جست‌وجوی استاد و پژوهشگر

Zhe Shandianمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning (Theory and Modeling)
• Physics-informed Machine Learning
• +۷ مورد دیگر

Zhe Shandian is an Associate Professor at the Kahlert School of Computing, University of Utah. His research focuses on advancing machine learning methodologies, particularly in physics-informed machine learning, operator learning, and Bayesian methods. He explores applications in differential equations, multi-omics data, and engineering simulations. His work emphasizes scalable algorithms and data efficiency in complex systems. Education details are not explicitly provided in the text, but his professional trajectory aligns with a strong computational and mathematical background. Research interests include developing novel frameworks for neural operators, symbolic regression, and functional Bayesian decomposition. Key research trends from his articles highlight advancements in physics-guided AI models, multi-fidelity simulation, and scalable kernel-based methods. His contributions span domains like material science, cancer prediction, and traffic flow modeling. No scientific awards are mentioned in the provided text. Zhe Shandian has not listed any advisees, and specific grants or labs are not detailed here. His work often involves collaboration across disciplines, leveraging both theoretical and applied machine learning techniques.

Hua Wangمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Medical Image Computing
• +۶ مورد دیگر

Hua Wang is a Professor and Graduate Director in the Department of Computer Science at the Colorado School of Mines. He joined the university in 2012 after completing his Ph.D. in Computer Science & Engineering at the University of Texas at Arlington. Prior to academia, he worked in industry as a Signal Processing engineer at Motorola for four years. Education B.S. in Electronic Engineering, Tsinghua University (Beijing, 1999) M.S. in Signal Processing, Nanyang Technological University (Singapore, 2003) Ph.D. in Computer Science & Engineering, University of Texas at Arlington (2012) Research Interests : Dr. Wang focuses on developing efficient machine learning and data mining algorithms with theoretical guarantees for large-scale data challenges. His work spans applications in medical image computing, health informatics, cheminformatics, material informatics, additive manufacturing, bioinformatics, and computer vision. He emphasizes practical solutions for interdisciplinary problems such as drug discovery, disease diagnosis, and robotics. Advising & Grants : While no specific student advisees or grant details are listed, his publications reflect extensive collaboration across domains like healthcare, materials science, and robotics. His work often involves multi-institutional efforts addressing real-world data-driven challenges. Labs & Teams : His research group likely focuses on algorithmic development for medical imaging and multi-modal data fusion, though specific lab affiliations are not explicitly mentioned in the text.

Klara Stokesمشاهده پروفایل

دانشیار

• Incidence Geometry
• Combinatorics
• Graph Theory
• +۷ مورد دیگر

Klara Stokes is an Associate Professor in Mathematics at the Department of Mathematics and Mathematical Statistics, Umeå University. She holds a qualification as a Recognised University Teacher. Her research focuses on incidence geometry, combinatorial structures, and their geometric realizations, with an emphasis on symmetries and motions. She also explores algebraic and geometric graph theory, finite field geometry, and applications in coding theory, cryptography, data privacy, and kinematics. Her affiliations include membership in the Discrete Mathematics research group. Key research themes involve incidence geometries with trialities, projective rigidity of configurations, and privacy-preserving graph database models. She collaborates on topics such as rigidity circuits, Assur graphs, and geometric decoding of subspace codes. Publications highlight her work on flag transitive geometries, pebble game algorithms for sparse posets, and structural rigidity analysis. Her research bridges pure mathematics and applied areas like data privacy through probabilistic models and clustering techniques. Stokes has contributed to foundational work on pentagonal geometries, minimal genus graph embeddings, and combinatorial configurations. She remains active in advancing geometric and combinatorial methods with interdisciplinary applications.

Maggie Chengمشاهده پروفایل

استاد

• Machine Learning
• Complex Networks Analysis
• Applied Statistics
• +۱۱ مورد دیگر

Maggie Cheng is a Professor of Applied Mathematics and Director of the Center for Interdisciplinary Scientific Computation at Illinois Institute of Technology (IIT). She holds a Ph.D. from the University of Minnesota at Twin Cities. Her research focuses on interdisciplinary applications of data science and network science, including machine learning for power grids, cybersecurity, and complex systems analysis. She leads NSF-funded projects on real-time data analytics for energy systems and human dynamics inference from data streams. Key areas of expertise include machine learning, complex networks analysis, applied statistics, optimization algorithms, and wireless sensor networks. Her work bridges theoretical foundations with practical applications in power systems, communications, and biomedical informatics. She serves as Associate Editor for Frontiers in Big Data , IEEE Journal of Biomedical and Health Informatics , and International Journal of Sensor Networks . Current sponsored research includes projects on energy cyber-physical systems, network security analysis, and big data inference methodologies. She actively mentors Ph.D. students in applied mathematics and interdisciplinary computational science, emphasizing novel machine learning approaches for critical infrastructure resilience.

