Konrad Burchardi is Professor of Economics at the Institute for International Economic Studies (IIES) at Stockholm University. His academic work focuses on understanding economic development processes and the social foundations of economic behavior, with significant contributions to development economics and migration studies. Professor Burchardi obtained his PhD in Economics from the London School of Economics (LSE), with prior studies conducted in Munich, Aix-en-Provence, and at LSE. His educational background reflects a strong international foundation in economic theory and development perspectives. His research primarily centers on productivity growth at early stages of economic development and the social underpinnings of economic interactions. Key contributions include field experiments on sharecropping contracts in developing countries, demonstrating how output-sharing rules affect agricultural productivity, and research showing how migration ancestry influences foreign direct investment patterns. His work spans agricultural economics, migration studies, experimental economics, and economic networks, often employing rigorous field and natural experimental designs to establish causal relationships. Analysis of Professor Burchardi's publication record reveals consistent contributions to top economics journals with a focus on development and labor economics. His work shows an evolution from examining micro-level economic behaviors in developing countries to broader questions about migration networks and their economic impacts. His recent publications indicate increasing attention to historical economic data and financial inclusion, with work scheduled through 2025.
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Gustav Henterمشاهده پروفایل
استادیار
Gustav Henter is an Assistant Professor in Intelligent Systems at KTH Royal Institute of Technology, specializing in Machine Learning. He is affiliated with the Division of Speech, Music and Hearing (TMH) within the School of Electrical Engineering and Computer Science. His research focuses on deep generative models for applications like speech synthesis, 3D character animation, and human-computer interaction. He holds a Docent degree from KTH and has held post-doctoral positions at the University of Edinburgh and the National Institute of Informatics in Tokyo. Education: PhD in Electrical Engineering (KTH, 2013), MSc in Engineering Physics (KTH, 2007). He supervises doctoral students in areas like gesture synthesis and multimodal interaction. His work is supported by grants from the Wallenberg AI, Autonomous Systems, and Software Program (WASP) and South Korea's MOTIE. He co-founded Motorica AB to commercialize motion synthesis research. Awards include Best Paper Awards at ICMI 2020 and IVA 2020, and recognition for student theses. His research spans generative AI, perceptual evaluation, and robust statistical models. He organizes the GENEA Challenge and Workshop series for gesture generation benchmarking.
Kathrin Kaufhold is an Associate Professor in Applied Linguistics at Stockholm University's Department of English. Her research examines academic writing practices in multilingual and interdisciplinary contexts, focusing on knowledge recontextualization, translanguaging strategies, and institutional communication dynamics. She coordinates undergraduate thesis projects and teaches academic writing, research methods, and sociolinguistics. Current research explores participation in academic writing dialogues Investigates English's role in multilingual university environments Examines institutional communication mediation processes Her work bridges theoretical frameworks from Vygotskian socio-cultural theory and genre pedagogy with practical applications in writing center operations and healthcare communication. She leads the Language and Power research network addressing contemporary linguistic challenges in polarized academic environments. Key collaborations include projects with: Karolina Wirdenäs (healthcare communication) Niina Hynninen (academic writing evolution) Maria Kuteeva (language policy in Nordic universities) Her teaching emphasizes: Academic writing development Research methodology applications Discourse analysis frameworks Applied linguistics principles
Nicolò Dell'Unto serves as Professor of Archaeology at Lund University's Department of Archaeology and Ancient History within the Faculty of Humanities and Theology. His research pioneers digital methodologies for archaeological analysis, specializing in 3D visualization, spatial technology, and virtual reality applications that transform how we perceive and investigate the past. Based at Helgonavägen 3 in Lund (Room LUX:A122), he directs the Digital Archaeology Laboratory (DARKLab) and oversees undergraduate/postgraduate digital archaeology programs. His educational background includes: Archaeology studies at University of Rome, La Sapienza PhD in Technology and Management of Cultural Heritage from IMT Lucca, Italy Postdoctoral fellowship at University of California Merced Dell'Unto's research focuses on how laser scanners, photogrammetry, GIS, and virtual reality technologies fundamentally reshape archaeological practice. His work bridges technical innovation with theoretical frameworks in landscape archaeology, emphasizing practical field applications while addressing methodological challenges in data interpretation. Key themes include digital documentation standards, 3D data management, and the cognitive impact of visualization tools on archaeological reasoning. His publication trends reveal a shift toward AI integration in archaeological data interpretation, infrastructure development for 3D data sharing, and cross-disciplinary collaborations examining Mediterranean connectivity. Recent works emphasize practical frameworks for implementing digital tools in fieldwork while addressing sustainability challenges in digital heritage preservation. Award highlights: Einar Hansen Prize for Humanities (2017) Royal Physiographic Society of Lund election (2024) Best Paper Award (2014) Highly Cited Research recognition (2017) Dell'Unto supervises PhD and master's students while leading major projects including RE-OSTRAKON (3D artifact scanning), TETRARCHs (data reuse), and AIR (Archaeological Interactive Report). His DARKLab serves as Sweden's national infrastructure for digital archaeology, collaborating with institutions like University of Oslo's Museum of Cultural History where he holds a visiting professorship since 2019. He actively shapes digital archaeology policy as Domain Specialist for Swedish National Data Service, Board Member for Statens historiska museer, and Open Science Champion at Lund University, driving national standards for archaeological data management and open science practices.
