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Simon MillsView profile

Associate Professor

• Philosophy of Technology
• Media Theory
• Gilbert Simondon
• +11 more

Simon Mills is an Associate Professor in New Media at the Leicester Media School, Faculty of Computing, Engineering and Media, De Montfort University (DMU), UK. He is a leading scholar in the philosophy of technology and media theory, with a specialized focus on the work of Gilbert Simondon. His research explores the philosophical underpinnings of digital media, information, and technical individuation, contributing significantly to contemporary debates on AI, big data, and ethics. PhD, University of the West of England — Gilbert Simondon: Causality, Ontogenesis & Technology PGCert(HE), De Montfort University MSc in Multimedia, Nottingham Trent University MA in Writing (Practice & Issues), Nottingham Trent University BA Hons Philosophy, University of Nottingham Simon Mills' research centers on the philosophy of technology, particularly the work of Gilbert Simondon, and its application to contemporary issues in digital media, AI, and ethics. He investigates concepts such as individuation, transduction, and the transindividual, bridging continental philosophy with media and technology studies. His work critically engages with cybernetics, information theory, and the ontology of technical objects, especially in software and data systems. His recent publications, including the monograph Gilbert Simondon: Information, Technology and Media (2016) and articles like 'Simondon and Big Data' (2015), demonstrate a consistent trajectory in analyzing the philosophical implications of big data, AI, and digital culture through Simondonian frameworks. His scholarship reveals a deep engagement with interdisciplinary themes, connecting philosophy, media studies, and computer science to critique and reimagine technological development. Simon Mills has served on the editorial advisory board of Writing Technologies and has conducted peer reviews for journals such as AI & Society , Information, Communication and Society , and Sociological Review . He has delivered invited talks and keynotes at international conferences, including events at Nankai University, Edinburgh University Press, and the University of Kent, affirming his standing in the academic community. He teaches courses in New Media theory, AI & Society, Cybernetics, and Digital Publishing. He is affiliated with the Media & Culture Research Centre (MCRC) at DMU, contributing to a vibrant research environment focused on media, technology, and culture. His ongoing work includes a forthcoming chapter in The Edinburgh Companion to Simondon (2025), where he explores the optative dimension of technical and ethical invention.

Jens KrauseView profile

Professor

• Behavioural Ecology
• Collective Intelligence
• Social Networks
• +7 more

Jens Krause is a Professor and Head of Department at the Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB) in Berlin, leading the Research Group on Mechanisms and Functions of Group-Living. He holds a full professorship in Fish Ecology at Humboldt-Universität zu Berlin, Faculty of Life Sciences, Thaer-Institute, and since 2018 has been an Adjunct Professor at Technical University Berlin within the Excellence Cluster 'Science of Intelligence'. His research is centered on collective intelligence, social networks, decision-making, and behavioural ecology in fish and other animals. Full Professor in Fish Ecology, Humboldt-Universität zu Berlin Adjunct Professor at Technical University Berlin (since 2018) Head of Department, IGB Berlin PhD, University of Cambridge Diploma, Free University Berlin His work integrates experimental biology, network analysis, and biomimetic robotics to understand how animals make collective decisions. His expertise spans animal behaviour, evolution, and ecological physiology, with a strong focus on group-living dynamics. Recent research explores group hunting, predator evasion, social foraging, and the impact of environmental stressors on collective behaviour. The analysis of his recent publications reveals a strong trend in understanding collective behaviour in fish, including escape waves, social foraging, group hunting in marlins and sailfish, and the use of robotic agents to study social integration. His interdisciplinary approach combines marine biology, physics, robotics, and data science to uncover the mechanisms behind collective intelligence in both animal and human systems. Editorial Board, Behavioral Ecology Editorial Board, Fish and Fisheries Executive Board, Excellence Cluster 'Science of Intelligence' Advisory Board, Bimini Biological Field Station Foundation He advises numerous PhD students and postdoctoral researchers, and leads major research projects, including 'Developing exploration behaviour' funded by the Excellence Cluster. His work has been supported by extensive collaborations across Europe and North America, and he frequently publishes in top-tier journals such as Nature , Science Advances , Proceedings of the Royal Society , and Current Biology . His lab employs cutting-edge methods including automated tracking, social network analysis, and interactive robotics to study animal groups. His research group, 'Mechanisms and Functions of Group-Living', is embedded within the Excellence Cluster 'Science of Intelligence', where they investigate collective cognition, social information use, and the role of individual differences in group performance. The team combines field studies with laboratory experiments and computational modelling to understand the evolution and function of collective behaviour across species.

