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Neil JohnsonView profile

Professor

• Complex Systems
• Many-Body Physics
• Online Social Networks
• +4 more

Neil Johnson is a Professor of Physics at George Washington University, leading a Complexity and Data Science initiative. His research focuses on complex systems, online networks, and quantum information processing, with applications to social media dynamics, conflict analysis, and financial markets. He holds a BA/MA from St. John's College, Cambridge, and a PhD from Harvard University as a Kennedy Scholar. He has been a Research Fellow at the University of Cambridge, a Professor at the University of Oxford (1992–2007), and later at the University of Miami before joining GW in 2018. Johnson is an APS Fellow and recipient of the 2018 Burton Award. His work spans theoretical physics, computational social science, and interdisciplinary research through initiatives like CABDyN (Complexity Science) and the Oxford Centre for Financial Complexity (OCCF). He has authored over 300 papers, supervised 25+ PhD students, and written books such as Financial Market Complexity and Simply Complexity . His current lab, the Dynamics Online Networks Lab, explores collective behavior in digital ecosystems, including AI-driven misinformation and hate networks. Key research areas include modeling conflict dynamics, predicting social media trends, and analyzing extremisim's online ecology. His recent work addresses AI governance, technosocial systems, and the interplay between online and offline human behavior.

• Machine Learning
• Biomedical Informatics
• Data Science
• +3 more

Claus Horn is an Associate Research Scientist in the Department of Biomedical Informatics & Data Science at Yale School of Medicine. His research focuses on integrating machine learning with biomedical applications, including enzyme design and deep learning frameworks for protein-ligand interactions. Previously, he held roles at CERN (ATLAS collaboration) and Stanford University, contributing to particle physics breakthroughs like the Higgs boson discovery. He has also pioneered data science education, founding an open education platform with 3,000+ members and teaching over 300 students in AI and machine learning courses. Education: PhD in Physics (University of Hamburg, 2006), Postdoctoral Researcher at Stanford University (2010), DipEd in University Didactics (PH Zurich, 2021), MBA in Management & Leadership (HWZ Zurich, 2023). Research interests span interdisciplinary fields: developing GNN frameworks for drug discovery (e.g., GEMS), optimizing protein structures via geometric methods, and applying AI to drone signal analysis and indoor farming simulations. His particle physics work includes searches for supersymmetry, microscopic black holes, and long-lived particles using ATLAS data. Publications highlight contributions to both biomedical innovation and high-energy physics, with recent work emphasizing AI-driven systems and collaborative data science education. He has no explicitly listed scientific awards but contributed to Nobel Prize-winning Higgs boson research.

Siamak F. ShahandashtiView profile

Senior Lecturer

• Cyber Security
• Privacy
• Cryptography
• +3 more

Siamak F. Shahandashti is a Senior Lecturer (Associate Professor) in the Department of Computer Science at the University of York, UK. He leads the Cyber Security and Privacy Research Group and is involved in academic leadership roles such as Programme Lead for the MSc Cyber Security. His research focuses on cyber security, privacy, cryptography, and blockchain technology, with notable contributions to post-quantum cryptography, secure authentication frameworks, and privacy-preserving systems. Education: PhD in Computer Science, MSc and BSc in Electrical Engineering. His work integrates technical innovation with human-centered design, addressing challenges in user privacy, secure protocols, and resilient systems. Key research areas include: Cryptography and Post-Quantum Security Blockchain and Distributed Ledger Technology User-Centric Privacy Solutions Cyber Security Resilience in IoT and Edge Computing Current projects include PRIME (protecting minority ethnic communities online) and PHAWM (participatory harm auditing methodologies). He has been recognized for collaborative achievements, including a best paper award for work on DoS-resilient Bitcoin mempools and contributions to password manager governance frameworks. Teaching includes the MSc module 'Cryptography Theory and Practice', emphasizing both theoretical foundations and real-world applications.

