جست‌وجوی استاد و پژوهشگر

Rajiv Satsangiمشاهده پروفایل

دانشیار

• Special Education
• Mathematics Education
• Assistive Technology
• +۳ مورد دیگر

Dr. Rajiv Satsangi is an Associate Professor at George Mason University's Helen A. Kellar Institute for Human disAbilities, within the College of Education and Human Development. He holds a PhD from Purdue University and focuses on special education with an emphasis on mathematics instruction for students with disabilities. His work emphasizes the use of technology-based interventions, such as virtual manipulatives and video modeling, to enhance learning outcomes for students with learning disabilities, intellectual disabilities, and other exceptionalities. Dr. Satsangi's research interests center on inclusive education strategies, assistive technology integration, and evidence-based practices in mathematics education. He has extensively studied the efficacy of virtual tools like number lines, graphic organizers, and algebraic manipulatives in improving mathematical proficiency among students with disabilities. His work also addresses policy implementation (e.g., NCTM/CEC guidelines) and teacher training in inclusive pedagogy. His recent publications highlight trends toward technology-driven solutions for teaching algebra, fractions, and geometry to students with learning challenges. He has explored comparative studies between virtual and concrete manipulatives, as well as video modeling versus traditional instruction methods. Dr. Satsangi collaborates closely with the Helen A. Kellar Institute to develop adaptive curricula and interventions that promote educational equity in STEM fields. No awards or grants are explicitly listed in the provided texts. His teaching focuses on special education coursework related to general curriculum adaptations. While no advising records are mentioned, his research consistently involves collaborative efforts to improve educational access through innovative methodologies.

J. Andreas Bærentzenمشاهده پروفایل

دانشیار

• Computer Graphics
• Shape Modelling
• Real-Time Rendering
• +۵ مورد دیگر

Professor J. Andreas Bærentzen is an Associate Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), where he has held academic positions since 2001. His research focuses on computer graphics, shape modeling, real-time rendering, and geometry processing. He holds a MSc Eng (1998) and PhD (2003) from DTU. His work emphasizes topological adaptability in shape representation and efficient geometry processing, with notable contributions to structural topology optimization, 3D reconstruction, and virtual reality applications. Education: MSc Eng, Technical University of Denmark, 1991–1998 PhD, Technical University of Denmark, 1998–2003 Research Interests: Interactive 3D modeling and real-time graphics Topology optimization and structural infill design Shape modeling using distance fields and deformable hypersurfaces Applications in biomedical imaging (e.g., white matter dynamics) and environmental engineering (e.g., tree geometry simulation) His recent publications highlight advancements in neural network-based shape representation, skeletonization for tree reconstruction, and inverse-designed structural infill for engineering. He supervises multiple PhD students and leads projects on robotic manufacturing and virtual reality visualization. Bærentzen collaborates internationally, including visits to Stony Brook University (USA) and Padova University (Italy). Advisees & Projects: PhD Students: Rui Cui, Thomas D. V. Christiansen, Elias Theil Gæde Key Projects: 'Generative Methods for Brain Tissue Phantoms', 'Neural Form Representation', 'Graph Algorithms with Geometric Applications' His work bridges theoretical geometry processing with practical applications in architecture, biomedical engineering, and environmental science, leveraging DTU's interdisciplinary research ecosystem.

Serkan Hostenمشاهده پروفایل

استاد

• Commutative Algebra
• Combinatorics
• Algebraic Statistics

Serkan Hosten is a Professor in the Department of Mathematics at San Francisco State University's College of Science and Engineering. His research focuses on Commutative Algebra, Combinatorics, and Algebraic Statistics. He serves as an adviser for the Applied Math BS program and maintains active seminar engagements at UC Berkeley. His research explores the intersection of algebraic geometry with statistical optimization, leveraging combinatorial structures to solve problems in information theory and machine learning. Recent publications demonstrate consistent work in toric geometry, matroid theory, and computational algebra. Publications over the past decade show a strong emphasis on: (1) geometric methods in statistical inference, (2) combinatorial optimization frameworks, (3) algebraic approaches to machine learning, and (4) computational aspects of commutative algebra. This interdisciplinary work bridges pure mathematics with data science applications.

