جست‌وجوی استاد و پژوهشگر

• Algebraic Geometry
• Numerical Analysis
• Homotopy Methods
• +۳ مورد دیگر

Kisun Lee is a Research Fellow in Mathematical and Statistical Sciences at Clemson University's College of Science. His research develops computational methods for solving polynomial systems using homotopy continuation, certification algorithms, and tropical geometry approaches. Research focuses on: Certified numerical methods for algebraic systems Homotopy continuation and monodromy techniques Tropical geometry applications Implementation of computational algebra tools Real solution finding for polynomial equations Publications demonstrate innovations in certified computation methods, including alpha theory certification using interval arithmetic and Krawczyk-based homotopy tracking. Developed the NumericalCertification package for Macaulay2 and contributes to computational algebra toolkits for polynomial system solving.

Peimeng Yinمشاهده پروفایل

استادیار

• Computational Mathematics
• Applied Mathematics
• Numerical Analysis
• +۷ مورد دیگر

Peimeng Yin is an Assistant Professor in the Department of Mathematical Sciences at the University of Texas at El Paso (UTEP). His research focuses on computational mathematics and applied mathematics, emphasizing numerical analysis, partial differential equations, scientific computing, and data science. He designs and implements numerical algorithms for PDEs with applications in physics, astrophysics, engineering, biology, energy, and oncology. Key interests include discontinuous Galerkin methods, structure-preserving numerical techniques, dynamical low-rank approximation, and PDE-based data-driven modeling. His work bridges rigorous mathematical theory with practical computation, addressing challenges like high derivatives, singularities, and multiscale phenomena. Recent research involves adaptive finite element methods, positivity-preserving schemes, and machine learning-enhanced algorithms for parabolic equations. Yin's studies often emphasize energy stability and error estimation in gradient flow systems, with applications ranging from fluid dynamics to biomedical decision support systems. Notable contributions include advancements in biharmonic problem solvers, neutrino kinetic equations, and surgical outcome prediction models. He maintains an active publication record in top-tier computational mathematics journals, with a focus on developing robust numerical frameworks for complex physical systems.

Miao Yinمشاهده پروفایل

استادیار

• Spatial Intelligence Systems
• Multimodal Intelligence Systems
• Large-Scale Neural Model Optimization
• +۳ مورد دیگر

Dr. Miao Yin is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington (UTA), part of the College of Engineering. He holds a PhD in Computer Engineering from Rutgers University and has industry experience at Google, Amazon Web Services, and Samsung Research America. His research focuses on high-performance and energy-efficient spatial/multimodal intelligence systems, leveraging higher-order tensor decomposition and algorithm-hardware co-optimization. Education: PhD in Computer Engineering, Rutgers University (2023). Research Interests include Spatial Intelligence Systems, Large-Scale Neural Model Optimization, and High-Performance Computing. His lab is equipped with advanced computing resources, including H100 GPU nodes and Ada6000/4090 servers. Awards include the UT System Rising STARs Award ($100k), and multiple travel grants from top conferences. He serves on committees for MLSys, PPoPP, DAC, and ICCD, and reviews for CVPR, NeurIPS, and IEEE journals. Advises PhD students including Sunny Shree and Wei Lin, and collaborates on projects funded by UTA and NSF grants. His lab emphasizes cross-disciplinary collaboration in AI and scientific computing.

Gregory Ongieمشاهده پروفایل

استادیار

• Machine Learning
• Medical Imaging
• Computational Imaging
• +۵ مورد دیگر

Dr. Gregory Ongie is an Assistant Professor in the Department of Mathematical and Statistical Sciences at Marquette University. He holds a Ph.D. in Applied Mathematical and Computational Sciences from the University of Iowa (2016), an M.S. in Mathematics (2011), and a B.S. in Mathematics and Physics from Coe College (2008). His research focuses on machine learning, optimization, and compressed sensing applied to medical imaging, particularly in MRI and CT reconstruction. He has developed algorithms for inverse problems in imaging, including deep equilibrium models and Neumann networks. Research Interests: Mathematics of deep learning, applied algebraic geometry in data science, machine learning for medical image reconstruction, and optimization in computational imaging. His work bridges theoretical foundations of neural networks with practical applications in medical imaging, including model observer analysis and compressed sensing techniques. Grants and Awards: NSF CISE Research Initiation Initiative Grant (2022), Way Klinger Early Career Award (2023), D. C. Spriestersbach Dissertation Prize (2018), and Best Student Paper Award at ISBI 2015 for MRI super-resolution work. Teaching: Courses include Applied Linear Algebra, Differential Equations, and Mathematics of Medical Imaging. He has mentored multiple graduate and undergraduate students in imaging-related projects. Active in service roles, including Marquette's Faculty Hiring Committee and seminar coordination. Labs & Collaborations: Engaged in collaborative research with institutions like the University of Chicago and University of Michigan, focusing on computational imaging and machine learning. GitHub repositories include tools for matrix completion and MRI reconstruction.