Andrew ShepherdView profile

Professor

• Cryosphere Studies
• Satellite Remote Sensing
• Climate Change
• +3 more

Andrew Shepherd is the Head of the Department of Geography and Environment at Northumbria University, and Director of the NERC Centre for Polar Observation and Modelling. He is a Principal Scientific Advisor to the European Space Agency's CryoSat mission and co-leads the ESA-NASA Ice Sheet Mass Balance Inter-comparison Exercise. His research focuses on measuring changes in Antarctic and Greenland ice sheets, their interaction with the climate system, and contributions to sea level rise. He also works on satellite sensor calibration/validation, collaborating with space agencies, glaciologists, and government agencies. Shepherd holds a PhD in Physics from the University of Leicester. He has held academic positions at UCL, Cambridge, Edinburgh, and Leeds. His honors include the Philip Leverhulme Prize (2008), Royal Society Wolfson Award (2014), and the International Glaciology Society Richardson Medal (2021). He supervises PhD students researching topics like Arctic sea ice variability, proglacial lakes, and climate change adaptation. His work is frequently featured in media, including TV documentaries like Climate Change: The Facts .

Kostas StathisView profile

Professor

• Artificial Intelligence
• Distributed Systems
• Software Engineering
• +7 more

Kostas Stathis is a Professor of Artificial Intelligence at the Department of Computer Science, Royal Holloway, University of London. He serves as Vice-Dean for Research and Knowledge Exchange in the School of Engineering, Physical, and Mathematical Sciences, and is the Interim Director of the Centre for AI and Skills. His research focuses on autonomous systems, cognitive agent models, game-theoretic strategies, and multi-agent environments, with applications in e-markets, regulation, and legal reasoning. Education details are not explicitly provided in the text. His research interests include knowledge representation, strategic interaction models, and programmable agent societies. He leads the DICE Lab, exploring AI-driven solutions for complex systems. Recent projects include iRealHAT-PEGA (human-agent teaming via eye gaze analysis) and collaborations on AI in regulatory decision-making for healthcare professions. He has supervised 9 research students and contributed to over 132 publications, including work on reinforcement learning in negotiation and LLM-enhanced legal reasoning frameworks. His work aligns with UN Sustainable Development Goals, particularly those addressing innovation and industry (Goal 9) and reducing inequalities through technological access (Goal 10).

• AI Security
• Cybersecurity
• Artificial Intelligence
• +12 more

Petar Radanliev is a part-time academic and project supervisor at the Department of Computer Science, University of Oxford. He is actively involved in teaching and research, focusing on AI security, cybersecurity, and emerging technologies such as quantum computing and blockchain. His work bridges academic rigor with practical industry applications, and he supervises professional masters students. His research interests center on the security and ethical implications of artificial intelligence. Key areas include AI attack surfaces, agentic AI, quantum AI, malware analysis, and responsible AI practices. He explores vulnerabilities in machine learning systems and develops frameworks for secure and resilient AI deployment. His recent publications and courses reflect a strong trend toward interdisciplinary integration of AI with blockchain and quantum computing, emphasizing real-world threats, ethical considerations, and future-proofing digital systems. These works highlight a consistent focus on proactive defense, red teaming, and the societal impact of emerging technologies. Fulbright Fellowship Prince of Wales Innovation Scholarship Petar Radanliev has supervised numerous professional masters students and contributes to academic discourse through extensive publications, books, and public lectures. He is affiliated with major research platforms including ORCID, Google Scholar, and the Alan Turing Institute. His teaching includes advanced courses on AI security, quantum AI, and red teaming, often delivered through recorded events and online platforms. He is associated with research themes in Security, Artificial Intelligence, and Machine Learning at Oxford, and has contributed to projects such as SOCIAM. His work emphasizes practical, actionable insights for professionals and academics navigating the evolving landscape of AI and cybersecurity.

