جست‌وجوی استاد و پژوهشگر

Malin Palö Forsströmمشاهده پروفایل

عضو هیئت علمی

• Discrete Probability Theory
• Lattice Gauge Theories
• Mathematical Physics
• +۳ مورد دیگر

Malin Palö Forsström is a Visiting Lecturer at Gothenburg University and lectures at Chalmers University of Technology. She previously held a postdoctoral position at KTH Royal Institute of Technology, focusing on probability theory under supervision of Fredrik Viklund and Jonatan Lenells. Her PhD, completed in 2019, was at Chalmers University of Technology under Jeffrey E. Steif. She has also taught courses at multiple institutions, including Chalmers, KTH, and Gothenburg University. Malin Palö Forsström earned her PhD in 2019 from Chalmers University of Technology, supervised by Jeffrey E. Steif. Prior to that, she pursued academic training leading to her doctoral studies. Her primary research interests lie in discrete probability theory, particularly applying probabilistic methods to study lattice gauge theories and related models. She also investigates properties of Boolean functions, including noise sensitivity, volatility, and total influence. Her work bridges theoretical probability with applications in mathematical physics and statistical mechanics. Her recent publications focus on lattice gauge theories, exploring Wilson loops and Higgs models, as well as the analysis of Boolean functions' sensitivity properties. Her work often combines probabilistic techniques with insights from mathematical physics, contributing to both theoretical frameworks and applied models in statistical mechanics. Scientific Awards and Grants: 2021: Ruth och Nils-Erik Stenbäcks stiftelse 2019: Kungliga vetenskapsakademin, Stiftelsen G S Magnusons fond 2015: Stiftelsen Olle Engkvist byggmästare 2014: SveFuM Malin has received several grants supporting her research, including notable awards from the Ruth och Nils-Erik Stenbäcks Foundation and the Swedish Research Council. She has not listed specific advising roles or grants beyond those mentioned. Her work has been presented at numerous events, including the Nordic Congress of Mathematicians, the Mittag-Leffler Institute, and international conferences like ICMP 2021.

• Wood Chemistry
• Biomass Utilization
• Sustainable Materials
• +۵ مورد دیگر

Martin Lawoko is a Professor at KTH Royal Institute of Technology, specializing in Wood Chemistry. His research focuses on developing sustainable materials from biomass and lignin, aiming to replace fossil-based products. He collaborates with industries like Stora Enso and the Wallenberg Wood Science Center (WWSC), and contributes to initiatives such as Bioinnovation, advancing Sweden's bioeconomy goals. His work integrates multidisciplinary approaches to address challenges in lignin valorization, biorefinery processes, and material synthesis. Lawoko’s research emphasizes the structural and reactivity analysis of lignin-carbohydrate complexes (LCCs), exploring their applications in thermosets, bioplastics, and functional polymers. His studies often involve advanced techniques like molecular dynamics simulations, spectroscopy, and process engineering. Key areas include lignin extraction methods, modification strategies, and the development of bio-based materials with tailored properties for industrial use. Publications highlight advancements in lignin-based materials, including studies on kraft lignin utilization, oxidative carboxylation, and thiol-ene thermosets. His work underscores the importance of preserving lignin structure during extraction to maximize its potential in renewable products. While no specific awards or grants are listed, his contributions significantly impact sustainable development and the forestry sector’s product diversification efforts.

• Mathematical Statistics
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Jimmy Olsson is a Professor in the Department of Mathematical Statistics at KTH Royal Institute of Technology. His research focuses on generative models, statistical computational methods, and dynamic systems analysis, particularly in the context of artificial intelligence and machine learning. He specializes in state-space models, sequential Monte Carlo methods, and Markov chain Monte Carlo techniques. His work emphasizes foundational contributions to hidden Markov models, computational algorithms for statistical inference, and the intersection of mathematical statistics with modern data science challenges. Key research areas include developing efficient computational tools for analyzing complex, time-dependent data streams, with applications in AI and machine learning. Olsson’s publications span advanced topics such as diffusion models, particle filtering, and online variational learning, reflecting his expertise in bridging theoretical statistics with practical computational methods. He collaborates with researchers across disciplines, including engineering, computer science, and applied mathematics.

