جست‌وجوی استاد و پژوهشگر

Björn Hagströmerمشاهده پروفایل

استاد

• Financial Market Microstructure
• Asset Pricing
• Financial Econometrics
• +۳ مورد دیگر

Björn Hagströmer is Professor of Finance and Director of Studies for the Postgraduate Programme at Stockholm Business School , Stockholm University. He earned his PhD from Aston Business School in 2010 and has been with SBS since then. His primary teaching responsibility is the course Financial Market Structure . Research Focus : Hagströmer specializes in financial market microstructure , analyzing price formation, liquidity supply, and trading mechanisms. His work examines closing call auctions, high-frequency trading dynamics, bid-ask spread components, and information revelation in decentralized markets. Current projects include studies on market fragmentation , gold futures microstructure , and equity market information dissemination Recent publications explore volatility extensions in auctions, unbiased spread estimation, and network-based information flow analysis Awards : Recipient of the 2017 De la Vega Prize for research on effective spread overestimation. His work appears in leading journals including Journal of Financial Economics and Journal of Finance . Academic Leadership : Co-organizer of the Microstructure Exchange online seminar series and the 2017 Conference on the Econometrics of Financial Markets celebrating the 20th anniversary of Campbell-Lo-MacKinlay's influential work.

Markus Jänttiمشاهده پروفایل

استاد

• Income Distribution
• Wealth Distribution
• Poverty
• +۴ مورد دیگر

Markus Jäntti serves as Professor at Stockholm University's Institute for Social Research (SOFI), where his research centers on income and wealth distribution, poverty dynamics, and socio-economic mobility through comparative international lenses. He specializes in quantifying family background's influence on economic outcomes and leads pivotal projects including MapIneq (life-course inequality trends), PrecaNord (Nordic precarious work analysis), and TITA (austerity-era inequality assessment). His research portfolio spans income distribution , wealth inequality , intergenerational mobility , and comparative social policy , employing advanced econometric techniques and cross-national datasets like the Luxembourg Income Study. Work within SOFI's Labor Market Economics (AME) group extends to education, health, taxation, and gender equality, reflecting labor economics' interdisciplinary nature. Analysis of his 2020-2025 publications reveals dual emphases: methodological innovation (e.g., grouped-data inequality measurement) and empirical exploration of family-background effects, labor policies, and social transfers. His influential 2020 framework synthesizing four mobility approaches underscores conceptual rigor, while recent Nordic-focused studies address contemporary challenges like migrant labor exploitation. Scientific awards: No awards were documented in source materials. Major grant-funded projects include: MapIneq : Tracking intergenerational, educational, labor market, and health inequality drivers across lifespans PrecaNord : Multi-level analysis of precarious/informal work in Finland, Norway, and Sweden TITA : Consortium study of austerity's impacts on financial, health, and opportunity inequalities As core faculty in SOFI's AME group, he contributes to research spanning labor market outcomes (wages, employment), social transfers, crime, and political economy—operating within Stockholm University's broader social policy ecosystem while maintaining independent research leadership.

Marco L. Della Vedovaمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence and Machine Learning
• Interpretable and Explainable AI
• Geospatial AI
• +۷ مورد دیگر

