Sampling Theory Professors and Supervisors | ApplySharif

جست‌وجوی استاد و پژوهشگر

استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۷۲۱-۷۳۵ از ۶٬۵۴۴ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Kaius Sinnemäkiمشاهده پروفایل

استاد

University of HelsinkiFinland

Kaius Sinnemäki is a Professor of Quantitative and Comparative Linguistics at the University of Helsinki, affiliated with the Department of Languages under the Faculty of Arts. Since 2025, he has directed the strategic profiling action Diversity in Society and Life (DIVSOL) , funded by the Research Council of Finland, and previously led the ERC Starting Grant project Linguistic Adaptation (2019-2024). His research focuses on large-scale comparative linguistics, integrating language typology with sociolinguistics. Key areas include core argument marking, statistical methods in typology, linguistic complexity, language contact, and the interplay between language and religion. He teaches courses spanning language typology, evolution, sociolinguistics, and quantitative methods. Recent publications address replication in linguistic research, adaptation in contact ecologies, and complexity measures across phonological, morphosyntactic, and syntactic domains. His work often bridges theoretical linguistics with empirical data analysis, utilizing open datasets and computational tools. Scientific Awards: Prize for the Humanities (2021) He actively supervises academic theses, serves on editorial boards (e.g., Linguistic Typology ), and participates in international collaborations, peer reviews, and media engagements on linguistic diversity and secularism.

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Beat Rechsteinerمشاهده پروفایل

مدرس ارشد

University of ZurichSwitzerland

Beat Rechsteiner is a Senior Lecturer and Postdoctoral Researcher at the Institute of Education, University of Zurich, specializing in educational processes within schools. He serves as Project Lead for the SNSF Research Project R2 (Regulation of Routines in Teaching Development) and contributes to theoretical and empirical research on teacher collaboration, school improvement, and social network analysis. His work emphasizes self-regulated learning, instructional capacity, and adaptive strategies for educational challenges. Doctoral Program in Education (University of Zurich, 2018–2022) Master’s in Educational Science (University of Zurich, 2014–2018) Secondary School Teacher Training (Zurich University of Teacher Education, 2005–2009) His research focuses on: Teacher collaboration networks and their impact on school improvement Professional development dynamics through experience sampling Brokerage mechanisms in educational change Adaptation of routines during crises (e.g., pandemic effects on math competencies) Recent publications highlight trends in social network analysis, school reform, and collective regulation. Key themes include boundary-crossing activities, data-driven school development, and stress management in collaborative environments. Awards include the GRC Travel Grant (2022). He actively reviews for journals like Teaching and Teacher Education and Journal of Educational Change , participates in international exchanges (University of Antwerp), and teaches graduate courses on systematic reviews, school improvement routines, and educational research.

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Ullrich Mönichمشاهده پروفایل

پژوهشگر

Technical University of MunichGermany

Dr.-Ing. Ullrich Mönich is a Senior Researcher and Lecturer at the Technical University of Munich (TUM) , affiliated with the Chair of Theoretical Information Technology and leading research activities at the Advanced Communication Systems and Embedded Security Lab (ACES Lab) . Since 2019, he has been instrumental in shaping experimental and theoretical research in 6G communications, physical layer security, and signal processing. Education: Dr.-Ing. in Electrical Engineering, Technische Universität München (2011) – supervised by Prof. Holger Boche Previous affiliations include MIT (2012–2015) and TU Berlin Research Focus: His research spans signal processing, wireless communications, machine learning, and sampling theory , with a strong emphasis on physical layer security , computability in signal processing , and 6G communications . He explores theoretical foundations and practical implementations, including neuromorphic computing, digital twinning, and secure modular coding schemes. Publications & Trends: His recent publications (2023–2025) are heavily concentrated in 6G communications , integrated sensing and communications (ISAC) , semantic physical layer security , and digital twinning . These works often combine theoretical analysis with experimental validation using 5G/6G testbeds and neuromorphic hardware. Teaching & Supervision: Regularly teaches "Foundations of Analog, Digital, and Quantum Computers" (tutorials since 2018) Previously taught "Applied Functional Analysis" and "Advanced Signal Theory" Involved in practical courses like "Software Defined Radio Laboratory" Labs & Teams: He leads the ACES Lab at TUM, which focuses on experimental validation of advanced communication systems, including physical layer security, neuromorphic computing, and 6G testbeds. The lab collaborates with national and international partners, including MIT, and is supported by major funding bodies such as the German Federal Ministry of Education and Research (BMBF) and the German Research Foundation (DFG).

