جست‌وجوی استاد و پژوهشگر

• Computational Neuroscience
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Conor Houghton is an Associate Professor in Computer Science at the University of Bristol, affiliated with the School of Engineering Mathematics and Technology. His work bridges computational neuroscience, machine learning, and artificial intelligence. He explores neural mechanisms in cognition, language evolution, and network dynamics through interdisciplinary methods. Research interests include neural coding, evolutionary models, and applications of deep learning to cognitive processes. Key research themes involve modeling neural systems (e.g., cerebellar circuits, auditory processing), analyzing spike train data, and developing algorithms for information processing. His publications span topics like Bayesian inference in neural systems, cooperative evolutionary dynamics, and sparse autoencoder architectures. Collaborations with colleagues in neuroscience and computer science highlight his interdisciplinary approach. Publications demonstrate expertise in computational neuroscience (e.g., cerebellar state estimation, synaptic plasticity) and machine learning (e.g., reservoir computing, language models). His work often combines theoretical models with empirical data analysis, addressing questions in both biological and artificial systems.

• Power Electronics
• Renewable Energy Systems
• Energy Storage
• +۳ مورد دیگر

Prof. Dylan Lu is a Professor and Head of Discipline for Electrical Power and Energy Systems at the School of Electrical and Data Engineering, University of Technology Sydney (UTS). He holds a Ph.D. in Electronic and Information Engineering from The Hong Kong Polytechnic University (2004). Previously, he served as academic staff at the University of Sydney from 2006 to 2016, progressing from Lecturer to Associate Professor. His research focuses on power electronics, renewable energy systems, and grid integration, with an emphasis on converter topologies, control techniques, and power quality solutions for microgrids and energy storage systems. Prof. Lu's academic leadership includes roles as Associate Editor for IEEE Transactions on Industrial Electronics and IEEE Journal of Emerging and Selected Topics in Power Electronics. He chairs the IEEE NSW Joint Chapter PEL/IES/IAS and is a Senior Member of the IEEE. His teaching contributions include coordinating Renewable Energy Systems studios and supervising graduate projects in areas like electro-thermal modeling of power devices and three-port DC-DC converter synthesis. Research highlights include advancements in cascaded H-bridge converters, battery management systems, and grid-connected inverter control strategies. His work addresses challenges in high-efficiency power conversion, renewable integration, and energy storage reliability. Current projects explore fault-tolerant converter designs and predictive control algorithms for electric vehicle chargers.

Yam Siwakotiمشاهده پروفایل

دانشیار

• Power Electronics
• Renewable Energy Systems
• Multilevel Converters
• +۵ مورد دیگر

Associate Professor Yam Siwakoti holds a position at the University of Technology Sydney (UTS), leading research in power electronics and renewable energy systems. He has affiliations with the School of Electrical and Data Engineering and has collaborated with institutions like Fraunhofer ISE and the University of Oxford. Education: B.Tech in Electrical Engineering from National Institute of Technology, Hamirpur (2005) MEng in Electrical Power Engineering from Norwegian University of Science and Technology and Kathmandu University (2010) PhD in Electronic Engineering from Macquarie University (2014) Research focuses on high-efficiency power converters, multilevel inverters, and sustainable energy systems. Key interests include transformerless grid-connected inverters, battery management systems, and wide bandgap semiconductor applications. Notable achievements include the Friedrich Wilhelm Bessel Research Award (2022) and Green Talent Award (2016). He has supervised over 15 students and holds editorial roles in IEEE journals. His funded research projects focus on extending battery lifetimes and high-frequency magnetics design. Labs/Teams: Active in UTS' Power Electronics and Renewable Energy research groups, collaborating with industry partners like Re:Start on EV charging solutions.

