جست‌وجوی استاد و پژوهشگر

Malka Gorfine Orgadمشاهده پروفایل

استاد

• Survival Data
• Non-parametric Statistics
• Biostatistics
• +۵ مورد دیگر

Professor Malka Gorfine Orgad is a distinguished faculty member in the Department of Statistics and Operations Research at the School of Mathematical Sciences, Tel Aviv University. She has held the position of Professor since 2016, following her promotion from Associate Professor (2014-2016). Additionally, she maintains a long-standing affiliation as an Affiliate Investigator at the Division of Public Health Sciences, Fred Hutchinson Cancer Research Center in Seattle since 2003. Prof. Gorfine Orgad earned her MA (1994) and PhD (1999) in Statistics from the Hebrew University of Jerusalem under the supervision of Professors Banjamin Yakir and David Zucker. Her academic journey includes previous appointments as Associate Professor at Harvard School of Public Health (2011-2012) and at the Technion - Israel Institute of Technology (2010-2014), where she served as Senior Lecturer prior to that. Her research focuses on survival data analysis, non-parametric statistics, biostatistics, and machine learning. Specifically, she investigates inference based on Survival Deep Learning, natural experiment methods for causal inference, and various challenges of causal inference with survival outcomes. She has developed several innovative methodologies for survival analysis, particularly in the areas of competing risks, semi-competing risks, and illness-death models. Analysis of her recent publications reveals a strong emphasis on practical applications of survival analysis methodology to real-world health problems, particularly related to cancer and infectious diseases like COVID-19. Her work frequently bridges theoretical statistical development with practical implementation through software packages, demonstrating her commitment to making advanced methods accessible to applied researchers. Associate Editor of JASA Applications and Case Studies (2022 - present) Associate Editor of EJS (2022 - present) Associate Editor of Scandinavian Journal of Statistics (2021 - 2024) Co-Editor of Biometrics (2017 - 2019) Associate Editor of Biometrics (2009 - 2016) Member of Editorial Board of Lifetime Data Analysis (2013 - 2016) Prof. Gorfine Orgad actively mentors students at all levels, currently supervising one postdoc, two PhD students, and four MSc students, with numerous former students who have completed their degrees under her guidance. She serves as co-chair of the STRATOS TG8 (Survival Analysis Topic Group), which provides guidance on survival analysis methods for observational studies. She has also developed multiple software packages including PyMSM, PyDTS, frailty-LTRC, HHG, and others that implement her methodological contributions for the broader research community.

• Financial Economics
• Development Economics
• Capital Structure
• +۴ مورد دیگر

Professor Laurence Harris is a faculty member at the School of Finance and Management (FIM) at SOAS University of London. His research focuses on financial economics, development economics, and globalization studies with particular emphasis on policy formation and financial market dynamics in African and East Asian economies. Key Research Areas: Capital structure analysis, Basel III banking regulations, financial technology diffusion, and Keynesian policy frameworks in disarticulated economies. Affiliations: Active member of SOAS research outputs with multiple publications in economics and finance journals. His recent work applies micro-founded, stock-flow consistent models to study financial regulations in South Africa and explores fintech's role in financial inclusion. Publications span from 1996 to 2025, demonstrating sustained academic contributions in corporate finance, globalization effects, and economic development.

