جست‌وجوی استاد و پژوهشگر

Guoqiang Yuمشاهده پروفایل

استاد

• Machine Learning
• Pattern Recognition
• Optimization
• +۹ مورد دیگر

Guoqiang Yu is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. He holds a joint appointment at the Virginia Tech Research Center - Arlington. His research focuses on integrating machine learning, signal processing, and statistical methods to develop computational tools for analyzing multiplatform biomedical data. Key areas include neuroinformatics, bioinformatics, and systems biology, with applications in understanding human diseases through genomic, proteomic, and imaging data integration. Education: Ph.D. in Electrical Engineering, Virginia Tech (2011) Postdoctoral Fellowship at Stanford University (2012) M.S. Tsinghua University (2004) B.S. Shandong University (2001) Research Interests: Machine learning methodologies for biomedical data analysis, pattern recognition in complex datasets, optimization algorithms for high-dimensional data, stochastic signal processing, and their applications in neurodegenerative diseases (e.g., ALS, Alzheimer's), glial cell biology, and precision medicine. His work emphasizes developing open-source tools like ABDS, CAM3.0, and SynQuant for data normalization, deconvolution, and quantitative imaging analysis. Awards & Service: NSF Career Award (2018) Dean's Award for Excellence in Research (2022) Member of NIH BRAIN Initiative Consortium (2021–present) Associate Editor for BMC Bioinformatics (2017–present) Labs & Teams: Leads the Yu Lab at Virginia Tech, collaborating with multidisciplinary teams in neuroscience, bioengineering, and computational biology. Active in NIH-funded consortia focused on brain data science and large-scale neuroimaging initiatives.

Katie Morrisonمشاهده پروفایل

استاد

• Mathematical Neuroscience
• Neural Networks
• Neural Coding
• +۳ مورد دیگر

Katie Morrison is an Associate Chair and Professor in the Department of Mathematical Sciences at the University of Northern Colorado (UNC), part of the College of Natural and Health Sciences. Her research focuses on mathematical neuroscience, neural networks, and algebraic coding theory, with a particular emphasis on applying matrix codes and graph-based algorithms to understand neural dynamics. She has been funded by NSF DMS-1951599 and NIH R01 EB022862 grants. Education: Ph.D. in Mathematics (University of Nebraska-Lincoln, 2012), M.S. in Mathematics (2008), B.A. in Mathematics and Psychology (Swarthmore College, 2005), and studies at Budapest Semesters in Mathematics. Her work bridges abstract mathematics with applied neuroscience, including projects like the CTLN initiative. Research trends in her publications emphasize graph-theoretic approaches to neural network dynamics, pattern completion in threshold-linear networks, and algebraic analysis of neural codes. Collaborations with computational topologists and neuroscientists highlight interdisciplinary strengths. Grants and advising: Active in securing federal research funding and mentoring students in interdisciplinary projects. Her lab explores connections between network connectivity and emergent behaviors, with applications to both theoretical and applied neuroscience.

• Textile Engineering
• Composite Materials
• Sustainable Textiles
• +۳ مورد دیگر

Thomas Gries is a Professor at RWTH Aachen University 's Department of Textile Technology . He serves as the Director of the university's textile machinery department, leading research in composite materials, sustainable textiles, and advanced manufacturing technologies. Current Role: University Professor & Director, Chair of Textile Machinery Research Focus: Textile engineering, carbon fiber composites, sustainable manufacturing, digital twins His work spans experimental studies on fiber-reinforced composites, AI-driven process optimization, and lunar regolith-based fiber production for space applications. Collaborations include public research projects with industrial partners and events like the WIRKTag 2025 on AI in work design. Recent publications analyze: Mechanical behavior of natural/synthetic fiber composites Recycled thermoplastic composite thermoforming 3D-woven CFRP structural optimization Moon-based fiber production from lunar materials Environmental impact assessment tools for textiles