Marco L. Della Vedova is a Senior Lecturer in Applied Artificial Intelligence at Chalmers University of Technology, Sweden. He works in the Vehicle Engineering and Autonomous Systems division within the Department of Mechanics and Maritime Sciences, as part of Prof. Mattias Wahde's research group. Since 2025, he has served as Director of the Data Science and AI master's programme (MPDSC) at Chalmers, where he teaches courses including Introduction to Artificial Intelligence and Digitalization in Sports. Dr. Della Vedova earned his academic foundation at the University of Pavia, Italy, where he completed his BSc (2006), MSc (2009), and PhD (2013) in Computer Engineering. His doctoral research focused on "Real-Time Physical Systems and Electric Load Scheduling" under Prof. Tullio Facchinetti. During his PhD studies, he spent a year at U.C. Berkeley hosted by Prof. Francesco Borrelli at the Model Based Predictive and Distributed Control Lab. His research spans multiple AI domains with a strong emphasis on interpretability. Dr. Della Vedova develops interpretable methods for conversational AI, naturalness evaluation of forests using canopy height models, and geospatial applications. His work bridges theoretical AI with practical societal benefits, particularly in environmental monitoring, transportation systems, and orienteering. He has previously contributed to cloud computing, hate speech detection, and cyber-physical energy systems, demonstrating his interdisciplinary approach to AI research. Dr. Della Vedova's publication record reveals a consistent trajectory of impactful research across multiple domains of artificial intelligence. His recent work shows a strong focus on interpretability in AI systems, with significant contributions to natural language processing, geospatial analysis, and causal inference. The research demonstrates both theoretical depth and practical applications, particularly in environmental monitoring and social media analysis. His methodology often combines traditional machine learning approaches with novel interpretability techniques, creating bridges between complex AI systems and human understanding. Dr. Della Vedova has received several prestigious recognitions for his work: Best PhD thesis award from the Order of the Engineers of Bergamo (2013) Italian champion of Il Cervellone (2012) Top Italian performer in IEEEXtreme 6.0 programming competition (148th overall globally, 2012) Premio Arturo Schena award from Fondazione Credito Valtellinese (2010) With over 50 students supervised through bachelor's and master's theses, Dr. Della Vedova has established himself as a dedicated mentor in the AI community. His current PhD students include Minerva Suvanto working on interpretable NLP and Vivien Lacorre developing AI for railway infrastructure inspection. His supervision spans diverse topics from forest naturalness evaluation to hate speech detection and transportation optimization. Beyond formal supervision, he actively contributes to educational initiatives including serving as Director of Chalmers' Data Science and AI master's program and developing innovative teaching methods that connect theoretical concepts with real-world applications. Dr. Della Vedova is deeply embedded in both academic and professional communities. He leads the Applied Artificial Intelligence research group at Chalmers while maintaining strong connections with European research networks through projects like the ERASMUS+ EUrienteering initiative. His interdisciplinary approach is reflected in collaborations across computer science, environmental science, and social sciences. Notably, he applies his AI expertise to orienteering both as a researcher developing localization methods and as a licensed Event Advisor for the International Orienteering Federation, demonstrating how his professional and personal interests converge in innovative ways.

Mattias Villaniمشاهده پروفایل

استاد

• Bayesian Statistics
• Machine Learning
• Computational Methods
• +۳ مورد دیگر

Mattias Villani is Professor of Statistics at Stockholm University, specializing in Bayesian statistics and machine learning. He obtained his PhD in Statistics from Stockholm University in 2000 and has held positions at Sveriges Riksbank and Linköping University. Villani develops computationally efficient Bayesian methods for inference, prediction and decision-making with flexible probabilistic models. Research Interests: His work spans Bayesian computation (MCMC, HMC, variational inference), machine learning (Gaussian processes, mixture models), and applications in neuroimaging, transportation, and econometrics. Research focuses on scalable Bayesian methods for large datasets and complex models. Publication Focus: Recent articles concentrate on Bayesian neuroimaging analysis, transportation network modeling, and efficient MCMC algorithms. Methodological innovations in subsampling techniques for large-scale Bayesian computation represent a significant research trend. Student Advising: Supervises PhD students in statistical methodology development and applications. Current research groups focus on spatiotemporal modeling, locally stationary processes, and neuroimaging statistics.

Xiyu Jiaoمشاهده پروفایل

مدرس ارشد

• Econometrics
• Outlier Robustness
• Nonlinear Cointegration
• +۶ مورد دیگر

Xiyu Jiao is a Senior Lecturer in the Department of Economics at the University of Gothenburg, where he joined in 2023. Previously, he served as a Postdoctoral Research Fellow at Nuffield College, Oxford (2019–2023). He holds a DPhil (and MPhil) in Economics from the University of Oxford (2019), an MSc in Statistics (Research) with Distinction from the London School of Economics (2012), and a BSc in Computational Mathematics from Northwest University, China (2011). His research focuses on econometrics, statistics, and machine learning, with particular emphasis on outlier robustness, nonlinear cointegration, and model selection. His work bridges theoretical advancements and practical applications in addressing econometric challenges. He is affiliated with the Institute for New Economic Thinking and Climate Econometrics at the University of Oxford. Xiyu’s research trends reflect a strong emphasis on robust statistical methodologies, particularly in outlier detection and regression analysis. His articles explore asymptotic theory, instrumental variables, and algorithmic approaches to econometric problems. He has no listed academic awards but maintains active collaborations through his associations with interdisciplinary institutes. No grants or advising records are explicitly mentioned in the provided texts. Xiyu contributes to research teams at Climate Econometrics and the Institute for New Economic Thinking, focusing on econometric applications in climate and economic systems.

