جست‌وجوی استاد و پژوهشگر

Daniel Buncicمشاهده پروفایل

استاد

• Empirical Finance
• Macroeconomics
• Asset Pricing
• +۴ مورد دیگر

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).

Lantian Zhangمشاهده پروفایل

پژوهشگر ارشد

• Nonlinear Stochastic Systems
• Adaptive Control
• System Identification
• +۲ مورد دیگر

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.

Maria Jönssonمشاهده پروفایل

استاد

• Children's Literature
• Literature Didactics
• Gender Studies
• +۵ مورد دیگر

Maria Jönsson serves as Professor in Literature and Senior Lecturer at Umeå University's Department of Culture and Media Studies, where she coordinates literature education programs. Her academic work spans the Faculty of Arts with significant contributions to interdisciplinary humanities research, particularly through Health Humanities and Litum (Literacy research with educational sciences orientation). Her research centers on children's literature, literary didactics, and pedagogical approaches to literature education. She examines intersections of age, gender, and sexuality within literary representations of subjectivity and power dynamics. Current investigations focus on hormone narratives in women's transitions through the project It's the hormones. Women in transition through hormone narratives (2021-2026), exploring cultural constructions of menopause and aging. Analysis of her recent publications (2023-2025) reveals evolving scholarly trajectories: increasing integration of health humanities with literary studies, innovative temporal frameworks in children's literature, and pedagogical models emphasizing care ethics alongside critical irony. Her work demonstrates consistent engagement with contemporary societal issues through literary analysis while maintaining strong foundations in Scandinavian literary traditions. Jönsson actively participates in research infrastructure through Health Humanities and Litum groups, with current funding supporting hormone narrative research until 2026. Her academic service includes editorial contributions to journals like Barnboken and critical commentary in regional media such as Västerbottens-Kuriren . She maintains ORCID 0000-0001-6449-3738 and is recognized as a university teacher within Sweden's higher education framework.

• Stochastic Control Theory
• Artificial Intelligence for Optimization
• Power Systems Engineering
• +۳ مورد دیگر

Magnus Perninge is an Associate Professor in Electrical Engineering at Linnaeus University (LNU), Sweden. He previously completed his undergraduate and PhD studies at KTH Royal Institute of Technology and held postdoctoral/researcher positions at Lund University. His teaching includes courses in Automatic Control (basic and advanced), Electric Power Systems , and Electrical Machines . He supervises Alexander Marcial , a doctoral student in Mathematics. Current Affiliation: Linnaeus University (School of Engineering & Faculty of Technology) Past Affiliations: KTH Royal Institute of Technology, Lund University Research Interests: Magnus specializes in the optimal control of stochastic systems with applications in electrical energy systems . He integrates artificial intelligence and machine learning to solve large-scale optimization problems in power systems, aiming to enhance efficiency and sustainability. His work bridges stochastic analysis , operations research , and renewable energy integration . Research Projects: Efficient Hydropower Planning : Optimizing Sweden's hydropower for future intermittent energy scenarios Sustainable Energy & Food Production : Enhancing rural farm energy systems Smart Electrification : Supporting SMEs in Kronoberg and Kalmar regions Research Groups: AI and Machine Learning for Optimization and Operations Research Stochastic Analysis and Stochastic Processes Waves, Signals and Systems

• Mathematics Education
• Physics Education
• Engineering Education
• +۳ مورد دیگر

Oswald Fogelklou serves as a Lecturer at the Division of Basic Science Education, KTH Royal Institute of Technology, specializing in foundational science instruction for engineering preparatory programs. He holds a PhD in Mathematics from Uppsala University (2012) with a dissertation titled "Computer-Assisted Proofs and Other Methods for Problems Regarding Nonlinear Differential Equations" and extensive pedagogical training. His academic credentials include: PhD in Mathematics, Uppsala University, 2012 15 higher education credits in university pedagogy comprising: Pedagogical Basic Course for University Teachers II (7.5 credits, Uppsala University, 2010) Learning for Sustainable Development (4.5 credits, 2019) Developing Learning with Assessment Criteria (1.5 credits, 2018) Examinership for Courses at KTH (1.5 credits, 2018) Dr. Fogelklou's professional focus centers on mathematics and physics education for engineering students, integrating his expertise in applied mathematics—particularly nonlinear differential equations and computer-assisted proofs—into curriculum development. His work emphasizes effective pedagogical methods for foundational courses, including assessment design and sustainable development integration. He holds key roles across multiple courses: Course responsible and teacher for Algebra and Geometry (SF1624), Calculus in One Variable (SF1625) Examiner and teacher for Physics for Technical Preparatory Year I (HF0022/ML0022) and II (HF0025/ML0025) Examiner for Basic Programming (ML1602) Teacher for Programming and Numerical Tools (ML1309), Quality Technology and Improvement (ML1609), and Fundamental Industrial Statistics (ML1018) Former assistant for Differential Equations and Transform Methods II (SF1629) and Discrete Mathematics (SF1662) No scientific awards or honors were documented in the provided information. Dr. Fogelklou's responsibilities focus exclusively on course instruction, examination, and pedagogical development. The text indicates no graduate student advisement or research grant activity. His contributions support the Division of Basic Science Education's mission to deliver rigorous foundational science training for engineering students through evidence-based teaching practices and course administration.