Austin Napierمشاهده پروفایل

استاد

• Experimental Particle Physics
• High Energy Physics
• Computational Physics
• +۵ مورد دیگر

Austin Napier is a Professor of Physics & Astronomy at Tufts University, affiliated with the School of Arts and Sciences. He holds a PhD (1979) and BS (1969) in Physics from MIT. His research focuses on Experimental Particle Physics, High Energy Physics, and Computational Physics, with emphases on heavy quark studies, Standard Model tests, and collider data analysis. He has been at Tufts since 1980, progressing from Assistant to Associate Professor before achieving his current rank. Education: PhD Physics, MIT, 1979 BS Physics, MIT, 1969 Research Interests: Dr. Napier’s work explores experimental particle physics at colliders like the Tevatron, focusing on Higgs boson searches, top quark dynamics, and electroweak interactions. His computational physics research includes advanced data analysis techniques and simulation methodologies for high-energy experiments. Publications: His recent work includes studies of Higgs-like particles, top quark asymmetries, and exotic meson discovery. These contributions highlight his role in advancing precision measurements and collider-based physics. Teaching: Teaches introductory physics courses and lab modules, emphasizing hands-on experimental methods and computational techniques.

Hsin-Hsiung Huangمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Machine Learning
• Neuroimaging
• +۳ مورد دیگر

Dr. Hsin-Hsiung Huang is an Associate Professor in the Department of Statistics and Data Science at the University of Central Florida (UCF). His research focuses on Bayesian modeling, dimension reduction, neuroimaging, and threat detection. He holds a Ph.D. in Statistics from the University of Illinois at Chicago and multiple advanced degrees in Mathematics and Statistics from Georgia Tech and National Taiwan University. Dr. Huang has led projects funded by NSF's ATD program (e.g., NSF DMS-1924792 and DMS-2318925) and NIH's NINDS R01 award. His team achieved top placements in the ATD Challenge competitions from 2021 to 2023. Awards include the UCF Research Incentive Award (2021), College of Sciences Seed Grant (2023–24), and the 2025 OSCaR Award for Outstanding Scholarship. His research interests include Bayesian ultrahigh-dimensional variable selection, matrix/tensor regression, and spatiotemporal modeling. Key areas of application include neuroimaging reconstruction, threat detection algorithms, and medical data analysis. Publications span statistical methodologies, neuroimaging algorithms, and applications in healthcare and environmental risk modeling. He actively collaborates across disciplines, integrating machine learning with biomedical and ecological data challenges.

Gerrit Welperمشاهده پروفایل

استادیار

• Approximation Theory
• Neural Networks
• Gradient Descent
• +۲ مورد دیگر

Dr. Gerrit Welper is Assistant Professor of Mathematics at the University of Central Florida's College of Sciences, specializing in approximation theory and neural network optimization. His research bridges computational mathematics and machine learning, particularly analyzing gradient descent dynamics in neural networks and developing efficient reduced-order models for transport equations. Recent work establishes approximation guarantees for gradient-flow trained shallow networks, develops neural representations of entropy solutions, and creates transformed snapshot interpolation techniques for parametric systems. He has advanced adaptive Petrov-Galerkin methods for hyperbolic PDEs and developed rigorous performance bounds for reduced-order models. Dr. Welper's publications demonstrate increasing focus on physics-informed machine learning architectures and their mathematical foundations, with recent innovations in sparse backpropagation and low-rank neural representations.

Teng Zhangمشاهده پروفایل

دانشیار

• Statistics
• Machine Learning
• Signal Processing
• +۷ مورد دیگر

Dr. Teng Zhang is an Associate Professor in the Department of Mathematics at the College of Sciences, University of Central Florida. His research focuses on statistics, machine learning, and signal processing, with a strong emphasis on robust covariance estimation, matrix analysis, and phase retrieval. Robust statistical methods Machine learning algorithms Signal processing for complex systems Optimization in non-convex models Applications in cybersecurity and bioinformatics Recent publications highlight his work on low-rank matrix models, adversarial robustness in deep learning, and secure distributed estimation for cyber-physical systems. His research spans theoretical guarantees and practical implementations, with applications in computer vision, network security, and biomedical data analysis.

