جست‌وجوی استاد و پژوهشگر

Mark Snyderمشاهده پروفایل

استاد

• Computer Science
• Autonomic Mobile Sensor Networks
• Coverage Algorithms
• +۷ مورد دیگر

Mark Snyder is a Professor in the School of Business and Technology at Florida SouthWestern State College (FSW). His academic and professional background bridges advanced computer science research with real-world software development in high-impact industries such as travel technology and cybersecurity. Education: Ph.D. in Computer Science, Missouri University of Science & Technology, 2014 M.S. in Computer Science, Missouri University of Science & Technology, 2008 B.S. in Computer Science, University of Central Missouri (formerly Central Missouri State University), 1987 Dr. Snyder's research focuses on autonomic mobile sensor networks, coverage algorithms, fault tolerance, and cybersecurity, particularly in DNS and bandwidth attack defense. His work combines theoretical algorithm design with practical applications in distributed and mobile systems. He has published in journals and conferences including the International Journal of Space-Based and Situated Computing and IEEE LCN. His recent publications show a strong trend in developing robust, self-organizing algorithms for mobile sensor networks under mobility constraints, as well as applying game-theoretic models to network security. These works span topics from exploratory coverage to statistical defenses against cyberattacks, reflecting a deep integration of distributed computing and security. Dr. Snyder has no listed scientific awards in the provided text. He is actively involved in teaching software development to early-career and vocational rehabilitation students, preparing them for successful careers in tech. His courses include COP 1000, COP 1822, COP 2360, COP 2362, COP 2700, CGS 1100, CIS 2321, CTS 1131, and CTS 1133. He has also led innovative projects such as the Allegiance game at Microsoft Research and developed navigation software for cruise ships. No grants or advising relationships are detailed in the text. His industry experience includes key roles at Microsoft and Expedia, where he helped build the first online travel reservation system and architected major systems like Expedia Rewards and Cruise Reservation. He has also worked as a consultant on location-aware services for Princess Cruise Lines.

Müjdat Çetinمشاهده پروفایل

استاد

• Signal Processing
• Image Processing
• Data Science
• +۱۲ مورد دیگر

Müjdat Çetin is a Professor of Electrical and Computer Engineering and serves as the Robin and Tim Wentworth Director of the Goergen Institute for Data Science and Director of the New York State Center of Excellence in Data Science at the University of Rochester. He previously held faculty positions at Sabancı University and was a Research Scientist at MIT, with visiting roles at Boston University, Northeastern University, and MIT. Education: PhD in Electrical Engineering, Boston University, 2001 MS in Electrical Engineering, University of Salford, 1995 BS in Electrical Engineering, Boğaziçi University, 1993 His research lies at the intersection of signal processing, machine learning, and data science, with applications in biomedical imaging, radar, and brain-computer interfaces. He develops probabilistic and deep learning models for robust information extraction from noisy and complex data. His work emphasizes computational imaging, sparse representations, and multimodal data fusion. The recent publications reflect a strong trend toward integrating Bayesian methods and deep learning in imaging sciences, particularly in medical image reconstruction, neuroimaging analysis, and radar systems. His group actively explores transformer architectures, federated learning, and model-based deep learning for solving inverse problems in imaging. Scientific Awards and Honors: IEEE Fellow IEEE Signal Processing Society Best Paper Award IET Radar, Sonar and Navigation Premium Award Elsevier Signal Processing Best Paper Award Turkish Academy of Sciences Distinguished Young Scientist Award (GEBİP) ODTÜ Mustafa Parlar Foundation Research Incentive Award TÜBİTAK Career Award Boston University Best Engineering Research Award Professor Cetin has advised numerous PhD and Master’s students and led significant research grants in data science and imaging. He has served as a Senior Area Editor for IEEE Transactions on Image Processing and IEEE Transactions on Computational Imaging, and held editorial roles in several top journals. He has chaired major conferences including ICASSP, ICIP, and IVMSP workshops. He leads a multidisciplinary research group focused on data science and imaging, collaborating with neuroscientists and medical researchers. The team develops novel algorithms for brain-computer interfaces, medical image analysis, and remote sensing systems, often integrating machine learning with physical models of data acquisition.

