جست‌وجوی استاد و پژوهشگر

• Sports Analytics
• Biomechanics
• Sleep Science
• +۴ مورد دیگر

Minh Huynh is a Senior Lecturer in the Department of Econometrics & Business Statistics within the Faculty of Business and Economics at Monash University. His work bridges statistical methodology with practical applications in sports science, biomechanics, and health research. Position: Senior Lecturer Institution: Monash University Department: Econometrics & Business Statistics School: Faculty of Business and Economics Dr. Huynh's research focuses on the application of statistical models to real-world problems in sports performance and human health. His primary interests include sports analytics, biomechanics of cricket and football, sleep science, and the impact of lifestyle factors on athletic recovery. He employs advanced quantitative methods to analyze player performance, injury risk, and recovery strategies. His recent publications reveal a strong trend toward interdisciplinary research, particularly in validating new technologies (such as AI-based speed tracking), meta-analyses on sleep and alcohol, and biomechanical assessments in cricket. The articles span high-impact journals in sports science and medicine, indicating a rigorous, peer-reviewed research trajectory with practical implications for coaching and athlete management. While no specific scientific awards are listed in the provided text, his work has been widely disseminated, appearing in 45 research outputs with significant media and academic attention, including coverage by over 100 news outlets and engagement on social platforms. Dr. Huynh collaborates extensively with researchers in sports science and medicine, suggesting active involvement in research teams and potential supervision of students, though no advisees are explicitly named. His research likely involves data-driven projects in sports performance labs or collaborative health studies. He has not indicated any part-time status, retirement, or former staff designation, and remains an active academic contributor.

Carlo Dallapiccolaمشاهده پروفایل

استاد

• Experimental Particle Physics
• High-Energy Physics
• Beyond the Standard Model Physics
• +۷ مورد دیگر

Carlo Dallapiccola is a Professor and Graduate Program Director in the Department of Physics at the University of Massachusetts Amherst, actively contributing to the ATLAS experiment at CERN's Large Hadron Collider. His research is central to advancing experimental particle physics through searches for new phenomena and precision measurements of Standard Model processes. University: University of Massachusetts Amherst School: College of Natural Sciences Department: Department of Physics Academic Rank: Professor Emails: carlod@physics.umass.edu, carlo.dallapiccola@cern.ch Location: Lederle Graduate Research Tower, Amherst, MA His research interests lie at the forefront of high-energy physics, focusing on experimental searches for physics beyond the Standard Model , particularly long-lived new particles that decay with displaced vertices, a signature of models like gauge-mediated supersymmetry breaking and dark sectors. He is deeply involved in the performance and upgrade of the ATLAS detector , including the muon spectrometer and the new inner tracker (ITk) for the High-Luminosity LHC. His work also encompasses Higgs boson physics , top quark studies , and precision electroweak measurements , all requiring advanced data analysis and detector calibration techniques. The recent articles highlight a strong trend toward precision Higgs physics , including searches for rare decays (H→μμ, H→ZZγ), Higgs self-coupling via pair production, and CP properties in tau decays. There is also a significant focus on exotic and beyond-Standard-Model signatures , such as leptoquarks and vector-like quarks, alongside the development of advanced data analysis methods using machine learning and neural simulation-based inference for parameter estimation and jet flavor tagging. His leadership is evident in collaborative efforts, with no individual scientific awards listed in the provided text. He advises graduate students as Graduate Program Director, fostering the next generation of physicists. His work is supported by major international collaborations and grants from agencies funding high-energy physics research, though specific grants are not detailed here. He is a key member of the ATLAS collaboration and has contributed to major detector upgrade projects like the New Small Wheel and the ITk. His research integrates data from the LHC with sophisticated computing frameworks, placing him at the heart of one of the largest scientific endeavors in history.

