جست‌وجوی استاد و پژوهشگر

K.C. Changمشاهده پروفایل

استاد

• Data Fusion
• Bayesian Networks
• Probabilistic Reasoning
• +۹ مورد دیگر

K.C. Chang is a Professor in the Department of Systems Engineering and Operations Research at George Mason University's Volgenau School of Engineering. He is the Director of the Sensor Fusion Lab and the coordinator of the Financial Engineering Program at GMU, reflecting his interdisciplinary research focus on data fusion and probabilistic reasoning. Education: PhD, Electrical Engineering, University of Connecticut MS, Electrical Engineering, University of Connecticut BS, Communication Engineering, National Chiao-Tung University Dr. Chang's research centers on distributed data fusion, Bayesian networks, and decision-making under uncertainty, with applications in sensor networks, wireless communications, satellite systems, and financial engineering. His work integrates statistical modeling, machine learning, and systems engineering principles to solve complex, real-world problems involving uncertainty and large-scale information processing. The recent publications reflect a strong trend in applying probabilistic reasoning to dynamic spectrum access, satellite communication, and electronic warfare. His research leverages copula functions and information genealogy to enhance fusion accuracy in non-Gaussian and dependent environments. Applications span defense, aerospace, and finance, demonstrating the versatility of his methodological contributions. Scientific Awards: IEEE Fellow (2010) for contributions to sensor data fusion and Bayesian probabilistic inference Dr. Chang has advised numerous graduate students and led major research initiatives funded by the Office of Naval Research (ONR), National Science Foundation (NSF), Army Research Office (ARO), Air Force Office of Scientific Research (AFOSR), and industry partners like Intelligent Fusion Technology Inc. and Impact Technologies, LLC. His editorial roles include serving as Editor for Tracking/Navigation Systems and Large Scale Systems in IEEE Transactions on Aerospace and Electronic Systems, and as Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics. He was also Technical Program Co-Chair of the 2009 International Conference on Information Fusion. He leads the Sensor Fusion Lab at George Mason University, a research group focused on advancing the theory and application of multi-sensor, multi-source information fusion. The lab works on foundational algorithms as well as real-world implementations in defense, communication, and financial systems. The lab collaborates with government agencies and private sector organizations to translate theoretical advances into operational technologies.

Serge Stollمشاهده پروفایل

مدرس ارشد

• Environmental Chemistry
• Soft Matter Science
• Colloid and Polymer Chemistry
• +۷ مورد دیگر

Serge Stoll is a Senior Lecturer in Analytical, Environmental Physical Chemistry, and Soft Matter Science at the Department F.-A. Forel for Environmental and Aquatic Sciences, University of Geneva. He teaches colloid and polymer chemistry, environmental chemistry, and water quality courses at both undergraduate and graduate levels, including within the Master of Environmental Sciences program. Research Interests: His research focuses on understanding the fate, transport, aggregation, and sedimentation of colloids, biopolymers, and manufactured nanoparticles in aquatic environments. He employs both experimental techniques and advanced computational modeling (Monte Carlo, molecular dynamics) to link molecular-scale processes with macroscopic environmental behavior. Key areas include microplastics, nanoparticle toxicity, and applications in water treatment technologies. The recent publications highlight a strong trend in environmental nanotechnology, particularly the behavior of microplastics and engineered nanoparticles in urban water cycles and drinking water systems. The work spans from fundamental soft matter simulations to applied wastewater and drinking water treatment, often involving interdisciplinary collaborations and European research projects like NanoFASE and NanoMILE. Scientific Awards and Recognition: Represented Switzerland at the CERC3 Workshop on Colloidal Synthesis and Characterization, Bristol, UK Member of the selection committee for the Institut Universitaire de France Co-editor of the Journal of Colloid Science and Biotechnology Advising and Grants: Serge Stoll has supervised approximately 10 PhD theses, including recent graduates A.D.J. Negrete Velasco and G.J. Hul. His research is supported by the Swiss National Research Foundation and through participation in large-scale European projects such as NanoFASE and NanoMILE, indicating sustained funding and international collaboration. Laboratories and Research Teams: His group operates within the Institute of Environmental Sciences (ISE) at the University of Geneva and collaborates extensively with European and international research groups. The team combines experimental and computational approaches to study colloidal systems in environmental contexts, contributing to both fundamental science and practical water treatment solutions.