• Nonlinear Filtering
• Multi-Object Tracking
• Deep Machine Learning
• +۳ مورد دیگر

Lennart Svensson is a Professor at Chalmers University of Technology in the Signal Processing research group. His work focuses on nonlinear filtering, multi-object tracking, Bayesian statistics, and deep machine learning with applications in autonomous systems and sensor fusion. Research Interests Nonlinear Filtering and Bayesian Inference Multi-Object Tracking and Sensor Fusion Deep Learning for Autonomous Systems Performance Metrics (GOSPA, T-GOSPA) Lidar-Camera Fusion and Radiance Fields 5G SLAM and mmWave Sensing Publications Trends Recent work emphasizes uncertainty-aware multi-object tracking metrics, trajectory estimation using Poisson Multi-Bernoulli Mixtures, and sensor fusion techniques for autonomous driving. His research integrates Bayesian methods with deep learning for applications in automotive radar, lidar, and 5G positioning systems. Contact Email: lennart.svensson@chalmers.se

• GNSS Positioning Error Estimation
• Machine Learning Interpretability
• Algorithmic Fairness
• +۴ مورد دیگر

Alejandro Kuratomi is an Assistant Professor in Data Science at the Department of Computer and Systems Sciences (DSV), Faculty of Social Sciences, Stockholm University. His academic journey includes a Ph.D. in Machine Learning (2024), M.Sc. in Engineering Design: Mechatronics (2019), and dual B.Sc. degrees in Industrial and Mechanical Engineering (2014). Ph.D., Machine Learning – DSV, Stockholm University M.Sc., Mechatronics – KTH Royal Institute of Technology B.Sc., Industrial Engineering – Universidad de Los Andes B.Sc., Mechanical Engineering – Universidad de Los Andes Kuratomi’s research focuses on Machine Learning Interpretability , Algorithmic Fairness , and Multivariate Time Series Classification , with applications in GNSS error estimation and healthcare decision-making. He develops interpretable models like CRITS and ORANGE to address technical and ethical challenges in AI. His recent work explores Transformer/LLM interpretability , mechanistic explanations , and integer-justified counterfactuals . While no awards or students are mentioned, his publications highlight interdisciplinary efforts combining computer science, ethics, and engineering.

Jiabao Heمشاهده پروفایل

پژوهشگر

• Control Theory
• System Identification
• Information Theory
• +۵ مورد دیگر

Jiabao He is a Ph.D. Student in the Division of Decision and Control Systems at KTH Royal Institute of Technology , supervised by Prof. Håkan Hjalmarsson. He holds a Master's degree in Control Engineering (2021) and a Bachelor's degree in Mechanical Engineering (2018) from Tsinghua University. Education M.S. in Control Engineering (Tsinghua University, 2021) B.S. in Mechanical Engineering (Tsinghua University, 2018) His research interests lie in systems, control theory, and information theory , with current focus on subspace identification, finite-sample analysis , and data-driven control . Past work explored fuzzy control and descriptor systems . His publications reflect expertise in subspace methods , statistical learning , and Markov parameter identification . Conference Participation includes Reglermötet 2025 (Lund) , 63rd IEEE CDC 2024 (Milan) , 32nd ERNSI Workshop 2024 (Venice) , and SYSID 2024 (Boston) . He is a reviewer for journals and conferences like IEEE Transactions on Automatic Control and IFAC Symposium on System Identification . Scientific Awards IEEE TC SIAC Student Paper Award (2025) Travel Scholarship from Karl Engvers Foundation (2024) Travel Scholarship from Björns stiftelse (2024) He has served as a Teaching Assistant for courses such as EL1020 Automatic Control and EL2520 Control Theory and Practice , and supervised Bachelor theses on Learning in Dynamical Systems (2023-2024). His work bridges theoretical control methods with practical applications in descriptor systems and space robotics.