John Griesmerمشاهده پروفایل

استادیار

• Ergodic theory
• Additive combinatorics
• Harmonic analysis
• +۳ مورد دیگر

John Griesmer is a Teaching Assistant Professor in the Department of Applied Mathematics and Statistics at the Colorado School of Mines. He holds a PhD and MS from The Ohio State University and a BS from Miami University. His research focuses on ergodic theory, additive combinatorics, and harmonic analysis, with a particular emphasis on recurrence phenomena, Bohr sets, and structural properties of sumsets within abelian groups. His work bridges topological and measurable dynamics, exploring connections between algebraic structures and combinatorial patterns. Education: PhD in Mathematics, The Ohio State University MS in Mathematics, The Ohio State University BS in Mathematics, Miami University Research Interests: Ergodic theory: recurrence in dynamical systems, rigidity phenomena Additive combinatorics: sumset structures, intersective sets, sparse configurations Harmonic analysis: Bohr neighborhoods, Fourier analysis on groups Recent work examines distinctions between topological and measurable recurrence, properties of difference sets in abelian groups, and applications of ergodic methods to combinatorial problems. His publications often explore foundational questions in these areas, with contributions to both theoretical frameworks and specific case analyses. Current courses include Math 112 (Calculus II) and Math 301 (Introduction to Analysis), reflecting his commitment to both foundational mathematics education and advanced theoretical training.

Junsong Yuanمشاهده پروفایل

استاد

• Computer Vision
• Pattern Recognition
• Video Analytics
• +۶ مورد دیگر

Junsong Yuan is a Professor and Director of the Visual Computing Lab at the Department of Computer Science and Engineering, University at Buffalo (SUNY). He previously served as an Associate Professor and Nanyang Assistant Professor at Nanyang Technological University (NTU), Singapore. His research focuses on computer vision, pattern recognition, video analytics, and large-scale visual search. He holds a Ph.D. from Northwestern University, M.Eng. from National University of Singapore, and B.Eng. from Huazhong University of Science and Technology. Education : Ph.D., Electrical and Computer Engineering, Northwestern University, 2009 M.Eng., Electrical and Computer Engineering, National University of Singapore, 2005 B.Eng., Communication Engineering, Huazhong University of Science and Technology, 2002 Research Interests : Dr. Yuan's work spans computer vision, video analytics, gesture/action analysis, and multimodal learning. He emphasizes scalable visual search, 3D reconstruction, and federated learning for medical imaging. His lab develops algorithms for autonomous systems, generative media processing, and human-AI interaction. Awards : IEEE Fellow (2021) IAPR Fellow (2018) SUNY Chancellor's Award for Excellence in Scholarship (2022) Advising & Grants : He has advised numerous PhD students, including Sudhir Yarram, Nan Xi, and Tianyu Luan. His research is supported by grants focusing on video generation, medical image analysis, and federated learning. He serves as Editor-in-Chief of JVCI and on editorial boards of T-PAMI, T-IP, and T-CSVT. Labs & Teams : Director of the Visual Computing Lab, leading projects in generative models, 3D scene understanding, and healthcare AI. Collaborates with industry partners like Meta, Amazon, and Accenture on applied vision systems.

Gopal Guptaمشاهده پروفایل

استاد

• Programming Languages
• Software Engineering
• Parallel and Distributed Processing
• +۴ مورد دیگر

Dr. Gopal Gupta is the Erik Jonsson Chair Professor of Computer Science at The University of Texas at Dallas (UT Dallas), leading the Department of Computer Science since 2009. He holds a Ph.D. from the University of North Carolina at Chapel Hill (1991) and has held academic roles since 1992, including Department Head and Associate Head at UT Dallas. His research focuses on Programming Languages, Software Engineering, Parallel Processing, Human-Computer Interaction, and Artificial Intelligence , with notable contributions in logic programming, constraint programming, and neuro-symbolic AI. He has developed influential systems such as the NEXUS parser , DAWN , and VoxBoox , and co-founded Interoperate , a startup translating legacy code into modern formats. His honors include the Junior Faculty Enhancement Award (1992) and multiple best paper awards. Gupta’s work bridges theory and application, emphasizing explainable AI, commonsense reasoning, and system assurance. Key Projects : Leader of the Condicutve Logic Programming initiative Co-developer of the E-plan system for chemical hazard response Pioneer in voice-based accessibility tools like VoxBoox Advocate for entrepreneurial ventures in academia His publications span logic-based AI, parallel computing, and software verification, with recent work in trustworthy AI, causal counterfactuals, and neuro-symbolic frameworks.

