has coordinated European Networks (1992–2006) and led projects funded by EPSRC and the Royal Society. Corrigan is a Fellow of the Royal Society (FRS, 1995), the Institute of Physics (FInstP, 1999), and the Institute of Mathematics and its Applications (FIMA, 2013). His work bridges theoretical physics and mathematics, emphasizing integrability and exact solutions. He has supervised numerous students and remains active in academic leadership and research.

Haewon Jeongمشاهده پروفایل

استادیار

• Machine Learning
• Responsible AI
• Algorithmic Fairness
• +۷ مورد دیگر

Haewon Jeong is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara (UCSB), with an affiliation to the Department of Computer Science. She is the Co-director of the REAL AI initiative and leads the Jeong Lab, focusing on trustworthy machine learning systems, responsible AI, and AI for scientific discovery. Her research bridges information theory, distributed computing, and machine learning, with a recent NSF CAREER Award and Hellman Fellowship recognition. Education: PhD in Electrical and Computer Engineering from Carnegie Mellon University (2020), BS in Electrical Engineering and Computer Science from KAIST (2015). Research Interests: The lab’s core themes include Responsible AI (fairness, privacy, bias mitigation), Reliable Computing (fault-tolerant algorithms for classical and quantum systems), and ML for Science (accelerating scientific discovery via AI, particularly in quantum materials and physics). Recent Trends in Articles: Recent work addresses bias in neural compression models, differential privacy in distributed systems, and fault-tolerant quantum computing algorithms. Publications span topics like generative models, privacy-preserving computation, and educational equity in AI. Awards: NSF Early CAREER Award (2024) 2024-2025 Hellman Fellowship Advising & Grants: Advises PhD/MS students in AI fairness, distributed computing, and quantum ML. Current grants include NSF CAREER funding for trustworthy ML systems. Lab members have presented work at ISIT, ICML, and SIAM conferences. Labs & Collaborations: The Jeong Lab collaborates across disciplines, with ties to UCSB’s quantum computing and materials science groups. The REAL AI initiative emphasizes ethical AI development and societal impact.

• Computer Science
• Neuroscience
• Medical Imaging
• +۳ مورد دیگر

Fengyu Cong is a Visiting Professor at the Faculty of Information Technology of the University of Jyväskylä (Finland). His research focuses on interdisciplinary computational neuroscience, biomedical signal processing, and machine learning applications in healthcare. Key contributions include EEG/fMRI analysis for mental health disorders such as depression and autism spectrum disorder, as well as federated learning methods for privacy-sensitive medical data. He has presented at major conferences including the 6th Conference on Mismatch Negativity (2012) and ESCAN 2014 , showcasing work on preattentive facial expression processing in neurological populations. His affiliations include the Engineering and Secure Communications Engineering and Signal Processing groups within his faculty. Research interests span neural signal analysis, multimodal imaging techniques, and AI-driven diagnostics. Notable methodologies include tensor decomposition for fMRI preprocessing, HMM-based sleep stage classification, and federated learning frameworks for distributed healthcare applications. His work bridges computer science and clinical neurology, with publications in top-tier journals addressing topics like vascular cognitive impairment, neural oscillations in autism, and artifact removal in biomedical signals.

• Cognitive Psychology
• Multivariate Statistics
• Neural Networks
• +۵ مورد دیگر

Hervé Abdi is a full Professor in the School of Behavioral and Brain Sciences at the University of Texas at Dallas. He holds a Ph.D. in Mathematical Psychology from the University of Aix-en-Provence (France, 1980). His career includes roles as an assistant and full professor in French universities, adjunct professor at UT Southwestern Medical Center, and visiting scholar at institutions worldwide, including Brown University and the University of Geneva. Abdi’s research focuses on computational models of cognition, multivariate statistical techniques (e.g., PCA, correspondence analysis, PLS regression), and neuroimaging data analysis. He explores face and odor perception, brain imaging methodologies, and sensory evaluation. His work bridges cognitive science, statistics, and neuroscience, with over 327 publications, including 12 books and 13 edited volumes. He has mentored numerous Ph.D. students and postdoctoral researchers, many of whom hold academic and industry leadership roles. Key awards include two Fulbright Scholarships. His recent work emphasizes DISTATIS and STATIS methods for multi-table data, covSTATIS for network neuroscience, and applications in autism research and sensory analysis. Education: M.S. Psychology, University of Franche-Comté (1975) M.S. Economics, University of Clermond-Ferrand (1976) M.S. Neurology, University Louis Pasteur (1977) Ph.D. Mathematical Psychology, University of Aix-en-Provence (1980) Grants & Collaborations: Recipient of NIH-funded grants for autism neuroimaging studies and sensory evaluation projects. Collaborates internationally on brain-behavior relationships and multivariate statistical methodologies. Labs & Teams: Leads computational neuroscience and multivariate analysis research groups. Involved in the Face Lab at UTD, exploring face perception and cognitive modeling.