• Robust Methods
• Nonparametric Methods
• Uncertainty Quantification
• +۷ مورد دیگر

Omer Ozturk is a Professor of Statistics at The Ohio State University (OSU), affiliated with the Department of Statistics. He joined the faculty in 1996 and has held editorial roles at journals including Environmental and Ecological Statistics, and Communications in Statistics. His research focuses on robust and nonparametric statistical methods, particularly in developing efficient sampling designs that minimize costs while maximizing information through auxiliary variables and ranking techniques. He has been funded by the NSA and NSF and actively collaborates with the U.S. Census Bureau as a Summer at Census Scholar. Education: PhD in Statistics from Penn State University (1994). Research Interests: Omer’s work emphasizes statistical inference under relaxed distributional assumptions, including robust methods, nonparametric techniques, and uncertainty quantification. He specializes in finite population sampling designs, such as ranked set sampling and judgment post-stratification, which leverage auxiliary information to enhance efficiency. His contributions span meta-analysis, spatial statistics, and Bayesian mixture modeling, with applications in epidemiology, environmental science, and agriculture. Articles Overview: His recent work addresses meta-analysis of survival times, spatially balanced sampling, and Bayesian modeling with ranked set samples. He developed the R package 'metamedian' for median-based meta-analysis and explored trade-offs in spatial sampling efficiency. His research consistently emphasizes practical applications in reducing sampling costs while improving statistical precision. Awards: ASA Fellow (2010). Advising & Grants: Omer has received grants from NSA and NSF, and his work frequently involves collaboration with institutions like the U.S. Census Bureau. Although specific student advisees are not listed, his research outputs suggest involvement in training graduate students in statistical methodology and applications. Labs/Teams: While no specific lab names are mentioned, his collaborations span statistical methodologies in environmental and medical research contexts, leveraging interdisciplinary teams for applied problems.

Guy E. Blellochمشاهده پروفایل

استاد

• Parallel Computing
• Programming Languages
• Algorithms and Complexity
• +۵ مورد دیگر

Guy E. Blelloch is the U.A. and Hellen Whitaker University Professor at Carnegie Mellon University , affiliated with the Computer Science Department . He focuses on the intersection of algorithms and programming languages within parallel computing , with significant experimental and theoretical contributions. His recent work includes the PSCICO project (with Gary Miller, Bob Harper, Peter Lee) exploring high-level programming constructs in geometric algorithms, and continued development of the NESL programming language for parallel computation. Research spans Parallel Garbage Collection Work-Efficient Scheduling Deterministic Parallel Algorithms Processing-in-Memory Models Key publications since 2022 show trends in dynamic data structures , cache-oblivious algorithms , and concurrent memory management , with applications in graph processing (PIM-tree), geometric computation (tree-based sorting), and multiversioning systems. His work often bridges theory and practice, demonstrated in frameworks like Ligra , GBBS , and Aspen for large-scale graph analysis. Scientific awards include ACM Paris Kanellakis Theory and Practice Award (2023) IEEE Charles Babbage Award (2021) Best Paper Awards at PPoPP 2022, SPAA 2021 SCS Doctoral Dissertation Award (Honorable Mention) He has advised numerous PhD students including Magdalen Dobson (current student on nearest-neighbor problems), Daniel Anderson (parallel batch-dynamic algorithms), and Julian Shun (graph processing frameworks). His teaching includes advanced courses on Parallel and Sequential Data Structures and Parallel and Concurrent Algorithms .

Matt Fredriksonمشاهده پروفایل

دانشیار

• Machine Learning
• Security
• Privacy
• +۲ مورد دیگر

Matt Fredrikson is an Associate Professor in the Computer Science Department at Carnegie Mellon University , affiliated with CyLab and the Principles of Programming Group . His research bridges security, privacy, and formal methods in machine learning and software systems. PhD in Computer Science, University of Wisconsin–Madison (2015) M.S. in Computer Science, University of Wisconsin–Madison (2012) Bachelor's in Mathematics and Computer Science, Duquesne University (2007) His work focuses on privacy in machine learning , particularly adversarial inference and differential privacy limitations. He develops formal methods for privacy-aware programming , using logics with counting to model adversarial uncertainty. Additionally, he explores probabilistic program analysis to enhance machine learning security and reliability. Recent publications highlight his contributions to LLM security , including attacks on alignment and robustness certification. His 2025 paper LLM Whisperer reveals biases in LLM responses, while 2024 works address certifiable robustness and automated adversarial attacks on coding models. Best Paper Award, USENIX Security Symposium 2014 He advises students on topics spanning AI ethics , program verification , and IoT security . Courses taught include Software Foundations of Security and Privacy and Bug Catching: Automated Program Verification and Testing .