• Probability Theory
• Futurology
• Existential Risk
• +۴ مورد دیگر

Olle Häggström is a Professor of Mathematical Statistics at Chalmers University of Technology, specifically in the Department of Applied Mathematics and Statistics. His academic career spans several decades with a significant shift in research focus over time. Häggström's research interests have evolved from traditional probability theory to encompass broader future-oriented topics. Initially focused on mathematical statistics and probability theory, including percolation theory and stochastic processes, he has increasingly turned his attention to futurology, existential risk, and AI safety in recent years. His work demonstrates a unique interdisciplinary approach, bridging rigorous mathematical analysis with philosophical considerations about humanity's technological trajectory. The trends in Häggström's publications reveal a clear evolution from purely mathematical research toward interdisciplinary studies examining the societal implications of emerging technologies. His recent work focuses heavily on AI safety, existential risk assessment, and long-term futures thinking, while still maintaining connections to his mathematical foundations. This shift is evident in publications ranging from technical mathematical papers to broader philosophical discussions about technology's impact on civilization. Häggström has received research funding from notable sources including the FTX Foundation Future Fund for his project "Topics in the theory of xrisk and longtermism" (2022-2025), indicating recognition of the importance of his work in the existential risk community. His book "Here Be Dragons: Science, Technology and the Future of Humanity" (2016) represents a significant synthesis of his thinking on these topics. While specific details about his advising activities are not provided in the source material, his research projects suggest engagement with interdisciplinary teams working at the intersection of mathematics, computer science, and future studies. His work appears to influence both academic and policy discussions regarding technological risk and long-term planning.

Chun-Biu Liمشاهده پروفایل

دانشیار

• Computational Mathematics
• Statistical and Computational Methods in Biophysical Systems
• Information Theory
• +۴ مورد دیگر

Chun-Biu Li is an Associate Professor at the Department of Mathematics , Stockholm University , specializing in computational mathematics and statistical physics of biophysical systems. His research bridges information theory, machine learning, and nonequilibrium statistical mechanics to understand complex biological processes. Education: PhD in mathematical & statistical physics from the University of Texas at Austin (supervised by Ilya Prigogine), M.S. in mathematical physics from the University of Utah. Academic Experience: Associate Professor (2016–present) at Stockholm University, Associate Professor (2008–2016) at Hokkaido University, and JST/CREST Researcher (2005–2008) at Kobe University. Research Interests focus on data-driven statistical analyses, nonequilibrium biophysical systems, AI explainability, and fluctuation theorems. His work applies these methods to plant morphogenesis, molecular motor dynamics, and machine learning applications in life sciences. Teaching includes advanced courses on deep learning, reinforcement learning, and statistical methods for life science applications. His Supervision has guided over 30 students in topics ranging from protein mutation modeling to causal inference algorithms. Scientific Awards: Professional Development Award, University of Texas at Austin (2003) Summer Research Fellowship, University of Texas at Austin (2001) National Dean's List for Outstanding College Students (1997) Publications (110+) highlight his contributions to plant morphogenesis, single-molecule analysis, and statistical methods. Recent works examine DNA barcode clustering, rotary motor protein dynamics, and shape-aware data visualization techniques.