Louis-Daniel Papeمشاهده پروفایل

استادیار

• Industrial Organization
• Labor Economics
• Digital Economics
• +۳ مورد دیگر

Louis-Daniel Pape serves as an Assistant Professor at Télécom Paris within the CREST research center, specializing in Economics. His academic profile positions him as a permanent research member focused on empirical analysis at the intersection of digital platforms, labor markets, and competition policy. Affiliated with both Télécom Paris and CREST (Center for Research in Economics and Statistics), he contributes to France's leading economics research ecosystem. Pape's research centers on industrial organization and labor economics, with particular emphasis on digital economics and competition policy. His methodological approach combines reduced-form and structural modeling techniques applied to administrative datasets, web-scraped information, and firm-provided data. Current investigations examine platform self-preferencing practices, labor market concentration effects, and innovative econometric methodologies for handling zero values in regression analysis. His work demonstrates consistent engagement with real-world policy questions, particularly regarding digital market regulation and antitrust enforcement. Analysis of Pape's publication trajectory reveals a strong focus on digital economy regulation and labor market dynamics. His recent work on the Digital Markets Act's impact on Google Maps represents cutting-edge research in digital competition policy, employing difference-in-differences methodologies to assess regulatory interventions. Parallel investigations into labor market concentration, non-compete clauses, and wage collusion demonstrate methodological versatility across different market contexts. The recurring theme across his publications is the application of rigorous empirical methods to evaluate policy interventions in concentrated markets. Pape maintains active teaching responsibilities across multiple institutions. At Télécom Paris, he delivers courses in Applied Econometrics and Data Collection/Visualization for Master's students, while also teaching Big Data at École Polytechnique. His pedagogical approach integrates machine learning methods with traditional econometric techniques, reflecting the evolving nature of empirical economic research. Course materials demonstrate particular emphasis on instrumental variables, difference-in-differences designs, and structural modeling approaches. Pape's research infrastructure includes collaborations with major institutions including the French Ministry of Public Finances (DGFIP) on VAT fraud analysis for digital platforms. His methodological contributions, particularly the iterated Ordinary Least Squares (iOLS) framework for handling zero values in regression models, have gained recognition in the econometrics community with substantial downloads on SSRN. His software implementations for iOLS/i2SLS and Instrumented Differences-in-Differences (IVDID) methods are publicly available on GitHub, facilitating broader adoption of these techniques.

Erin Evelyn Gabrielمشاهده پروفایل

استاد

• Causal Inference
• Biostatistical Methods Development
• Randomized Trials
• +۷ مورد دیگر

Erin Evelyn Gabriel is a Professor in the Section of Biostatistics within the Department of Public Health at the University of Copenhagen's Faculty of Health and Medical Sciences. She holds a PhD in Biostatistics from the University of Washington awarded on August 10, 2012. She teaches Introductory Statistics and Data Analysis at both Masters and Doctoral levels. Her research focuses on biostatistical methods development with applications to infectious diseases, vaccination, cancer, and aging. Key research areas include: Causal inference methodologies Surrogate evaluation, particularly in the presence of interference Nonparametric causal bounds Designs and estimation methods for emulated and randomized clinical trials Evaluation of prediction-based decision rules Her publication record shows a strong trajectory in methodological biostatistics, with recent work (2022-2025) focusing on causal bounds, mediation analysis, age-period-cohort models, instrumental variables, and biases in randomized controlled trials. Her work appears in top-tier journals including the Journal of the American Statistical Association, Biostatistics, and Epidemiology. Dr. Gabriel has established collaborations across multiple institutions, as evidenced by her co-authorship patterns with researchers including Sachs, Sjölander, Follmann, and Ocampo. Her research impact is indicated by citations and downloads of her work, with some publications gaining attention on social media platforms and news outlets.

Jonathan Flemingمشاهده پروفایل

دانشیار

• Applied GIS
• Drone Mapping
• Environmental Geography
• +۴ مورد دیگر

Jonathan Fleming serves as Associate Professor in the Department of Geography and Sociology at Samford University's Howard College of Arts and Sciences. His academic work bridges environmental geography, geospatial technology, and ecological field studies with applications across diverse global ecosystems from the Dutch Caribbean to the American Midwest. His educational background includes: PhD in Forest Resources from Mississippi State University MS in Wildlife and Fisheries Science from Mississippi State University BS in Geographic Information Science from University of North Alabama Fleming's research integrates applied GIS methodologies , drone-based remote sensing , and field ecology to address contemporary environmental challenges. His expertise spans invasion ecology of aquatic macrophytes, environmental stewardship frameworks, and experiential learning through international field studies. Current projects emphasize spatial modeling of plant distributions, habitat suitability analysis, and sustainable management of freshwater ecosystems using cutting-edge geospatial tools. Analysis of his publication record reveals consistent contributions to Environmental Science and Ecology journals with growing emphasis on global macrophyte patterns (2025), spatial modeling of aquatic ecosystems (2021-2022), and invasive species management (2019-2015). His work demonstrates strong methodological continuity in geospatial analysis while expanding into broader ecological questions about biotic-abiotic interactions and conservation applications. Fleming actively engages students through field-based instruction across multiple continents and directs Samford's Graduate GIS Certificate program. As a certified GIS Professional (GISP) and FAA Part 107 Remote Pilot, he brings professional technical expertise to both classroom and research environments. His field studies in Tanzania, the Caribbean, and throughout North America provide students with immersive environmental science experiences while generating practical ecological data. Professional activities include leadership in geospatial education through the Graduate GIS Certificate program and application of drone technology for environmental monitoring. His research team focuses on connecting classroom learning with real-world conservation challenges through hands-on fieldwork and advanced spatial analysis techniques.

