جست‌وجوی استاد و پژوهشگر

Liam Solusمشاهده پروفایل

استادیار

• Combinatorics
• Algebraic and Geometric Combinatorics
• Discrete Geometry
• +۵ مورد دیگر

Liam Solus is an Assistant Professor of Mathematics at the Department of Mathematics, KTH Royal Institute of Technology in Stockholm, Sweden. He is affiliated with the ALGEBRA COMBINATORICS & TOPOLOGY division and the Digital Futures Faculty, focusing on research in combinatorics, discrete mathematics, algebraic statistics, and their applications in artificial intelligence and causal inference. His work is supported by grants from WASP (Wallenberg AI, Autonomous Systems, and Software Program), the Swedish Research Council, and the Göran Gustafsson Foundation. Education: PhD in Mathematics (University of Kentucky, 2015), postdoctoral fellowships at MIT, IST Austria, and the Max Planck Institute. Current research projects include causal AI for mobility behavior analysis (cAIMBER), combinatorial and geometric approaches to statistical models, and algebraic methods in machine learning. Research Interests: Algebraic and geometric combinatorics, lattice polytopes, causal inference, graphical models, algebraic statistics. Key contributions include studies on Ehrhart theory, Bayesian networks, and combinatorial sequences like Eulerian polynomials and Fibonacci numbers in causal structures. Awards: Large Prize for Young Researchers (Göran Gustafsson Foundation, 2023). Supervision includes PhD students Danai Deligeorgaki and former student Petter Restadh, along with postdocs in algebraic statistics and combinatorics. Labs/Teams: Co-PI of cAIMBER, member of Digital Futures, organizer of international workshops (e.g., AlCoVE 2024 in VR). Teaching includes courses on statistical learning, algebraic statistics, and probabilistic graphical models.

• Computer Vision
• Machine Learning
• Medical Image Analysis
• +۴ مورد دیگر

Fredrik Kahl is a Professor at Chalmers University of Technology, leading the Computer Vision Group under the Department of Signal Processing and Medical Technology. His research spans Computer Vision , Machine Learning , and Medical Image Analysis , with a focus on geometric deep learning and 3D reconstruction. University: Chalmers University of Technology Department: Signal Processing and Medical Technology Email: fredrik.kahl@chalmers.se His work addresses rotation equivariance , out-of-distribution detection , and privacy-preserving representations . Recent publications explore Gaussian splatting for 3D edge mapping, semi-supervised learning frameworks, and symmetry encoding in ReLU networks. Projects include collaborations with institutions like Wallenberg AI, Autonomous Systems and Software Program and grants from VINNOVA and Vetenskapsrådet (VR) .

Jose M Penaمشاهده پروفایل

عضو هیئت علمی

• Machine Learning
• Artificial Intelligence
• Probabilistic Graphical Models
• +۴ مورد دیگر

Jose M Pena is a Senior Associate Professor and Head of Unit at Linköping University's Department of Computer and Information Science, specializing in the Division of Statistics and Machine Learning (STIMA). His research focuses on probabilistic graphical models including Bayesian networks, Markov networks, and chain graphs within machine learning and artificial intelligence. His research interests center on causal inference, machine learning, and statistical modeling. He actively develops methods for causal discovery, counterfactual reasoning, and sensitivity analysis using normalizing flows and graphical models. His work bridges theoretical statistics with practical applications in complex data analysis. His recent publications (2020-2025) demonstrate a strong focus on causal machine learning, with particular emphasis on directed acyclic graphs (DAGs), counterfactual modeling, and handling unobserved confounding. Key trends include the integration of deep learning with causal structures, robust causal inference under distribution shifts, and scalable methods for high-dimensional causal analysis. As Head of Unit within STIMA, he contributes to the international master's programme in Statistics and Machine Learning. The division hosts significant research activities in modern data analysis and is part of Linköping University's Department of Computer and Information Science—one of northern Europe's largest departments in this field.