Johanna Rickneمشاهده پروفایل

استاد

• Gender Economics
• Labor Economics
• Political Economics
• +۳ مورد دیگر

Johanna Rickne is a Professor of Economics at the Institute for Social Research (SOFI), Stockholm University, where she conducts research at the intersection of labor economics, political economics, and gender studies. Her work examines gender disparities in labor markets and political representation, utilizing large-scale register data and natural experiments to analyze causal relationships in social phenomena. She actively contributes to SOFI's research environment through the Labor Market Economics (AME) and GAINS (Gender Analysis and Interdisciplinary research Network) groups. Rickne's research program centers on understanding systemic inequalities through interdisciplinary lenses. Her investigations span gender dynamics in career advancement, political selection mechanisms, and the socioeconomic impacts of family structures. Key methodological approaches include quasi-experimental designs leveraging Swedish administrative data, with particular attention to how gender-traditional norms affect professional trajectories and relationship stability. Current projects address labor market equity for marginalized groups—including women, immigrants, and transgender individuals—and the historical evolution of gender-balanced political representation. Analysis of her recent publications reveals consistent thematic focus on gendered career patterns, political elite formation, and workplace inequality. Her work demonstrates how structural barriers like the 'class ceiling' in politics and gender gaps in meaningful work persist despite formal equality measures. Methodologically, she pioneers the use of municipal-level political data and occupational survey evidence to isolate causal mechanisms, with growing emphasis on sexual harassment dynamics and intergenerational political transmission. Scientific recognition includes: CEPR Fellow Wallenberg Academy Fellow Rickne leads multiple significant research initiatives including 'En jämlik arbetsmarknad i teori och praktik' examining labor market outcomes for vulnerable demographics, 'Hundra års färd mot en jämställd politisk representation' analyzing historical political representation trends, and the Laborocto network fostering interdisciplinary working-life research. Her grant portfolio reflects sustained funding for projects investigating political recruitment barriers, gendered career divergence, and policy interventions for workplace equality. She actively mentors junior researchers through SOFI's collaborative environment while contributing to national policy debates on gender equity. Within SOFI's organizational structure, Rickne operates through the Labor Market Economics group studying education, health, taxation, and crime alongside core labor issues, and the GAINS network facilitating cross-disciplinary gender research. Her workspace in University Road 10 F serves as a hub for analyzing Swedish registry data and coordinating international collaborations with institutions like IZA and the Stockholm China Economic Research Center, where she maintains affiliate status.

• High-Dimensional Statistics
• Portfolio Optimization
• Bayesian Analysis
• +۳ مورد دیگر

Taras Bodnar is a Professor at the Department of Management and Engineering, Linköping University. His research focuses on high-dimensional statistical methodologies with applications in finance, portfolio optimization, and econometrics. He specializes in developing and analyzing advanced statistical models for asset allocation, risk management, and multivariate meta-analysis. Bodnar's work often involves Bayesian methods, shrinkage estimation techniques, and copula modeling to address challenges in financial data analysis. His recent contributions include the HDShOP package for portfolio selection and advancements in nonlinear shrinkage tests for large-dimensional covariance matrices. His research bridges theoretical statistics with practical financial applications, addressing issues such as dark uncertainty and efficient frontier estimation in high-dimensional settings. Key research interests include: High-Dimensional Portfolio Optimization Bayesian Analysis in Financial Contexts Covariance Matrix Estimation and Testing Uncertainty Quantification in Multivariate Analyses Statistical Software Development for Finance Recent publications (2024-2025) emphasize methodological innovations in portfolio selection, copula modeling, and robust statistical inference. His work has implications for both academic theory and practical investment strategies, particularly in managing large and complex financial datasets.

Andreea Enacheمشاهده پروفایل

استادیار

• Structural Econometrics
• Industrial Organization
• Auctions
• +۴ مورد دیگر

Andreea Enache is an Assistant Professor at the Department of Economics, Stockholm School of Economics (SSE), with affiliations to the House of Governance and Public Policy, Center for Resilient Health, and Karl-Adam Bonnier Center for Governance. She holds a PhD in Mathematical Economics and Econometrics from Paris School of Economics (2015) and has held academic positions at institutions like Bocconi University, the European University Institute, and the University of Chicago. Her research focuses on structural econometrics, industrial organization, auctions, health economics, digital economics, and cultural economics. Education: PhD in Mathematical Economics and Econometrics, Paris School of Economics, 2011–2015 MSc in Economics, Toulouse School of Economics, 2010–2011 MSc in Applied Econometrics and Statistics, University of Orléans, 2009–2010 Bachelor’s Degree in Finance, Academy of Economic Studies (Bucharest), 2005–2008 Research Interests: Her work integrates econometric methods with applications in auctions, healthcare markets, digital platforms, and cultural industries. She explores topics like market competition dynamics, pricing strategies, and policy impacts using structural models and advanced statistical techniques. Publications: Recent work includes studies on Swedish primary care ownership structures, mobile app consumer behavior, and auction models. These contributions highlight her expertise in linking theoretical frameworks to real-world economic phenomena. Awards: Recipient of the PhD Thesis Prize 'Agguire-Basualdo' (2015), recognizing her doctoral research on structural econometrics and nonlinear inverse problems. Advisees & Grants: While no formal advisee list is provided, her collaborations span institutions like CEPR and involve co-authored research on healthcare, digital markets, and cultural economics. Active participation in workshops like SweRIE (2025) underscores her engagement with academic communities. Labs/Teams: Affiliated with interdisciplinary centers like the Center for Resilient Health and Karl-Adam Bonnier Center for Governance, focusing on policy-relevant research in health systems and governance structures.

