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Ajim UddinView profile

Assistant Professor

• FinTech
• Machine Learning in Finance
• Financial Networks
• +5 more

Ajim Uddin is an Assistant Professor at the MT School of Management, New Jersey Institute of Technology (NJIT). His research focuses on FinTech, machine learning applications in finance, and financial network analysis. He holds a PhD in Business Data Science from NJIT (2022), an MBA and BBA in Finance & Banking from Shahjalal University of Science & Technology (SUST), and an MS in Economics & Finance from Southern Illinois University Edwardsville. Education Highlights: Ph.D., Business Data Science, NJIT (2022) M.S., Economics & Finance, SIUE (2017) M.B.A. & B.B.A., Finance & Banking, SUST (2016/2013) Dr. Uddin's research explores dynamic graph learning frameworks for asset pricing, signed networks in equity models, and institutional investor behavior. His work bridges financial theory with advanced computational methods, addressing topics like mutual fund performance prediction, NPL forecasting, and social media influence on stock markets. His methodologies often integrate graph neural networks, tensor completion algorithms, and attention mechanisms. His publications span journals like Quantitative Finance and Finance Research Letters . Recent trends show a focus on applying machine learning to traditional finance problems, with emphasis on network-based models and algorithmic solutions to market dynamics. Awards & Recognition: Best Contribution to Theory Award (2023, NEDSI Conference) Overall Best Conference Paper Award (2023, NEDSI Conference) While no formal advising relationships or grants are listed, his research actively engages with industry-relevant problems such as institutional investor decision-making and financial crisis prediction. His professional website at web.njit.edu/~au76 provides further research details.

Michael BerryView profile

Professor

• Data Science
• Machine Learning
• Text Mining
• +4 more

Michael Berry is a Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville, part of the Tickle College of Engineering. He holds a PhD in Computer Science from the University of Illinois at Urbana-Champaign, an MS in Applied Mathematics from North Carolina State University, and a BS in Mathematics from the University of Georgia. His research focuses on data science, machine learning, text mining, nonnegative matrix factorization, parallel computing, and their applications in biomedical and environmental domains. Notable contributions include work on tensor decomposition for big data analysis, algorithms for text mining, and computational tools like PolyLens and CodeAssessor. Berry's publications emphasize interdisciplinary applications, such as using nonnegative tensor factorization for biomedical literature analysis and developing GPU-accelerated methods for traffic flow analysis. His work spans conferences like the International Conference on Soft Computing in Data Science (SCDS) and journals in computational science. He has contributed to software tools like FutureLens for text visualization and SHEPPACK for interpolation algorithms. His research also addresses environmental challenges via parallel ecosystem modeling and spatial control problems. No scientific awards or grants are explicitly mentioned in the provided text.

• Commutative Algebra
• Non-Commutative Algebra
• Homological Algebra
• +1 more

Frank Moore is a Professor of Mathematics at Wake Forest University. His research focuses on homological algebra, commutative and non-commutative algebra, and algebraic software development. He holds a Ph.D. from the University of Nebraska (advisor: Lucho Avramov) and completed postdoctoral work at Cornell University (advisor: Irena Peeva). Research interests include homological questions in commutative algebra, non-commutative algebra inspired by commutative theory, and computational methods in algebra. His work often involves exploring structures like Hopf algebras, skew polynomial rings, and derived categories, with applications to algebraic geometry and singularity theory. Recent publications (2022–2014) highlight studies on Hopf algebras, support varieties, Taylor resolutions, and non-commutative Knörrer periodicity. No scientific awards are explicitly mentioned in the text. Moore has advised no listed students in the provided data. His grants and labs are not detailed here, though his software development efforts suggest involvement in computational algebra tools.

Chris H.Q. DingView profile

Professor

• Machine Learning
• Data Mining
• Bioinformatics
• +7 more

Chris Ding is a Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington, with a PhD from Columbia University. His research bridges Machine Learning, Data Mining, and Bioinformatics. Affiliations: University of Texas at Arlington; Lawrence Berkeley National Laboratory (staff computer scientist). Research Focus: Clustering algorithms (K-means, spectral clustering), matrix/tensor decompositions (NMF, Tucker), and bioinformatics applications (protein structure prediction, gene expression analysis). Professional Engagement: Organized KDD and ICDM workshops; served on NSF, DOE, and international grant review panels. Key Contributions: Established theoretical links between PCA/NMF and K-means; pioneered SVM use for protein 3D structure prediction. His work emphasizes practical applications in biological networks, dimensionality reduction, and optimization-based machine learning frameworks.