• Quantum Optics
• Quantum Information
• Quantum Machine Learning
• +۳ مورد دیگر

Anton Frisk Kockum is an Associate Professor in Applied Quantum Physics at Chalmers University of Technology. He holds a PhD in Physics from Chalmers (2014) and completed postdoctoral research at RIKEN in Japan (2014–2018), supported by a JSPS Fellowship. He joined the Wallenberg Centre for Quantum Technology in 2018, focusing on theoretical contributions to quantum computing and quantum program design. His research spans quantum optics, quantum information, and quantum machine learning, with emphasis on ultrastrong light-matter coupling, quantum acoustics, and circuit quantum electrodynamics. He explores quantum technologies for building scalable quantum computers and optimizing their applications. Recent work includes entanglement generation in hybrid systems, decoherence mitigation, and quantum process tomography advancements. His publications (67 total) address topics like giant atom dynamics, quantum amplification, and quantum algorithm optimization for real-world problems. He leads projects funded by EU, SSF, and VR, including studies on non-Hermitian topological phases and structured quantum environments.

• Non-equilibrium statistical physics
• Theoretical cell biology
• Theoretical computer science (unconventional computing)
• +۴ مورد دیگر

Zoran Konkoli is an Associate Professor at Chalmers University of Technology, affiliated with the Department of Microtechnology and Nanoscience (MC2) within the Electronic Materials division. His research spans interdisciplinary areas including non-equilibrium statistical physics, theoretical cell biology, and unconventional computing. He holds a PhD in Theoretical Chemistry from Gothenburg University and has held postdoctoral positions at Chalmers, NORDITA, and the Applied Physics Department at Chalmers. His academic journey includes roles such as assistant professor in Biological Physics (2002–2006) and a permanent position as Associate Professor since 2007. Konkoli’s research focuses on diffusion-controlled reactions, theoretical cell biology (e.g., HIV latency mechanisms), and computational models like cellular automata and memristor networks. His work intersects condensed matter theory, statistical physics, and computer science. Key projects include the EU-funded RECORD-IT (2015–2018) and SYMONE (2012–2015), exploring reservoir computing and biomimetic systems. He collaborates widely, emphasizing interdisciplinary networks to advance theoretical ideas into practical applications. His expertise includes mathematical logic, measure theory, and approximation theorems, reflecting his broad academic interests.

Chiara Trentiمشاهده پروفایل

پژوهشگر

• Cardiovascular Magnetic Resonance Imaging
• Aortic Hemodynamics
• Wall Shear Stress Analysis
• +۳ مورد دیگر

Chiara Trenti is a researcher at Linköping University, affiliated with the Department of Health, Medicine and Caring Sciences and the Division of Diagnostics and Specialist Medicine. She holds a PhD in Medical Sciences (2020), an MSc in Biomedical Engineering with a focus on Biomechanics and Biomaterials (Politecnico di Milano, 2019), and a BSc in Biomedical Engineering (Politecnico di Milano, 2016). Her research focuses on advancing cardiovascular magnetic resonance imaging (CMR) techniques, particularly 4D flow MRI, to study blood flow dynamics and their effects on aortic remodeling. Key interests include wall shear stress analysis, biomarker correlations in aortic diseases, and risk factor identification for vascular conditions like abdominal aortic aneurysms and aortic dilation. Her work has been recognized with awards including 2nd place for best presentation at the 2024 ISMRM Annual Meeting and the 2022 Potchen-Passariello Basic Science Award. She collaborates with the Center for Medical Image Science and Visualization (CMIV) and researchers like Petter Dyverfeldt and Tino Ebbers. Current research emphasizes decoding aortic disease mechanisms through enhanced MRI analysis and exploring hemodynamic factors influencing vascular remodeling. Publications highlight contributions to understanding oscillatory shear stress in bicuspid aortic valve patients, correlations between wall shear stress and biomarkers in aortic dilation, and risk factors for abdominal aortic aneurysms. Ongoing projects include follow-up studies on ascending aortic dilation progression and development of automated segmentation methods for 4D flow MRI.