• Human Resource Development (HRD)
• Adult Learning
• Workforce Development
• +۷ مورد دیگر

Meera Alagaraja is an Associate Professor in the Department of Educational Administration & Human Resource Development at Texas A&M University's College of Education. Her research focuses on adult learning, performance improvement, and strategic HRD, emphasizing workforce development and organizational well-being. She collaborates with nonprofits to align community well-being with workforce priorities and partners with private-sector organizations to align HR policies with strategic objectives. Her research spans three primary areas: 1) employee well-being programs and health policies, 2) strategic HRD frameworks for organizational performance, and 3) community-based workforce development initiatives. Notable projects include studies on Black American community well-being, empathic organizational leadership, and burnout prevention in military contexts. Dr. Alagaraja's recent work explores intersections between cultural identity (e.g., multiracial student belongingness) and organizational dynamics. Her articles analyze topics like lean management practices, global HRD strategies in emerging markets (e.g., Ghana), and compassion in leadership behavior. While no formal awards are listed, her prolific publication record indicates sustained scholarly contributions to HRD and adult education. Her advising and grant activities are not explicitly detailed in the text, though her collaborations suggest involvement in community-based research projects. No specific labs or teams are mentioned, though her work involves partnerships with non-profits and multinational organizations.

Amy Sappمشاهده پروفایل

مدرس

• Health Informatics
• Epidemiology
• Biostatistics
• +۴ مورد دیگر

Amy Sapp is a Lecturer in the Department of Public Health and Recreation at San José State University, part of the College of Health and Human Sciences. She has been teaching quantitative methods courses, including Epidemiology and Biostatistics, in both undergraduate and Master of Public Health (MPH) programs since 2012, with recent teaching in the Online MPH Program (2020–2022). She holds a Doctor of Science (ScD) in Social Epidemiology and a Master of Science (SM) in Health and Social Behavior from the Harvard School of Public Health, and a BA in Anthropology from Beloit College. Education: ScD in Social Epidemiology, Harvard School of Public Health SM in Health and Social Behavior, Harvard School of Public Health BA in Anthropology, Beloit College Her research and teaching interests center on health informatics, epidemiological methods, biostatistics, and the use of technology for public health promotion. She has contributed to the development of curriculum in epidemiology and biostatistics. Her work often explores the social and behavioral determinants of health, including workplace conditions, household roles, and social networks in relation to health behaviors and cancer outcomes. The recent publications reflect a consistent focus on translational behavioral medicine, social epidemiology, and occupational health. Themes include multiple health behavior change, workplace stress and smoking, dietary behavior in relation to household responsibilities, and quality of life among cancer survivors. Her methodological expertise includes multilevel modeling, cohort studies, and statistical analysis of public health data. Scientific Awards: None listed in the provided text. Advising and Grants: Amy Sapp has served as a statistical consultant for various organizations, providing services such as needs assessments, outcomes analyses, predictive modeling, scientific manuscript development, and grant application support (2004–2022). While no formal advisees are listed, her role in curriculum development and teaching suggests mentorship of public health students. There is no explicit mention of grants she has led, but her consulting work implies involvement in funded research projects. Labs and Research Teams: She has collaborated with researchers from institutions such as Harvard School of Public Health and has been part of research teams studying workplace health, cancer survivorship, and behavioral interventions. While no formal lab is mentioned, her work appears integrated within public health research groups focused on social and organizational determinants of health.

Daniel Barbaráمشاهده پروفایل

استاد

• Data Mining
• Machine Learning
• Databases
• +۳ مورد دیگر

Daniel Barbará is a Professor in the Department of Information and Software Engineering at George Mason University since 1997. He holds a PhD and MS in Computer Science from Princeton University, and a BS in Electrical Engineering from Universidad Metropolitana. Research Interests: Data mining, machine learning, cybersecurity, mobile computing, and fractal-based data analysis. His work focuses on clustering algorithms, intrusion detection, data compression, and handling high-dimensional datasets. Grants: Multiple NSF and Air Force grants including "Clustering by Impact" (2002-2005) and "Data Mining Support for NIAC" (1999-2000). Scientific Awards: Outstanding Researcher, CS Department, George Mason University Best Paper Finalist at WWW 2002 Projects: Developed ADAM (Intrusion Detection), COOLCAT (Categorical Clustering), Fractal Mining, and Quasi-Cubes (Approximate Query Processing).