Mark Iwenمشاهده پروفایل

استاد

• Computational Harmonic Analysis
• Mathematical Data Science
• Signal Processing
• +۱۲ مورد دیگر

Mark Iwen is a Full Professor with a dual appointment in the Department of Mathematics and the Department of Computational Mathematics, Science and Engineering (CMSE) at Michigan State University. His research bridges theoretical mathematics and computational science, focusing on high-dimensional data analysis, signal processing, and numerical algorithms. Research Interests: His work centers on computational harmonic analysis, mathematical data science, and algorithms for large-scale data. Key areas include sparse spectral methods, phase retrieval, tensor decomposition, compressive sensing, sublinear-time algorithms, and Johnson-Lindenstrauss embeddings. He develops provably accurate and efficient algorithms for problems in high dimensions, such as solving PDEs, function approximation, and manifold learning. Publications and Trends: His recent publications (2023–2025) emphasize sparse and low-rank tensor methods, phase retrieval, and high-dimensional PDE solvers. There is a strong focus on algorithmic efficiency, provable guarantees, and applications in imaging and data science. Many works involve collaborative efforts and include open-source code. Scientific Awards: Scholarship established in honor of Benjamin (Ben) Segal, reflecting his mentorship and academic impact Advising and Grants: He advises numerous students, including undergraduates and graduate researchers, many of whom are co-authors on publications and code repositories. While specific grants are not listed, his sustained research output and collaborative projects suggest active funding. He co-founded the One World MINDS Seminar, demonstrating leadership in the academic community. Labs and Teams: He leads a research group focused on algorithm development for high-dimensional problems, with strong ties to computational mathematics and data science. His team develops and shares code for sparse FFTs, phase retrieval, tensor methods, and PDE solvers, indicating a collaborative and open research culture.

Timothy B Roweمشاهده پروفایل

استاد

• Vertebrate Paleontology
• Evolutionary Biology
• Phylogenetic Systematics
• +۶ مورد دیگر

Timothy B. Rowe is a Professor (retired) at the Jackson School of Geosciences, University of Texas at Austin, affiliated with the Department of Earth and Planetary Sciences. He holds the J. Nalle Gregory Regents Professor title and has led the Vertebrate Paleontology Laboratory since 1998. His research focuses on vertebrate skeletal evolution, phylogenetic systematics, and high-resolution X-ray computed tomography (CT) applications for fossil analysis. Collaborations span global institutions, particularly in scanning significant fossils and developing digital morphology databases like DigiMorph.org. Education: Ph.D., Paleontology, University of California, Berkeley (1986) M.S., Anatomy, University of Chicago (1981) B.A., Geology, Occidental College (1975) Research Interests: Dr. Rowe’s work integrates evolutionary biology with cutting-edge imaging technologies. Key areas include mammalian and dinosaurian skeletal evolution, developmental patterns, and CT-based 3D morphological analysis. His fieldwork focuses on Mesozoic terrestrial sediments in Texas and the Southwest, uncovering critical fossil records. He pioneered open-access digital repositories for morphological data, revolutionizing paleontological research accessibility. Awards: Scientific American ‘World’s 50 Best Websites’ (2004) for DigiMorph.org UT Innovative Use of Instructional Technology Gold Award (2003) National Science Foundation Presidential Young Investigator Award (1989–1994) Advising & Grants: Supervised over 20 students (Ph.D., M.S., undergrad) and postdocs. Secured NSF grants for CT technology development, digital libraries, and fossil studies. Notable grants include the ‘Digital Library of Vertebrate Morphology’ (2002–2007) and ‘Acquisition of a High-Resolution Micro-CT Scanner’ (2007–2008). Labs & Collaborations: Directs the Vertebrate Paleontology Laboratory and co-founded DigiMorph.org. Active in field paleontology and international collaborations, particularly in Africa, South America, and Asia. His team’s work bridges traditional paleontology with digital informatics, emphasizing open data sharing.