Gerardo Schneider is a Full Professor in Computer Science at the University of Gothenburg, Sweden, and holds a joint appointment at Chalmers University of Technology. He serves as Head of the Data Science and Artificial Intelligence (DSAI) Division and has previously led the Formal Methods Division and acted as Director of Graduate Studies. University of Gothenburg: 2009–present Chalmers University of Technology: 2009–present Uppsala University: 2002–2003 University of Oslo: 2005–2009 His research focuses on formal methods for software engineering, including contract specification and analysis , privacy policy formalization , model checking , and runtime verification . He works on verification of real-time systems, embedded systems (e.g., smart Java cards), and blockchain-based smart contracts. Key projects include: X-LEGAL (2020–2023): Smart Legal Contracts (Swedish Research Council) PolUser (2016–2019): User-Controlled Privacy Policies (Swedish Research Council) ARVI (2014–2018): Runtime Verification Beyond Monitoring (ICT COST Action) ReMU (2013–2017): Reliable Multilingual Digital Communication (Swedish Research Council) He has supervised numerous PhD and Master’s students in formal methods, blockchain security, and privacy compliance. His tools include SPeeDI (Polygonal Hybrid Systems Verification), CLAN (Contract Normative Conflict Detection), and AnaCon (Controlled Natural Language Analysis).
Malena Janson is a Lecturer at the Department of Child and Youth Studies , Stockholm University , specializing in children's culture and moving image media. Her work spans teaching, research, and public engagement across multiple disciplines. Swedish Film Institute network member for film educational research Steering group member of BIN Norden (Nordic children's culture network) Co-editor on multiple children's culture anthologies Research Focus : Historical and contemporary children's film/TV aesthetics Child figure functions in moving images Child-animal relationships in media Narrative liminality and phantasmagoria Norm criticism in visual culture Adaptation and transmediality Recent Publications address Swedish children's cinema history, Bergman film analysis, and media's role in shaping democratic citizenship. Articles since 2022 explore posthumanist narratives and eco-activism in children's television. Teaching Materials developed for Swedish Film Institute include guides for films like Heartstone , Your Name , and Matilda , emphasizing aesthetic learning and critical thinking.
Fredrik Sandin is a Professor in the Department of Computer Science, Electrical and Space Engineering at Luleå University of Technology, where he leads the Machine Learning research group with approximately thirty members. His work focuses on neuromorphic technologies and the intersection of machine learning with computational physics to solve challenging real-world interaction problems. He coordinates the 'Teknisk fysik och elektroteknik' program at LTU and has been instrumental in establishing neuromorphic research activities at the university. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering Member of WASP (Wallenberg AI, Autonomous Systems and Software Program) and ELLIS (European Laboratory for Learning and Intelligent Systems) Coordinator of Neuromorphic Innovation Platform Sweden with KTH, Lund University, Uppsala University, FOI, ABB, Ericsson, and SAAB Fredrik earned his PhD in Physics from Luleå University of Technology in 2007, with thesis work focusing on dense states of matter in neutron stars. His academic journey began with an MSc diploma work in ATLAS at CERN in 2001, followed by postdoctoral research in computational physics at IFPA in Belgium (2008-2009) and brain-like computing at EISLAB with Prof. Jerker Delsing (2010-2011). Professor Sandin's research interests center around neuromorphic technologies, particularly neuromorphic computing and spiking neural networks. He investigates sensor/detector and intelligent systems co-design where constraints like energy, power, latency, and dynamic range challenge conventional digital approaches. His work spans mixed-signal neuromorphic circuits, algorithms, and systems, as well as machine learning projects involving industrial data and collaboration. He has been a key figure in establishing neuromorphic research at LTU, supported by The Kempe Foundations, particularly through the 2014 Gunnar Öquist Fellowship. His recent publications demonstrate a strong interdisciplinary focus spanning quantum phase transitions, particle physics detector optimization, renewable energy materials, and the integration of large language models into control systems. This diverse portfolio reflects his approach connecting machine learning with fundamental physics and practical engineering applications, particularly in neuromorphic computing and intelligent systems design, with emphasis on solving real-world problems through co-design of hardware and algorithms. Gunnar Öquist Fellowship Award and 3 MSEK grant from The Kempe Foundations ISSP award for an Original Work in Theoretical Physics (signed by Prof. 