• Biomaterials Engineering
• Biomedical Engineering
• Biomineralization
• +۶ مورد دیگر

Dr. Liza-Anastasia DiCecco is an Assistant Professor in the Department of Systems Design Engineering at the University of Waterloo, where she leads the Regenerative Biomaterials Innovation Group. Her research focuses on biomimetic implant development for hard tissues, biomineralization processes, and advanced materials characterization using liquid phase electron microscopy. She holds a PhD in Materials Engineering from McMaster University and completed postdoctoral training at Penn State University supported by the NSERC Banting Fellowship. Education: PhD, Materials Engineering, McMaster University (2023) MASc, Materials Engineering, University of Windsor (2019) BASc, Mechanical Engineering, University of Windsor (2017) Research Interests: Biomaterials design for musculoskeletal applications Biomineralization mechanisms at organic-inorganic interfaces In situ liquid transmission electron microscopy (TEM) for dynamic material analysis 3D printing of functionalized medical implants Computational modeling of biomaterial behavior Awards & Recognition: 2023 NSERC Banting Postdoctoral Fellowship 2019-2022 NSERC Vanier Canada Graduate Scholarship 2024 M&M Microanalysis Society Creative Canvas Award 2023 McMaster University Fall Valedictorian Professional Affiliations: Councillor-At-Large, Microscopy Society of Canada Core Member, Waterloo Institute for Nanotechnology Member, Canadian Biomaterials Society Lab Focus: The DiCecco Team develops novel biomaterial characterization workflows linking atomic-to-macro scale structures, with emphasis on hydrated environments. Current projects explore titanium-based implants with genistein coatings and collagen mineralization mechanisms.

Tiziano De Matteisمشاهده پروفایل

استادیار

• Parallel and Distributed Computing
• High-Performance Computing
• Energy Efficiency in Computing
• +۴ مورد دیگر

Tiziano De Matteis is an Assistant Professor in the @Large Research group at Vrije Universiteit Amsterdam's Faculty of Science, Department of Computer Systems. He also holds an affiliation with the Network Institute. His research focuses on overcoming post-Moore architecture challenges through parallel and distributed computing, high-performance systems, energy efficiency, and FPGA applications. Previously, he was a PostDoc at ETH Zurich's SPCL Group and earned his MSc/PhD from the University of Pisa. Education PhD in Computer Science, University of Pisa MSc in Computer Science, University of Pisa Research Interests Post-Moore architectures for distributed ecosystems Energy-aware parallel computing High-level abstractions for parallel software development FPGA-based hardware acceleration Data stream processing and distributed systems Recent Research Trends Recent work emphasizes: Data center risk analysis and sustainability Optimizing microservices and distributed scheduling LLM model offloading to NVMe storage Python-based data-centric programming productivity GPU interconnect performance in supercomputing Grants & Projects Participates in the EU-funded 'Extreme and Sustainable Graph Processing' project (2023-2025), exploring scalable graph algorithms and energy-efficient computing systems. Teaching Accelerator-Centric Computing Ecosystems Computer Organization Distributed Systems Systems Seminar

Hadi Salmasianمشاهده پروفایل

استاد

• Lie Groups
• Representation Theory
• Algebra
• +۴ مورد دیگر

Hadi Salmasian is a Full Professor in the Department of Mathematics and Statistics at the University of Ottawa, Faculty of Science. He holds a PhD from Yale University and specializes in Lie groups, Lie algebra representations, and related areas of algebra and mathematical physics. Education: PhD in Mathematics, Yale University Research Interests: Dr. Salmasian's research focuses on representation theory of Lie groups and Lie superalgebras, quantum groups, invariant theory, and applications to mathematical physics. His work explores topics such as Capelli eigenvalue problems, spherical functions on supergroups, and polynomiality of faithful dimensions for nilpotent groups. Recent Research Trends: His publications emphasize the interplay between algebraic structures (e.g., Weyl algebras, quantum homogeneous spaces) and representation-theoretic techniques, with applications to quantum computing and cryptography. Notable themes include classification of dual pairs in Lie supergroups and development of hash functions over finite groups. Awards: No awards explicitly mentioned in the provided texts. Advising & Grants: Supervised students include Manal Al-Zahrani, Mengyuan Cao (co-supervised with Monica Nevins), Dene Lepine, and Mitra Mansoori. Postdoctoral researchers include Ali Assem Mahmoud (co-supervised with Anne Broadbent and Monica Nevins). Research activities involve collaboration with groups in algebra and Lie theory. Labs/Teams: Active in research groups focused on algebra, Lie theory, and representation theory within the Department of Mathematics and Statistics at the University of Ottawa.