Artyom GrigoryanView profile

Associate Professor

• Quantum Computing
• Image Processing
• Signal Processing
• +3 more

Artyom Grigoryan is an Associate Professor at the Klesse College of Engineering and Integrated Design within The University of Texas at San Antonio, specializing in the Department of Electrical and Computer Engineering. His research bridges quantum computing and advanced image/signal processing techniques, with a focus on novel transforms and mathematical frameworks. Ph.D. in Electrical Engineering from Moscow Institute of Physics and Technology Contact: BSE 1.522, UTSA Research Interests : Quantum image processing, signal-induced heap transforms, quaternion-based algorithms, and thermal imaging enhancements. His work explores quantum representations of signals, fast transforms for multi-qubit systems, and entropy-driven segmentation methods applicable to medical and underwater imaging. Recent Publications highlight trends in quantum circuit design, color restoration for Renaissance art, lightweight neural networks for solar systems, and mathematical tools for quantum signal analysis. Notable contributions include quantum edge detection, state-to-state transformations, and golden ratio applications in vector similarity.

• Computer Science
• Artificial Intelligence
• Information Theory
• +7 more

Anatoly V. Anisimov is a Professor and current Dean of the Faculty of Computer Science and Cybernetics at Taras Shevchenko National University of Kyiv, with a career spanning over five decades. He previously served as Dean of the Faculty of Cybernetics (2004-2016) and has held academic roles ranging from Assistant Professor to Head of Department of Mathematical Informatics. Candidate of Physical and Mathematical Sciences (1972) Doctor of Science (Dr. Sc.) in Physics and Mathematics (1994) Internship at Stanford University (1976-1977) under Donald Knuth His research interests focus on information theory, cryptography, data compression, natural language processing, and algorithm design , with recent work exploring multidelimiter prefix codes, two-base numeration systems, and lightweight authentication protocols for IoT. Over 150 publications and book chapters reflect his contributions to computational linguistics and theoretical computer science. Key article trends reveal a consistent emphasis on encoding techniques, mathematical modeling, and parallel computing , including applications in error correction, semantic analysis, and tensor factorization . His work bridges classical algorithmic theory with modern cybersecurity and AI challenges. Scientific honors include: Glushkov Prize (1994) Soros Professor Grant (1994-1995) State Prize of Ukraine (1998) Honored Worker of Science and Technique (2005) Distinguished Professor Title (2008) Lebedev Prize (2008) Corresponding Member, National Academy of Sciences (2009) Order of Merit (2018) Advising and grants show collaborations with researchers like I.O. Zavadskyi and O.O. Marchenko. He secured international grants (e.g., Soros) and led projects in computational linguistics, information security, and matrix factorization .

• Neuropathology
• Neurodegenerative Diseases
• Alzheimer's Disease
• +4 more

Lea T. Grinberg, MD, PhD is a Full Professor of Neurology at the University of California San Francisco and Professor of Pathology at the University of São Paulo. She leads the UCSF Neurodegenerative Disease Brain Bank , directs the Human Biology Validation Core for the NIH/U54 Tau Consortium, and co-leads the Neuropathology Core for the LEADS project. Her research focuses on: Brainstem neurodegeneration in Alzheimer's disease Sleep disturbances in neurodegenerative conditions Tau and amyloid-beta pathophysiology Neuroinflammatory mechanisms Global brain health disparities Neuropathological biomarker validation Her 15 most recent publications span tau PET imaging, synaptic degeneration mechanisms, and genetic risk factors for FTLD-TDP subtypes. She has received the UNESCO-L'Oréal Award for Women in Science and the John Douglas French Alzheimer Foundation Distinguished Research Scholar Award, and contributes to: International dementia research initiatives Neuropathological consensus criteria development Global brain health policy frameworks Neurodegenerative disease education programs