John Mitchellمشاهده پروفایل

استاد

• Optimization
• Integer Programming
• Conic Optimization
• +۶ مورد دیگر

John Mitchell is a Professor and Graduate Program Director in the Department of Mathematical Sciences at Rensselaer Polytechnic Institute. His research focuses on optimization theory and applications, including integer programming, conic optimization, and complementarity constraints. He holds an additional affiliation with the Department of Industrial and Systems Engineering. Key research interests include sparse/low-rank optimization with applications in compressed sensing, genome-wide association studies, and financial optimization. His work extends to practical domains like disaster management (resource allocation, humanitarian logistics) and resilient supply chain design. Mitchell's methods address both theoretical challenges (e.g., exact formulations) and real-world problems such as network interdiction and infrastructure restoration. Publications from 2015-2025 highlight interdisciplinary applications: from ridesharing algorithms and trafficking network disruption to community detection in graphs and neuromorphic computing optimization. His work bridges mathematical rigor with societal impact in areas like emergency evacuation modeling and fair division approaches for humanitarian logistics. His research emphasizes algorithm development (e.g., branch-and-cut methods) and convex reformulations for nonconvex problems. Current projects explore quantitative resilience metrics for multi-echelon supply chains and ground-truth community detection via modularity optimization.

• Behavioral Finance
• Portfolio Optimization
• Algorithmic Trading
• +۴ مورد دیگر

Dr. Steve Yang is an Associate Professor at the School of Business, Stevens Institute of Technology. He holds a PhD in Systems and Information Engineering from the University of Virginia, specializing in Financial Engineering. His research focuses on behavioral finance, algorithmic trading, and financial systemic risk, employing tools like Markov decision processes and reinforcement learning. He leads the Center for Research toward Advancing Financial Technologies (CRAFT), a NSF-funded initiative with Rensselaer Polytechnic Institute. Education: PhD (2012) University of Virginia (Systems and Information Engineering) MS (2008) University of Virginia (Systems and Information Engineering) MS (1997) Virginia Tech (Computer Science) BA (1992) Beihang University (Aerospace Engineering) Research Interests: Dr. Yang investigates market irrationality's impact on trading strategies, portfolio optimization, and systemic risk using AI and decision science. His work bridges computational methods with financial systems, addressing fraud detection, liquidity dynamics, and regulatory policy through agent-based modeling and Hawkes processes. Funding & Grants: NSF CRAFT Center ($862K, 2021-2026) Northrop Grumman ($130K, 2014-2017) CFTC & SEC collaborations Awards & Roles: Director of CRAFT Associate Editor, European Journal of Finance Former Visiting Scholar at SEC Labs & Teams: Leads CRAFT, collaborating with federal agencies and industry on financial technology and regulatory challenges. Engages in NSF review panels and editorial boards.

Mikhail Belkinمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Feature Learning
• +۵ مورد دیگر

Mikhail Belkin is a Professor at the Halicioglu Data Science Institute and the Computer Science and Engineering Department at the University of California San Diego (UCSD), as well as an Amazon Scholar. His research focuses on foundational aspects of machine learning, particularly deep learning and large language models, emphasizing understanding phenomena like feature learning, over-parameterization, and generalization. He serves as editor-in-chief of the SIAM Journal on Mathematics of Data Science (SIMODS) and was elected an ACM Fellow in 2023. Research Interests: Generalization theory in deep learning Optimization dynamics and convergence Feature learning mechanisms in neural networks Kernel methods and their connections to neural networks Double descent phenomenon and model complexity Key Contributions: Belkin's work has advanced understanding of interpolation in over-parameterized models, revealing how zero-training-error models can generalize well despite classical statistical concerns. His analyses of optimization landscapes and loss surfaces have clarified why gradient-based methods succeed in deep learning. He introduced the Average Gradient Outer Product (AGOP) framework to explain feature learning in neural networks, bridging classical kernel methods and modern architectures. Publications: His recent work explores feature learning in neural networks, convolutional architectures, and the theoretical underpinnings of LLMs. Over 15 recent papers are listed, spanning topics like kernel regression, matrix completion, and the mathematical foundations of deep learning. Awards & Grants: ACM Fellow (2023) NSF/Simons Collaboration on Theoretical Foundations of Deep Learning PI in the TILOS AI Institute (NSF-funded) Advising & Teams: Belkin collaborates with interdisciplinary teams at UCSD and institutions like the Broad Institute’s Eric and Wendy Schmidt Center, focusing on AI’s societal implications and scientific applications. His work emphasizes bridging theory and practice, with algorithms like Recursive Feature Machines (RFM) and EigenPro showcasing scalable, interpretable machine learning solutions.

