Statistical Regression Modelling Professors and Supervisors in Sweden

Maria Karlssonمشاهده پروفایل

دانشیار

Umea UniversitySweden

Maria Karlsson is an Associate Professor at the Department of Statistics, Umeå School of Business, Economics and Statistics (USBE), Umeå University. She also serves as a Student Counsellor. Her research focuses on statistical methodology, particularly in truncated and censored regression models, as well as pedagogical innovations in statistics education. She has contributed to projects like Stat4Reg and led initiatives such as Analysis of demand equations using mixtures of Tobit models (2013–2015) and Estimation of labor market policy program effects in the presence of interference between individuals (2013–2014). Educational Background: Holds the title of Distinguished university teacher , reflecting her expertise in pedagogy. Her academic qualifications emphasize both statistical rigor and educational effectiveness. Research Interests: Her work bridges statistical theory and practical applications, including labor market policy evaluation, causal inference in observational studies, and integrating flipped classrooms with MOOCs. She also explores ethical frameworks for AI in education and innovative writing practices in statistics curricula. Publications Overview: Karlsson's publications span statistical methodology (e.g., truncSP R package development) and pedagogical advancements. Recent work highlights include addressing ethical AI challenges and advancing active learning strategies in higher education. Awards: Recognized as a Distinguished university teacher , underscoring her contributions to educational excellence. Grants & Projects: Led two major research projects on labor market policies and demand modeling, demonstrating her impact on both theoretical and applied statistics. Labs/Teams: Active member of the Stat4Reg research group, focusing on statistical methods for regression analysis and policy evaluation.

پروفایل رسمی دانشگاه

Mathias Lundinمشاهده پروفایل

دانشیار

Umea UniversitySweden

Mathias Lundin is an Associate Professor at the Umeå School of Business, Economics and Statistics (USBE), specializing in the Department of Statistics at Umeå University. His research focuses on causal inference methodologies in observational studies, sensitivity analysis, and their applications in social sciences evaluation. Key application areas include labor market policy evaluation, education impact analysis, and health outcomes research. He contributes to research groups such as Stat4Reg and has led projects like the evaluation of national teacher development programs and labor market policy effects in the presence of interference between individuals. His work integrates advanced statistical techniques with real-world policy questions, emphasizing methodological rigor. Lundin’s publications span epidemiological studies on cancer recurrence, methodological advancements in causal inference, and educational pedagogy innovations combining flipped classrooms with MOOCs. His research bridges theoretical statistics with practical policy analysis, particularly in understanding complex interactions in observational data.

پروفایل رسمی دانشگاه

Xavier de Lunaمشاهده پروفایل

استاد

Umea UniversitySweden

Xavier de Luna is a Professor of Statistics at Umeå School of Business, Economics and Statistics (USBE) at Umeå University in Sweden. He leads the Stat4Reg Lab and is part of the Umeå SIMSAM Lab and the SINGS graduate school. His research focuses on causal inference, machine learning, and statistical methodology applied to social and health sciences. He serves as Editor of the Scandinavian Journal of Statistics (2025-2027) and has held roles as Associate Editor for Biometrics, Computational Statistics and Data Analysis, and Observational Studies. Education: PhD in Statistics from Swiss Federal Institute of Technology (1996). Prior roles include Head of the Department of Statistics at Umeå University (2009-2011) and affiliation with institutions like IFAU and the Institute of Labour Market Policy Evaluation. Research interests span causal machine learning, covariate selection, and register-based studies in health and social sciences. Key projects include using generative AI for open science and machine learning for causal discovery. His work addresses challenges like unobserved confounding and high-dimensional data. Notable contributions include methodological advances in causal inference (e.g., covariate balancing, neural networks for causal effects) and applications in health economics, retirement effects, and brain imaging studies. He has advised over 15 PhD students and secured grants from the Swedish Research Council and Wallenberg Foundation. Labs/Teams: Stat4Reg Lab (causal inference), Umeå SIMSAM Lab (register-based research), SINGS (interdisciplinary graduate school).

پروفایل رسمی دانشگاه

Alemseged Abdissaمشاهده پروفایل

پژوهشگر

Lund UniversitySweden

Alemseged Abdissa serves as a Researcher and Supervisor in Clinical Infection Medicine at Lund University's Faculty of Medicine, Sweden, with research focused on HIV/AIDS management in Ethiopian healthcare contexts. His primary research domains include: HIV/AIDS pathogenesis and treatment outcomes Epidemiological modeling of viral suppression Biostatistical analysis of antiretroviral therapy efficacy Patient retention challenges in resource-limited settings He employs advanced statistical methodologies including odds ratio calculations, confidence interval analysis, and logistic regression to investigate healthcare facility dynamics. His recent work examines low-level viremia as a predictor of treatment failure and care discontinuation among Ethiopian HIV patients. As an academic supervisor, he mentors students in clinical infection medicine research though specific advisee names are not documented. His collaborative network includes researchers from Global Health Action journal publications with active institutional partnerships in Africa.