Huaizu JiangView profile

Assistant Professor

• Computer Vision
• Computer Graphics
• Natural Language Processing
• +5 more

Huaizu Jiang is an Assistant Professor at Khoury College of Computer Sciences, Northeastern University. His research bridges computer vision, graphics, and natural language processing to develop AI systems that understand and reconstruct 3D visual environments. Prior to joining Northeastern, he was a Postdoc Researcher at Caltech and Visiting Researcher at NVIDIA. He holds a Ph.D. from UMass Amherst (advised by Prof. Erik Learned-Miller), and M.E./B.E. degrees from Xi'an Jiaotong University. His research focuses on fundamental challenges in 3D scene understanding, including geometry reconstruction, semantic interpretation, novel view synthesis, motion generation, and optical flow estimation. Core interests span video processing, human-object interactions, multimodal reasoning, and efficient edge-device implementations. Recent publications emphasize diffusion models for motion/scene generation, transformer-based 3D perception, and video interpolation. Key trends include multi-view consistency techniques, text-to-3D synthesis, and efficient real-time algorithms for robotics applications. Awards & Honors: Winner of the VQA Challenge 2020 He advises 15+ graduate students on projects spanning 3D reconstruction, motion synthesis, and vision-language models. His group collaborates with institutions like NVIDIA and Caltech, focusing on generative AI for dynamic scene understanding.

• Information Systems
• Model-Driven Engineering
• Requirements Engineering
• +2 more

Alberto Rodrigues da Silva is a Professor at the Institute Superior Técnico , part of the University of Lisbon . He teaches Fundamentals of Information Systems , primarily during the 1st Semester of the 2025/2026 academic year. His scientific interests revolve around Information Systems , Model-Driven Engineering (MDE), Requirements Engineering (RE), Social Computing , and Software Engineering . He has extensively contributed to the development of rigorous requirements specification languages like RSL (Requirements Specification Language) and its extensions (e.g., RSL-IL4Privacy for privacy policies). His collaborative work spans automated acceptance testing, GDPR compliance, and domain-specific languages (DSLs) for applications such as mobile development , digital twins , and legal contexts (e.g., LegalLanguage ). His research trends focus on integrating model-driven engineering with privacy policies , IoT applications , and low-code platforms . He has also explored tools like Maestro for data classification and usability testing, and RiverCure for flood simulation. Email: alberto.silva@tecnico.ulisboa.pt .

Aleksandar MihajlovicView profile

Researcher

• Visual Communication
• Graphic Design
• Digital Marketing
• +5 more

Aleksandar Mihajlovic is a researcher and Art Director at Singidunum University, Serbia. With a doctoral degree in Contemporary Business Decision-Making (2022), a master's in Business Economics (2014), and a bachelor's in Computer Graphics and Design (2008), he combines academic rigor with creative leadership in the university's marketing strategy. Doctoral studies: Contemporary Business Decision-Making, Singidunum University (2022) Master studies: Business Economics, Singidunum University (2008–2014) Undergraduate: Computer Graphics and Design, Faculty of Informatics and Management (2005–2008) High school: Robotics and Flexible Production Systems Technician, Polytechnic Academy (1995–1999) His research spans visual communication , digital marketing , and artificial intelligence applications in creative industries. Key contributions include Co-authoring 11 academic papers (2015–2025) on topics like Instagram ad effectiveness, techno-feudalism, and responsive logo design. Developing the scientific research portal 'Singipedia' and international magazine 'SingiLogos'. Participating in 7 global projects including Erasmus+ and TEMPUS initiatives. His scientific awards include the JISA Discobolos Special Award (2010), IT Globus Award (2010), and Grafima Fair Special Award (2025). He serves on the organizing committee for conferences like Sinteza and Sitcon , and has judged marketing competitions while volunteering for NGOs like the City Organization of the Deaf of Belgrade.

Katarina DomijanView profile

Associate Professor

• Bayesian applications to data analysis
• Machine learning
• Statistical computing
• +3 more