Jeffrey K. Hollingsworthمشاهده پروفایل

استاد

• High Performance Computing (HPC)
• Parallel Programming
• Operating Systems
• +۵ مورد دیگر

Jeffrey K. Hollingsworth is a Professor in the Computer Science Department at the University of Maryland and serves as Vice President for Information Technology and Chief Information Officer (CIO) for the university. He holds appointments in CS, UMIACS (University of Maryland Institute for Advanced Computer Studies), and ECE (Electrical and Computer Engineering). His research focuses on High Performance Computing (HPC), parallel programming environments, and system architecture. He received a Ph.D. from the University of Wisconsin at Madison (1994) and a B.S. in Electrical Engineering from UC Berkeley. Hollingsworth leads the university’s IT infrastructure, overseeing critical services like networking, cybersecurity, and support for research and teaching. He has held leadership roles in professional organizations, including past chair of the ACM Special Interest Group on HPC (SIGARCH) and board positions with Internet2 and the Computing Research Association. His awards include IBM Faculty Partnership Awards (2016, 2001), an NSF CAREER Award (1997), and IEEE Senior Member status (2003). Key contributions include advancing HPC education through programs like the SC Student Cluster Competition and developing tools for performance analysis (e.g., PIPER, Chapel profilers). He has authored over 150 papers and has been cited for innovations in auto-tuning, parallel algorithms, and system optimization.

Niels Aageمشاهده پروفایل

دانشیار

• Topology Optimization
• Acoustics and Vibroacoustics
• Biomechanics
• +۵ مورد دیگر

Niels Aage is an Associate Professor in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU). His research focuses on topology optimization, biomechanics, and multiphysics modeling, with applications in acoustic devices, biomedical implants, and microelectromechanical systems (MEMS). He holds roles such as Vice President of the International Society for Structural and Multidisciplinary Optimization (2023–2027). Education and Professional Background: Conducted a 5-month research visit at the University of Colorado, Boulder (USA) in 2010. Specializes in giga-scale numerical modeling, finite element methods, and topology optimization algorithms. Research Interests: Develops novel methods for topology optimization of fluidic, thermal, and acoustic systems. Explores applications in patient-specific spinal implants, metamaterials with vibroacoustic bandgaps, and nonlinear dynamic substructuring. His work integrates machine learning and reduced-order modeling for efficient simulation. Publications: Over 100 peer-reviewed articles, including recent contributions on connectivity promotion in topology optimization (2025), vibroacoustic metamaterial design (2025), and anatomically conforming spinal fusion cages (2024). Research emphasizes high-resolution modeling and bridging computational design with additive manufacturing. Scientific Awards: ISSMO Haftka Young Investigator Award (2021), Equinor Prize 2020, and Hyperion Innovation Excellence Award (2017). Recognized for contributions to structural optimization and computational mechanics. Advising and Grants: Supervises multiple PhD projects, including work on vibroacoustic shape optimization, quantum-opto-mechanical systems, and smart hearing aid modeling. Engages in collaborative projects funded by industry and academia. Labs/Teams: Collaborates with DTU’s Solid Mechanics group and industry partners on projects involving topology optimization, multiphysics simulation, and biomedical engineering. Active in international conferences and serves on editorial boards.

Brian Kellerمشاهده پروفایل

استادیار

• Missing Data Analysis
• Bayesian Statistics
• Statistical Computing
• +۳ مورد دیگر

Brian Keller is an Assistant Professor in the Department of Educational, School & Counseling Psychology at the University of Missouri's College of Education & Human Development. His research focuses on statistical methodologies, particularly missing data analysis, Bayesian statistics, structural equation modeling, and multilevel modeling. He is the developer of Blimp statistical software, which supports advanced statistical computing methods. Key areas of expertise include missing data imputation techniques, Bayesian computational methods, and the application of statistical models to educational and psychological data. His work emphasizes methodological innovation, software development, and the rigorous analysis of complex datasets. Recent research trends in his articles highlight advancements in latent variable modeling, nonlinear effects, and the integration of computational tools like Blimp. His contributions address challenges in multilevel models, missing data mechanisms, and model comparison criteria such as DIC and WAIC. No specific scientific awards are listed for Brian Keller in the provided information. His professional affiliations include the College of Education & Human Development and the Blimp Statistical Software initiative. He advises students in the department but no specific advisee names are provided. Research grants and funding details are not explicitly mentioned in the text. Blimp Statistical Software is a key collaborative project, reflecting his commitment to advancing statistical methods and software solutions for researchers.