پروفایل رسمی دانشگاه

Olof Elvstamمشاهده پروفایل

پژوهشگر

Lund UniversitySweden

Olof Elvstam is a Researcher in Clinical Infection Medicine at Lund University, focusing on epidemiological studies of HIV and its comorbidities. His work emphasizes low-level HIV viremia dynamics, aging-related complications in HIV patients, and register-based population studies in Sweden. He leads national projects analyzing HIV-related cardiovascular risks and collaborates with the RESPOND cohort in Copenhagen. Research Interests: HIV pathophysiology, antiretroviral therapy outcomes, infectious/non-infectious comorbidity patterns, aging in HIV-positive populations, and molecular epidemiology. Uses epidemiological methods to bridge clinical data with public health policy. Recent Article Trends: Focus on HIV-cardiovascular links, viremia's impact on treatment adherence, and cross-border HIV epidemiology in Sweden/Ethiopia. Collaborates internationally with groups in Denmark, Austria, and South Africa. Grants & Advising: Completed a postdoctoral project (2017-2021) on low-level viremia mechanisms. Currently involved in collaborative cohort studies (RESPOND) and Swedish national HIV surveillance (InfCareHIV). Labs/Teams: Works within Lund University's clinical infection medicine department, contributing to national/international HIV research networks.

پروفایل رسمی دانشگاه

Tomas McKelveyمشاهده پروفایل

استاد

Chalmers University of TechnologySweden

Tomas McKelvey is a Full Professor and Deputy Head of the Department of Electrical Engineering at Chalmers University of Technology, where he leads the Signal Processing research group. He has been with Chalmers since 2000 and has held a full professor position in Signal Processing since 2006. Professor McKelvey received his Electrical Engineering education at Lund University between 1987 to 1991 and earned his PhD in Automatic Control at Linköping University in 1995. Between 1995 and 1999, he held research and teaching positions at Linköping University, where he became docent in 1999. He was a visiting researcher at the University of Newcastle, Australia between 1999 and 2000 before joining Chalmers University of Technology. Professor McKelvey's research spans multiple domains within signal processing and control systems. His primary interests include model-based signal processing, statistical signal processing, system identification, and automatic control. His work has significant applications across several fields including radar systems, biomedical engineering, power systems, and automotive propulsion. He has developed innovative methods for signal processing in challenging environments, particularly in radar and power systems applications, often bridging theoretical advances with practical implementation challenges. His recent publications demonstrate a strong trend toward integrating machine learning and deep learning approaches with traditional signal processing methods, particularly for applications in radar systems, power grid stability, and automotive control. His work shows increasing sophistication in handling non-stationary signals, developing efficient computational methods, and creating robust solutions for real-world engineering problems. Professor McKelvey has been actively involved in numerous research projects, as indicated by his extensive publication record spanning multiple disciplines. His leadership position as Deputy Head of Department reflects his significant standing within the academic community. His research group maintains strong connections with both academic and industrial partners, as evidenced by the applied nature of much of his work. The cross-disciplinary nature of his publications indicates collaborations across engineering fields and with medical researchers, particularly in the biomedical applications of microwave technology.

پروفایل رسمی دانشگاه

Gustav Nilsonneمشاهده پروفایل

پژوهشگر

Stockholm UniversitySweden

Gustav Nilsonne is a researcher affiliated with Stockholm University (Department of Psychology, Division for Psychoneuroimmunology) and holds an associate professor position at Karolinska Institutet , while also being affiliated with Stanford University . His work bridges neuroscience and metascience , focusing on sleep and diurnal rhythms , brain-immune communication, and methodological rigor in scientific research. He actively investigates reproducibility, open science practices, and analytical pitfalls in longitudinal studies. Neuroscience Research : Explores bidirectional communication between the brain and immune system, including studies on stress-related diagnostics like exhaustion disorder. Metascience Contributions : Analyzes statistical artifacts in ability tilt studies, self-esteem-depression causal links, and publication ethics, advocating for robust analytical frameworks. Recent Publications (2022–2024) span psychoneuroimmunology, reproducibility crises, and methodological interventions like discrepancy review in peer assessment. Collaborative efforts, including the Psychological Science Accelerator and global pandemic response datasets, highlight his commitment to cross-cultural and large-scale empirical validation. His work challenges assumptions in genetic/environmental interpretations and emphasizes transparency in academic publishing. Leadership & Ethics : Contributed to debates on editorial self-publication practices, psychoneuroimmunology methods, and policy recommendations for improving scientific reliability. No listed students or awards in the provided text.

پروفایل رسمی دانشگاه

Daniel Buncicمشاهده پروفایل

استاد

Stockholm UniversitySweden

Daniel Buncic is Professor of Finance at Stockholm Business School, Stockholm University, specializing in empirical finance, macroeconomics, and econometrics. He holds a Ph.D. in Economics from the University of New South Wales and has held positions at Sveriges Riksbank and the University of St. Gallen. His research integrates machine learning with traditional econometric methods for financial forecasting and policy analysis. Research Focus: Buncic's work spans asset pricing, volatility forecasting, exchange rate dynamics, and macroprudential policy. He employs advanced techniques like Bayesian econometrics, nonlinear time-series models, and high-dimensional data analysis to address questions in financial stability and market predictability. His recent work critiques methodological approaches in natural rate estimation and equity return prediction. Publication Trends: His articles frequently address econometric methodology, financial market volatility, and monetary policy transmission, with a growing emphasis on machine learning applications in finance. Common themes include forecasting under structural breaks, model robustness, and cross-market interdependencies. Grants & Advising: He secured a 1.74M SEK grant (2020-2023) from the Jan Wallander & Tom Hedelius Foundation. Currently advising PhD student Qinglin Ouyang, he previously directed Stockholm University's Master’s in Banking and Finance program (2019-2022).