Ulf Kargénمشاهده پروفایل

استادیار

• Information Security
• Systems Security
• Software Security
• +۴ مورد دیگر

Ulf Kargén is an Assistant Professor at Linköping University's Department of Computer and Information Science (IDA). His research focuses on information security, particularly systems and software security, with an emphasis on dynamic analysis of binary-code programs. He is a PhD student under the supervision of Professor Nahid Shahmehri, exploring topics such as scalable dynamic analysis methods and security flaw detection in software. He leads courses in software security (TDDC90) and information security (TDDD17) at Linköping University. His work includes developing tools like desync-cc for program obfuscation and analyzing Android app decompilation performance. He is part of the Security and Networks Group at ADIT, which emphasizes secure and reliable information systems. Key research areas include code obfuscation detection, malware analysis, and empirical studies of decompiler effectiveness. His publications span topics like autonomous vehicle compliance testing, binary code analysis, and compiler-level security mechanisms.

Fredrik Lindstenمشاهده پروفایل

دانشیار

• Machine Learning
• Statistical Machine Learning
• Approximate Bayesian Inference
• +۷ مورد دیگر

Fredrik Lindsten is a Senior Associate Professor in Machine Learning and Head of the Division of Statistics and Machine Learning at Linköping University's Department of Computer and Information Science (IDA). His research focuses on statistical machine learning, emphasizing probabilistic modeling and uncertainty quantification in methods such as approximate Bayesian inference, representation learning, and graph-based approaches. Applications span weather forecasting, materials science, biochemistry, and automotive industry challenges. Education: MSc (2008), PhD (2013) in Automatic Control from Linköping University; Postdoctoral roles at University of Cambridge, UC Berkeley, and University of Oxford. Affiliations: WASP (Wallenberg AI, Autonomous Systems and Software Program), ELLIIT (Lab for Information and Communication Technology). Research Interests: Lindsten’s work bridges statistical methodology and machine learning, particularly in quantifying uncertainty in predictions. His team explores method development across diverse applications, including spatio-temporal models and graph-based techniques. Recent projects include probabilistic weather forecasting with graph neural networks and cryo-EM reconstruction techniques. Publications: Over 25+ peer-reviewed articles, with recent highlights in Nature Methods , NeurIPS , and Physical Review Materials , focusing on Bayesian methods, generative models, and computational statistics. Awards: Ingvar Carlsson Award (Swedish Foundation for Strategic Research), Benzelius Award (Royal Society of Sciences in Uppsala). Grants & Teams: Supervises 7+ PhD students; active in collaborative projects funded by WASP, ELLIIT, and VR (Swedish Research Council). Labs/Teams: Leads the Division of Statistics and Machine Learning (STIMA) at IDA, fostering interdisciplinary research in probabilistic machine learning.

Andreas Larssonمشاهده پروفایل

پژوهشگر

• Educational Development
• Technology Education
• Educational Science
• +۳ مورد دیگر

Andreas Larsson is a Postdoc researcher at Linköping University's Department of Behavioural Sciences and Learning (IBL), within the Division of Learning, Aesthetics, and Natural Science (LEN). His work focuses on educational development, particularly in science and technology education, and he actively contributes to the Swedish National Centre for Science and Technology Education (NATDID). His research emphasizes bridging educational research with practical teaching methods, including the integration of technology, programming pedagogy, and the role of spatial metaphors in instruction. Education: While specific academic degrees are not detailed in the provided texts, Larsson's role as a Postdoc indicates advanced qualifications in educational sciences or related fields. Research Interests: Larsson specializes in technology education, programming pedagogy, and the use of gestures and metaphors in teaching. He explores how spatial constructs and linguistic metaphors aid abstract concept comprehension, particularly in programming and STEM education. His work also addresses curriculum continuity and material resources in Swedish vocational training programs. Articles Overview: Larsson’s recent publications (2022–2025) focus on metaphor analysis in programming education, gesture-based teaching strategies, and the material foundations of effective technology education. His research highlights the interplay between embodied cognition and instructional design. Awards: No scientific awards are explicitly mentioned in the provided texts. Advising & Grants: While specific grants are not listed, Larsson collaborates with NATDID to translate research into practical educational tools. His involvement in conferences like the WERA Task Force Global Research in Extended Education (2025) underscores his contribution to educational policy and outreach. Labs/Teams: Active in LEN and NATDID, he contributes to projects on AI literacy in teacher education and the greening of working life (GROWL). His work intersects with interdisciplinary initiatives exploring digital tools in education and workplace dynamics.