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.

Alan Edelmanمشاهده پروفایل

استاد

• Julia Programming
• High Performance Computing
• Linear Algebra
• +۳ مورد دیگر

Alan Edelman is a Professor at the Massachusetts Institute of Technology (MIT), renowned for his work in high-performance computing, linear algebra, and random matrix theory. He is a co-creator of the Julia programming language, which emphasizes efficiency and versatility for scientific computing. His research integrates mathematics and computer science, focusing on numerical methods, parallel computing, and applications in fields like quantum control and climate modeling. Edelman leads projects such as Oceananigans.jl for geophysical fluid dynamics and Circuitscape for connectivity analysis in conservation biology. His academic contributions span theoretical advancements in matrix theory and practical implementations of computational tools. Edelman collaborates across disciplines, bridging scientific computing with machine learning and quantum physics. His work on differentiable programming frameworks and GPU acceleration has impacted both academic research and industry applications. Key Projects: Julia programming language, Oceananigans.jl, Circuitscape Research Themes: High-performance computing, numerical linear algebra, random matrices, scientific machine learning Affiliations: MIT’s Theory of Computation Community of Research, PI of multiple NSF-funded projects Edelman’s recent work emphasizes interdisciplinary applications, including climate policy modeling and automated materials discovery. His publications reflect a blend of foundational mathematics and cutting-edge computational techniques.

Martin Fergieمشاهده پروفایل

مدرس

• Machine Learning
• Computer Vision
• Medical Imaging
• +۵ مورد دیگر

Martin Fergie is a Lecturer in Healthcare Sciences at the University of Manchester's Division of Informatics, Imaging & Data Sciences. His research focuses on applying machine learning and computer vision to medical imaging for disease outcome prediction, including breast cancer risk assessment via mammograms and multiplex immunofluorescence imaging for cancer outcomes. He holds a PhD in machine learning from the University of Manchester (2008–2012), followed by roles as CTO at DigitalBridge and CEO at Spotlight Pathology Ltd. He is actively involved in the InnovateUK ICURe program to reduce pathologists' workload through AI in digital pathology. Education: PhD in Machine Learning (University of Manchester, 2013), MSc/Molecular Pathology Teaching Role. Research interests include predictive modeling for disease outcomes, AI-driven pathology tools, and imaging biomarker development. Key projects include breast cancer risk prediction from mammograms and histology-based clinical outcome modeling for follicular lymphoma and head/neck cancers. Recent articles highlight advancements in AI for breast density assessment, extracellular matrix phenotyping, and H&E-to-multiplex immunohistochemistry translation. His work contributes to UN Sustainable Development Goals related to health and innovation. Awarded the 2023 Teaching Excellence Award (Highly Commended) for contributions to medical education. Collaborates on projects like the 'Developing targeted strategies for precision breast cancer prevention' initiative. External roles include Non-executive Director at VREvo Ltd and CEO at Spotlight Pathology Ltd. Associated with research platforms like Digital Futures and the Lydia Becker Institute.