Miju Ahnمشاهده پروفایل

استادیار

• Mathematical Optimization
• Statistical Learning
• Nonconvex Programming
• +۳ مورد دیگر

Miju Ahn is an Assistant Professor in the Department of Operations Research & Engineering Management at the Lyle School of Engineering, Southern Methodist University. She holds a Ph.D. in Industrial and Systems Engineering from the University of Southern California (2018) and a B.A. in Applied Mathematics from UC Berkeley (2008). Her research focuses on mathematical optimization methods with applications in statistical learning, power systems, finance, and healthcare. Education: Ph.D., Industrial and Systems Engineering, University of Southern California (2018) B.A., Applied Mathematics, UC Berkeley (2008) Research Interests: Designing computational algorithms for large-scale optimization problems Nonconvex programming and its applications Mathematical modeling for decision-making systems Scientific Awards: NSF CRII grant recipient for research in optimization and decision-making

Michael Mumaمشاهده پروفایل

استاد

• Robust Data Science
• Signal Processing
• Machine Learning
• +۵ مورد دیگر

Michael Muma is a Professor in the Department of Electrical Engineering and Information Technology at Technische Universität Darmstadt. His research focuses on robust data science theory and methods applied to signal processing and machine learning in biomedicine and engineering. He leads the ERC Starting Grant ScReeningData project, developing methods for reproducible information discovery in biomedical databases, and is a Principal Investigator in the LOEWE center emergenCITY and BMBF cluster curATime. Prior roles include Independent Junior Research Group Leader (Athene Young Investigator) and Lecturer at TU Darmstadt from 2017 to 2022, and Research Associate (Post-Doc since 2014) from 2009 to 2017. His research interests span robust statistical methods, high-dimensional data analysis, emergency response systems, and biomedical signal processing. Notable projects include FDR-controlled portfolio optimization, ECG delineation algorithms, and radar-based vital sign estimation. Muma has contributed to distributed sensor networks, robust clustering, and sparse regression techniques. His work addresses challenges in multi-source detection, financial data analysis, and genomics through interdisciplinary approaches combining signal processing, machine learning, and robust statistics. Recent publications emphasize scalable solutions for high-dimensional problems, including applications in robotics, cardiology, and financial index tracking.

Shuchin Aeronمشاهده پروفایل

دانشیار

• Data Science
• Statistical Signal Processing
• Inverse Problems
• +۸ مورد دیگر

Shuchin Aeron is an Associate Professor in the Department of Electrical and Computer Engineering at Tufts School of Engineering, with joint appointments in the Departments of Computer Science and Mathematics. He holds a Ph.D. from Boston University (2009) and completed postdoctoral research at Schlumberger Doll Research, focusing on borehole acoustic signal processing. His research spans statistical signal processing, machine learning, compressed sensing, and information theory, with applications in geophysics, bioengineering, and imaging. Aeron has authored over 175 publications and holds patents in acoustic signal processing. He received the NSF CAREER Award (2016) and is a Senior Member of the IEEE. Educations: Ph.D., Electrical Engineering, Boston University, 2009 M.S., Electrical Engineering, Boston University, 2004 B.Tech., Indian Institute of Technology, 2002 Research Interests: Statistical signal processing (SSP), inverse problems, compressed sensing, information theory, convex optimization Machine learning applications in geophysical signal processing, imaging, and bioengineering His work emphasizes optimal sampling and recovery of multidimensional signals, with contributions to compressed sensing architectures and generative models for particle physics experiments. He leads NSF-funded projects on data science and domain generalization, and collaborates with industry partners like Schlumberger and Mitsubishi Electric Research Labs. Awards: NSF CAREER Award (2016) Mitsubishi Electric Research Lab Research Gift (2015) Grants and Funding: NSF HDR TRIPODS (2019–2023) AFOSR: Enabling Trusted Human-Like Artificial Teammates (2018–2023) NSF: Optimal Sampling and Recovery for Multilinear Signals (2013–2016) Aeron teaches advanced courses in probabilistic systems analysis, information theory, and machine learning. He directs the Tufts Data Science undergraduate and graduate programs, and serves on editorial boards of journals including Frontiers in Signal Processing and IEEE Transactions on Geoscience and Remote Sensing .