Armin Zareمشاهده پروفایل

استادیار

• Control Theory
• Fluid Mechanics
• Stochastic Dynamical Modeling
• +۳ مورد دیگر

Armin Zare is an Assistant Professor at the University of Texas at Dallas in the Department of Mechanical Engineering , affiliated with the Center for Wind Energy . He obtained his PhD in Electrical Engineering from the University of Minnesota (2016) and previously held a postdoctoral position at the University of Southern California. Education : PhD (2016), MS (2016) in Electrical Engineering from University of Minnesota; BS (2010) in Electrical Engineering from Sharif University of Technology Research Interests : Modeling, dynamics, and control of large-scale and distributed systems; optimization; dynamics and control of complex fluid flows; renewable energy generation. His work focuses on stochastic modeling of turbulent flows and wind farm dynamics using control theory. Recent Publications highlight advancements in wind farm turbulence modeling, stochastic receptivity analysis, and covariance completion algorithms, reflecting his expertise at the intersection of control systems and fluid dynamics. Scientific Awards Young Investigator Program Award, Air Force Office of Scientific Research (2023) Doctoral Dissertation Fellowship, University of Minnesota (2015) Best Student Paper Award Finalist, American Control Conference (2014) Advisees include PhD students working on topics like wall turbulence modeling, wind farm forecasting, and stochastic dynamical systems, with several former students having completed theses on wind turbine wake modeling and fluid flow uncertainties.

Sean Chesterمشاهده پروفایل

استادیار

• Data Management
• Parallel Computing
• Algorithm Engineering
• +۵ مورد دیگر

Sean Chester is an Assistant Professor in the Department of Computer Science at the University of Victoria, Canada. He is affiliated with the Faculty of Engineering and Computer Science and specializes in scalable data analytics, with a focus on data management, parallel computing, and algorithm engineering. His research interests include GPU-native algorithms, multicore optimization, spatio-temporal data processing, and graph-based analysis. He actively contributes to open-source projects and course materials on platforms like GitHub, emphasizing open science and education. Recent work highlights include advancements in skyline computation, GPU-accelerated algorithms, and efficient processing of large-scale datasets. His publications span topics such as kNN optimization, social network anonymization, and vectorized k-core decomposition. Sean is involved in teaching courses like CSC 485C/586C on data management on modern hardware and CSC 370 on database systems. No scientific awards or grants are explicitly mentioned in the provided texts. He collaborates with students and researchers through platforms like GitHub, where he maintains repositories related to algorithm engineering and educational materials.

Simone Brugiapagliaمشاهده پروفایل

دانشیار

• Mathematics of Data Science
• Computational Mathematics
• Numerical Analysis
• +۵ مورد دیگر