Katarina Domijan is an Associate Professor in Statistics at the Department of Mathematics and Statistics, Maynooth University, Ireland. She holds a PhD in Statistics from Trinity College Dublin (2008) and has been affiliated with Maynooth University since 2008, transitioning from Lecturer/Assistant Professor to her current role in 2024. Her academic career includes editorial roles as Associate Editor for The R Journal (2021–present) and the Journal of Computational and Graphical Statistics (2015–2024). Research Interests focus on Bayesian methods for high-dimensional data, particularly in classification problems. She specializes in feature selection and model visualization, with applications spanning agricultural data analysis (e.g., hyperspectral imaging for lactose prediction), medical diagnostics (e.g., sepsis and cancer detection), and space physics (e.g., Saturn Kilometric Radiation classification). Her work bridges theoretical statistics with real-world challenges, including socio-economic studies and forensic science. Key Research Areas Bayesian statistical inference Machine learning for large feature spaces Statistical computing and model interpretability Data visualization and chemometrics Scientific Contributions include leading projects like VistaMilk Phase II (2024–2030, €152,300) and Measuring Carbon Sequestration (2024–2028, €174,788.90). Her 15 most recent publications highlight advancements in ensemble modeling, spatial statistics, and medical diagnostics. Scientific Awards Associate Editor, The R Journal (2021–present) Associate Editor, Journal of Computational and Graphical Statistics (2015–2024) Student Supervision includes PhD and MSc graduates such as Dr. Bruna Wundervald (2024) and Dr. Mark O’Connell (2017). She also collaborates with researchers across disciplines, including Dr. Nadim Akasheh in food hypersensitivity studies.

Max PellertView profile

Assistant Professor

• Computational Social Science
• Digital Traces
• Affective Expression
• +9 more

Max Pellert is a computational social scientist and cognitive scientist with faculty appointments at multiple institutions. Since 2022, he has served as an Assistant Professor at the Chair for Data Science in the Economic and Social Sciences at the University of Mannheim . He previously held an interim Professor position at the University of Konstanz and an Assistant Researcher role at Sony Computer Science Laboratories Rome. His work bridges computational methods with social science theory. Education M.Sc., University of Vienna (2017) in Middle European interdisciplinary Master's program in Cognitive Science (with distinction) Ph.D., Medical University of Vienna (2022) in Medical Informatics, Biostatistics & Complex Systems Research interests center on Computational Social Science , Digital Traces , and Natural Language Processing for emotion and sentiment analysis. He develops Temporal Adapters for tracking longitudinal emotional patterns and FAULTANA pipeline for polarization studies. His AI Psychometrics framework assesses personality-like traits in large language models. Recent publications include: (1) 2025 ACL work on political bias in LLMs; (2) 2025 ICWSM study of temporal emotion analysis; (3) 2024 Perspectives on Psychological Science paper on LLM psychometrics; (4) 2024 PNAS Nexus polarization analysis; (5) 2023 Emotion cross-cultural study of pandemic emotions. Scientific Awards Habilitation candidate status at University of Mannheim Teaching includes IS 616: Large Scale Data Analysis , IS 809: Advanced Text Mining Lab , and IS 723: Data Science Seminar at master’s and PhD levels. His Barcelona Supercomputing Center role focuses on principal investigator duties for computational social science projects.

Yeonghyeon GuView profile

Assistant Professor

• Artificial Intelligence
• Meta Learning
• Transfer Learning
• +2 more

Yeonghyeon Gu serves as Assistant Professor in the Department of Artificial Intelligence Data Science at Sejong University, South Korea, a position held since 2022 after progressing from Principal Researcher (2014-2019) to Acting Professor (2019-2022). He maintains active affiliation with the university's AI Convergence Research Center and has produced 84 research outputs with 795 Scopus citations and an h-index of 14. His academic credentials include: B.A. from Sejong University (2004) M.A. from Sejong University (2006) Ph.D. from Sejong University (2014) Dr. Gu's research centers on Artificial Intelligence with specialization in Meta Learning, Transfer Learning, and Deep Learning methodologies. His work demonstrates strong interdisciplinary application across robotics, agricultural technology, energy systems, and meteorology. Key contributions include district heater load forecasting using parallel CNN-LSTM attention, image-based hot pepper disease diagnosis, and potato late blight prediction models. Analysis of his 2024-2025 publications reveals concentrated innovation in hybrid AI architectures, particularly combining graph networks with reinforcement learning for blockchain security and integrating physical models with deep learning for weather prediction. His work consistently addresses real-world engineering challenges through novel neural network applications while maintaining strong theoretical foundations in transfer learning frameworks. No scientific awards were documented in the source materials. While specific student advisees and grant details weren't listed, his extensive publication record (29 outputs in 2025 alone) and international collaborations suggest active mentorship and research funding. His work shows particular strength in cross-institutional projects with researchers from Turkey, Nigeria, Saudi Arabia, and South Korea. As a core member of Sejong University's AI Convergence Research Center, Dr. Gu contributes to institutional initiatives bridging AI theory with practical implementation across multiple sectors. The center's structure facilitates his interdisciplinary approach, connecting computer science with engineering, agriculture, and environmental science domains through shared computational infrastructure and collaborative research frameworks.