• AI and Robotics
• Human-Robot Interaction (HRI)
• Autonomous Agents
• +7 more

Dr. Abolfazl Zaraki is a Senior Lecturer in AI and Robotics at the University of Hertfordshire's Department of Computer Science, part of the School of Physics, Engineering & Computer Science. He leads the Robotics Research Group and previously held roles at Cardiff University's School of Engineering and the IROHMS Research Centre. His academic journey includes a Master's in Mechatronics from University Technology Malaysia (2010) and a PhD in Automatic Robotic and Bioengineering from the University of Pisa (2014). He has held Research Fellow positions in Italy and the UK until 2019. Dr. Zaraki's research focuses on AI-driven autonomous systems, social robotics, and assistive technologies. Key projects include the EASEL, BabyRobot, and JAMES EU initiatives, alongside the Innovate UK-funded InSight project. His work emphasizes Human-Robot Interaction (HRI), trusted autonomy, and applications in healthcare and industrial contexts. Notable contributions include the development of the Kaspar humanoid robot for autism therapy and advancements in reinforcement learning for robotic control. His recent publications (2021–2025) explore agentic AI, memory-driven systems, and personalized LLMs for HRI, alongside technical advancements in robotic control, communication systems, and bio-inspired robotics. His research bridges theoretical AI innovation with practical applications in healthcare, education, and industrial automation. Zaraki has collaborated internationally across institutions and industry partners, contributing to 35+ research outputs. His work aligns with global trends in ethical AI, explainable systems, and human-centric robotics design.

Kristina HarthallerView profile

Professor

• Consumer Behavior
• Digital Marketing
• Multi-Channel Management
• +3 more

Kristina Harthaller is a Professor of Omnichannel Marketing & Sales at MCI Management Center Innsbruck, leading the Marketing & Sales Department since 2023. She holds a Dr. oec. HSG from the University of St. Gallen (2019) and previously served as an Associated FH Professor (2021–2024) and Senior Lecturer (2021) at MCI. Her academic career includes a postdoc role (2019–2021) at the University of St. Gallen's Research Center for Retail Management. She teaches courses on Consumer Psychology, Digital Transformation, and Omnichannel Commerce, focusing on practical retail innovations. Her research emphasizes customer journey dynamics, multi-channel strategies, and the impact of AI on retail. Notable awards include the 2021 AMA Retail SIG Best Paper Award and the 2020 Journal of Retailing Davidson Award. She advises over 30 students in bachelor and master theses, exploring topics like social commerce, frontline employee inspiration, and impulse buying. Kristina collaborates with industry partners like TIROLER Wirtschaftskammer, delivering workshops on AI in business and retail. She chairs the Bachelor's program in Luxury Hotel Management at Bodensee Campus and contributes to academic associations including the American Marketing Association and European Marketing Academy.

Ashok AgrawalaView profile

Professor

• Computer Systems
• Distributed Algorithms
• Real-Time Systems
• +10 more

Ashok Agrawala is a Professor in the Department of Computer Science at the University of Maryland, with joint affiliations in the University of Maryland Institute for Advanced Computer Studies (UMIACS) and the Department of Electrical Engineering. He directs the Maryland Information and Network Dynamics (MIND) Lab, a leading research group focused on real-time systems, network performance, and wireless localization technologies. Education: B.E. and M.E. in Electrical Engineering, Indian Institute of Science, Bangalore M.A. and Ph.D. in Applied Mathematics, Harvard University His research interests span computer systems, distributed algorithms, real-time operating systems, network performance modeling, traffic shaping, and indoor localization . He is renowned for pioneering the Ricart-Agrawala distributed mutual exclusion algorithm, developing the Maruti real-time operating system, and creating the Horus and Locus indoor positioning systems. His work has led to significant advances in temporal guarantees, jitter-free data delivery, and calibration-free localization. His recent publications show sustained innovation in wireless localization, context-aware computing, breath analytics, and energy-efficient IoT systems , reflecting an evolving research trajectory toward smart health and urban computing. He has authored over 200 papers and seven books, with consistent publication activity into 2024. Scientific Recognition: Fellow, IEEE Member, ACM Member, AAAS Member, Sigma Xi Agrawala has advised over thirty Ph.D. students, many of whom now lead research in academia and industry. His research has been funded by DARPA, NSF, and AFOSR, with corporate partnerships from IBM, Novell, and AT&T. He previously worked at Honeywell Information Systems, where he designed an Optical Character Reader. He has also served as a consultant to the UNDP and the Government of India, and as a technical expert in legal cases. He leads the MIND Lab, which focuses on mobile computing, wireless networks, and context-aware systems , continuing to innovate in real-time and networked systems.