Laure Daviaudمشاهده پروفایل

دانشیار

• Formal Verification
• Automata Theory
• Models of Computation
• +۴ مورد دیگر

Laure Daviaud is an Associate Professor in the School of Computing Sciences at the University of East Anglia (UEA). She holds associate membership in the Algebra, Number Theory, Logic, and Representations (ANTLR) group and the Computational Biology team, and is a member of UEA's Data Science and AI initiatives. Her academic background includes a Doctor of Philosophy from IRIF - CNRS/Université de Paris, focusing on the asymptotic behavior of min-plus and max-plus automata (2014). Her research interests center on Formal Verification, Automata Theory, Logic, Automata Learning, and Decidability. Recent work explores decidability questions in max-plus automata, probabilistic automata containment, and neural network analysis. She has contributed to quasi-polynomial lower bounds for parity games and the study of counting mechanisms in ReLU RNNs. Current projects include the Engineering and Physical Sciences Research Council-funded 'Learning, Approximating and Minimising Streaming Automata for Large-scale Optimisation' (2023–2024). She collaborates internationally, including as a Visiting Research Fellow at City, University of London since 2023. Her publications span over 25 peer-reviewed articles, with recent focus on weighted automata, max-plus algebra, and formal methods. She actively participates in program committees for conferences like ICALP and FoSSaCS, and delivers invited talks at events such as Highlights of Logic, Games and Automata.

Tommi Linnansaariمشاهده پروفایل

استاد

• Atlantic salmon ecology
• Biotelemetry
• Stream and river ecology
• +۴ مورد دیگر

Tommi Linnansaari is a Professor at the Department of Forestry and Environmental Management, University of New Brunswick. He holds the Atlantic Salmon Research Chair and coordinates the Collaboration for Atlantic Salmon Tomorrow (CAST) consortium. His research focuses on Atlantic salmon ecology, biotelemetry, and the ecological effects of hydropower. He completed his M.Sc. in Fisheries Science at the University of Helsinki and his Ph.D. in Biology at UNB. Education: M.Sc. in Fisheries Science, University of Helsinki, Finland Ph.D. in Biology, University of New Brunswick Research Interests: Atlantic salmon population dynamics in natural and hydropower-regulated rivers Migratory fish ecology (e.g., American eel, striped bass) Environmental flows and habitat modeling Hydropower management and fish passage solutions Articles Trends: Recent work emphasizes hydropower impacts on fish migration, thermal adaptation strategies of salmon, and innovative tracking technologies. Studies often integrate biotelemetry, hydrodynamic modeling, and interdisciplinary collaboration to address conservation challenges. Labs/Teams: Leads projects within the CAST consortium and the Atlantic Salmon Research Joint Venture, collaborating on large-scale river management and species conservation initiatives.

James D. Stameyمشاهده پروفایل

استاد

• Bayesian Methods
• Statistical Applications in Healthcare
• Pharmaceutical Research
• +۴ مورد دیگر

Dr. James D. Stamey serves as Chairman and Professor of the Department of Statistical Science at Baylor University. He holds a B.S. in Mathematics from Northwestern State University (1995), an MBA from Baylor University (1997), and a Ph.D. in Statistics from Baylor (2000). His research focuses on applying Bayesian methods to imperfectly measured data, with applications in pharmaceutical research, epidemiology, economics, and political science. Recent work emphasizes correcting misreporting in healthcare and public health datasets, structural zeros in contingency tables, and cost-effectiveness analysis in clinical trials. He advises graduate students on pharmaceutical-related dissertation topics, blending academic rigor with real-world problem-solving. Dr. Stamey is actively involved in Baylor's academic and community life, including consulting services and departmental leadership. His publications span Bayesian modeling innovations, misclassification correction techniques, and interdisciplinary applications in healthcare and social sciences. He collaborates with industry partners on nonclinical trial methodologies and contributes to regulatory guidelines through working groups like the DIA/ASA-BIOP Nonclinical Bayesian Working Group. Dr. Stamey’s work bridges theoretical statistics with practical challenges in public health, economics, and biomedical research. Outside academia, he enjoys family activities, tennis, pickleball, and supporting Baylor athletics. His wife and two Baylor-educated sons are active members of their local Catholic community.