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Aaron A. Kingمشاهده پروفایل

استاد

University of Michigan-Ann ArborUnited States

Aaron A. King, Ph.D. , is the Nelson G. Hairston Collegiate Professor of Ecology and Evolutionary Biology, Complex Systems, and Mathematics at the University of Michigan and an External Professor at the Santa Fe Institute . He is a Fellow of the American Association for the Advancement of Science and a Biological Sciences Scholar at the University of Michigan. His research integrates mathematical modeling, statistical inference, and empirical data to study ecological and epidemiological systems. Education: Ph.D. in Applied Mathematics, University of Arizona, 1999 M.A. in Mathematics, University of Hawai'i, 1992 B.A. summa cum laude in Mathematics, Rice University, 1989 Research Interests: Dr. King's lab focuses on the dynamics of ecological, epidemiological, and evolutionary systems . His work includes: Modeling host-pathogen systems (COVID-19, influenza, dengue, pertussis, cholera, malaria) Antibiotic resistance in hospital settings Statistical inference for ecological and epidemiological data Integration of genomic and epidemiological data Mathematical frameworks for understanding parasite infections and immune responses Scientific Awards: Fellow of the American Association for the Advancement of Science Biological Sciences Scholar, University of Michigan Teaching and Mentorship: Dr. King teaches courses in Mathematical Ecology , Adaptive Systems , and Statistical Inference . He mentors graduate students and postdoctoral fellows, including Avinash Subramanian and Madeline Peters , and is affiliated with multiple interdisciplinary centers at the University of Michigan. Lab and Collaborations: He leads the King Laboratory of Theoretical Ecology & Evolution , which emphasizes reproducible research and rigorous theoretical approaches. The lab collaborates with institutions like the Santa Fe Institute and accepts students from programs such as Ecology & Evolutionary Biology , Applied & Interdisciplinary Mathematics , and Data Science .

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Sadegh Talebiمشاهده پروفایل

استادیار

University of CopenhagenDenmark

Sadegh Talebi is a Tenure Track Assistant Professor in the Machine Learning Section at the Department of Computer Science, University of Copenhagen . His research focuses on theoretical aspects of reinforcement learning, Markov decision processes, online learning, stochastic multi-armed bandit problems, and resource allocation in networks. Education BSc in Electrical Engineering (minor: Electronics) from Iran University of Science and Technology (IUST) (2004) MSc in Electrical Engineering (minor: Communication Systems) from Sharif University of Technology (2006) PhD in Electrical Engineering from the Department of Automatic Control at KTH Royal Institute of Technology (supervised by Alexandre Proutiere and Mikael Johansson) Research Specializes in theoretical foundations of reinforcement learning and online learning Key contributions in stochastic optimization, MDPs, and bandit algorithms Collaborates on applications in resource allocation and quantum computing Publications include high-impact work on offline RL, differentially private exploration, and scalable MDP solutions in journals like Neural Processing Letters and conferences such as NeurIPS and UAI.

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Karlheinz Fleischerمشاهده پروفایل

استاد

University of MarburgGermany

Prof. Dr. Karlheinz Fleischer is a full professor at Philipps University of Marburg, where he holds the Chair of Statistics within the Department of Business Administration. He leads the Statistics research group (AG Fleischer) and maintains office hours by appointment during lecture periods. His primary research focuses on: Statistical sampling theory and survey methodology Data fusion techniques and multivariate analysis Quantitative methods in economics and finance Statistical estimation techniques and distribution theory Computational statistics and simulation methods An analysis of his 15 most recent publications (1993-2000) reveals consistent focus on statistical theory and applications: 73% concern sampling/estimation methods, 20% focus on financial/econometric applications, and 7% address computational statistics. Common themes include ratio estimation, survey methodology, and distribution theory with applications ranging from stock market analysis to industrial optimization. He leads a research team including Dr. Karl-Heinz Schild (retired 2024), Vladlena Prysyazhna (research associate), Robert Scherf (scientific staff), and Ute Bendix (secretary). The group offers courses in descriptive statistics, econometrics, and statistical programming using R at both bachelor's and master's levels.

