جستجوی استاد راهنمای دانشگاهی

• Plant Evolutionary Biology
• Cytogenetics
• Systematics
• +۳ مورد دیگر

Hanna Schneeweiss is a Professor at the Department of Botany and Biodiversity Research , University of Vienna. Her work focuses on plant evolutionary biology, cytogenetics, and systematics, with particular emphasis on chromosomal evolution, hybridization, and biogeography. Current Teaching: Courses include Plant Chromosome Structure, Genome Evolution, and Systematic Botany Research Themes: Chromosomal speciation, polyploidy, and ecological-genomic interactions Her recent research explores the interplay between chromosomal changes and species diversification across multiple plant lineages. A recurring theme in her publications involves analyzing repetitive DNA elements, karyotype dynamics, and phylogenetic relationships in polyploid complexes. Key trends in her work include: Investigating chromosomal evolution in genera like Melampodium , Dactylorhiza , and Prospero Developing molecular cytogenetic techniques such as formamide-free genomic in situ hybridization Integrating biogeography with genomic data to understand long-distance dispersal patterns

• Nonparametric predictive inference
• Nonparametric statistics
• Modeling dependence
• +۳ مورد دیگر

Professor Tahani Coolen-Maturi is a faculty member in the Department of Mathematical Sciences at Durham University and Fellow of the Durham Research Methods Centre. Her research focuses on advanced statistical methodologies with applications across multiple domains. Her primary research interests include: Nonparametric predictive inference and statistics Reliability and survival analysis using survival signatures Diagnostic accuracy and ROC analysis Modeling dependence structures Uncertainty quantification through imprecise probability Statistical reproducibility frameworks Analysis of her recent publications reveals a strong trend toward statistical reproducibility research, particularly in hypothesis testing and educational trial methodologies. Her work bridges theoretical statistics with practical applications in reliability engineering, medical diagnostics, and educational assessment. A significant portion of her recent output develops nonparametric predictive inference frameworks for complex systems and censored data. Professor Coolen-Maturi actively supervises doctoral students, with ten current supervisees including Azza Alzahrani, Fatemah Alammar, and Hadeer Ghonem. Her collaborative research includes major educational trials funded by the Education Endowment Foundation, focusing on statistical power calculations and meta-analysis of intervention effectiveness.

• Machine Learning
• Statistics
• Artificial Intelligence
• +۶ مورد دیگر

Michael Irwin Jordan is a Professor at the University of California, Berkeley, ranked #1 worldwide in Machine Learning and Distributed Computing, #36 in Computer Science, and #9 in Statistics according to Academic Influence rankings. His interdisciplinary research bridges theoretical foundations with practical applications across machine learning, statistics, and artificial intelligence. His educational background includes a PhD in Computer Science from the University of California, San Diego, a Master's in Mathematics from Arizona State University, and a Bachelor's in Psychology from Louisiana State University. This diverse foundation has shaped his approach to integrating perspectives from computer science, statistics, neuroscience, and psychology. Professor Jordan's research spans several decades, beginning with foundational work in neural networks and connectionist models. He made seminal contributions to probabilistic graphical models, variational inference, and Bayesian nonparametrics. His 2001 paper 'Latent Dirichlet Allocation' has received over 34,000 citations, becoming one of the most influential papers in machine learning. More recently, his work has focused on applications to biological data analysis, particularly single-cell transcriptomics, where he has developed probabilistic models and software tools like scvi-tools. Member of the National Academy of Engineering (2010) Ranked #1 worldwide in Machine Learning Ranked #1 worldwide in Distributed Computing Ranked #36 worldwide in Computer Science Ranked #9 worldwide in Statistics As an advisor, Professor Jordan has mentored numerous PhD students who have become leaders in academia and industry. His recent work through 2022 shows continued innovation across multiple domains, including uncertainty quantification, theoretical analysis of transfer learning, and scalable frameworks for reinforcement learning. He also contributes significantly to data science education, having helped develop Berkeley's undergraduate data science curriculum.

• Bayesian Statistics
• Approximate Bayesian Computation
• Statistical Modeling
• +۶ مورد دیگر

Christopher Drovandi is a Professor at Queensland University of Technology and a Chief Investigator in the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). His research spans statistical methodology development and applications across diverse fields including ecology, biomedicine, and environmental science. Dr. Drovandi's primary research interests focus on advancing Bayesian statistical methods, particularly in the areas of: Approximate Bayesian Computation (ABC) and likelihood-free inference Synthetic likelihood methods Sequential Monte Carlo algorithms Bayesian experimental design Statistical computing for complex models His recent work demonstrates a strong emphasis on addressing model misspecification in Bayesian inference, developing robust computational methods for intractable likelihoods, and applying advanced statistical techniques to solve real-world problems in ecology, biomedicine, and environmental science. Drovandi has made significant contributions to both the theoretical foundations and practical applications of modern Bayesian statistics. Dr. Drovandi has received research funding through multiple Australian Research Council grants and collaborates extensively with researchers across disciplines. His work has been published in top-tier statistical and interdisciplinary journals including the Journal of the American Statistical Association, Bayesian Analysis, and PLOS Computational Biology.