Joon Jin Songمشاهده پروفایل

استاد

• Statistical Science
• Bayesian Analysis
• Spatial Statistics
• +۴ مورد دیگر

Dr. Joon Jin Song is a Professor of Statistical Science at Baylor University. His work focuses on Bayesian methods, spatial statistics, and their applications in environmental science, biostatistics, and public health. He holds a Ph.D. from Texas A&M University (2004), an M.S. from Kyungpook National University (1999), and a B.S. from Yeungnam University (1997). His research integrates advanced statistical techniques to address challenges in spatial data analysis, including rainfall estimation, disease mapping, and environmental monitoring. Notable projects include Bayesian correction for misclassification in count data and radar-rain gauge merging for high-resolution precipitation estimation. He has also contributed to studies on crime fear reduction through spatio-temporal modeling and public health surveillance during the COVID-19 pandemic. Dr. Song's publications span computational statistics, environmental modeling, and health informatics. His methodologies emphasize robustness to measurement error, high-dimensional data, and spatial dependencies. Collaborations include interdisciplinary work in meteorology, epidemiology, and bioinformatics.

Julia E. Faمشاهده پروفایل

استاد

• Conservation Science
• Wildlife Hunting
• Wild Meat Use
• +۳ مورد دیگر

Julia E. Fa is Professor of Biodiversity and Human Development at Manchester Metropolitan University and Senior Research Associate at the Center for International Forestry Research (CIFOR). Her research examines relationships between wildlife conservation and human development, focusing on wildlife hunting impacts, zoonotic diseases, and community-based conservation across Africa, Latin America, and Europe. She leads the Bushmeat Research Initiative at CIFOR, developing evidence-based solutions for sustainable wildlife use. Current projects include WARBIO (armed conflicts and biodiversity in Angola), Sustainable Wildlife Management in Cameroon, and Amazon conservation initiatives. Her work integrates ecological science with policy development and community engagement. Recent publications (2023-2025) address leprosy transmission through wild meat, COVID-19 impacts on food security, ethical insect trade, and Indigenous knowledge integration. Research consistently emphasizes balancing biodiversity protection with human wellbeing in tropical regions.

Mini Dasمشاهده پروفایل

استاد

• Medical Imaging
• Biomedical Engineering
• Optical Physics
• +۷ مورد دیگر

Mini Das is the Moores Professor in the Department of Physics at the University of Houston, with affiliations in the Cullen College of Engineering's Biomedical Engineering and Electrical and Computer Engineering departments. She holds a Ph.D. from the Indian Institute of Technology, Delhi. Her research focuses on advanced imaging techniques at the intersection of optical physics, computational methods, and engineering, with applications in medical diagnostics, defense/security, and global health. Key areas include X-ray phase contrast imaging, photon-counting detectors (collaborating with CERN), virtual clinical trials, and psychophysical models for human perception in medical imaging. Her work aims to improve cancer screening accessibility in underserved regions through innovative imaging systems. Das leads interdisciplinary projects combining quantum detection, material decomposition algorithms, and virtual patient modeling. She is a leader in developing cutting-edge imaging tools and has pioneered methods for low-dose and high-resolution biomedical imaging. Education : Ph.D., Indian Institute of Technology, Delhi Research Interests : Experimental/Optical design and computational imaging, virtual clinical trials, psychophysics/image science, vision science-driven imaging systems, and global health applications in cancer screening. Her lab develops advanced imaging modalities like multi-contrast X-ray phase mammography and spectral CT material decomposition systems. Grants & Collaborations : Works with CERN to optimize quantum detectors, FDA on regulatory-compliant imaging systems, and non-profits to deploy screening tools in low-resource areas. Her lab also collaborates on eye-tracking studies for human observer performance modeling in radiology. Labs/Teams : Directs a lab focused on quantum detection systems, computational imaging platforms, and bio-inspired optical designs. Active in multi-university consortia for biomedical imaging innovation.