• Clinical Prediction Models
• Sample Size Calculation
• Statistical Methods in Healthcare
• +۴ مورد دیگر

Lucinda Archer serves as an Assistant Professor in Biostatistics at the Applied Health Sciences Institute for Interdisciplinary Data Science and AI, University of Birmingham. She is actively engaged in methodological research focused on clinical prediction models and statistical methodology for healthcare applications. Her research interests center on developing robust statistical methods for clinical prediction modeling, with particular emphasis on sample size calculations, validation of prediction models, and ensuring fairness in AI-based healthcare tools. Her work bridges biostatistics, machine learning, and clinical medicine to improve risk prediction and decision support in healthcare settings. Analysis of her recent publications reveals a strong focus on methodological advancements in clinical prediction models, particularly regarding sample size requirements for developing and validating prediction tools. Her research addresses critical challenges in translating statistical models to clinical practice, with applications in hypertension management, electrolyte disorders, and time-to-event prediction. Dr. Archer has secured significant research funding as Co-Investigator on multiple major projects including: AI-enabled frontline workers (FLWs): benchmarks & tool evidence (Bill & Melinda Gates Foundation) BRC CF11: Developing and validating predictive models for clinical outcomes in mild autonomous cortisol secretion (NIHR) Systematic Reviews and Meta-Analysis of Prognosis Studies (REVAMP) (UK Research and Innovation) Sample Size guidance for UPDATing clinical prediction models (SS-UPDATE) (Medical Research Council) NIHR BRC Theme - Data, Diagnostics & Decision Tools She collaborates extensively with leading researchers in the field, particularly with R.D. Riley, J. Ensor, and K.I.E. Snell, contributing to high-impact publications in journals including Diagnostic and Prognostic Research, Journal of Hypertension, BMC Medical Research Methodology, and The Lancet Digital Health.

• Information Systems
• Business Intelligence
• Data Mining
• +۴ مورد دیگر

Maria Isabel Calapez Cabrita Leal Seruca serves as an Associate Professor at Portucalense University's Science and Technology Department in Porto, Portugal, where she teaches Information Systems and coordinates the Informatics Engineering and Information Systems for Management programs. She additionally holds the role of Department Coordinator for the Erasmus+ Programme. As a Senior Researcher at Universidade do Minho's Centro Algoritmi, she contributes to the IST R&D Group and ISTTOS R&D Lab, bridging academic and research domains across institutions. Her academic foundation includes a degree in Applied Mathematics and Computation from Portucalense University, complemented by an MSc and PhD in Computation from the University of Manchester Institute of Science and Technology (UMIST). This technical background underpins her research trajectory in digital systems and organizational transformation. Her research focuses on Information Systems with specialized expertise in Business Intelligence, Data and Web Mining, Digital Transformation, and Reuse and Patterns. She has pioneered models like E-swim (Enterprise Semantic Web Implementation) and ToOW (Training of Organizations in the Web) to address organizational challenges through technology adoption. Her work consistently integrates practical applications in educational settings and industry contexts, particularly through international Erasmus collaborations. Analysis of her 2019-2025 publications reveals a dominant emphasis on Digital Transformation in educational mobility systems (notably Erasmus+ projects), alongside applied Data Mining in domains including tourism, pharmaceutical distribution, and waste management. Her scholarship demonstrates a trajectory from foundational semantic web research toward contemporary organizational digitalization challenges, with increasing focus on sustainable development frameworks. While no major individual scientific awards are documented in the source material, her research output includes 41 publications with an h-index of 7 and 164 citations, reflecting steady scholarly impact in Information Systems journals and conferences. She actively leads Erasmus+ initiatives including the KA220 project 'Data Science We Teach Data' and has coordinated multiple Erasmus Intensive Programmes on Sustainable Digital Transformation (2022-2023), Business Intelligence (2008-2010), Web Mining (2011-2013), and Data Science (2014). Her grant work consistently targets curriculum development for digital competencies in higher education. Within research infrastructure, she contributes to the Digital Transformation and Innovation in Organisations (DTIO) Group at REMIT (Portucalense University) and the ISTTOS Lab at Centro Algoritmi (Universidade do Minho), focusing on organizational agility through technology adoption and pattern-based solutions.