• Verification and Validation
• Search-Based Software Engineering
• Autonomous Driving
• +3 more

Shaukat Ali serves as Research Professor and Head of the Department of Engineering Complex Software Systems at Simula Research Laboratory, concurrently holding the title of Chief Research Scientist. His academic leadership drives innovation at the critical nexus of quantum computing, artificial intelligence, and software engineering, with concentrated expertise in verification, validation, and testing methodologies for complex systems including cyber-physical infrastructures and autonomous robotics. His primary research domains encompass: Verification and Validation Search-Based Software Engineering Autonomous Driving Systems Cyber-Physical Systems Engineering Digital Twin Technologies Quantum Software Engineering Analysis of recent publications (2024-2025) reveals a decisive trend toward quantum-AI convergence in software engineering, particularly through quantum software testing frameworks and AI foundation models applied to cyber-physical systems. His work systematically addresses noise mitigation in quantum hardware, uncertainty quantification in adaptive robotics, and novel testing paradigms using vision-language models for industrial robotics—demonstrating both theoretical rigor and industrial applicability. As department head, Ali spearheads strategic research directions in complex software systems, fostering cross-disciplinary collaboration while actively shaping quantum software engineering through workshops like QAI2024 and Q-SANER 2024. His invited presentations at venues including JYU Quantum Electronics and EU-Korea Quantum Forums underscore his influence in defining emerging research landscapes.

Azadeh HaghighiView profile

Assistant Professor

• Smart manufacturing
• Additive manufacturing
• In-space manufacturing
• +3 more

Azadeh Haghighi serves as an Assistant Professor in the Department of Mechanical and Industrial Engineering at the University of Illinois Chicago, with her office located in the Engineering Innovation Building (EIB) at 945 W Taylor St, Chicago, IL 60608. She can be contacted via email at ahaghi3@uic.edu or by phone at 312.355.8306. Her academic credentials include: Ph.D. in Industrial Engineering and Operations Research from the University of Illinois Chicago (2020) M.Sc. in Mechanical Engineering (Production Engineering and Management) from KTH Royal Institute of Technology, Sweden (2013) B.Sc. in Industrial Engineering from Sharif University of Technology (2011) Dr. Haghighi's research program pioneers next-generation manufacturing systems with emphasis on: Smart manufacturing integrating IoT and real-time analytics Additive manufacturing for complex component fabrication In-space manufacturing for extraterrestrial environments Printed electronics for flexible circuit production Multi-robot coordination in additive processes AI-driven decision support for intelligent production She leads research activities through the Smarture Lab, which focuses on the convergence of robotics, data science, and advanced manufacturing methodologies. Information regarding academic advising, grant funding, and scientific awards is not specified in the available documentation.

Sriraam NatarajanView profile

Professor

• Artificial Intelligence
• Machine Learning
• Healthcare Applications
• +5 more

Sriraam Natarajan is a Professor and Director of the Center for Machine Learning and StaRLing Lab at The University of Texas at Dallas (UTD), part of the Erik Jonsson School of Engineering & Computer Science. He holds additional roles as a hessian.AI Fellow at TU Darmstadt and an RBCDSAI Distinguished Faculty Fellow at IIT Madras. His expertise spans Artificial Intelligence, Machine Learning, and their applications in healthcare, with a focus on Relational Learning, Reinforcement Learning, and Graphical Models. He has been honored as an AAAI Fellow (2025), elected to the AAAI Executive Council, and recognized with the UTD Outstanding Graduate Teaching Award. Education: Completed his PhD in Computer Science at Oregon State University in 2007 under Prof. Prasad Tadepalli. Postdoctoral work at the University of Wisconsin-Madison with Professors Jude Shavlik and David Page. Previously served as faculty at Indiana University and Wake Forest School of Medicine. Research: Active in developing AI systems for healthcare, including predictive models for gestational diabetes and cardiac arrest in children. His work emphasizes integrating human knowledge into machine learning (e.g., Human-in-the-Loop systems) and advancing statistical relational AI frameworks like Markov Logic Networks and Probabilistic Circuits. Publications: Over 100 peer-reviewed articles, including notable works on causal learning, relational reinforcement learning, and knowledge graph construction. Recent focuses include explainable AI and scalable probabilistic models. Awards: AAAI Fellow, RBCDSAI Distinguished Fellowship, UTD Teaching Excellence Award. Advising and Collaboration: Mentored over 30 students, many now in academia and top institutions like IBM Research, Facebook, and Microsoft. Collaborates globally on projects like GLAD (Glocalized Anomaly Detection) and StaRLing Lab initiatives. Labs/Teams: Leads the StaRLing Lab, focusing on statistical relational AI, and directs UTD's Center for Machine Learning. Engaged in interdisciplinary projects with healthcare, robotics, and data science communities.