Hadi Safari Katesariمشاهده پروفایل

استادیار

• Bayesian Statistics
• Multivariate Time Series Analysis
• High-Dimensional Data Analysis
• +۷ مورد دیگر

Hadi Safari Katesari is a Teaching Assistant Professor in the Department of Mathematical Sciences at Stevens Institute of Technology within the Charles V. Schaefer, Jr. School of Engineering and Science. He joined Stevens in August 2021 after completing his PhD in Statistics at Southern Illinois University and teaches core statistics courses for Data Science, Mathematics, and Business graduate programs. His educational background includes: PhD in Mathematics with concentration in Statistics, Southern Illinois University (2021) Dr. Safari Katesari develops statistical tools for analyzing complex dynamic and dependent data, with emphasis on dimension reduction for multivariate time series. His research spans Bayesian statistics, copula models for dependency structures, factor analysis, and high-dimensional techniques. Key research domains: Bayesian inference and multivariate time series modeling Factor analysis and cluster analysis for latent structures Copula-based approaches in actuarial science and business analytics His publication trends (2015-2022) reveal consistent innovation in copula methodology applied across actuarial science, insurance, and agricultural analytics, with recent work focusing on count data modeling, survival analysis, and IBNR loss reserving through advanced dependency frameworks. His honors include: Doctoral Fellowship Award (2019-2021) from Southern Illinois University Dr. Safari Katesari actively contributes as a reviewer for the Journal of Mahani Mathematical Research, Risk and Decision Analysis Journal, and Mathematical Reviews. His institutional service encompasses assessment of internship projects, graduate open house coordination, and domestic student recruitment events for data science master's programs. No specific research laboratories or dedicated teams were mentioned in the source materials.

Guohua Yanمشاهده پروفایل

استاد

• Cluster Analysis
• Linear Clustering
• Statistical Modeling
• +۲ مورد دیگر

Guohua Yan is Professor in Mathematics and Statistics at the University of New Brunswick, specializing in statistical methodologies for pattern recognition and complex data structures. His research develops innovative approaches to cluster analysis with applications across biological and medical sciences. Primary contributions include advancing linear clustering techniques that identify linear relationships within datasets, such as allometric studies in biology and genotyping analysis. Complementary research creates statistical models for clustered count data with excessive zeros, incorporating random effects to account for subject-level heterogeneity in medical studies. Current methodologies address challenges including missing data, measurement errors, and spatial dependencies in large-scale epidemiological research.

Xia Wangمشاهده پروفایل

استاد

• Bayesian Methodology
• Categorical Data Analysis
• Scalable Modeling
• +۶ مورد دیگر

Xia Wang is a Professor in the Department of Mathematical Sciences at the University of Cincinnati, specializing in Bayesian methodology, categorical data analysis, and scalable modeling of high-dimensional data. She holds dual Ph.D.s in Statistics (2009) and Economics (2007) from the University of Connecticut, followed by postdoctoral training at the National Institute of Statistical Sciences (NISS) from 2009 to 2011. Education: Ph.D. in Statistics, University of Connecticut, 2009 Ph.D. in Economics, University of Connecticut, 2007 Research Interests: Bayesian methodology and computation Categorical data analysis Scalable modeling for complex data Applications in genomics, proteomics, climate science, and biomedical studies Grants & Collaborations: NIH Grant (R01MH119814): Collaborator in studying stress resilience via neurocircuit mechanisms (2019–2024) Students & Advising: Supervised 5 doctoral students (e.g., Leo Duan, Dan Li, Weizhe Su) and multiple undergraduates Research focuses on Bayesian nonparametric methods, survival analysis, and machine learning applications Labs & Teams: Active collaborations with the CCHMC (Cincinnati Children’s Hospital Medical Center) and NISS, with interdisciplinary projects in proteomics, climate modeling, and healthcare analytics.