• Computational Social Science
• Natural Language Processing
• Social Network Analysis
• +۳ مورد دیگر

Roshni Chakraborty is an Assistant Professor at the Institute of Computer Science, University of Tartu, Estonia, where she leads research in the Computational Social Science Group. Her work bridges natural language processing, social network analysis, and machine learning to address challenges in social media analytics, disaster response, and misinformation detection. Research Interests: Her expertise spans: Computational Social Science : Analyzing social dynamics via digital footprints. Disaster Informatics : Developing NLP tools for crisis-related tweet summarization (e.g., ATSumm, IKDSumm). Political & Media Analysis : Detecting biases in community-driven fact-checking and news stance. Network Science : Advancing signed network modeling (SigGAN) and cross-network alignment (HCNA). Publication Trends: Recent articles (2022-2025) focus on generative AI for fact-checking, disaster tweet summarization benchmarks, and political bias analysis. Her work emphasizes practical NLP solutions for social good, leveraging transformer models, adversarial learning, and ontology-driven approaches. Academic Activities: She actively recruits PhD students and research interns for projects in social computing. Her group collaborates on datasets like BD2TSumm and Adsumm, fostering open research in crisis informatics.

Mubarak Shahمشاهده پروفایل

استاد

• Computer Vision
• Video Surveillance
• Visual Tracking
• +۷ مورد دیگر

Dr. Mubarak Shah is the Trustee Chair Professor of Computer Science and Founding Director of the Center for Research in Computer Vision (CRCV) at the University of Central Florida. His research focuses on computer vision, including video surveillance, visual tracking, human activity recognition, and UAV video analysis. He has held prestigious fellowships from ACM, NAI, AAAS, IAPR, and IEEE. Roles: Trustee Chair Professor, Director of CRCV, Graduate Faculty Member Affiliations: University of Central Florida, College of Engineering and Computer Science Research interests span visual analysis of crowded scenes, video registration, and privacy-aware diffusion models. He has pioneered geolocalization techniques and multimodal learning frameworks. His work impacts security, autonomous systems, and medical imaging. Recent publications emphasize diffusion models, 3D object detection, and adversarial learning. His work has been presented at CVPR, ECCV, and NeurIPS. Awards: Pegasus Professor (2006) ACM SIGMM Technical Award (2019) Multiple fellowships and recognitions from IEEE, AAAS, and others Advising and mentorship include over 50 graduate students and NSF REU programs. He leads CRCV, a hub for interdisciplinary vision research, and contributes to high-impact datasets like MAVREC. Labs/Teams: CRCV hosts cutting-edge projects in geolocalization, action recognition, and medical vision. Collaborative efforts include global partnerships in AI and robotics.

• Artificial Intelligence
• Optimization Techniques
• Reinforcement Learning
• +۲ مورد دیگر

Michele Ciavotta is an Associate Professor at the University of Milano-Bicocca's Department of Computer Science, Systems, and Communication, specializing in AI-driven optimization for complex systems. His research integrates reinforcement learning, graph neural networks, and metaheuristics applied to distributed computing and physical systems like smart mobility and production lines. Research spans cloud/edge computing optimization, industrial production systems, smart city applications, and graph-based learning methods. Recent publications demonstrate focus on decentralized AI systems, geospatial data processing, and hypergraph neural networks for chemical and urban applications. Extensive involvement in European R&D projects addresses challenges in cloud computing infrastructure, Industry 4.0 implementations, and distributed AI solutions.

Przemyslaw Musialskiمشاهده پروفایل

دانشیار

• geometric design
• computer graphics
• digital fabrication
• +۱ مورد دیگر

Przemyslaw Musialski is an Associate Professor of Computer Science at New Jersey Institute of Technology (NJIT), USA. His research focuses on geometric design, computer graphics, digital fabrication, and machine learning, aiming to develop algorithmic solutions for computational design and fabrication processes. Previously, he led the Computational Fabrication group at TU Wien's Center for Geometry and Computational Design (GCD), serving as an assistant professor there. He holds a PhD in Computer Science from TU Wien (2010) and an MSc in Media Sciences from Bauhaus University Weimar (2007). His academic journey includes roles at VRVis Research Center, Arizona State University as a postdoc, and KAUST as a visiting researcher. His work bridges computational geometry, fabrication-aware design, and AI applications in architecture and engineering. Notable contributions include methods for procedural facade generation, elastic geodesic grid structures, and neural surface reconstruction techniques. He actively contributes to international conferences and journals in computer graphics and geometric design. Research interests emphasize geometric algorithms, digital fabrication workflows, and integrating machine learning to enhance design automation. His lab explores applications in architectural design, 3D printing, and interactive modeling tools.