Mihaela Vajiacمشاهده پروفایل

استاد

• Complex Analysis
• Hypercomplex Analysis
• Differential Geometry
• +۳ مورد دیگر

Mihaela Vajiac is a Professor and Program Director for Mathematics at Chapman University's Schmid College of Science and Technology. She serves as Director of the Center of Excellence in Complex and Hypercomplex Analysis (CECHA) and organizes the Math/Physics/Computation Seminar. Her educational background includes a Ph.D. from Boston University and a B.S. from the University of Bucharest. Dr. Vajiac's research spans: Complex/Hypercomplex Analysis : Investigating Dirac-type operators, quaternionic systems, and applications in physics and engineering. Algebraic Computational Methods : Developing algebraic tools for PDEs including Maxwell and Cauchy-Fueter systems. Differential Geometry : Exploring integrable systems, curvature invariants, and symplectic structures. Her recent publications focus on bicomplex tensor products, spectral factorization in hypercomplex spaces, and geometric invariants, reflecting sustained innovation in operator theory and Clifford analysis. She co-organizes international workshops like IWOTA 2021 and maintains active collaborations with global researchers.

Elina Robevaمشاهده پروفایل

استادیار

• Algebraic Statistics
• Tensor Decomposition
• Applied Algebraic Geometry
• +۴ مورد دیگر

Elina Robeva is an Assistant Professor in the Department of Mathematics at the University of British Columbia, Faculty of Science. She is an active researcher in algebraic statistics, tensor decomposition, and applied algebraic geometry, with a focus on structured tensor models and their statistical and computational properties. Her research lies at the intersection of algebra, statistics, and data science. She investigates mathematical structures in high-dimensional data, particularly through the lens of tensor decompositions that generalize matrix factorizations. Her work includes studying orthogonally decomposable tensors, symmetric tensor eigenvalues, and connections to algebraic varieties and optimization methods such as the tensor power method. She also explores extensions to incoherent and frame-based tensor models with applications in machine learning and signal processing. The available article from her 2021 seminar talk indicates a strong trend in theoretical and computational aspects of tensor analysis, emphasizing algebraic structure, spectral properties, and algorithmic approaches to decomposition. Her work bridges abstract algebra with practical data modeling challenges. While no specific awards or students are listed in the provided text, her invited seminar presentation reflects recognition in the mathematical sciences community. She is involved in advancing the theoretical foundations of statistical learning through algebraic methods. Elina Robeva contributes to the academic mission through research and scholarly dissemination, collaborating across mathematical disciplines to develop new tools for understanding complex data structures.

Monique Laurentمشاهده پروفایل

استاد

• Discrete Mathematics
• Optimization
• Polynomial Optimization
• +۵ مورد دیگر

Monique Laurent is a Tilburg University professor and senior researcher at CWI (Centrum Wiskunde & Informatica), focusing on discrete mathematics and optimization . Her work bridges algebra, geometry, and computer science to solve complex combinatorial and polynomial optimization problems. Part-time full professor at Tilburg University (since 2009) Group leader of Networks and Optimization at CWI (2005-2016) Current member of CWI Management Team Research Focus: Semidefinite programming hierarchies, noncommutative polynomial optimization, quantum information theory, and matrix factorization. Her recent work explores applications in quantum entanglement , graph parameters , and combinatorial data analysis . Key Publications: 15+ articles from 2017-2024 address topics like copositive matrices , sum-of-squares convergence , and hypergraph optimization . Collaborations span institutions in the Netherlands, France, Germany, and the U.S. Awards: 2023 Khachiyan Prize SIAM Fellow (2017) KNAW member (2018) EUROPT Fellow (2021) Grants & Projects: Leads EU-funded initiatives TENORS (2024) and POEMA (2019), with prior NWO and Marie Curie grants. Organizes international workshops on polynomial optimization and quantum information.