Johan Lyhagenمشاهده پروفایل

استاد

• Time series econometrics
• Cointegration analysis
• Panel data analysis
• +۶ مورد دیگر

Johan Lyhagen is a Professor of Statistics at the Department of Statistics, Uppsala University, within the Faculty of Social Sciences. His contact information includes mobile phone: +46 70 425 02 02 and email: Johan.Lyhagen@statistik.uu.se. He has been a full Professor of Statistics since 2010, having been promoted to professor specializing in time series econometrics two years prior. Professor Lyhagen's main research area is time series econometrics, with particular focus on cointegrated relationships in economic equilibriums. His work examines how to empirically test for and estimate these equilibriums, with recent interest in how short-term dynamics may differ across business cycles. Beyond his primary focus, he has conducted research in psychometrics (non-linear structural equation models, robust inference), optimal binning in histograms, and measurement error problems. His research demonstrates a strong methodological foundation with applications across multiple economic and social domains. His recent publication trends (2016-2025) show a diverse research portfolio that spans econometrics, statistics, and interdisciplinary applications. His work demonstrates expertise in spatial econometrics, climate change impacts on agriculture, robust statistical methods, structural equation modeling, and policy evaluation. The research shows a consistent methodological focus on time series analysis, panel data, and statistical modeling across various substantive domains including economics, environmental science, health, and energy markets. Professor Lyhagen has substantial experience in academic administration, having served as head of department, member of faculty board, and on committees for the recruitment of professors. He supervises PhD students and is actively involved in teaching, demonstrating a commitment to both research excellence and academic development.

Kitsuchart Pasupaمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Chemoinformatics
• +۷ مورد دیگر

Professor Kitsuchart Pasupa, Ph.D. , is a royal-appointed full professor and Vice Dean for Research & Innovation and International Affairs at the School of Information Technology (SIT) , King Mongkut's Institute of Technology Ladkrabang (KMITL) , Thailand. He has held academic positions at the University of Southampton, University of Sheffield, University College London, IIT Patna, and several European institutes, and is a senior member of both IEEE and the Asia Pacific Neural Network Society (APNNS). Education Ph.D. in Automatic Control & Systems Engineering, University of Sheffield, UK (2007) M.Sc.(Eng) in Control Systems, University of Sheffield, UK (2004) B.Eng. in Electrical Engineering, Sirindhorn International Institute of Technology, Thammasat University, Thailand (2003) Research Focus Professor Pasupa’s research lies at the intersection of machine-learning theory and real-world applications . He designs and deploys algorithms for chemoinformatics, bioinformatics, eye-tracking analytics, cyber-bullying detection, Thai natural-language processing, multimodal fusion, time-series forecasting , and healthcare AI . His work spans from foundational reservoir computing and extreme learning machines to large-scale deep learning systems operating on mobile devices and cloud platforms. Across more than 150 refereed publications , a clear trend emerges: leveraging limited or noisy data, handling concept drift, and delivering interpretable AI solutions for socially impactful domains such as medical imaging, precision livestock farming, financial analytics, and smart-city water management. Scientific Awards & Fellowships APNNS Young Researcher Award 2019 Multiple Best Paper Awards (ICITEE 2024, KST 2019, IC2IT 2018, NCIT 2015, NCCIT 2014) KMITL Honorable Fame Awards (2014–2022) Teaching Excellence Award 2017, KMITL Commander (Third Class) of the Most Noble Order of the Crown of Thailand (2020) Companion (Fourth Class) of the Most Exalted Order of the White Elephant (2017) Grants & Supervision He has attracted >30 M THB in research funding from Thai government agencies, EU (FP7), Indian DST, and industry partners (SCB, KBTG). He currently leads a 2025-2026 KMITL grant on multimodal financial Q/A for Hindi and Thai and has completed projects on cyber-bullying in ASEAN languages, car-parts segmentation, eye-tracking, SNP analytics, waste classification, and smart farming . PhD advisees: 3 current + 6 completed Master’s & Bachelor’s student supervision: >60 theses and projects Internship teams: guided award-winning student teams in international programming contests (NAPROCK, NSC, TICTA) Laboratory & Teams His Intelligent Systems & Machine Learning Research Group at SIT-KMITL collaborates closely with IIT Patna, University of Malaya, Aalto University, and University of Leoben, maintaining an active visiting-scholar program and hosting joint workshops and data challenges. The lab focuses on reproducible research and open datasets in Thai NLP, medical imaging, and smart-city analytics.