Oliver van Kaickمشاهده پروفایل

دانشیار

• Computer Graphics
• Computer Vision
• Computer Games
• +۴ مورد دیگر

Oliver van Kaick is an Associate Professor at the School of Computer Science, Carleton University, Canada. He joined in 2014 after postdoctoral research at Tel Aviv University (Azrieli Fellow) and Simon Fraser University. His research focuses on geometric modeling, shape analysis, and computer graphics, with applications in 3D content creation, medical imaging, and game development. He holds a PhD from Simon Fraser University and degrees from the Federal University of Paraná, Brazil. Education: PhD in Computing Science, Simon Fraser University (2011) M.Sc. and B.Sc. in Computer Science, Federal University of Paraná (2005 and 2003) Research interests include shape segmentation, correspondence, and similarity estimation, with a focus on learning shape properties through geometry, topology, and functionality. His work has been published in top venues like SIGGRAPH and Eurographics. He leads the Graphics, Imaging, and Games Lab (GIGL) and serves on program committees for major conferences. Key Awards: Azrieli Fellowship (Tel Aviv University) CVMJ 2024 Best Paper Award Advising & Grants: Supervised PhD and Master’s students including Akshai Srinivasan and Tianshu Zhao. Active in research grants related to 3D modeling and computer vision. Labs/Teams: Graphics, Imaging, and Games Lab (GIGL), collaborating on projects like procedural modeling and functionality-aware shape evolution.

Neil Spencerمشاهده پروفایل

استادیار

• Network Analysis
• Bayesian Modeling
• Forensic Statistics
• +۲ مورد دیگر

Dr. Neil Spencer is an Assistant Professor in the Department of Statistics at the University of Connecticut. His research integrates Bayesian inference, network analysis, and computational statistics, with applications ranging from forensic science to neurological disorders. He earned a PhD in Statistics and Machine Learning from Carnegie Mellon University, MSc from University of British Columbia, and BScH from Acadia University. Research focuses on developing novel methods for network data analysis (latent position models, efficient MCMC), robust Bayesian inference, and forensic statistics. Publications demonstrate consistent innovation in computational techniques for complex data structures and interdisciplinary applications. Teaching includes STAT5410 (Statistical Computing) and STAT3345Q (Probability Models for Engineers). He co-advised PhD candidate Tolani Olarinre and participates in the New England Statistical Society's NextGen committee. Research publications emphasize methodological innovations in network modeling, Bayesian computation, and experimental design, with significant applications in neuroscience and forensic science.

Myron Minn-Thu-Ayeمشاهده پروفایل

دانشیار

• Undergraduate Mathematics Education
• Representation Theory
• Active Learning in Mathematics
• +۳ مورد دیگر

Myron Minn-Thu-Aye is an Associate Professor in Residence in the Department of Mathematics at the University of Connecticut, College of Liberal Arts and Sciences. He holds a B.A. in Mathematics and Computer Science from Williams College, and an M.S. and Ph.D. in Mathematics from Louisiana State University. His research focuses on undergraduate mathematics education and geometric representation theory, with a doctoral thesis on perverse coherent sheaves on the nilpotent cone. He is committed to innovative teaching methods, including inquiry-based and active learning approaches to enhance student engagement and collaboration. Myron has taught a wide range of courses, from Calculus to Abstract Algebra and Multivariable Calculus, emphasizing non-traditional pedagogy to foster independent problem-solving. He actively participates in advising and supports initiatives like the Women in Math, Science, and Engineering (WiMSE) learning community. His teaching philosophy prioritizes student-led inquiry and collaborative learning environments.

• Parallel and distributed processing
• Massively parallel systems
• Computer architecture
• +۴ مورد دیگر

K. Wendy Tang is an Associate Professor and Associate Chair in the Department of Electrical and Computer Engineering at Stony Brook University, affiliated with the College of Engineering and Applied Sciences. She also serves as the Associate Provost for Online Education. Her research focuses on parallel and distributed processing, computer architecture, neural networks, and wireless sensor networks. Her academic career includes advising PhD students such as Mahdin Mahboob Divith A. Babu and former advisees like Dongsoo Kim (Microsoft) and Jaewook Yu (Intel Labs). She has published extensively on graph theory applications in communication networks, including works on Borel Cayley Graph topologies and routing protocols. Notable contributions include the 'Best Paper Award' at the 1997 IEEE International Performance, Computing, and Communications Conference. Key Research Themes: Distributed systems, network topology design, and algorithm optimization Collaborations: Multidisciplinary work in healthcare (e.g., speech recognition for disabilities education) and robotics. Her recent articles emphasize scalable communication topologies for multi-agent systems and energy-efficient wireless sensor networks. She has pioneered graph-based resizing algorithms for ultrafast information distribution.