Pierre Nyquistمشاهده پروفایل

دانشیار

• Probability Theory
• Mathematical Statistics
• Applied Mathematics
• +۸ مورد دیگر

Pierre Nyquist is an Associate Professor and docent in the Department of Mathematical Sciences at Chalmers University of Technology and Gothenburg University. His research is sponsored by the Swedish Research Council, the Swedish e-science Research Center (SeRC), and the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP). He is also an elected member of the Young Academy of Sweden for the period 2024-2029 and has served as a scientific ambassador for EURANDOM since November 2021. Dr. Nyquist's research interests lie at the intersection of probability theory, mathematical statistics, and applied mathematics. His main expertise is in probability theory, with a focus on large deviations theory and stochastic numerical methods. He has a general interest in all aspects of probability theory and much of what is categorized as applied mathematics, particularly questions related to partial differential equations, optimization, and stochastic optimal control. Recently, he has become increasingly interested in the mathematical foundations of complex data analysis and modeling, and the interplay with ideas from physics. His current research interests include large deviations, gradient flows and their generalizations, stochastic numerical methods, statistical learning theory, stochastic processes, and random dynamical systems. Pierre Nyquist has received research funding from several prestigious sources including the Swedish Research Council, the Swedish e-science Research Center (SeRC), and the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP). His publications demonstrate a consistent focus on theoretical aspects of probability with applications to computational methods and data analysis, showing increasing integration with machine learning techniques in recent years. elected member of the Young Academy of Sweden (2024-2029) scientific ambassador for EURANDOM (since November 2021) Dr. Nyquist is actively involved in mentoring the next generation of researchers. He currently supervises several PhD students including Cinja Arndt (starting Aug. 2025), Niki Wilhemlson (started Aug. 2024), and Viktor Nilsson (started Aug. 2020). He has previously supervised successful PhD students such as Federica Milinanni (Aug. 2020-May 2025) and Carl Ringqvist (Aug 2015-June 2021). He regularly teaches graduate-level courses including "Modern methods of statistical learning" and has supervised numerous MSc theses on topics ranging from deep learning for time-series radar signals to neural network embedding in insurance pricing. His research group is active in both theoretical developments and practical applications, with current projects spanning from mathematical foundations of probability to applications in machine learning and data science. Dr. Nyquist maintains strong international collaborations, as evidenced by his frequent travel for conferences and research visits to institutions such as Brown University and TU Delft.

Leif Nilssonمشاهده پروفایل

دانشیار

• Statistical Learning
• Defect Detection
• Occupational Health
• +۵ مورد دیگر

Leif Nilsson is an Associate Professor at the Department of Mathematics and Mathematical Statistics, Umeå University. His research focuses on statistical learning methods for industrial quality control and occupational health risk assessment. Statistical learning for defect detection Exposure modeling for occupational hazards Biostatistical analysis of health outcomes Recent work applies probabilistic classifiers and spline smoothers to automate surface finish inspection. He also investigates exposure variability in hand-arm vibration and its health impacts through epidemiological studies. Nilsson collaborates with clinical teams on stress recovery interventions and contributes to methodological developments in biological monitoring. His statistical expertise spans Bayesian modeling, resampling techniques, and spatio-temporal data analysis.