• Organizational Behavior
• Strategic Human Resource Management
• Career Development Theory
• +۵ مورد دیگر

JUNG, Yuhee is Professor at Waseda University’s School of Social Sciences (Faculty of Social Sciences) where he has taught since April 2020. Holding a Doctorate in Business Administration, he previously served as Associate Professor at Ritsumeikan University and Professor at Gakushuin University. He is an editorial board member of the Asia Pacific Journal of Management and has chaired the Association of Japanese Business Studies annual conference. Education: Doctor of Business Administration (Ph.D.) Research Interests: Jung’s work integrates micro-organizational behavior with strategic HRM, emphasizing career development, newcomer socialization, top-management diversity, and cross-cultural comparisons between Japan and Korea. Recent projects examine how HR attributions shape older-worker engagement, how “reading the air” ( kuuki-wo-yomu ) affects Japanese workplaces, and what triggers the first appointments of women executives in Japanese firms. Publication Trends: Across 15 recent articles (2009-2025) his scholarship tracks three dominant streams: (1) employee career self-management and organizational support across the lifespan, (2) diversity attributes of top-management teams (age, tenure, gender) and their performance outcomes, and (3) nuanced social-exchange mechanisms linking HR practices (training, family-friendly policies) to attitudinal and behavioral outcomes in Japanese and Korean samples. Scientific Awards: Conference Excellence Award, Japan Association of Administrative Science (2023) JAIOP 3rd Best Paper Award (2023) EURAM OB SIG Best Paper Runner-up (2019) AJBS / Palgrave Macmillan Best Paper Awards (2017, 2019 runner-up) Emerald Literati Network Awards for Excellence (2017) Multiple best-paper awards from AJBS, Academy of Management OB Division, and Japanese academic societies (2007-2016) Grants & Projects: JSPS KAKENHI (B) “Integrative research on gig-work careers” (2025-29, Principal Investigator) JSPS KAKENHI (B) “Sustainability paradigm HRM” (2023-27, Co-I) JSPS KAKENHI (B) “Organizational attractiveness and recruitment” (2021-25, PI) JSPS KAKENHI (B) “Minority executives and firm performance” (2020-25, Co-I) Internal Special Research Projects on older-worker engagement, learning orientation, and COVID-19 career shifts (2020-24) Labs & Teams: Jung leads an active OB/HRM lab at Waseda that collaborates with a cross-university network (Takeuchi at Gakushuin, Tanikawa at Seikei, Kim at KUBS) on JSPS-funded projects; the group hosts international workshops and doctoral seminars each semester.