Magnus WesterlundView profile

Senior Lecturer

• Trustworthy AI
• Cybersecurity
• IT Security
• +6 more

Magnus Westerlund is a Senior Lecturer in Information Technology and Director of the Laboratory for Trustworthy AI at Arcada University of Applied Sciences in Helsinki, Finland. His industry background spans telecom and information management, and he holds a doctoral degree in Information Systems from Åbo Akademi University. He actively contributes to the Z-Inspection® network, focusing on ethical AI implementation and governance. Westerlund’s research emphasizes trustworthy AI, cybersecurity, and distributed systems. Key areas include AI regulatory compliance (e.g., EU AI Act), healthcare AI applications, blockchain security, and IoT edge solutions. His work bridges academia and industry, such as the Valohai-CSC collaboration for machine learning infrastructure in Finnish academia. His publications highlight practical AI assessment methods, ethical AI integration, and decentralized technologies. Notable contributions include frameworks for sustainable AI development, privacy-preserving autonomous systems, and smart contract-based IoT security protocols. Westerlund also explores educational innovations, such as integrating large language models (LLMs) into coding education. His research consistently addresses real-world challenges like pandemic-era healthcare AI, edge computing for IoT, and cybersecurity in autonomous systems.

• Machine Learning
• Reinforcement Learning
• Bandit Algorithms
• +5 more

Dr. Kevin G. Jamieson is a faculty member at the University of Washington , School of Computer Science , with prior affiliations at the University of California, Berkeley (Department of Electrical Engineering and Computer Sciences) and the University of Wisconsin-Madison (Department of Electrical and Computer Engineering). His work spans machine learning, reinforcement learning, bandit algorithms, and robotics. Current university: University of Washington Academic rank: Professor His research focuses on: Bandit algorithms and sequential decision-making Optimization in non-stationary environments Reinforcement learning with real-world applications Multi-agent systems and game theory Efficient data selection for multimodal learning Human-in-the-loop AI systems Recent publications highlight his expertise in pure exploration strategies, robotic manipulation, and bridging simulation-to-reality gaps in RL. He has mentored numerous collaborators, though formal student advising details are not explicitly listed here. No scientific awards are mentioned in the provided data.

Panagiota AntonopoulouView profile

Professor

• Sports Communication
• Political Communication
• Media Studies
• +6 more

Panagiota Antonopoulou is a Professor at the University of Peloponnese , affiliated with the Faculty of Human Movement Sciences and Quality of Life and the Department of Sports Organization & Management . Her work bridges media studies , political communication , and sports management , with a focus on place branding and digital media impacts . Education: Doctoral degree in Sociology from Panteion University (dissertation: The role of television in national elections , Grade: Excellent) Research interests include: Sports, media, and political communication interactions Place branding through ultra-distance events like Spartathlon Crisis management in sports organizations Sustainable tourism and urban identity (e.g., Piraeus, Sparta) Strategic HRM in sports events and facilities Recent publications highlight: Algorithmic journalism in sports reporting (2018) Media's role in shaping perceptions of sports violence (2012–2017) Place branding strategies for regional development (2017) Evolution of Greek sports press and radio (2008–2012) She teaches courses such as: "Sports Journalism and Media" (2015–present) "Tourism Politics and Strategy" (2015–present) "Communication and Writing Skills" (2010–present)

Diogo BarradasView profile

Assistant Professor

• Network Traffic Analysis
• Internet Censorship Circumvention
• Anonymous Communication
• +5 more

Diogo Barradas is an Assistant Professor in the Department of Computer Science at the University of Waterloo. His research focuses on network security, internet censorship circumvention, and anonymous communication systems. He holds a Ph.D., M.Sc., and B.Sc. from Instituto Superior Técnico, Universidade de Lisboa, Portugal. His work addresses challenges such as website fingerprinting defenses, programmable network security, and steganographic techniques for censorship resistance. Education: Ph.D., Instituto Superior Técnico, Universidade de Lisboa (2021) M.Sc., Instituto Superior Técnico, Universidade de Lisboa (2016) B.Sc., Instituto Superior Técnico, Universidade de Lisboa (2014) Research interests include: Network traffic analysis and obfuscation Security of programmable network infrastructures Digital forensics and information hiding Covert channels in multimedia protocols His recent publications explore cutting-edge topics like time series analysis for website fingerprinting detection, distributed traffic correlation on programmable networks, and satellite-based censorship circumvention. These contributions highlight advancements in both theoretical frameworks and practical tools for privacy-preserving communication.