Dehan Chenمشاهده پروفایل

دانشیار

• Inverse Problems in PDEs
• Regularization Theory
• Evolution Equations
• +۷ مورد دیگر

Dehan Chen is an Associate Professor at the School of Mathematics & Statistics, Central China Normal University, and currently a Researcher at the University of Duisburg-Essen. His research lies at the intersection of applied analysis and inverse problems, with a strong focus on partial differential equations and regularization theory. His research interests include: Inverse Problems in PDEs Regularization Theory Evolution Equations Ill-Posed Problems Mathematical Modeling Nonlinear Dynamics The analysis of his recent publications reveals a consistent and deep engagement with variational source conditions, Tikhonov regularization in Banach spaces, and inverse problems in both elliptic and parabolic systems. His work often bridges theoretical analysis with applications in physics and biology, such as electromagnetic modeling and population dynamics. A strong emphasis is placed on convergence rates and stability in ill-posed settings. His notable scientific recognition includes the prestigious Alexander von Humboldt Fellowship. This award highlights his international research impact and collaborative work in Germany. While no formal students are listed, his frequent collaborations with prominent mathematicians such as Jun Zou, Bernd Hofmann, and Irwin Yousept suggest active participation in research teams and potential advisory roles. He has not received mention of external grants, but his sustained publication record in top-tier journals indicates strong research productivity. He is affiliated with research groups focusing on optimal control and inverse problems at the University of Duisburg-Essen, particularly within the AG Optimal Control of Partial Differential Equations, contributing to a vibrant mathematical research environment.

Dustin Richmondمشاهده پروفایل

استادیار

• Computer Architecture
• FPGA
• RISC-V
• +۴ مورد دیگر

Dustin Richmond is an Assistant Professor in the Department of Computer Science and Engineering at the Baskin School of Engineering, University of California, Santa Cruz. His work focuses on secure, usable hardware systems with applications in FPGA acceleration, RISC-V architectures, and side-channel analysis. Email: drichmond@ucsc Office: Engineering 2, Room 221 Research Interests: Secure hardware systems FPGA-based computing Manycore processors High-level synthesis Side-channel vulnerabilities Notable Article Trends: Recent publications emphasize cloud FPGA security, manycore design optimization, and hardware security. Earlier works focus on RISC-V acceleration, OpenCL compiler enhancements, and heterogeneous computing systems. GitHub Contributions: Maintains open-source projects like RISC-V-On-PYNQ and PYNQ-HLS, addressing FPGA programming challenges and RISC-V integration. Active in resolving community issues related to toolchain compatibility and hardware-software interfaces.

Irena Orovicمشاهده پروفایل

پژوهشگر

• Compressive Sensing
• Sparse Signal Processing
• Signal Reconstruction
• +۳ مورد دیگر

IRENA OROVIC is a researcher affiliated with COPELABS (Cognitive and People-centric Computing), focusing on signal processing and sparse signal reconstruction. She actively contributes to advanced research in compressive sensing, time-frequency analysis, and machine learning applications. Research Interests: Her work spans Compressive Sensing Sparse Signal Processing Signal Reconstruction Time-Frequency Analysis Watermarking Machine Learning Publications: Recent research outputs include advancements in adaptive compressive sensing, Hopfield network optimization, and Hermite transform applications, reflecting her expertise in sparse signal analysis and multidisciplinary computational methods.

Karen Gundersonمشاهده پروفایل

دانشیار

• Graph theory
• Combinatorics
• Random graphs
• +۱۰ مورد دیگر

Karen Gunderson is an Associate Professor in the Department of Mathematics at the University of Manitoba's Faculty of Science. Her research spans graph theory, combinatorics, random graphs, percolation, hypergraphs, and extremal combinatorics. Research Focus : Graph theory, combinatorics, random graphs, percolation, hypergraphs, extremal combinatorics Academic Role : Associate Professor, Acting Associate Head Graduate Contact : Karen.Gunderson@umanitoba.ca , karen.gunderson@umanitoba.ca Her work includes bootstrap percolation , random geometric graphs , and extremal hypergraph problems , with applications in network modeling and probabilistic combinatorics. Recent publications focus on adversarial burning densities, Erdos-Ko-Rado robustness, and Turán numbers in switching contexts. Academic Leadership : Co-organizer of the University of Manitoba Combinatorics Seminar and key organizer for the 2023 CanaDAM conference and Movement & Symmetry in Graphs retreat.