پروفایل رسمی دانشگاه

Lantian Zhangمشاهده پروفایل

پژوهشگر ارشد

KTH Royal Institute of TechnologySweden

Lantian Zhang is a postdoctoral researcher at KTH Royal Institute of Technology's Department of Mathematics, working under Assistant Professor Silun Zhang since September 2024. He is affiliated with the Division of Numerical Analysis, Optimization and Systems Theory within the School of Engineering Sciences. His research focuses on identification and adaptive control of nonlinear stochastic systems , adaptive estimation under quantized observations , and applications in machine learning . Dr. Zhang received his Ph.D. in systems theory from the Academy of Mathematics and Systems Science at the Chinese Academy of Sciences in 2024. Primary research area: Nonlinear stochastic systems and adaptive control Specialization: Quantized observations and binary-valued output systems Current projects: Advanced adaptive identification methods under saturated output constraints His publication record shows a strong focus on adaptive identification techniques under challenging observation conditions, with multiple papers published between 2022-2025 addressing saturated output observations, non-iid data, and binary-valued output systems. This research has applications in both theoretical control systems and practical implementations where measurement constraints exist. Dr. Zhang is part of the research group led by Assistant Professor Silun Zhang, which also includes other postdocs and PhD students working on related topics in networked systems and control theory. His work contributes to KTH's broader research initiatives in WASP (Wallenberg AI, Autonomous Systems and Software Program) and Digital Futures.

پروفایل رسمی دانشگاه

Richard Sawatzkyمشاهده پروفایل

استاد

University of GothenburgSweden

Richard Sawatzky is an Affiliated Professor at the University of Gothenburg's Department of Nursing Sciences and Health within the Faculty of Medicine. His academic work bridges nursing science, health services research, and clinical practice with a strong emphasis on patient perspectives. His research centers on patient-reported outcomes (PROs), quality of life measurement, and person-centered care methodologies across multiple clinical contexts. Sawatzky has developed innovative approaches to analyzing PRO data, particularly in response shift phenomena and symptom pattern identification using latent class analysis. His work spans cancer care, palliative settings, dialysis treatment, and mental health in chronic illness. Analysis of his recent publications (2023-2025) reveals a strong focus on methodological advancements in PRO research alongside practical applications in clinical settings. He has contributed significantly to understanding how to implement PROs effectively in diverse healthcare contexts while maintaining patient-centered perspectives. His work frequently addresses vulnerable populations including older adults with cancer, dialysis patients with mental health concerns, and migrants navigating healthcare systems. Sawatzky maintains extensive international collaborations, particularly between Swedish and Canadian institutions, reflecting the global relevance of his methodological contributions to patient-centered outcomes research.

پروفایل رسمی دانشگاه

Anupama Unnikrishnanمشاهده پروفایل

مدرس

Blekinge Institute of TechnologySweden

Anupama Unnikrishnan is a University Lecturer in the Department of Industrial Economics at Blekinge Institute of Technology (BTH), where she has been employed since September 2021. She began her association with BTH as an intern at TIEK through Jobbsprånget (IVA) program. Her academic home is within the Faculty of Engineering, specifically in the Department of Industrial Economics, where she contributes to both teaching and research activities in industrial economics and management. Dr. Unnikrishnan completed her doctoral studies in Economics from Mumbai School of Economics & Public Policy, India. Her dissertation focused on capital inflows and monetary policy response in BRICS economies. Prior to joining BTH, she worked as a Research Assistant at the Indian Council of Social Science Research (ICSSR) between 2015-2017. She also pursued a summer course in financial risk management from the London School of Economics & Political Science in 2019. Anupama's research primarily centers on industrial economics and productivity analysis, with recent work focusing on productivity growth, technical progress, and efficiency change in industrialized countries. She has been actively researching regional variation in total factor productivity in Sweden, particularly in the tourism industry. Her earlier work examined capital inflows in BRICS economies, monetary policy responses, and sterilization policies. She currently leads research on productivity analysis in Sweden funded by the Swedish Entrepreneurship Forum. Her scholarly output shows a clear progression from international finance and capital flows in emerging economies toward more focused productivity analysis in industrialized nations, particularly Sweden. Recent publications increasingly address practical economic measurement and policy implications for Swedish firms and regional development. Dr. Unnikrishnan teaches a diverse range of courses including Financial Modelling, Financial Economics, Finance and Investment Analysis, Globalization and Digitalization, Research Methodology, Statistics and Econometrics. She also supervises Master's theses in industrial economics and management, demonstrating her commitment to developing the next generation of economists and business analysts.

پروفایل رسمی دانشگاه