Shing-Chi Cheungمشاهده پروفایل

استاد

• Software Engineering
• Program Analysis
• Software Testing
• +۷ مورد دیگر

Shing-Chi Cheung is a Professor of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST), School of Engineering. He founded the CASTLE research group and co-founded the International Workshop on Automation of Software Testing (AST) in 2006. His leadership includes serving as General Chair of FSE 2014 and chairing multiple APSEC conferences. His research focuses on software quality enhancement through program analysis, testing, debugging, and AI techniques, targeting Android apps, open-source software, deep learning systems, smart contracts, and spreadsheets. Current projects include metamorphic testing frameworks, binary analysis tools, and vulnerability detection systems for emerging technologies. His publication portfolio demonstrates consistent contributions to software engineering since 2016, with recent work emphasizing AI-integrated testing methodologies, smart contract security, and deep learning system reliability. Key trends show increasing focus on cross-language analysis, data visualization quality, and compiler-level verification for modern software stacks. Distinguished Member of the ACM Fellow of the British Computer Society Editorial board member: Science of Computer Programming (SCP), Journal of Computer Science and Technology (JCST) Former editorial board member: IEEE Transactions on Software Engineering (2006-2009), Information and Software Technology (2012-2015) Four patents in China and the United States Cheung actively mentors through the CASTLE research group and serves on program committees for major conferences including ICSE, ESEC/FSE, and ISSTA. His work bridges academic research with practical applications through industry collaborations and tool development. He has contributed to numerous workshops and symposia as steering committee member and program chair.

• Hierarchical Bayesian models
• Spatial statistics
• Multilevel data analysis
• +۷ مورد دیگر

Emanuela Dreassi is a Full Professor of Statistics at the University of Florence, where she currently serves as Director of the Department of Statistics, Computer Science, and Applications 'G. Parenti' (DiSIA) from November 1, 2024, to October 31, 2028. She is affiliated with the School of Economics and Management and has held various institutional roles including Director of the Bachelor's Program in Statistics (2014-2022) and Vice-President of the School of Economics and Management (2019-2022). Her research spans hierarchical Bayesian models and spatial statistics, with methodological advancements in specifying and estimating models for multilevel data, missing data, and latent variable models. She has made significant contributions to robust analysis in small area estimation, compatibility of conditional distributions, semicontinuous data modeling, Bayesian predictive inference, and knockoffs construction. Her work bridges theoretical statistics with practical applications in epidemiology, environmental studies, and medical research. Dreassi's recent publications (2020-2025) reveal a strong focus on methodological innovations in Bayesian statistics, spatial analysis, and knockoff filters for variable selection. She has published extensively in top statistical journals while also collaborating on interdisciplinary research in medical fields including plastic surgery and epidemiology. Her work demonstrates a consistent trajectory of advancing statistical methodology while maintaining strong connections to real-world applications across multiple domains. As an active referee for numerous prestigious journals including Biometrics, Journal of the Royal Statistical Society, and Statistics in Medicine, Dreassi contributes significantly to the scholarly community. She has coordinated research units for PRIN and HORIZON2020 projects and participated in numerous national and international conferences as organizer, scientific committee member, and session chair.

Fereydoun Daneshgaranمشاهده پروفایل

استاد

• Wireless Communications
• Digital Communications
• Coding Theory
• +۷ مورد دیگر

Fereydoun Daneshgaran is a Professor and Chairman of the Electrical & Computer Engineering Department at California State University, Los Angeles, where he has served since 2006. He also directs the fiber and nonlinear optics research laboratory and has held various academic leadership positions including chairman of the Communications group and acting chairman of the ECE Department. Dr. Daneshgaran's educational background includes: Ph.D. in Communications, VLSI, and Optimization from UCLA (1992) M.S. (Magna Cum Laude) in Communications, Solid State Electronics, and Control Systems from Cal State LA (1985) B.S. (Magna Cum Laude) in Electrical and Mechanical Engineering from Cal State LA (1983) Dr. Daneshgaran's primary research interests focus on wireless communications, digital communications, and information theory. His work spans multiple specialized areas including coding theory (particularly Turbo Codes and LDPC codes), quantum key distribution, cognitive radio, and network localization. His research has significant applications in wireless networking protocols, particularly IEEE 802.11 standards, with numerous publications analyzing throughput performance under various network conditions. He has also made substantial contributions to optical communications and signal processing techniques. Dr. Daneshgaran's publication record shows a progression from foundational work in Viterbi decoding and turbo codes to more recent applications in quantum communications and advanced wireless network protocols. His research consistently bridges theoretical communications theory with practical implementations, as evidenced by both his extensive journal publications and industry experience. His scientific achievements include: Best Paper Award at First International Conference on Advances in Satellite and Space Communications (SPACOMM 2009) U.S. Patent No. US 2007/0079223 A1 for "A method and system for Information processing" Dr. Daneshgaran has supervised numerous Ph.D. students and visiting scholars, primarily from Politecnico University of Turin (POLITO), Italy, fostering international research collaborations. His academic leadership extends to developing and teaching the entire graduate sequence in digital communications at Cal State LA, along with specialized courses in wireless communications, cognitive radio, and optical communication systems. He has also maintained industry connections through various consulting roles and entrepreneurial ventures in communications technology. As director of the fiber and nonlinear optics research laboratory from 1994 to 2015, Dr. Daneshgaran led research in optical communications and related technologies. His work has bridged theoretical communications research with practical implementations, as evidenced by both his academic publications and industry experience founding companies like EuroConcepts S.r.l. and Quantum Bit Communications, LLC.