• Bayesian Statistics
• Uncertainty Quantification
• Experimental Design
• +۳ مورد دیگر

Dr. Arved Bartuska is a Researcher at the Department of Mathematics, RWTH Aachen University, working in the Chair for Mathematics for Uncertainty Quantification under Prof. Raúl Tempone. His research focuses on developing efficient computational methods for Bayesian optimal experimental design, particularly in the presence of nuisance uncertainty. Education: PhD (Dr. rer. nat.) from RWTH Aachen University, supervised by Prof. Raúl Tempone, with co-advisors Prof. Luis Espath (University of Nottingham) and Prof. Robert Scheichl (University of Heidelberg). Dissertation: Hierarchical Methods for Bayesian Optimal Experimental Design . M.Sc. and M.A. (fields not specified in the text). Dr. Bartuska's research centers on Bayesian statistics and uncertainty quantification . He addresses computational challenges in Bayesian optimal experimental design by developing approximations like Laplace methods and leveraging Monte Carlo techniques to handle high-dimensional parameters and nuisance uncertainties in complex models. His 2022 publication introduced a small-noise approximation for Bayesian experimental design with nuisance uncertainty, significantly reducing computational burden by avoiding nested Monte Carlo sampling. This enables practical application to engineering problems through efficient expected information gain computation. Dr. Bartuska actively presents at international venues including the SIAM Conference on Uncertainty Quantification 2022 and the Stochastic Numerics Workshop at King Abdullah University. He maintains collaborations with institutions across Saudi Arabia, the UK, and Germany within the DFG-funded IRTG 2379 framework. He is a core member of the Mathematics for Uncertainty Quantification chair, contributing to the International Research Training Group 2379 Modern Inverse Problems focused on interdisciplinary approaches to inverse problems from geometry, data, and models.

• Substance Misuse Prevention
• Addiction Epidemiology
• Statistical Methodology
• +۷ مورد دیگر

Associate Professor Gary Chung Kai Chan is an NHMRC Emerging Leadership Fellow at the National Centre for Youth Substance Use Research, The University of Queensland. His primary research focuses on substance misuse prevention, addiction epidemiology (including vaping, cannabis, and gaming disorders), and advanced statistical methods for longitudinal analysis and causal inference. He has secured over $13 million in research funding and authored more than 200 publications. His research interests span substance use prevention, statistical methodology, behavioral addiction, and public health policy. Key areas include: Epidemiology of vaping, cannabis, and tobacco use Application of causal inference methods in addiction research Global trends in substance use and policy impact evaluation Digital health interventions for substance cessation Recent publications demonstrate a strong emphasis on substance use trends, policy analysis, and innovative methodologies. Key thematic clusters include: Cannabis and vaping epidemiology across diverse populations Social media's influence on substance-related behaviors Systematic reviews and meta-analyses of treatment interventions Wastewater-based drug consumption monitoring Honors include the NHMRC Emerging Leadership Fellowship . He serves as Deputy Statistical and Methodology Editor for the journal Addiction and has consulted for the United Nations Office on Drugs and Crime. He leads multiple national and international collaborations with institutions including the University of Washington, University College London, and Murdoch Children's Research Institute. His work has directly influenced global drug policy frameworks and data collection methodologies.

Hyung-Jo Yoonمشاهده پروفایل

دانشیار

• Phraseology
• Computational Text Analysis
• Second Language Writing
• +۳ مورد دیگر