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Jon Andoni Duñabeitiaمشاهده پروفایل

استاد

UiT The Arctic University of NorwayNorway

Jon Andoni Duñabeitia is a Full Professor at the School of Languages and Education of Universidad Nebrija in Madrid. He serves as Director of the Centro de Investigación Nebrija en Cognición (CINC) and the International Chair in Cognitive Health . With an h-index of 43 (Scopus), he has published 170+ articles across psycholinguistics, multilingualism, cognitive training, and virtual reality applications in education. His research examines how language processing interacts with cognitive load, emotional modulation, and technological innovation. Principal Investigator for 8+ projects funded by Spanish Government, Basque Government, BBVA Foundation Associate Editor and Editorial Board Member of high-impact journals Recognized among Spain's top 3% scientists across all disciplines Recent publications span topics including: Second-language reading dynamics in VR environments Multilingual cognitive interactions in neurological conditions Emoji/typographic effects on word processing Computerized cognitive assessment and training systems He actively contributes to scientific meetings as invited speaker across Europe, Asia, and Americas. His work bridges basic research in psycholinguistics with applied technologies for cognitive health.

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Ting-Chung Poonمشاهده پروفایل

استاد

Virginia Polytechnic Institute and State UniversityUnited States

Ting-Chung Poon is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. His research focuses on optical scanning holography (OSH), digital holography, and 3D imaging applications. He leads the Optical Scanning-Holographic Imaging Group (OSIG), which explores OSH for 3D imaging, processing, and display, emphasizing 2D optical heterodyne scanning techniques. Education: Ph.D., University of Iowa, 1982 M.S.E.E., University of Iowa, 1979 B.A., University of Iowa, 1977 Research Interests: Optical Scanning Holography (OSH) and its applications in 3D imaging Computer-Generated Holography (CGH) Quantitative Phase Imaging Optical Cryptography Efficient Hologram Algorithms His work spans theoretical advancements and practical implementations, including encryption systems, noise reduction techniques, and high-resolution 3D reconstruction. Recent Research Trends: Focus on polygon-based CGH algorithms for faster rendering Integration of machine learning for speckle noise reduction and hologram classification Development of adaptive and compressive holography methods for industrial and biomedical applications Labs & Teams: Optical Scanning-Holographic Imaging Group (OSIG) at Virginia Tech Collaborations with institutions globally, including conferences on digital holography and photonics

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Yinqiu Heمشاهده پروفایل

استادیار

University of Wisconsin-MadisonUnited States

Yinqiu He is an Assistant Professor in the Department of Statistics at the University of Wisconsin-Madison. They hold affiliations with the School of Computer, Data & Information Sciences and the Data Science Institute at Columbia University (2021-2022 postdoc). Their research focuses on developing statistical methodologies for high-dimensional and complex data, with applications in genomics, metabolomics, and network analysis. Key areas include mediation pathway analysis, asymptotic theory for U-statistics, and functional connectivity modeling. Education includes a B.S. in Statistics from the University of Science and Technology of China (2016) and a Ph.D. in Statistics from the University of Michigan-Ann Arbor (2021), advised by Professors Gongjun Xu and Xuming He. They were awarded the ProQuest Distinguished Dissertation Award (2022) and received multiple travel grants from the Institute of Mathematical Statistics and ASA. Teaching includes core Ph.D. courses like STAT 849 (Regression Analysis) and applied courses like STAT 456 (Multivariate Statistics). Current mentoring includes MS student Yuhan Zheng (now pursuing UW-Madison Ph.D.) and Xiangyi Liao (Ph.D. in Educational Psychology). Research outputs include foundational work on adaptive U-statistics testing frameworks and scalable methods for large-scale genomic data analysis. Active in methodological contributions to biostatistics, their work bridges statistical theory and computational efficiency. Recent projects involve dynamic functional connectivity estimation from fMRI data and latent space modeling in heterogeneous networks. GitHub repository 'Adaptive-U-stats' hosts open-source implementations of high-dimensional testing algorithms developed in their research.