Guangliang Chenمشاهده پروفایل

دانشیار

• Subspace/manifold clustering
• Dictionary clustering
• Classification
• +۶ مورد دیگر

Guangliang Chen is an Associate Professor in the Department of Mathematics and Statistics at San José State University (SJSU), part of the College of Science. His research focuses on subspace/manifold clustering, dictionary learning, and classification with applications in image and document analysis. He earned a Ph.D. in Applied Mathematics from the University of Minnesota (2009) and a B.S. in Mathematics from the University of Science and Technology of China (2003). Key research contributions include scalable spectral clustering algorithms, geometric multi-resolution analysis, and advancements in compressive sensing. His work has been recognized with a Best Paper Award at the 2009 ICCV workshop for Kernel Spectral Curvature Clustering (KSCC). He teaches courses in applied statistics, machine learning, and data visualization at both undergraduate and graduate levels. Dr. Chen's recent projects involve developing efficient SVM classification techniques, large-scale spectral clustering frameworks, and MATLAB implementations for scalable algorithms. His research also extends to anomaly detection in hyperspectral imaging and functional genomics analysis through multiscale methods.

David C. Anastasiuمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• High Performance Computing
• +۴ مورد دیگر

Dr. David C. Anastasiu is an academic with a focus on data mining, machine learning, and high performance computing. He served as an Assistant Professor in the Department of Computer Engineering at San José State University from 2016 to 2019 before moving to Santa Clara University. He holds a Ph.D. in Computer Science from the University of Minnesota (2016), and earlier degrees from Texas State University. His research emphasizes scalable methods for sparse data analysis, including nearest neighbor algorithms, time series forecasting, and applications in computational genomics and urban computing. He has contributed to the AI City Challenge, advancing computer vision and traffic analysis systems. His work spans theoretical advancements and practical implementations, such as hardware acceleration for ML inference (ML-NIC) and graph-based clustering for single-cell data (CosTaL). Key projects include CityFlow, a city-scale benchmark for multi-camera vehicle tracking, and the development of efficient cosine similarity search algorithms (L2AP). He also explored user-centric search result organization and collaborative learning platforms to enhance educational engagement. His lab, DMLab, focuses on interdisciplinary applications of machine learning and data science. Anastasiu’s work bridges algorithmic innovation with real-world challenges in healthcare, transportation, and environmental monitoring.

• Bayesian Models
• Complex Networks
• Disclosure Risk Assessment
• +۳ مورد دیگر

Francesca Panero is an Assistant Professor (RTT) in Statistics at Sapienza University's Department of Methods and Models for Economics, Territory and Finance (MEMOTEF), with a concurrent role as Visiting Fellow at the London School of Economics (LSE) Department of Statistics. She holds a PhD in Statistics from the University of Oxford and prior degrees from the University of Turin and Collegio Carlo Alberto. Her research focuses on Bayesian models applied to complex networks , disclosure risk assessment , and Gaussian process modeling . Current projects include Bayesian nonparametric frameworks for sparse networks, spatio-temporal food insecurity prediction, and fair machine learning methodologies. She leads the GENIAL initiative at LSE, exploring GenAI's educational impact. She teaches courses on Probability and Stochastic Processes (Sapienza) and Deep Learning/Artificial Intelligence (LSE). Awards include the 2025 USI Visiting Lectureship and j-ISBA Chair Elect (2025-2026) . Her work is supported by grants such as the LSE RISF fund for food insecurity research. Publications span topics like graphex processes, fair ML algorithms, and optimal risk assessment methodologies. She actively engages in policy discussions through collaborations with organizations like the UN World Food Programme Hunger Monitoring Unit.

Bruno Carpentieriمشاهده پروفایل

دانشیار

• Numerical Linear Algebra
• High-Performance Computing
• Computational Electromagnetics
• +۵ مورد دیگر

Bruno Carpentieri is an Associate Professor in Applied Mathematics at the Faculty of Computer Science , Free University of Bozen-Bolzano , Italy. His work focuses on Numerical Linear Algebra , High-Performance Computing , and Preconditioning Techniques for large-scale scientific problems. Education : Laurea in Applied Mathematics (1997, University of Bari Aldo Moro); PhD in Computer Science (National Polytechnic Institute Toulouse). Research Interests : Sparse linear systems, Krylov subspace methods, computational electromagnetics, PageRank problems, and fractional calculus applications in nonlinear engineering. His publications (1998–2025) emphasize efficient solvers for multi-shifted systems, parallel computing, and preconditioning strategies. Recent works (2025) explore fractional-order methods and multilayer network centrality . He has collaborated on ITER tokamak simulations (F4E/MIUR grants) and biomedical finite element models . Grants : F4E-2008-OPE-06-06-11 (ITER analysis); MIUR PRIN 2010SPS9B3. Key Contributions : VBARMS preconditioner (2014), hybrid block GMRES variants (2018), and chaos-enhanced fractional solvers (2025).