Dinesh Mehtaمشاهده پروفایل

استاد

• Algorithms
• Data Structures
• VLSI Design Automation
• +۴ مورد دیگر

Dinesh Mehta is a Professor in the Department of Computer Science at Colorado School of Mines, where he has been since 2000. His research focuses on algorithms and data structures applied to interdisciplinary fields such as VLSI design automation, cheminformatics, computational materials, and cyber-physical systems. He holds a B.Tech. from the Indian Institute of Technology Bombay (1987), an M.S. from the University of Minnesota (1990), and a Ph.D. from the University of Florida (1992). His research emphasizes algorithmic solutions for big graph analytics, including parallel algorithms, subgraph pattern matching, and network science applications. He has contributed to advancements in data structures for cheminformatics and VLSI layout optimization. His work bridges computer science with domains like materials science and geophysics, demonstrating strong interdisciplinary impact. Key research themes include optimizing algorithms for large-scale networks, predictive modeling in fracture mechanics, and efficient graphlet counting. His recent papers reflect a focus on parallel computing, machine learning applications in materials science, and algorithmic competitions in education. No significant scientific awards are listed in the provided texts. His advising and grants are not detailed, but his extensive publication record indicates active research and collaboration. He has no listed labs or teams, though his work likely involves interdisciplinary teams across Mines and industry partners.

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.

Kaixiong Zhouمشاهده پروفایل

استادیار

• Machine Learning
• Graph Representation Learning
• Language Models
• +۴ مورد دیگر

Kaixiong Zhou is an Assistant Professor in the Department of Electrical and Computer Engineering at North Carolina State University (NC State). He previously held a postdoctoral position at the Institute for Medical Engineering and Science (MIT) and earned his Ph.D. in Computer Science from Rice University in 2023. His academic journey includes a Master’s in Electrical Engineering from University of Science and Technology of China (USTC, 2018) and a Bachelor’s in Electrical Engineering from Sun Yat-Sen University (2015). Awards : ORAU Ralph E. Powe Junior Faculty Enhancement Award (2025), Student Travel Award (KDD 2022), Outstanding Graduate Awards (USTC 2018), National Scholarship (2017). His research focuses on graph representation learning , language models , and AI for science & engineering , with applications in synthetic biology, drug discovery, and network analysis. Recent work spans graph neural networks, large language models (LLMs), and privacy-preserving AI, emphasizing efficiency and trustworthiness. Publications in ICML, NeurIPS, and ICLR highlight innovations in structured pruning, knowledge editing, and fairness-aware systems. He actively mentors prospective students and collaborates on interdisciplinary projects.

Anastasis Kratsiosمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Statistical Learning
• +۵ مورد دیگر

Anastasis Kratsios is an Assistant Professor in the Department of Mathematics and Statistics at McMaster University. His research focuses on the analytic and statistical foundations of deep learning, particularly in encoding structures from natural and social sciences like stochastic processes, games, and PDEs. He teaches courses including Computational Finance, Mathematical Reasoning, and Calculus for Science. Research interests span machine learning theory, approximation methods, and their applications in finance and geometry. Notable work includes neural operator design for PDEs, causal deep learning models, and federated learning frameworks. He frequently publishes in top venues like NeurIPS, ICLR, and Journal of Machine Learning Research. Teaching responsibilities include MFM 713 (Computational Finance II), MATH 1C03 (Introduction to Mathematical Reasoning), and MATH 1A03 (Calculus for Science I). He has developed innovative curricula blending theoretical rigor with practical computational finance tools. Publications emphasize theoretical guarantees for neural networks, kernel methods, and geometric deep learning. Recent work explores universal approximation under constraints, adversarial learning in market models, and scalable graph representation techniques. His research bridges mathematical foundations with real-world applications in finance, physics, and engineering.