Eduardo Eyrasمشاهده پروفایل

استاد

• RNA Biology
• Cancer Research
• Long-read Sequencing Technologies
• +۴ مورد دیگر

Eduardo Eyras is a Professor at the Australian National University (ANU) and EMBL Australia Group Leader, leading research in computational RNA biology and cancer genomics. He directs the Centre for Computational Biomedical Sciences and is part of the Shine-Dalgarno Centre for RNA Innovation. His work focuses on transcriptome and epitranscriptome analysis using long-read sequencing, machine learning, and computational methods to study cancer mechanisms. Eyras holds a PhD in Mathematics from the University of Groningen (1999) and previously led research at the Sanger Institute and Pompeu Fabra University. Affiliations: Director, Centre for Computational Biomedical Sciences Researcher, Shine-Dalgarno Centre for RNA Innovation Member, Division of Genome Sciences and Cancer Leader, The Eyras Group - Computational RNA Biology Research Interests: Development of algorithms for long-read sequencing Machine learning applications in RNA biology Epitranscriptomic modifications and cancer Therapeutic mRNA platform steering Key Projects: Novel algorithms for transcriptome variation analysis Predictive models of RNA modifications in disease Ribosomal DNA variation analysis Advisees & Grants: Supervises PhD students (e.g., Favour Oyelami, Stefan Prodic) and leads ARC-funded projects on mRNA diagnostics and epitranscriptomic therapies. Collaborates with global teams on forensic genomics, cancer drug resistance, and AI-driven translational research. Labs/Teams: Leads the Eyras Group, collaborating with the Hannan Group (Cancer Therapeutics) and Shirokikh Group (Protein Biosynthesis).

Ehsan Kharati Koopaeiمشاهده پروفایل

مدرس ارشد

• Statistical Inference
• Generalized Linear Models
• Machine Learning
• +۳ مورد دیگر

Dr. Ehsan Kharati Koopaei is a Senior Lecturer (Associate Professor) in Statistics/Data Science at the School of Computing and Mathematics, Manchester Metropolitan University. He serves as the Early Career Researchers (ECR) Representative and holds a FHEA (Fellow of the Higher Education Academy) and PGCert. His academic journey includes a BSc in Statistics from Iran, an MSc in Mathematical Statistics, and a PhD in Statistics from Italy. His research focuses on statistical inference, generalized linear models, and machine learning applications in public health and clinical trials. He has contributed to projects funded by the National Institute for Health and Care Research (NIHR), including studies on exercise interventions for abdominal aortic aneurysms, psychoeducation for psychosis, and suicide prevention in autistic adults. Dr. Koopaei has collaborated on over £9 million in research grants, serving as a statistician in trials such as the TACTIC telomerase study and the UK Mini Mitral valve surgery trial. His work emphasizes bridging statistical rigor with real-world healthcare applications. Awards include FHEA and PGCert. He teaches undergraduate courses in Computer Science and Mathematics and supervises potential PhD students in statistical data science.

• Statistical Inference
• Numerical Optimization
• Multidimensional Scaling
• +۳ مورد دیگر

Michael W. Trosset is a Professor of Statistics at Indiana University in Bloomington, IN. He holds a Ph.D. in Statistics from the University of California at Berkeley and has previously worked at the Arizona Media Arts Center and the College of William & Mary. Educational background includes Princeton High School, Rice University (B.A. in Mathematics), and UC Berkeley (Ph.D. in Statistics). Research Interests: Statistical inference, numerical optimization, manifold learning, high-dimensional data analysis, and stochastic simulation. His work bridges classical statistical theory with modern computational methods, focusing on dimensionality reduction, network analysis, and algorithmic validation. Publication Trends: Recent articles emphasize geometric approaches to statistical computing, including continuous multidimensional scaling, latent structure inference in random graphs, and rehabilitating manifold learning techniques like Isomap. His work often integrates theoretical rigor with practical implementation. Academic Contributions: He has developed courses in statistical computing, multivariate analysis, and statistical learning, emphasizing both theoretical foundations and real-world applications in text mining, microarray analysis, and network inference.