Mengchu Zhouمشاهده پروفایل

استاد

• Systems Engineering
• Automation
• Discrete Event Systems
• +۲ مورد دیگر

Dr. Mengchu Zhou is a Distinguished Professor in the Department of Electrical and Computer Engineering at the New Jersey Institute of Technology. His research spans discrete event systems, industrial IoT, optimization algorithms, and intelligent manufacturing. With over 30 years of experience, he has made significant contributions to Petri net theory and its industrial applications. Research interests include intelligent automation, manufacturing systems, optimization algorithms, and quantum computing applications. His work frequently addresses scheduling problems, robotic systems, and IIoT implementations using advanced computational methods. Publication trends demonstrate consistent focus on scheduling optimization, manufacturing automation, and emerging computing paradigms. Recent works increasingly explore quantum computing applications and security aspects of industrial systems. Honors include election as National Academy of Inventors Fellow and the IEEE Norbert Wiener Award. His research has been recognized through multiple patents and technology transfer implementations. Dr. Zhou maintains strong industry connections and has advised numerous graduate students through his laboratory focused on automation and intelligent systems.

Shuai Zhangمشاهده پروفایل

استادیار

• Machine Learning
• Edge Computing
• Reinforcement Learning
• +۳ مورد دیگر

Shuai Zhang is Assistant Professor in the Department of Data Science at New Jersey Institute of Technology (NJIT). He earned his Ph.D. in Electrical and Computer Engineering from Rensselaer Polytechnic Institute under Dr. Meng Wang, with industry experience at IBM Thomas J. Watson Research Center and MIT-IBM Watson AI Lab. Research focuses on theoretical foundations and practical applications of machine learning, including reinforcement learning algorithms, transformer architectures, edge computing optimization, and underwater robotics. Key contributions address model efficiency, theoretical guarantees in deep learning, and multi-agent systems for challenging environments. Recent publications demonstrate expertise in: provable learning guarantees for transformers and neural networks, federated learning frameworks for non-IID data, reinforcement learning for marine robotics, and efficient model architectures for edge deployment. Emerging themes include real-time underwater systems, federated self-supervised learning, and algorithmic fairness in imbalanced data scenarios. Practical implementations include simulator development for underwater pursuit-evasion games (UPEGSim) and collaborative frameworks for unmanned marine vehicles operating in extreme conditions.

Jing Qinمشاهده پروفایل

دانشیار

• Mathematical Optimization
• Variational Image Processing
• Compressive Sensing
• +۳ مورد دیگر

Jing Qin is an Associate Professor in the Department of Mathematics at the University of Kentucky, within the College of Arts & Sciences. His research focuses on Mathematical Optimization, Variational Image Processing, Compressive Sensing, and computational analysis of imaging data. His work addresses challenges in signal recovery, tensor decomposition, hyperspectral imaging, and machine learning applications. Recent publications emphasize advancements in tensor recovery algorithms, hyperspectral band selection, and graph-based signal processing. These contributions highlight innovations in iterative methods, regularization techniques, and deep learning models for structural analysis and robotic systems. Qin’s research bridges theoretical optimization frameworks with practical applications in remote sensing, medical imaging, and computer vision. His articles reflect a strong focus on interdisciplinary solutions, combining mathematical rigor with computational efficiency. Despite no explicitly listed awards or grants, his prolific publication record underscores sustained scholarly impact. Further details are available via his faculty webpage: http://ms.uky.edu/~jqi229/ .

Alex Bihloمشاهده پروفایل

دانشیار

• Geometric Numerical Integration
• Differential Equations
• Machine Learning
• +۶ مورد دیگر

Dr. Alex Bihlo is an Associate Professor at the Department of Mathematics and Statistics, Memorial University of Newfoundland, with a cross-appointment to the Department of Computer Science (2021–2024). He holds a Tier II Canada Research Chair in Numerical Analysis and Scientific Computing (2015–2025) and is affiliated with Memorial’s Centre for Numerical Analysis and Scientific Computing and Data Science Research Group . Research Interests: His work bridges geometric numerical integration of differential equations, symmetry/conservation law preservation in numerical schemes, and machine learning for geophysical fluid dynamics. Key applications include weather prediction, tsunami modeling, and turbulence parameterization. Recent efforts focus on physics-informed neural networks (PINNs) and deep operator networks (DeepONets) that conserve physical properties. Publications & Awards: With over 30 peer-reviewed articles, his 2025 Physics-informed neural networks for tsunami inundation modeling and 2024 PinnDE paper demonstrate cutting-edge integration of ML and classical physics. Awards include the CAIMS-PIMS Early Career Award (2018) and Austria’s Sub Auspiciis Praesidentis distinction (2011). He has supervised 15+ PhD and MSc students across numerical methods, ML applications, and geophysical modeling. Grants & Collaborations: Funded by NSERC, CRC, and IgniteR&D, he collaborates with institutions including UBC, McGill, University of Vienna, and Imperial College London. His teaching portfolio spans advanced scientific programming, deep learning, and geometric numerical integration.