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Harsha Gangammanavarمشاهده پروفایل

دانشیار

Southern Methodist UniversityUnited States

Harsha Gangammanavar is an Associate Professor in the Department of Operations Research and Engineering Management (OREM) at Southern Methodist University (SMU), affiliated with the Data Science Institute. He holds a Ph.D. in Operations Research and M.S. in Electrical Engineering from The Ohio State University, and a B.E. in Electronics and Communication Engineering from Visvesvaraya Technological University, India. Education: Ph.D. in Operations Research, The Ohio State University M.S. in Electrical Engineering, The Ohio State University B.E. in Electronics and Communication Engineering, Visvesvaraya Technological University Research Interests: His work focuses on stochastic programming, large-scale computational optimization, and their applications in infrastructure systems, healthcare, and wireless communication. Key areas include optimization under uncertainty, stochastic decomposition methods, and scalable algorithms for power systems and renewable energy integration. Recent Activities & Awards: Recipient of NSF XTRIPODS grant for collaborative research in data science (2024). DOE Office of Science grant for multiscale stochastic optimization (2022). ONR grant for decomposition-based stochastic models in discrete-event systems (2022). Awarded INFORMS Undergraduate Student Paper Award (2021) and INFORMS Minority Affairs Poster Competition (2016). Advising & Grants: Current advisees include Ph.D. students Ishara A.A.D.H., Jackson Forner, Chhavi Sharma, and Zhiyuan Zhang. Former students Niloofar Fadavi, Sakitha Ariyarathne, and others have secured roles in industry and academia. Active in securing grants from AFOSR, ONR, DOE, and NSF. Labs & Teams: Leads research on stochastic optimization algorithms, power grid resilience, and healthcare applications through interdisciplinary collaborations. Open to motivated students for Ph.D. research in optimization and data science.

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Ted Mouwمشاهده پروفایل

استاد

University of North Carolina at Chapel HillUnited States

Ted Mouw is a Professor of Sociology at the University of North Carolina at Chapel Hill. He holds a B.A. in English Literature from Oberlin College, an M.A. in Economics, and a Ph.D. in Sociology from the University of Michigan. His research focuses on demography, social stratification, and economic sociology, with emphasis on labor markets, globalization’s impact, and immigration. Current projects include studies on social mobility in the U.S., economic effects of globalization in Indonesia and Mexico, and labor market dynamics for Hispanic immigrants in North Carolina. He has taught courses such as Social Stratification, Economy and Society, and Applied Regression Analysis, reflecting his expertise in quantitative methods and social theory. His work frequently intersects with the Carolina Population Center and addresses topics like occupational segregation, wage inequality, and transnational social networks. Mouw’s research also explores the interplay between spatial diffusion, migration patterns, and racial/ethnic integration in urban areas.

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Lianne Bakkumمشاهده پروفایل

استادیار

Vrije University AmsterdamNetherlands

Dr. Lianne Bakkum is an Assistant Professor at the Vrije Universiteit Amsterdam within the Faculty of Behavioural and Movement Sciences, holding affiliations in Clinical Child and Family Studies, APH - Mental Health, and LEARN! - Child rearing. She earned her PhD in Public Health and Primary Care from the University of Cambridge (2017–2021), focusing on attachment and trauma in the Adult Attachment Interview. Her research explores intellectual disability, child protection systems, mental health interventions, and the impact of digital social contact during crises like the COVID-19 pandemic. Her work contributes to UN Sustainable Development Goals related to quality education, good health, and reduced inequalities. Key projects include reducing involuntary care in intellectual disability settings and analyzing the effectiveness of EMDR therapy for PTSD in adults with intellectual disabilities. She teaches courses on family studies, child-rearing practices, and scientific reasoning rooted in attachment theory. Bakkum’s publications emphasize interdisciplinary approaches to disability care, trauma resolution, and policy evaluation. She leads and collaborates on projects funded by academic institutions, focusing on improving outcomes for vulnerable populations through evidence-based strategies.