't Hooft and Prof. Zichichi) New-Talents award for original work in theoretical physics at the International School of Subnuclear Physics in Erice Professor Sandin has supervised numerous PhD students working on topics ranging from neuromorphic TinyML to materials for neuromorphic computing, privacy-preserving machine learning at the edge, and intelligent fault diagnosis. He has secured substantial research funding from various sources including Vinnova, ÅForsk, Kempe Foundations, WASP-WISE, and EU programs like ECSEL JU Arrowhead Tools and ITEA3 AutoDC. His current major projects include the Neuromorphic Innovation Platform Sweden and several initiatives focused on neuromorphic condition monitoring and computing, with total funding exceeding 30 MSEK in the past five years. He leads the Machine Learning group at LTU, which collaborates extensively with industry partners including ABB, Ericsson, SAAB, SKF, and RISE. The group is active in developing neuromorphic technologies for wireless sensor networks, condition monitoring systems, and next-generation intelligent systems that address energy, power, and latency constraints that challenge conventional digital approaches.
Marco L. Della Vedovaمشاهده پروفایل
مدرس ارشد
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 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.
Tobias Dalbergمشاهده پروفایل
مدرس ارشد
Tobias Dalberg is a Senior Lecturer at Uppsala University's Department of Pedagogy, Didactics and Educational Sociology, with research focusing on sociology of education, social stratification, and higher education policy. His work examines gender differentiation in course selection, curricular blind spots, and historical transformations in academic disciplines. Contact: tobias.dalberg@edu.uu.se | Personal Website Research: Utilizes graph theory, natural language processing, and geometric data analysis for educational pathway studies Current research areas include: Gendered patterns in major selection and course enrollment Educational policy analysis (1950-2020) Academic field institutionalization processes Disciplinary legitimacy struggles (1935-1980) Recent publications (2021-2024) reveal trends in: Higher education marketization and gender segregation Curriculum structure impacts on student trajectories Disciplinary evolution in social sciences and humanities Assessment consistency in educational evaluation As member of international research collaborations, Dalberg works with networks studying educational stratification and transnational academic markets. His methodological contributions include applications of network analysis for studying epidemic spread in educational contexts.
Nadeem Abbasمشاهده پروفایل
مدرس ارشد
Nadeem Abbas is a Senior Lecturer at the Department of Computer Science and Media Technology, Faculty of Technology, Linnaeus University, Sweden. He earned his PhD in Computer and Information Science from Linnaeus University and has been working with software systems since 2001. His primary research interests include Self-Adaptive Software Systems, Dynamic Software Product Lines, Software Reuse, Requirements Engineering, Software Architecture and Design, and Architectural Analysis and Reasoning. He is actively involved in multiple research groups including: AdaptWise - focusing on foundations and engineering of self-adaptive software systems Engineering Resilient Systems (EReS) Research Lab - investigating system resilience Smart Industry Group (SIG) - an interdisciplinary group focusing on production and product innovation His recent publications show a strong trend in self-adaptive systems with expansion into health inequality research and environmental monitoring applications. His work bridges theoretical software engineering with practical industrial applications, particularly evident in his survey of industry practices in self-adaptation. Nadeem teaches several courses including: 1DV532 - Starting Out with Java 1DV533 - Structured programming with C++ 1DV534 - Object-Oriented Programming with C++ 2DV600 - Foundations of Software Technology 4DV610 - Adaptive Software Systems 2DV604 - Software Architectures 1DV607 - Object-Oriented Analysis and Design using UML He currently supervises multiple research projects related to self-adaptive systems, architectural analysis tools, and health inequality mitigation through digital solutions. His research portfolio demonstrates strong connections between academic research and practical industry applications, particularly in software architecture and adaptation techniques.