• Robotics
• Auditory Perception
• Multimodal Perception
• +3 more

Sylvain Argentieri serves as an Associate Professor at the Institute for Intelligent Systems and Robotics (ISIR) within Sorbonne University, where he has been a member of the ASIMOV research group ("Architectures and Models for Adaptation and Cognition") since 2008. His office is located at ISIR, Campus Pierre et Marie Curie, 4 place Jussieu, BC173, 75005 Paris. He maintains an active research presence with publications spanning from 2006 to 2024. His educational background includes obtaining the highest teaching diploma in France (Agrégation externe) in Electrical Engineering from the École Normale Supérieure in 2002, followed by a Ph.D. in Computer Science from Paul Sabatier University (Toulouse) in 2006, and his Habilitation à Diriger des Recherches (HDR) in 2018. Argentieri's research focuses on artificial perception in robotics with special emphasis on auditory perception, active multimodal perception approaches, and sensorimotor integration. His work demonstrates a consistent trajectory exploring how robots can develop perceptual capabilities through sensorimotor experiences, with particular attention to binaural sound localization, head movement control, and the emergence of spatial representations from uninterpreted sensory signals. His publications reveal a strong interest in how naive agents can structure their understanding of the world through active exploration and sensory prediction. His publication record shows a clear progression from foundational work on sound source localization to more complex investigations of sensorimotor contingencies and embodied cognition. The research spans multiple venues including IEEE conferences, Robotics and Autonomous Systems, and Frontiers journals, with recent work incorporating transformer architectures and neural network approaches to auditory processing. Argentieri maintains active collaborations with researchers including Nicolas Obin, Bruno Gas, and Valentin Marcel, as evidenced by his co-authored publications. His laboratory work takes place within the ASIMOV team at ISIR, which focuses on architectures and models for adaptation and cognition in robotic systems.

Kazi Lutful KabirView profile

Assistant Professor

• Data Mining
• Machine Learning
• Computational Biology
• +3 more

Kazi Lutful Kabir is an Assistant Professor in the Department of Information Sciences and Technology at George Mason University (GMU). He previously served as an Assistant Professor at Stevens Institute of Technology and held a research fellowship at Los Alamos National Laboratory (LANL). His research focuses on applying data mining and machine learning techniques to address biological questions, particularly in protein structure prediction and molecular dynamics analysis. Kabir holds a PhD and MS in Computer Science from GMU and a BS in Computer Science and Engineering from the Military Institute of Science and Technology (MIST), Bangladesh. Education: PhD in Computer Science, George Mason University MS in Computer Science, George Mason University BS in Computer Science and Engineering, MIST, Bangladesh Research Interests: His work bridges computational methods and biological systems, emphasizing machine learning applications in bioinformatics. Key areas include protein structure quality estimation, molecular energy landscape analysis, and antibody dynamics modeling. He also explores quaternion-based methods in pedestrian path analysis and graph theory for structure prediction. Publications: His recent work highlights advancements in non-negative matrix factorization, tensor factorization, and unsupervised learning for molecular structure analysis. Notable themes include protein decoy selection, antibody-antigen interaction dynamics, and integrative approaches combining energy landscapes with Markov state models. Professional Experience: Assistant Professor, Stevens Institute of Technology (prior to GMU) Research Fellow, LANL Labs/Teams: While specific lab affiliations are not detailed, his research aligns with computational biology and data science initiatives at GMU’s College of Science.