Simone Brugiapaglia is an Associate Professor in the Department of Mathematics and Statistics at Concordia University in Montréal, Canada. His academic journey includes a PhD in Mathematical Models and Methods from Polytechnic University of Milan (2016), an MSc in Mathematics from University of Pisa (2012), and a BSc in Mathematics from University of Pisa (2010), all earned cum laude . Prior to his current role, he held postdoctoral positions at École polytechnique fédérale de Lausanne (2016) and Simon Fraser University (2016-2019). Dr. Brugiapaglia's research bridges mathematics, data science, and computational methods. Key interests include: Foundations of deep learning and neural networks Compressed sensing and sparse recovery algorithms High-dimensional approximation theory Numerical methods for PDEs and diffusion equations Physics-informed machine learning Optimization techniques for large-scale problems His work develops rigorous mathematical frameworks for data-driven algorithms. His publications (30+ including two books) consistently focus on high-dimensional computation , featuring recent advances in neural network theory (e.g., generalization bounds, rank collapse), compressed sensing techniques (e.g., greedy algorithms, unrolled networks), and physics-informed learning. A strong trend involves combining traditional numerical methods with deep learning for PDE solutions. Awards & Fellowships: Concordia Research Fellow (2023) Leslie Fox Prize for Numerical Analysis (2nd place, 2019) PIMS Postdoctoral Fellowship (2016-2018) Multiple INdAM scholarships during graduate/undergraduate studies He has supervised over 20 trainees across postdoctoral, graduate, and undergraduate levels. While specific grants aren't detailed, his fellowship history indicates sustained research funding. No explicit research labs or teams are mentioned, but his supervision record and collaborative publications suggest active leadership in research groups focused on computational mathematics.

Francesco Iorioمشاهده پروفایل

پژوهشگر

• Computational Biology
• Pharmacogenomics
• Cancer Biology
• +۳ مورد دیگر

Francesco Iorio is a Research Group Leader in Computational Biology at Human Technopole (Milan, Italy), leading research in computational cancer pharmacogenomics and therapeutic target discovery. He holds a PhD from the University of Salerno and TIGEM (Naples), focusing on computational methods for drug discovery. Previously, he was a Senior Bioinformatician at EBI, leading CRISPR-Cas9 screen analyses across cancer cell lines to identify synthetic lethalities and therapeutic targets. He led the Cancer Dependency Map Analytics team at Wellcome Sanger Institute, developing algorithms for integrating large-scale cancer datasets. Since 2019, he serves as a Scientific Advisor to the Cancer Research Horizon–AstraZeneca Functional Genomics Centre. His research integrates computational methods with experimental data to uncover cancer dependencies, drug mechanisms, and biomarkers. Key contributions include tools like CRISPRcleanR for bias correction in CRISPR screens, and CELLector for selecting cancer cell line models. His work spans pharmacogenomics, functional genomics, and CRISPR technologies, with a focus on translating findings into clinical applications. Publications highlight advancements in CRISPR screen analysis, cancer dependency mapping, and drug repositioning. Current projects aim to advance precision medicine through computational frameworks for understanding tumor heterogeneity and therapeutic vulnerabilities.

LIU Boxiangمشاهده پروفایل

استادیار

• Genomics
• Bioinformatics
• Machine Learning
• +۳ مورد دیگر

Asst Prof LIU Boxiang holds the position of Assistant Professor and NUS Presidential Young Professorship at the Department of Pharmacy and Pharmaceutical Sciences, National University of Singapore (NUS), within the Faculty of Science. His research focuses on integrating multi-omics approaches with computational methods to study complex diseases such as coronary artery disease and age-related macular degeneration. He specializes in developing statistical and machine learning tools for genomic analysis, including eQTL mapping and deep learning architectures for gene expression regulation. Education: BA in Biophysics (Illinois Wesleyan University), MS and PhD in Bioinformatics (Stanford University). He contributed to the GTEx consortium and is part of the Asian Immune Diversity Atlas (AIDA) initiative. His lab develops methods like ANTseq for ancestry determination and scPrediXcan for cell-type-specific transcriptome studies. Research Interests: Functional genomics, eQTL analysis, deep learning in biomedicine, and computational tools for omics data integration. His work bridges disciplines such as natural language processing and computer vision with biological questions. Scientific Awards: NUS Presidential Young Professorship (2021). His lab's innovations include ParaMed, a biomedical translation dataset, and LinearDesign for optimized mRNA stability. Advising and Grants: Leads the Liu Lab (boxiangliulab.com), focusing on single-cell genomics, mitochondrial dynamics, and computational biomedicine. Collaborates on projects like the RESET cohort study for cardiovascular disease prevention.