Frédéric PASCALمشاهده پروفایل

استاد

• Statistical Signal Processing
• Machine Learning
• Adaptive Radar Detection
• +۵ مورد دیگر

Frédéric Pascal is a Full Professor at CentraleSupélec, part of the University of Paris-Saclay, and a member of the Laboratoire des Signaux et Systèmes (L2S). His research focuses on statistical signal processing, machine learning, and robust estimation techniques, with applications in radar detection, covariance matrix estimation, and information geometry. He has held roles including Coordinator of AI activities at CentraleSupélec and Head of the "Signals and Statistics" group at L2S. His academic journey includes a PhD from University Paris X – Nanterre (2006) and an HDR (2012) from University Paris-Sud. His work emphasizes adaptive signal processing in non-Gaussian environments, with contributions to robust covariance estimation, M-estimators, and applications in radar systems and biomedical signal processing. He has authored over 100 journal/conference papers and serves as an Associate Editor for IEEE Transactions on Signal Processing and Elsevier Signal Processing. Current research interests include AI transparency, data-driven methods for industry 4.0, and health-related signal analysis.

Hédi Hadijiمشاهده پروفایل

دانشیار

• Online Decision-Making
• Adaptivity in Bandits
• Online Learning
• +۳ مورد دیگر

Hédi Hadiji is an Associate Professor at the Laboratoire des Signaux et Systèmes (L2S) at CentraleSupélec. His research focuses on the mathematics of online decision-making, particularly adaptivity in bandits and online learning. Prior to this role, he was a postdoctoral researcher at the University of Amsterdam under Tim van Erven. He holds a PhD in Mathematics from Université Paris-Saclay, supervised by Gilles Stoltz and Pascal Massart. Education: PhD in Mathematics, Université Paris-Saclay (2020) Masters in Probability and Statistics, Université Paris-Saclay (2017) Part III Mathematical Tripos, University of Cambridge (2016) École Polytechnique (2012–2016) Research Interests: Hédi’s work addresses theoretical foundations of online learning, including bandit algorithms, reinforcement learning, and optimization. He explores adaptive strategies in dynamic environments, with applications to stochastic and adversarial settings. Key themes include minimax optimal algorithms, regret analysis, and the interplay between exploration and exploitation. Recent Publications: His recent work includes minimax optimal algorithms for linear bandits, tracking solutions in time-varying systems, and diversity-preserving strategies in K-armed bandits. These contributions advance theoretical understanding of adaptive decision-making in complex systems. Teaching: He teaches courses on the theoretical principles of deep learning, reinforcement learning, and mathematical foundations at the graduate level. His lectures emphasize rigorous analysis of generalization, optimization, and the neural tangent kernel. Labs & Affiliations: His primary affiliation is with L2S, where he engages in collaborative research groups such as MODESTY, COMEDY, and SYCOMORE. His work intersects signal processing, control systems, and communication networks.

Arnaldo Delli Carriمشاهده پروفایل

دانشیار

• Nonlinear Dynamics
• Finite Element Analysis
• Nonlinear Vibration Analysis
• +۳ مورد دیگر

Arnaldo Delli Carri is an Associate Professor at the CEES School of Engineering, serving as Curriculum Lead and actively involved in academic leadership. His research focuses on Nonlinear Dynamics and Finite Element Analysis, with expertise in structural analysis, modal testing, and vibration dynamics. He is currently accepting PhD students in areas like Nonlinear Vibration Analysis and FE model validation. Research interests include advanced topics such as nonlinear system identification, model upgrading, and machine learning applications in structural performance prediction. Notable projects involve analyzing planetary gearboxes, 3D printing opportunities in Africa, and laser vibrometer-based nonlinear detection. His work integrates experimental and computational methods to address challenges in mechanical systems and biomedical engineering. Collaborations span international partnerships, focusing on structural dynamics and nonlinear systems. His contributions include over 17 publications since 2011, with a focus on vibration analysis, finite element validation, and additive manufacturing applications. He currently supervises PhD candidates exploring nonlinear dynamics and FE formulations.