Cynthia MatuszekView profile

Assistant Professor

• Robotics
• Machine Learning
• Human-Robot Interaction
• +5 more

Cynthia Matuszek is an Assistant Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). She directs UMBC’s Interactive Robotics and Language lab, focusing on enabling robots to learn from human interaction using natural language. Her research spans robotics, machine learning, human-robot interaction (HRI), and natural language processing (NLP). Notably, she was recognized in IEEE's 2018 '10 to Watch in AI' list. Dr. Matuszek holds a Ph.D. and M.S. in Computer Science & Engineering from the University of Washington (2014, 2010) and a B.S. in Computer Science from the University of Texas at Austin (2000). Her work bridges theoretical and applied domains, addressing challenges in grounded language learning, multimodal data fusion, and ethical AI deployment. Her recent publications emphasize cybersecurity (e.g., malware detection using compact features), neurosymbolic reasoning, and the integration of large language models into robotics. The lab’s work often explores VR-based human-robot collaboration scenarios, leveraging photogrammetry and simulation for reproducible studies. Key thematic trends in her articles include: Advancing grounded language learning through cross-modal alignment and interactive labeling Developing ethical AI systems capable of moral reasoning and command rejection Pioneering domain-specific cybersecurity solutions using tensor factorization and vector stores Expanding multilingual and multimodal capabilities for robots in unstructured environments Her research also addresses societal impacts, including studies on gender stereotypes in AI datasets and privacy implications of pervasive robotics. The lab’s work often intersects with NSF-funded initiatives, such as the CAREER award for inclusive robotic learning environments.

Laura GrigoriView profile

Professor

• High-Performance Scientific Computing
• Numerical Linear Algebra
• Communication-Avoiding Algorithms
• +6 more

Laura Grigori is a Professor of Applied and Computational Mathematics at EPFL and Head of the Laboratory for Simulation and Modeling at the Paul Scherrer Institute (PSI), Switzerland. She holds a Ph.D. in Computer Science from Université Henri Poincaré (2001) and has been a leading figure in high-performance numerical algorithms and communication-avoiding methods. Her research focuses on numerical linear algebra, randomized algorithms, and scalable scientific computing for applications in astrophysics, molecular simulations, and exascale systems. Affiliations: EPFL Chair of High-Performance Numerical Algorithms & Simulation; Head, PSI Laboratory for Simulation and Modeling. Education: Ph.D. in Computer Science, Université Henri Poincaré (2001); Postdoctoral Researcher, UC Berkeley/LBNL (2001–2003); Senior Researcher at INRIA (2004–2023). Research Interests: Communication-avoiding algorithms, randomized numerical linear algebra, parallel preconditioners, tensor computations, and exascale computing challenges. Awards: SIAM Fellow (2020), ERC Synergy Grant (2018), SIAM Supercomputing Career Prize (2024), and Best Paper Awards (2016, 2023). Grants & Leadership: PI of the ERC Synergy Project EMC² (Extreme-scale Computational Chemistry); Chair of PRACE Scientific Steering Committee (2021–2022); SIAM SIAG Supercomputing Chair (2016–2017). Labs/Teams: Alpines Group (INRIA/Sorbonne University), EMC² Collaboration (Cancès, Maday, Piquemal).

Longxiu HuangView profile

Assistant Professor

• Data/Signal Processing
• Applied Harmonic Analysis
• Machine Learning
• +4 more

Longxiu Huang is an Assistant Professor at Michigan State University (MSU), holding dual appointments in the Department of Computational Mathematics, Science and Engineering (CMSE) and the Department of Mathematics. Prior to this, she was an Assistant Adjunct Professor at UCLA's Department of Mathematics. Her research focuses on Data/Signal Processing, Applied Harmonic Analysis, Machine Learning, Random Matrix/Tensor Analysis, Dynamical Sampling, and Optimization. Education : Ph.D. in Mathematics, Vanderbilt University (2014-2019) M.S. in Mathematics, Fudan University (2012-2014) B.S. in Mathematics, Sun Yat-Sen University (2009-2012) B.S. in Software Engineering, Sun Yat-Sen University (2008-2009) Research Interests : Huang's work bridges theoretical mathematics and applied computational methods. She explores signal reconstruction in dynamical systems, tensor decompositions for high-dimensional data, robust matrix/tensor completion, and optimization algorithms for ill-conditioned problems. Her contributions include CUR-based methods for low-rank approximations and sampling strategies for sparse signals on graphs. Professional Activities : She co-organizes the One World MINDS Seminar, a global initiative in mathematical data science. Her research has been published in top journals like SIAM Journal on Imaging Sciences and Applied and Computational Harmonic Analysis, with a focus on theoretical guarantees and practical applications in imaging and machine learning. Lab/Teams : Active collaborator in interdisciplinary projects at MSU, particularly in computational mathematics and machine learning applications.