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Shuvo Bakarمشاهده پروفایل

مدرس ارشد

University of SydneyAustralia

Dr. Shuvo Bakar is a Senior Lecturer in the Sydney School of Public Health at the University of Sydney, within the Faculty of Medicine and Health. He holds a PhD in Statistics from the University of Southampton, UK, and has prior experience as an Assistant Professor at Yale University, Lecturer at the Australian National University, and Scientist at Data61 (CSIRO). His research focuses on statistical methods applied to public health challenges, including Bayesian hierarchical modeling, machine learning, spatio-temporal analysis, and their applications in epidemiology, clinical trials, and environmental health. Dr. Bakar's research interests span statistical methodologies such as Bayesian adaptive designs, small area estimation, and spatial risk modeling, alongside applications in child health, infectious diseases, and extreme weather impacts on health. He is an active member of academic communities, including the Royal Statistical Society (RSS Fellow), Statistical Society of Australia, and the Australian Trials Methodology Research Network. His work also involves collaborations on grants totaling millions in funding, addressing topics like climate change impacts on health inequity and cardiovascular disease prevention in remote regions. Education: PhD in Statistics (University of Southampton, UK) Key Research Themes: Obesity, Diabetes, Cardiovascular Disease; Reproductive, Maternal & Child Health Grants/Projects: Includes NHMRC-funded trials on respiratory infections in First Nations children and MRFF grants for cardiovascular risk reduction in regional Australia. Dr. Bakar's contributions extend to editorial roles for Nature Scientific Reports and Discover Public Health , and his research has been published in journals like PloS One , Climatic Change , and Journal of the Royal Statistical Society .

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Hemant Purohitمشاهده پروفایل

دانشیار

George Mason UniversityUnited States

Hemant Purohit is an Associate Professor in the Department of Information Sciences and Technology at George Mason University, and Director of the Humanitarian Informatics Lab. He focuses on developing interactive intelligent systems to support emergency services and humanitarian organizations by analyzing non-traditional data sources like social media, web, and IoT using data mining, NLP, and human-centered computing. His work integrates social-psychological theories to enhance human capabilities in crisis contexts. Purohit holds a PhD in Computer Science and Engineering from Wright State University. His research has been recognized through prestigious awards including the ITU Young Innovator Award (2014), NSF CRII Award (2017), and a best paper award at IEEE/WIC/ACM Web Intelligence (2018). His lab is supported by grants from NSF and international agencies. Key research interests include crisis informatics, social computing, and AI ethics. He has led projects on adversarial scam detection, inclusive cybersecurity, and human-AI teaming for disaster response. Purohit serves on editorial boards for journals like Elsevier's Information Processing & Management and Frontiers in Big Data, and actively contributes to international conferences in his field. His work emphasizes real-world impact, bridging technical innovation with societal needs through collaborations between researchers and practitioners. Current projects address challenges in multilingual data analysis, social media activism, and resilience data repositories.

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Will Fleesonمشاهده پروفایل

استاد

Wake Forest UniversityUnited States

Will Fleeson is a Professor of Psychology at Wake Forest University, specializing in personality dynamics, moral behavior, and the integration of trait theories with dynamic systems. His research focuses on understanding how personality traits manifest in real-world behaviors, particularly in contexts involving morality, psychopathology, and virtue ethics. He leads the Fleeson Lab, exploring topics such as honesty, virtue profiles, and the interplay between personality and psychological well-being. Central to his work is the Whole Trait Theory, which emphasizes the dynamic nature of personality as both structural and process-oriented. Fleeson employs methodologies like experience sampling and multilevel modeling to examine intra-individual variability and situation-based contingencies. His studies often bridge philosophical and psychological perspectives, particularly in defining moral exceptionalism and intellectual virtues. Key research themes include the dynamics of borderline personality disorder symptoms, the measurement of honesty and virtue, and the motivational underpinnings of moral behavior. Fleeson’s contributions challenge traditional static trait models, advocating for frameworks that account for temporal fluctuations and situational influences. Despite the absence of listed awards or grants in the provided text, his extensive publication record underscores his influence in advancing dynamic personality theory and moral psychology. His lab’s website (linked) highlights ongoing projects and collaborations, though specific advisees or teams are not detailed here. Fleeson’s work consistently emphasizes the practical implications of personality research for understanding mental health, ethical behavior, and individual flourishing.

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