Jiyoung Kimمشاهده پروفایل

استادیار

• Entrepreneurship
• Innovation
• Resource Inequality
• +۳ مورد دیگر

Jiyoung Kim is an Assistant Professor at Jönköping University, affiliated with the Centre for Family Entrepreneurship and Ownership (CeFEO). She is currently on a leave of absence but maintains her academic position. Her research focuses on entrepreneurship ecosystems, institutional support mechanisms, and the socio-economic impacts of entrepreneurial networking. Key themes include resource inequality, policy effects on startups, and computational models of social network dynamics. Her work integrates interdisciplinary methods from economics, management, and data science to explore how external enablers (e.g., policies, networks) influence entrepreneurial success. Recent studies emphasize simulation-based analysis of country-level support systems and the unintended consequences of institutional interventions. She holds a strong publication record in entrepreneurship and innovation studies, with a focus on actionable insights for policymakers and practitioners. No scientific awards or grants are explicitly listed in the provided information. Her professional activities include contributions to CeFEO’s research initiatives and collaborations with academic and industry partners.

• Analytic Number Theory
• Computational Number Theory
• Dynamical Methods
• +۳ مورد دیگر

Pankaj Vishe is a Research Fellow (Gustafsson Postdoc) at the KTH Royal Institute of Technology, affiliated with the Department of Mathematics under the School of Engineering Sciences. He collaborates with Prof. Par Kurlberg and focuses on analytic number theory, computational number theory, and dynamical methods with applications to L-functions and the circle method. Education: PhD in Mathematics from the Courant Institute of Mathematical Sciences, NYU (2005–2010) under Prof. Akshay Venkatesh. Previous positions include Visiting Researcher roles at the Max Planck Institute for Mathematics (MPIM, Bonn, 2011) and École Polytechnique Fédérale de Lausanne (EPFL, 2010). Research Interests: His work bridges analytic number theory and dynamical systems, particularly leveraging computational techniques to study L-functions, the circle method, and their applications in number fields. Recent projects include collaborative work with Andreas Strömbergsson and Tim Browning on cubic hypersurfaces and algorithmic advancements for L-function computations. Teaching Experience: At NYU, he instructed courses in probability, statistics, calculus, and real/complex analysis between 2006–2010, including roles as grader, teaching assistant, and course instructor. Affiliations: Active in KTH’s Analysis and Dynamics Seminar (DNA), linked to DNA-L research groups, and maintains ties with colleagues like Tristan Freiberg and Samuel Holmin.

Gaurav Raiمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Computer Vision
• Image Processing
• +۳ مورد دیگر

Gaurav Rai is a Ph.D. researcher at Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), focusing on computer graphics, computer vision, and GPU computing. He has contributed to real-time animation frameworks and diffusion models for motion transfer. Ph.D. in Computer Science and Engineering (2020–present), IIIT Delhi B.Tech in Computer Science and Engineering (2016–2020), GKV Haridwar His research emphasizes creative algorithms for sketch-based animation and dynamic deformation. Recent work involves diffusion models with temporal consistency and motion-guided frameworks. Gaurav has published in top-tier conferences like SCA, GRAPP, and ICME, with a focus on computer graphics, deep learning, and multimedia. Earlier publications include applications in medical imaging and spam filtering. ACM Student Travel Grant for ICME 2025 CHANAKYA Doctoral Fellowship He has served as a teaching assistant for courses in Computer Graphics, Scientific Computing, and Linear Algebra at IIIT Delhi.