Ahmad AbdelfattahView profile

Academic

• High Performance Computing
• Parallel Numerical Algorithms
• GPU Computing
• +3 more

Dr. Ahmad Abdelfattah is a Research Assistant Professor at the University of Tennessee's Innovative Computing Laboratory (ICL). He holds a PhD in Computer Science from KAUST (2015), an MS in Computer Engineering from Ain Shams University (2010), and a BS in Computer Engineering (2006). His research focuses on high-performance computing, parallel numerical algorithms, and GPU acceleration, particularly in dense and sparse linear algebra libraries. He leads the MAGMA library and has contributed to NVIDIA's cuBLAS and AMD's rocBLAS libraries. His work emphasizes exascale computing, mixed-precision arithmetic, and optimizing batched operations on GPUs. His research spans algorithm design for GPUs, including batched matrix operations, sparse linear algebra, and high-order finite element methods. He has developed libraries like libCEED and SLATE, targeting exascale systems. His contributions address challenges in memory-bound computations, irregular patterns in matrices, and efficient use of tensor cores. Key trends in his publications include optimizing GPU kernels for small matrices, exploring mixed-precision techniques for solving linear systems, and enabling portable performance across diverse architectures. His work bridges theoretical numerical methods with practical implementation on modern hardware, aiming to accelerate scientific applications. Awards and recognitions include acknowledgments from NVIDIA and AMD for contributions to their BLAS libraries. His advising and grants involve advancing exascale computing through collaborations with ECP (Exascale Computing Project), focusing on CEED-enabled applications and performance tuning. He leads initiatives to enhance software tools for high-order discretizations and scalable linear algebra solutions. Abdelfattah is affiliated with the Innovative Computing Laboratory, contributing to both academic and industrial partnerships. His research impacts fields like computational science, computer vision, and robotics through optimized algorithms and libraries.

Kaie KubjasView profile

Assistant Professor

• Algebraic Geometry
• Statistics
• Computational Biology
• +2 more

Kaie Kubjas is an Assistant Professor at Aalto University in Finland. Her research focuses on applying algebraic geometry to statistical and biological problems, with particular expertise in matrix and tensor decompositions. She specializes in nonnegative, positive semidefinite, and subspace-constrained decomposition methods. Her research bridges pure mathematics and applied sciences, working on theoretical foundations of matrix/tensor factorizations and their practical implementations in data analysis. Current interests include log-concave maximum likelihood estimation and geometric methods for statistical inference. Dr. Kubjas maintains active collaborations with the Max Planck Institute for Mathematics in the Sciences (MPI Leipzig), notably partnering with Alex Heaton and Stefana Sorea on the project 'Exact Solutions in Log-Concave Maximum Likelihood Estimation'. She previously visited MPI Leipzig in 2017 for workshops and reading groups on algebra-science interfaces.

Andreas MetzView profile

Professor

• Theoretical Nuclear Physics
• Quantum Chromodynamics (QCD)
• High-Energy Physics
• +3 more

Andreas Metz is a Professor of Physics at Temple University, specializing in Theoretical Nuclear and Hadronic Physics. His research focuses on the quark and gluon structure of strongly interacting particles like protons, with an emphasis on QCD factorization, lattice-QCD calculations, and Monte Carlo-based data analysis. He has contributed significantly to understanding parton correlation functions, power corrections, and the proton's mass decomposition. Education: Ph.D. in Physics, University of Mainz (1997) Research Interests: Multi-dimensional quark/gluon structure of hadrons QCD factorization and non-perturbative effects Lattice QCD applications Proton spin and mass decomposition Parton fragmentation functions Publication Trends: His work emphasizes Generalized Parton Distributions (GPDs), lattice-QCD studies of proton structure, and theoretical support for upcoming Electron-Ion Collider (EIC) experiments. Recent studies explore axial-vector GPDs, twist-3 effects, and proton tomography via lattice methods. Awards: 2023 APS Fellow (Topical Group on Hadronic Physics) Advising & Grants: While no advisees are listed, his research aligns with major initiatives like the EIC Theory Alliance and Jefferson Lab projects. Collaborative efforts focus on interpreting experimental data from high-energy facilities. Labs/Teams: Involved in LHCSpin project and EIC-related theoretical collaborations, though specific lab affiliations are not explicitly stated.