Hyung-Jo Yoon is an Associate Professor in the Department of Linguistics/TESL at California State University, Northridge. His research focuses on phraseology, computational text analysis, second language writing, assessment, and task-based language teaching. He developed the Authorial Voice Analyzer , an automated tool for interactional metadiscourse analysis, which serves as a pedagogical resource for learners. Ph.D., Second Language Studies, Michigan State University (2013-2017) M.Ed., Teaching English to Speakers of Other Languages, University of Pennsylvania (2011-2013) B.A., English Education, Hankuk University of Foreign Studies (2004-2010) TEFL Certificate, University of California, Irvine (2009) His recent work investigates corpus-based measures as alternatives to traditional accuracy metrics in L2 writing, emphasizing pedagogical applications. Collaborative projects address professional development frameworks for ESL teachers, focusing on skill and knowledge requirements for effectiveness. Key research trends include automation of linguistic complexity metrics, validation of multi-word combinations for accuracy assessment, and cross-genre analysis of L2 writing. Subfields span syntactic complexity, metadiscourse profiling, genre differentiation, and cognitive task modeling. 2020 Research Article Award Winner: Cognitive validity of child English-language tests Yoon’s work bridges computational methods with language teaching, advocating for data-driven approaches to both assessment and pedagogy. His AVA tool exemplifies this integration, enabling systematic analysis of authorial voice in academic writing.

Mário Boto Ferreiraمشاهده پروفایل

استاد

• Judgment and Decision Making
• Social Cognition
• Memory
• +۴ مورد دیگر

Mário Boto Ferreira serves as Full Professor at the University of Lisbon's Faculty of Psychology, holding key leadership positions including Director of the Experimental Psychology Laboratories, President of the School Council, and Coordinator of CICPSI research center. His work bridges experimental cognitive psychology with societal applications, particularly in consumer decision-making and forensic contexts. His research centers on Judgment and Decision Making with emphasis on dual-process models, Social Cognition regarding impression formation, and Memory mechanisms including false memories. Ferreira actively investigates how cognitive biases manifest in real-world scenarios like financial scarcity, misinformation susceptibility, and moral judgment, aiming to translate laboratory findings into interventions for societal issues such as over-indebtedness. Recent publications demonstrate growing focus on societal applications of cognitive science, particularly through meta-analyses on financial scarcity effects and experimental studies on moral intuition. His work increasingly examines how social contexts amplify cognitive distortions in ratio perception and moral reasoning, while maintaining strong methodological rigor in experimental design. Scientific Awards: No awards were documented in the source materials. Advising and Grants: The provided texts contain no information about graduate students, postdoctoral mentees, or funded research grants. Ferreira directs the Experimental Psychology Laboratories where cognitive experiments on judgment and memory are conducted, and coordinates CICPSI (Centro de Investigação em Psicologia Social e das Organizações), fostering interdisciplinary research on social cognition and organizational behavior within the University of Lisbon's research ecosystem.

• Classics
• Ancient History
• Papyrology
• +۵ مورد دیگر

Mark Depauw is a Professor at the Faculty of Arts , KU Leuven. He leads the Research Group Antiquity, Leuven and is deeply involved in digital humanities initiatives, particularly through the Trismegistos project and its extensions like Trismegistos+ and STUDIUM.AI . His work bridges classical studies with computational methods. Research Focus: Quantitative analysis of classical text reception in early modern Europe Network analysis of ancient literary and social connections Development of digital infrastructures (GLAUx Treebank, Trismegistos) for ancient textual studies Application of transformer models and named entity recognition to Greek and Latin texts Membership & Collaborations: Member of the Faculty Council of Arts Part of KU Leuven Institute for Digital Society (DigiSoc) Core contributor to LECTIO for textual transmission studies Collaborator on international projects like CLARIAH-VL+ and NIKAW Teaching: He contributes to courses in historical data management, Greek papyrology, and digital research methods. His pedagogical approach integrates computational tools with classical scholarship.

Bystrov Alexander Alexandrovichمشاهده پروفایل

دانشیار

• Stochastic integrals
• Statistics of dependent observations
• Limit theorems
• +۱ مورد دیگر