Zihan Zhouمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Image Processing
• +۸ مورد دیگر

Zihan Zhou is an Assistant Professor at the College of Information Sciences and Technology, Penn State University, specializing in computer vision, machine learning, and 3D reconstruction. His research bridges geometric modeling, image processing, and human-computer interaction, with applications in assistive technology and creative design. Email: zuz22@psu.edu His work focuses on robust face recognition, sparse representation, and vision-language approaches for converting 2D CAD drawings into 3D parametric models. Recent projects include neural rendering for wireframe-to-image translation and data-driven 3D scene modeling. The 15 most recent publications highlight his contributions to end-to-end floorplan generation, depth estimation, trajectory prediction, and structured 3D modeling. These works integrate convolutional neural networks, graph construction, and optimization algorithms. Projects like Building Energy Savings by Tuning Indoor Lighting underscore his interdisciplinary approach, combining computer vision with environmental sustainability.

José Carlos Cabaleiro Domínguezمشاهده پروفایل

استاد

• High performance computing
• Parallel systems architecture
• Parallel algorithms
• +۶ مورد دیگر

Dr. José Carlos Cabaleiro Domínguez is a Full Professor in the Department of Electronics and Computing at the University of Santiago de Compostela's Faculty of Computing, Spain. He has been a member of CiTIUS (Centro singular de investigación en tecnoloxías da información e comunicación) since 2010 and was promoted to Full Professor in 2022 after serving as an Associate Professor since 1994. His academic journey began with a BS and PhD in Physics from the University of Santiago de Compostela in 1989 and 1994 respectively, with initial teaching experience at the University of A Coruña from 1990-1994. His research focuses on high performance computing, particularly in parallel systems architecture, development of parallel algorithms for irregular problems with sparse matrices, performance prediction and improvement of parallel applications, memory hierarchy optimization, and applications for grid and cloud computing. He has developed significant expertise in 3D point cloud processing from remote sensors like LiDAR, with applications in urban infrastructure analysis, powerline detection, and route planning. Analysis of his recent publications reveals a strong emphasis on optimizing resource allocation for big data frameworks, developing deep learning applications for point cloud classification, and creating efficient algorithms for powerline detection in LiDAR surveys. His work bridges theoretical computer science with practical applications in geospatial analysis and infrastructure monitoring. His research has been published in top-tier journals including IEEE Transactions, ISPRS Journal of Photogrammetry and Remote Sensing, and Future Generation Computer Systems, reflecting his significant contributions to the field of high performance computing and its applications. Dr. Cabaleiro actively collaborates with researchers across multiple institutions, as evidenced by his extensive publication record with co-authors from various universities and research centers. His work demonstrates a consistent trajectory of advancing parallel computing techniques while applying them to increasingly complex real-world problems involving large-scale geospatial data.

Piotr Indykمشاهده پروفایل

استاد

• Computer Science
• Electrical Engineering
• High-dimensional Computational Geometry
• +۵ مورد دیگر

Piotr Indyk is a Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT). He joined MIT in 2000 after completing his PhD at Stanford University and earning a Magister degree from Uniwersytet Warszawski in 1995. Education: Magister, Uniwersytet Warszawski (1995) PhD, Stanford University His research focuses on designing and analyzing efficient algorithms, particularly addressing challenges in high-dimensional computational geometry, sketching and streaming algorithms, and sparse recovery. He has pioneered advancements in compressive sensing and locality-sensitive hashing (LSH), which have applications in machine learning, databases, and computer vision. In 2016, he delivered a keynote at the LIG laboratory discussing novel data-dependent hashing techniques for similarity search in high dimensions, surpassing traditional LSH bounds. His work on sparse Fourier sampling was recognized in Technology Review's "TR10" (2012). Scientific Awards: Sloan Fellowship (2003) Packard Fellowship (2003) Simons Investigator Award (2013) Kanellakis Theory and Practice Award (2012)