Frank Woodمشاهده پروفایل

دانشیار

• Probabilistic Programming
• Deep Generative Models
• Reinforcement Learning
• +۴ مورد دیگر

Frank Wood is an Associate Professor of Computer Science at the University of British Columbia and a Canada CIFAR AI Chair at AMII. He directs the Pacific Laboratory for Artificial Intelligence (PLAI) research group and co-founded Inverted AI, a spin-out focused on advanced simulation for autonomous vehicles. His research focuses on probabilistic programming, deep generative models, and reinforcement learning, with applications in autonomous driving, robotics, and vision. Wood teaches courses such as Machine Learning and Data Mining (CPSC 340) and Topics in Artificial Intelligence (CPSC 532W). He has been on academic leave from January to December 2025. His work integrates theoretical advancements with practical applications, including projects like PLAICraft, a large-scale embodied AI dataset. Key awards include the ICML Best Paper Honourable Mention and contributions to probabilistic programming systems like Anglican. His research group collaborates on tools such as TorchDriveEnv for autonomous driving benchmarking and explores cutting-edge methods in diffusion models and continual learning.

Roderic D Pageمشاهده پروفایل

استاد

• Taxonomy
• Biodiversity Informatics
• Phylogenetics
• +۳ مورد دیگر

Professor Roderic D Page is a faculty member at the University of Glasgow, holding the position of Professor of Taxonomy (Evolution & Diversity) within the School of Biodiversity, One Health & Veterinary Medicine. His primary affiliation is with the Institute of Biodiversity, Animal Health & Comparative Medicine. He is based in Glasgow, Scotland, and can be contacted via email at Roderic.Page@glasgow.ac.uk or through his ORCID profile (0000-0002-7101-9767). Professor Page's research focuses on biodiversity informatics, taxonomy, and phylogenetics. He is a leading advocate for open science, particularly in improving access to taxonomic literature and integrating biodiversity data through knowledge graphs. His work includes projects such as BioStor (digitizing historical biodiversity literature), the Biodiversity Heritage Library (BHL), and initiatives to link persistent identifiers (e.g., LSIDs, DOIs, ORCIDs) across taxonomic databases. Key themes in his research include: Enhancing interoperability between biodiversity datasets Developing tools for phylogenetic visualization and analysis Addressing challenges in DNA barcoding and taxonomic classification Promoting open access to biodiversity data and legacy literature His recent work explores machine learning applications in taxonomy, the limitations of current data-sharing practices in phylogenetics, and the role of knowledge graphs in synthesizing biodiversity knowledge. He frequently critiques siloed approaches to data management and advocates for global collaboration in biodiversity informatics. Grants and funding for his research include support from initiatives related to the Biodiversity Heritage Library, the LOTUS project (open natural products research), and the Global Biodiversity Information Facility (GBIF). His contributions have advanced both computational methods and policy frameworks for biodiversity data integration. Professor Page is associated with the BioNames and BHL projects, and his lab focuses on bridging gaps between taxonomic databases, genomic data, and geospatial information. He actively participates in international efforts to standardize biodiversity data and improve accessibility for researchers worldwide.

H. Metin Aktulgaمشاهده پروفایل

دانشیار

• High Performance Computing
• Parallel Computing
• Big Data Analytics
• +۳ مورد دیگر

H. Metin Aktulga is an Associate Professor in the Department of Computer Science and Engineering at Michigan State University's College of Engineering. His research focuses on high-performance computing, parallel algorithms, and numerical methods for large-scale scientific applications. He leads interdisciplinary projects involving collaborations with computational physicists and materials scientists to develop scalable software systems. His work includes the development of PuReMD, a reactive molecular dynamics code, and DOoC+LAF, a task-based middleware for data analytics. He explores parallel computing on emerging architectures, emphasizing energy efficiency and performance optimization. His research spans applications in molecular modeling, nuclear physics, and computational biology. Awards and grants are not explicitly listed, but his contributions are highlighted through collaborations with projects like MFDn (nuclear structure) and SHINES (electronic structure computations). He advises students in computational methods and leads efforts to automate force field optimization using machine learning and big data analytics. Labs and teams include the High-Performance Computing group at MSU, with active participation in interdisciplinary initiatives to bridge simulation and data-driven discovery in materials science and quantum systems.