Yonghao Xuمشاهده پروفایل
استادیار
Yonghao Xu is an Assistant Professor at the Department of Electrical Engineering , Linköping University , and affiliated with the Computer Vision Laboratory (CVL) and the Wallenberg Autonomous Systems Program (WASP) . His research bridges remote sensing , machine learning , and AI security . Research Trends Xu's recent publications focus on adversarial attacks and defenses in remote sensing, domain adaptation for semantic segmentation, and benchmark dataset creation (e.g., Sen2Fire). His work addresses challenges in urban sustainability , geospatial data analysis , and deep learning robustness . Labs & Programs He is associated with the Computer Vision Laboratory (CVL) , contributing to autonomous systems through the Wallenberg Autonomous Systems Program (WASP) , a major Swedish initiative in AI and robotics.
Thomas Hellstrom is a Professor at the Department of Computer Science , Umeå University, Sweden. He leads the Intelligent Robotics group and is affiliated with the Center for Transdisciplinary AI . His research spans human-robot interaction (HRI) , deep learning applications , robot ethics , and field robotics for agricultural and forestry automation. Coordinated EU projects: INTRO (FP7/ITN), SOCRATES (H2020), CROPS, SWEEPER Developed intelligent walker for stroke patients with CMTS/MT-FoU/Umeå Stroke Center Key contributions in robot learning , causal reasoning , and natural language understanding Research Focus : His work emphasizes understandability in robot behavior, including causal modeling , multi-modal communication , and ethical frameworks for autonomous systems. Current project ROCC (Swedish Research Council) explores robot causality, while SOCRATES addressed social robotics in eldercare. Scientific Awards : • Erdös-Bacon-Sabbath number ≤ 13 Grants & Funding : • ROCC (2023, 3.7M SEK, Principal Investigator) • SCAI (2022, 3.7M SEK, Co-Applicant) • VINNOVA (2019, 3.47M SEK, Co-Applicant)
Kim Hammarمشاهده پروفایل
پژوهشگر
Kim Hammar is a postdoctoral researcher at KTH Royal Institute of Technology, with affiliations at the University of Melbourne (2025-2028) and Imperial College London. He works under Prof. Tansu Alpcan and Prof. Emil Lupu, focusing on the intersection of game theory, control theory, and large-scale systems for networking and security applications. Previously, he completed his Ph.D. at KTH under Prof. Rolf Stadler and Prof. Pontus Johnson. His research spans cybersecurity, networked systems, and adaptive control mechanisms. Key contributions include applying optimal stopping reinforcement learning conjectural online learning causal modeling to intrusion response and network security. His 2025-2024 publications highlight advancements in automated security through game-theoretic and control-theoretic approaches, with a focus on dynamic environments. Kim received the VR International Postdoctoral Fellowship in 2025. He has served as an assistant for Computer Networks (EP111U) Computer Systems (EP121U) at KTH.
- Game Theory
- Control Theory
- Cybersecurity
- +۵ مورد دیگر
Karl Palmskog is a Lecturer at KTH Royal Institute of Technology in the Division of Theoretical Computer Science and the STEP research group. His work focuses on program verification and proof engineering, with particular emphasis on developing techniques and tools based on proof assistants for constructing functionally correct and secure software systems. Palmskog received his Ph.D. in Computer Science in 2014 from KTH, advised by Mads Dam, and his M.Sc. in Computer Science and Engineering from KTH in 2007. Prior to his current position, he was a postdoc at The University of Texas at Austin and University of Illinois at Urbana-Champaign. His research interests span programming languages, software engineering, and formal verification, with a particular focus on developing techniques and tools based on proof assistants. He is an avid user of the Coq proof assistant for both proving and programming, often complemented by OCaml, and also utilizes HOL4 and other ML family dialects. His work bridges theoretical foundations with practical applications, particularly in the domains of blockchain systems, distributed systems, and automotive software verification. Analysis of his recent publications reveals a strong focus on Coq-based verification, with significant contributions to proof engineering tools and methodologies. His work includes developing tools for regression proving, change impact analysis, mutation testing for Coq projects, and lemma name suggestion using deep learning. There's also a growing trend toward applying formal methods to real-world systems like blockchain protocols and automotive software. Palmskog has been involved in several research projects, including Coq-community Proof Engineering and Distributed Components. His past projects include Trustfull (SSF), Model-based Event Driven Scalable Programming for the Mobile Cloud (NSF), Highly Adaptable and Trustworthy Software (EU FP7), and 4WARD Future Internet (EU FP7). As an educator, Palmskog has served as examiner, course responsible, teacher, and assistant for various courses including Algorithms, Data Structures and Complexity; Degree Projects; Game Theory; Parallel and Distributed Computing; and Programming Paradigms. His work on Chip, a Coq formalization of change impact analysis, demonstrates his commitment to creating practical, certified tools that bridge formal methods with software engineering practice.