Andrew Linمشاهده پروفایل

دانشیار بالینی

• Cardiac Imaging
• Coronary Artery Disease
• Atherosclerosis
• +۷ مورد دیگر

Andrew Lin is an Adjunct Clinical Associate Professor at the Victorian Heart Institute (VHI), Monash University, where he conducts advanced research in cardiac imaging and coronary atherosclerosis. He is actively involved in clinical and translational research, particularly in the application of artificial intelligence and radiomics in computed tomography for personalized cardiovascular care. Doctor of Philosophy, Monash University (2020) Bachelor of Medicine & Bachelor of Surgery, University of Melbourne (2008) Bachelor of Medical Science, University of Melbourne (2008) Dr. Lin's research focuses on the intersection of cardiovascular disease and cutting-edge imaging technologies. His key areas include coronary artery disease, atherosclerosis, cardiac CT, radiomics, and the integration of artificial intelligence for noninvasive phenotyping and risk prediction. His work aims to improve early diagnosis, monitor treatment response, and guide personalized interventions. His recent publications (2018–2025) demonstrate a consistent trajectory in AI-enhanced cardiac imaging, with emphasis on pericoronary adipose tissue, plaque characterization, and longitudinal monitoring using CT. These studies reflect a strong commitment to innovation in cardiovascular diagnostics and precision medicine. Dr. Lin has secured multiple research grants as a Primary Chief Investigator, including ongoing and completed projects on AI-driven atherosclerosis imaging and statin effects on plaque. His collaborative network includes leading researchers in cardiology and imaging science. Artificial intelligence-enabled quantitative atherosclerosis imaging by computed tomography to guide personalised management of coronary artery disease: a randomised clinical trial (2026–2030) Effects of statins on coronary atherosclerotic plaques: longitudinal, radiomics-based precision phenotyping (2023–2024) The Association of Patterns of Dyslipidaemia with Coronary Artery Plaque on Computed Tomography Coronary Angiography (2018–2021) He is affiliated with the Victorian Heart Institute at Monash University, a hub for cardiovascular innovation and research excellence. His lab and team focus on developing AI-powered tools for CT-based cardiovascular risk assessment and plaque analysis, contributing to global efforts in reducing cardiovascular morbidity and mortality.

• Software Engineering
• Source Code Analysis
• Software Clone Detection
• +۱۲ مورد دیگر

James R. Cordy is a Professor in the School of Computing at Queen's University, Faculty of Engineering and Applied Science, Kingston, Canada. He is a leading researcher in software engineering, with a focus on source code analysis, software clone detection, model-driven engineering, and program transformation. He has been actively publishing since 1977, with a sustained record of contributions in top-tier venues such as ICSE, MoDELS, and WCRE. His research interests include software clone detection, model transformation, Simulink models, source transformation, software maintenance, and grammatical inference. He has developed and contributed to influential tools such as TXL and NiCad, and his work often involves empirical studies and tool evaluation in real-world software systems. The most recent articles highlight trends in model transformation, clone detection, verification of state machines, and migration of legacy systems. His work increasingly integrates formal methods and empirical validation, particularly in automotive and safety-critical domains. He has also explored applications in healthcare software, such as artificial pancreas systems. Most Influential Paper Award, SCAM 2001 (awarded in 2019) He has advised numerous students, including Manar H. Alalfi, Matthew Stephan, and Chanchal K. Roy, who have co-authored multiple publications with him. His research is often collaborative, involving teams from Queen's University and other institutions. He has also contributed to workshops and special issues, demonstrating leadership in the software engineering community.