Bystrov Alexander Alexandrovich is an Associate Professor at the Department of Probability Theory and Mathematical Statistics, Mechanics and Mathematics Faculty, Novosibirsk State University (NSU). He has been a full-time employee since 2006 and has held his current position since 2008. Education: Graduated with honors from Novosibirsk State University (2001) PhD in Probability Theory and Mathematical Statistics (01.01.05) from NSU (2006), thesis: Stochastic integrals and asymptotics of canonical von Mises statistics constructed from dependent observations Research Interests: His work focuses on theoretical aspects of probability and statistics, including multiple stochastic integrals for non-random functions, statistical methods for dependent observations, limit theorems, and inequalities for U- and V-statistics. These topics bridge stochastic analysis with foundational statistical theory. Publications: His articles (2005–2014) concentrate on stochastic integration theory and asymptotic statistics, particularly involving Gaussian processes, von Mises statistics, and deviation probabilities. Recurring themes include generalizations of stochastic integrals and limit theorems for dependent data. Academic Activities: He supervises undergraduate and graduate students at NSU's Department of Probability Theory and Mathematical Statistics. No grants or awards are documented in the provided text.

• Industrial Dynamics
• Firm Growth and Survival
• Entrepreneurship
• +۵ مورد دیگر

Alex Coad is Professor at Waseda University’s Graduate School of Business and Finance, Faculty of Commerce, Tokyo. A 2007 PhD graduate of Université Paris 1 Panthéon-Sorbonne, he specialises in industrial dynamics, entrepreneurship and applied econometrics, with field-leading work on high-growth firms, innovation policy and firm survival. Education PhD student, Université Paris 1 Panthéon-Sorbonne, 2004-2007 Research Interests Coad’s research straddles three tightly linked domains: (i) the life-cycle of firms—how they start, grow, innovate and exit; (ii) the identification and support of high-growth enterprises (gazelles, scale-ups) and their role in job creation and productivity; and (iii) the econometric evaluation of innovation and entrepreneurship policies, including post-crisis resilience after COVID-19. He employs large micro-data panels, quasi-experimental designs and, more recently, machine-learning techniques to uncover robust empirical regularities. Publication Landscape Across 119 Scopus-indexed papers (h-index 44) and 17 000+ Google-Scholar citations (h-index 59), Coad’s recent output reveals three dominant streams: (1) policy-oriented studies on European high-growth firms and scale-ups; (2) COVID-related analyses of firm vulnerability, investment expectations and recovery paths; and (3) methodological contributions linking random-forest prediction to gazelle identification. His work consistently blends rigorous econometrics with actionable policy insight. Honours & Awards 2021 Waseda Research Award Teaching & Doctoral Supervision At Waseda he teaches graduate courses in Firm Growth & Innovation, Econometrics, Time-Series Analysis, Data-Science for Management and Japan Industry Studies, and supervises research seminars for the MSc in Finance. No individual student names are listed in the supplied material. Grants & Teams Coad leads or co-leads large EU-funded micro-data projects on productivity, innovation and COVID resilience, often involving cross-country research consortia; specific grant numbers or budgets are not disclosed in the text.

• Software Engineering
• Model Checking
• Formal Verification
• +۴ مورد دیگر

Professor Tomoji Kishi is a distinguished faculty member at Waseda University's School of Creative Science and Engineering, where he has been serving since 2009. Previously, he held academic positions at Japan Advanced Institute of Science and Technology (2003-2009) following a 21-year career at NEC Corporation (1982-2003). He earned his Ph.D. in Information Science from Japan Advanced Institute of Science and Technology in 2002, building upon his earlier engineering graduate studies at Kyoto University. Professor Kishi's research focuses on software engineering, particularly in software product line development, model checking, formal verification, and aspect-oriented modeling. His work bridges theoretical formal methods with practical applications in embedded systems, automotive software, and IoT technologies. He has made significant contributions to scalability challenges in model checking for configurable systems and has pioneered approaches to variability management and approximate modeling techniques. His publication record demonstrates remarkable consistency and evolution, with 42 papers and 153 citations according to Scopus data (h-index: 7), spanning from foundational work in software architecture in the 1990s to cutting-edge research on AI-enhanced verification methods in 2025. His recent work shows increasing application of machine learning techniques to traditional formal methods problems, particularly in the context of highly configurable systems and IoT applications. ITS Standardization Activity Merit Prize (2022) from Society of Automotive Engineers of Japan IPSJ/ITSCJ Standardization Contribution Award (2017) IPSJ/ITSCJ Project Editor Award (2016 and 2013) Information Processing Society of Japan Society Activity Contribution Award (2010) IPA/SEC Journal Best Paper Award (2007) Information Processing Society of Japan Yamashita Memorial Research Award (1998) Professor Kishi has led multiple JSPS-funded research projects, including recent work on 'variability management methods prioritizing usability through variability mining' (2020-2023) and 'utility-first modeling method' (2017-2020). His industry collaborations, particularly with automotive systems developers, demonstrate the practical impact of his research. He maintains active membership in major professional societies including IEEE Computer Society, ACM, and the Information Processing Society of Japan.