Sylvain Sardyمشاهده پروفایل

دانشیار

• statistical machine learning
• sparsity
• optimization
• +۶ مورد دیگر

Sylvain Sardy is an Associate Professor in the Department of Mathematics at the University of Geneva, where he conducts research at the intersection of statistics, optimization, and machine learning. He is affiliated with the Analysis, Mathematical Physics and Probability research group and has held significant editorial positions including Associate Editor for Computational Statistics and Data Analysis since 2020. Professor Sardy's research focuses on statistical machine learning, sparsity, and optimization with applications spanning astronomy, chemometrics, finance, and tomography. His work develops innovative methods for high-dimensional data analysis, particularly using wavelet-based approaches and LASSO regularization techniques for feature selection, denoising, and model selection. His publications reveal a consistent focus on finding sparse signals in complex datasets across diverse scientific domains. Professor Sardy has mentored numerous graduate students, currently supervising PhD candidate Maxime van Cutsem and having previously guided Dr. Xiaoyu Ma, Dr. Pascaline Descloux, Prof. Jairo Diaz Rodriguez, and Dr. Caroline Giacobino. His Master's students include Jairo Diaz (now Professor at Universidad del Norte, Colombia), Jean-Luc Baeriswyl, and others who have pursued careers in academia, industry, and education. His academic service includes leadership roles as Swiss representative at the European Regional Committee of the Bernoulli Society (2014-2018), President of the Doctoral School of Applied Statistics and Probability (2010-2013), and Student Advisor for the Mathematics Section (2008-2015). His teaching portfolio includes Optimization with Applications I, Statistical Machine Learning, and Pharmaceutical Statistics and Methodology, reflecting his expertise in statistical methodology and its practical implementation.

Rongrong Wangمشاهده پروفایل

دانشیار

• Applied and Computational Harmonic Analysis
• Machine Learning
• Neural network training and generalization
• +۶ مورد دیگر

Rongrong Wang serves as Associate Professor in both the Department of Computational Mathematics, Science and Engineering (CMSE) and Department of Mathematics at Michigan State University, based in the Engineering Building with contact email wangron6@msu.edu . Her academic journey includes: B.S. in Mathematics and B.A. in Economics from Peking University, Beijing Ph.D. in Applied Mathematics from University of Maryland College Park under John Benedetto and Wojciech Czaja Postdoctoral fellowship at University of British Columbia with Ozgur Yilmaz and Felix Herrmann Her research spans Applied and Computational Harmonic Analysis , Machine Learning , and Compressed Sensing with focus areas including neural network training dynamics, learning theory, tensor analysis, and inverse problems. She investigates theoretical foundations of deep learning while developing applications for medical imaging and signal processing. Recent publications (2024-2025) demonstrate strong interdisciplinary work at the intersection of deep learning theory and medical imaging, particularly exploring edge-of-stability phenomena in neural networks and diffusion-guided reconstruction techniques. Her work also advances tensor decomposition methods and in-context learning mechanisms in language models. Professor Wang actively recruits self-motivated graduate and undergraduate students with backgrounds in mathematics, computer science, or electrical engineering for research opportunities in her lab.

David Donohoمشاهده پروفایل

استاد

• Theoretical Statistics
• Computational Statistics
• Signal Processing
• +۲ مورد دیگر

David Donoho is the Anne T. and Robert M. Bass Professor of Humanities and Sciences and Professor of Statistics at Stanford University. His work bridges theoretical and computational statistics, signal processing, and harmonic analysis, with a focus on advancing reproducible research through innovative software development. His research explores the detection of rare but weak phenomena across multiple test statistics, addressing challenges in statistical inference and signal recovery. He has pioneered methods to transparently capture and share data analyses, promoting openness in computational science. Donoho's contributions to statistics and signal processing are foundational, particularly in high-dimensional data analysis and sparse representation. His expertise intersects disciplines such as applied mathematics, machine learning, and information theory.