• Educational Technology
• Mathematics Education
• Programming Pedagogy
• +۳ مورد دیگر

Ana Fuentes Martinez is a Senior Lecturer at Halmstad University, affiliated with the School of Education, Humanities and Social Sciences. Her work focuses on educational technology, programming pedagogy, and the intersection of mathematics and computing in teaching. She has extensively published on pandemic-era pedagogical adaptations, teacher training, and interdisciplinary curriculum design. Her research emphasizes practical acceptance models and the role of programming in fostering critical thinking in K-12 education. Research Themes Educational Technology Integration Programming in Mathematics Education Covid-19 Impact on Teaching Practices Teacher Professional Development Recent publications analyze the dual challenges and opportunities of online learning during crises, the role of soft skills in interdisciplinary PhD programs, and gender dynamics in programming education. Her work bridges theoretical educational frameworks with practical classroom applications.

• Machine Learning
• Predictive Maintenance
• Forklift Activity Recognition
• +۵ مورد دیگر

Thorsteinn Rögnvaldsson is a Professor of Computer Science and Deputy Vice Chancellor for Research at Halmstad University. He leads the Center for Applied Intelligent Systems Research (CAISR), focusing on machine learning applications in predictive maintenance, healthcare, and satellite-based poverty analysis. His research spans meta-learning, survival analysis, and synthetic data generation. Education: Not explicitly listed in the provided texts, but inferred to include doctoral training in computer science or related fields given his academic rank. Research Interests: Machine learning for knowledge creation, predictive maintenance for heavy vehicles, forklift activity recognition, synthetic healthcare data, and satellite imagery analysis for poverty mapping. Collaborations include projects with Lund University on neural networks for satellite data interpretation. Articles Trends: Recent work emphasizes meta-learning frameworks, survival analysis model interpretability, and synthetic data evaluation. Key domains include healthcare, industrial IoT, and cross-domain adaptation. Awards: None explicitly listed in the provided texts. Grants/Advising: Coordinates CAISR's research projects and supervises doctoral students in applied machine learning. Active in industry partnerships, particularly with automotive and healthcare sectors. Labs/Teams: Director of CAISR, which hosts interdisciplinary teams in AI, IoT, and big data applications.

Anthony H. Aletrasمشاهده پروفایل

استاد

• Cardiovascular Magnetic Resonance Imaging
• Fetal Diagnostics
• Medical Imaging Technologies
• +۳ مورد دیگر

Anthony H. Aletras is a dual-affiliated academic serving as a Researcher at Lund University's Lund Cardiac MR Group (Medical Faculty) and as Director/Professor of Biomedicine and Imaging Technologies at Aristotle University of Thessaloniki's School of Medicine. He holds a BSc in Electrical Engineering from Aristotle University and a PhD in Biomedical Engineering from The Ohio State University (1996). His postdoctoral work at NIH's Laboratory of Cardiac Energetics (until 2010) and prior role as Chairman/Professor at University of Thessaly (until 2014) further highlight his academic trajectory. Research focuses on cardiovascular MRI technology, fetal diagnostics, and innovations like DENSE strain imaging and CEST contrast agents. His work contributes to UN SDG goals in health and well-being. Notable achievements include pioneering Area-At-Risk determination via intramyocardial edema and advancing CMR perfusion mapping techniques. Recent projects include an advanced fetal imaging initiative (Swedish Research Council, 2024–2028) and technical developments in exercise cardiac MRI. His 91+ publications span MRI simulation, fetal blood oxygenation measurement, and infarct quantification validation across centers/vendors. Award-winning mentorship includes supervising student T. Lala in MRI research. His labs integrate medical imaging with AI and computational methods, emphasizing cloud-based simulation tools like coreMRI. Active collaborations span global institutions, reflecting his leadership in cardiovascular imaging innovation.