Christina Tzengمشاهده پروفایل

استادیار

• Cognition
• Cognitive Science
• Spoken Language Perception
• +۳ مورد دیگر

Christina Tzeng serves as an Assistant Professor in the Department of Psychology within San Jose State University's College of Social Sciences. Her research investigates the cognitive mechanisms enabling human comprehension of spoken language despite inherent variability from social factors including age, gender, and regional origin. She examines how listeners rapidly decode speech signals while simultaneously processing speaker identity and contextual information. Her primary research focuses on cognition, cognitive science, and spoken language perception, with emphasis on how social diversity in speech inputs shapes perceptual organization and auditory expectations. Key investigations include perceptual learning of non-native accents, lexically guided recalibration, integration of linguistic/non-linguistic speech properties, and sound symbolism. Her work demonstrates that speech variability—often perceived as noise—actually facilitates memory organization and predictive processing in human cognition. Dr. Tzeng's publication record (2010-2024) reveals consistent exploration of perceptual adaptation mechanisms, with recent work emphasizing attentional modulation in accent processing (2024), cumulative statistical learning in speech perception (2021-2023), and cross-modal correspondences in prosody (2016-2018). Her research trajectory shows increasing sophistication in modeling how social context interfaces with core speech processing systems. Scientific Awards: No awards documented in source material Advising and Grants: Source text provides no details regarding graduate student mentorship or research funding. Her background notes undergraduate research experience in human communication and memory labs, suggesting potential mentorship focus areas. Dr. Tzeng directs the Cognition of Language Processing (CLAP) Lab, which employs experimental methodologies to investigate spoken language perception. The lab's research framework positions speech diversity not as interference but as fundamental to human perceptual systems, with implications for understanding communication in multicultural contexts and developing more robust speech recognition technologies.

• Survey Methodology
• Economic Statistics
• Big Data Analysis
• +۳ مورد دیگر

Daniele Toninelli is an Associate Professor of Economic Statistics (SECS-S/03) at the University of Bergamo's Department of Economics, where he also serves as Director of the 'Data Analyst for Strategic Decisions' program, Internship Manager for the department, and member of the School of Economics and Management's Joint Teacher-Student Commission. His educational background includes a PhD in Marketing for Business Strategies (University of Bergamo, 2009), Master's in Statistics for Market Research (University of Milan-Bicocca, 2004), and Degree in Statistical and Economic Sciences (University of Milan-Bicocca, 2003). Toninelli's research focuses on: Survey and web survey methodology design and optimization Integration of big data for economic and social indicators Development of price indexes and composite indicators Social media analytics for economic measurement His work bridges statistical theory with applications in labor markets, sustainability metrics, and environmental data analysis. Teaching activities encompass undergraduate and graduate courses including Economic Statistics, Quantitative Methods for Business Data Analysis, Advanced Business Statistics, and specialized 'Data Skills' modules covering visualization, survey methods, and SAS certification preparation. International research collaborations include visiting positions at Statistics Canada (2008-2013), University of Ottawa (2012-2013), VŠB-Technical University of Ostrava (2012-2013), RECSM at Universitat Pompeu Fabra (2014), and guest lectures at University of Ljubljana (2018). He previously served on the management committee of the WEBDATANET network (COST Action IS1004). Professional experience includes roles at PiTre Milan, IBM Semea/Celestica, and Multiplex Arcadia, applying statistical expertise in industrial settings.