Marco Rianiمشاهده پروفایل

استاد

• Robust Statistics
• Regression Analysis
• Time Series Analysis
• +۵ مورد دیگر

Marco Riani is Full Professor of Statistics at the Department of Economic and Business Sciences, Faculty of Economics, University of Parma since 2006. He directs the Interdepartmental Center Ro.S.A. (Robust Statistics Academy) and has held academic positions including Post Doctoral Fellow (1996-1998), Lecturer (1998-1999), and Associate Professor (1999-2006) at the same institution. Education: Bachelor of Science in Economics (cum laude), University of Parma (1986-1990) PhD in Statistics, University of Florence (1992-1995) His research centers on robust statistics with methodological focus on robust regression , time series , and multivariate analysis . Applied work spans risk management , quality evaluation , international trade monitoring , and medical statistics (neonatal health parameters). He pioneers outlier detection without arbitrary thresholds and robust machine learning. Recent publications highlight robust transformations for regression, explainable AI, and applications in environmental/medical fields. Key trends include automated fraud detection in trade data, soil moisture estimation through time series, and biomarker analysis for birth anomalies. Scientific awards: Best Italian PhD thesis in Statistics (1997) Best contribution at MATLAB expo 2016 (Milan) As advisor, he has supervised five PhD theses and over 100 degree theses. His grant leadership includes Horizon 2020 PrimeFish (2014-2019) and multiple Italian Ministry projects on robust methods. External expertise spans EU Automated Monitoring Tool projects (AMT3-AMT5) for trade fraud detection. Principal Investigator: 11 national/EU projects (2000-2019) Steering roles: ICORS, SIS CLADAG, Ro.S.A. center He co-developed the FSDA toolbox (MATLAB/R) with the European Commission, enabling robust multivariate analysis for institutions like the Joint Research Centre. The Ro.S.A. center drives his collaborative work on outlier detection and data mining.

Angela Carolloمشاهده پروفایل

پژوهشگر

• Demography
• Statistics
• Survival Analysis
• +۴ مورد دیگر

Angela Carollo is a Researcher at the Max Planck Institute for Demographic Research (MPIDR) in Rostock, Germany, affiliated with the Laboratory of Fertility and Well-Being. Her work focuses on developing advanced statistical methodologies for demographic analysis, particularly in survival and event-history models with multiple time scales. Her research interests span demography, statistics, and population health, with emphasis on: Survival analysis and competing risks modeling Event-history frameworks with multidimensional time Fertility dynamics and family transitions Mortality patterns and health outcomes Statistical software development for demographic applications Carollo's publication record demonstrates consistent innovation in handling complex demographic data structures. Her recent work shows strong trends toward interdisciplinary collaboration (spanning statistics, gerontology, and public health) and methodological rigor in modeling time-dependent phenomena. Key contributions include the TwoTimeScales R package and novel approaches to hazard smoothing across multiple temporal dimensions, applied to critical demographic questions like partnership transitions and mortality prediction. No scientific awards were documented in the source material. Her collaborative research involves extensive work with international teams across Europe, though no formal student advising or grant management details were provided. Current projects include dissertation work on "Multiple Time Scales in Survival and Event-History Models" within the Laboratory of Fertility and Well-Being. Carollo operates within MPIDR's Laboratory of Fertility and Well-Being, which investigates how demographic processes like fertility and partnership transitions interact with individual well-being across the life course, leveraging advanced statistical techniques for population-level insights.

Weijing Tangمشاهده پروفایل

استادیار

• Network Analysis
• Machine Learning
• Survival Analysis
• +۵ مورد دیگر

Weijing Tang is an Assistant Professor in the Department of Statistics & Data Science at Carnegie Mellon University (CMU), affiliated with the Dietrich College of Humanities and Social Sciences. His research focuses on developing statistical methodologies for network analysis, machine learning, and survival analysis, addressing challenges in large-scale interdisciplinary datasets across health and social sciences. He holds a Ph.D. in Statistics from the University of Michigan (2022) and a B.S. in Mathematics from Tsinghua University (2016). Prior to CMU, he was a Postdoctoral Research Fellow in Biostatistics at Harvard University (2022-2023). Education: Ph.D. in Statistics, University of Michigan, 2022 B.S. in Mathematics, Tsinghua University, 2016 Research Interests: Network analysis and signed network dynamics Survival analysis and Cox models Machine learning applications in biostatistics and public policy Optimization and scalable statistical methods Nonparametric methods and subgroup heterogeneity analysis Key Awards: ProQuest Distinguished Dissertation Award (2023) IMS Hannan Graduate Student Travel Award (2022) Multiple awards from ASA and ENAR (2021-2020) Grand Prize in American Heart Association's COVID-19 Data Challenge (2020) Funding: NSF Grant DMS-2412853 (sole PI) CMU Dietrich College Seed Grant (co-PI)