• Statistical learning in high dimensions
• Statistical learning on networks
• Functional data analysis
• +۱۲ مورد دیگر

Charles Bouveyron is a Full Professor of Statistics at Université Côte d'Azur, Nice, France. He holds a Chair in Artificial Intelligence and serves as Director of the Institut 3IA Côte d'Azur, while also leading the Inria research team MAASAI on Statistical Learning and Artificial Intelligence. Université Côte d'Azur: Full Professor of Statistics (2019-present) Institut 3IA Côte d'Azur: Director (2019-present) Inria: MAASAI Team Leader (2019-present) His research focuses on statistical learning methodologies for high-dimensional data, network analysis, functional data modeling, and deep latent variable approaches. Key applications include medical diagnosis through radiomics, ecological monitoring, astrophysics data analysis, and humanities data science. Recent publications emphasize dynamic co-clustering techniques for count data streams, deep latent position models for network analysis, and discriminative clustering frameworks using generalized mutual information. His work integrates Bayesian sparsity, probabilistic PCA, and robust classification methods. Software development remains a core activity with tools like HDclassif, FunLBM, and Linkage.fr platform for network analysis with textual edges. He has mentored over 20 PhD/postdoc researchers in statistical learning and AI applications.

Karl Roheمشاهده پروفایل

استاد

• Machine Learning
• Data Science
• Statistical Theory
• +۳ مورد دیگر

Karl Rohe is a Professor of Statistics at the University of Wisconsin–Madison, with courtesy appointments in the School of Journalism and Mass Communication, Electrical & Computer Engineering, and Educational Psychology. His research focuses on modern data analysis challenges, including embeddings, PCA, and machine learning. He leads the Rohe Lab, which develops methods for network analysis and graph dimensionality estimation. Key contributions include the gdim package for estimating graph dimensions and work on spectral clustering. His work is supported by grants from the National Science Foundation (DMS-1309998, DMS-1612456, DMS-1916378) and the Army Research Office (W911NF-15-1-0423, W911NF-20-1-0051). Research interests span post-modern data science, leveraging linear algebra for statistical methods, and applications in social networks. His lab’s GitHub repositories include implementations of algorithms like fastadi and invertiforms , reflecting his commitment to open-source software.

Paul Russellمشاهده پروفایل

مدرس

• Combinatorics
• Ramsey Theory

Dr. Paul Russell is a Lecturer in the Department of Pure Mathematics and Mathematical Statistics at the University of Cambridge. He is affiliated with the Faculty of Mathematics and the Combinatorics research group. His research primarily focuses on combinatorial mathematics, with specialized expertise in Ramsey theory, partition regularity, and set systems. Russell's work explores foundational aspects of combinatorial structures, including monochromatic sumsets, intersecting families, and geometric configurations in Euclidean Ramsey theory. His publications demonstrate consistent engagement with problems related to infinite sets, additive combinatorics, and extremal set theory. While no awards or supervised students are mentioned, his research collaborations include notable mathematicians such as Imre Leader and Mary Walters. Russell maintains an active presence at Cambridge with office facilities and academic responsibilities.

Liza Levinaمشاهده پروفایل

استاد

• High-Dimensional Statistical Inference
• Statistical Network Analysis
• Neuroimaging
• +۳ مورد دیگر

Liza Levina is the Vijay Nair Collegiate Professor and Chair of the Department of Statistics at the University of Michigan. She is affiliated with the Michigan Institute for Data and AI in Society and the Center for the Study of Complex Systems. Her expertise lies in high-dimensional statistical inference and statistical network analysis, with applications in neuroscience and imaging. She earned her PhD in Statistics from UC Berkeley in 2002 and has been at the University of Michigan since then. Her research focuses on network analysis, statistical learning, and big data, particularly in neuroimaging. She has received notable awards, including the ASA Junior Noether Award, and is a Fellow of both the ASA and IMS. She was an invited speaker at the 2018 International Congress of Mathematicians and an IMS Medallion lecturer. Levina’s work bridges theoretical advancements and practical applications, such as analyzing brain connectivity networks and developing methods for community detection in complex systems. She has contributed to methodologies for network cross-validation, pseudo-likelihood approaches, and conformal prediction in network-assisted regression. Her affiliations include leadership roles in the Department of Statistics and collaborations across interdisciplinary institutes. Her research has been recognized with prestigious acknowledgments, reflecting her impactful contributions to statistical science and its applications.