Patrick Le TallecView profile

Professor

• Computational Mechanics
• Nonlinear Mechanics
• Domain Decomposition Methods
• +5 more

Patrick Le Tallec is a Professor of Mechanics at École Polytechnique in France, where he currently serves as Dean of the Bachelor Program and is a member of the M3DISIM project. His distinguished academic career spans multiple institutions including Université Paris Dauphine, INRIA (French National Institute for Research in Digital Science and Technology), and international universities such as Stanford University, University of Wisconsin, and Shanghai Jiao Tong University. He has held leadership positions including Vice President for Education and Head of the Laboratory of Solid Mechanics at École Polytechnique. His educational background includes: Graduate from École Polytechnique Ph.D. in Engineering Mechanics from The University of Texas at Austin (1980) Thèse d'Etat in Applied Mathematics from Université Pierre et Marie Curie in Paris (1981) Professor Le Tallec's research focuses on computational mechanics and applied mathematics with expertise in nonlinear mechanics, domain decomposition methods, and multiscale modeling. His work bridges theoretical mathematics with practical engineering applications, particularly in material science and fluid-structure interactions. He has developed advanced numerical methods for elasticity, viscoelasticity, and fluid dynamics with applications in industrial manufacturing and biomedical engineering. His recent publications demonstrate progression from foundational numerical methods to sophisticated multiscale approaches addressing complex engineering challenges in material science. The research shows particular emphasis on rubber mechanics, fatigue analysis, and computational methods for nonlinear structures, reflecting his ongoing commitment to solving real-world engineering problems through mathematical innovation. His scientific honors include: CISI award in Scientific Computing Prize Blaise Pascal of the French Academy of Sciences Chevalier des Palmes Académiques Chevalier de la Légion d'Honneur Officier de l'Ordre National du Mérite Professor Le Tallec has directed over 40 Ph.D. students from 10 different nationalities, demonstrating significant impact in academic mentoring. His research has been supported through extensive collaborations with industrial partners including Michelin, PSA Group, and Dassault Aviation, as well as scientific advisory roles at the French Alternative Energies and Atomic Energy Commission. He has served as president of the French Society of Applied and Industrial Mathematics and held editorial positions with leading journals in his field. His laboratory work centers around computational mechanics research, particularly through the M3DISIM project at École Polytechnique. His research team brings together mathematicians, engineers, and computer scientists to develop innovative solutions for complex problems in material science and structural mechanics, with applications ranging from industrial tire manufacturing to biomedical engineering.

• High Performance Computing
• Performance Engineering
• Performance Modeling
• +7 more

Gerhard Wellein is a Professor for High Performance Computing at the Department of Computer Science of Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He is the head of NHR@FAU (Erlangen National Center for High Performance Computing) and a member of the board of directors of the German NHR-Alliance. Since 2024, he has also served as a Visiting Professor for HPC at the Delft Institute of Applied Mathematics, Delft University of Technology. He holds a PhD in theoretical physics from the University of Bayreuth and has over two decades of experience in HPC education and research. Research Interests: His research focuses on performance modeling and engineering, architecture-specific code optimization, novel parallelization techniques, and the development of hardware-efficient building blocks for sparse linear algebra and stencil solvers. His work bridges computer science, applied mathematics, and computational physics, aiming to maximize efficiency on current and future HPC architectures, including exascale systems. Publication Trends: His recent publications emphasize analytical performance modeling (e.g., Roofline, oscillator models), energy efficiency, GPU optimization, and scalable linear algebra. They reflect a strong focus on both theoretical modeling and practical implementation, with applications in CFD, quantum physics, and molecular dynamics. Scientific Awards: 2011 Informatics Europe Curriculum Best Practices Award (shared with Jan Treibig and Georg Hager) for outstanding teaching contributions in HPC. Grants and Advising: He has led numerous third-party funded projects from the EU, BMBF, and DFG, including EoCoE-III, ESSEX, EXASTEEL, and ProPE. These projects focus on exascale software, performance engineering, fault tolerance, and multiscale simulation. He has mentored multiple researchers and students, contributing to the development of tools such as LIKWID, ClusterCockpit, and GEOPM. Labs and Teams: He leads the HPC research group at FAU and is deeply involved in national and international HPC initiatives. His team collaborates extensively on open-source HPC software and performance tools, fostering a strong community-driven approach to performance engineering.

Andreas StergiouView profile

Research Fellow

• Quantum Field Theory
• Conformal Field Theory
• Renormalization Group
• +7 more

Dr. Andreas Stergiou is a Senior Research Fellow in the Department of Mathematics at King’s College London, affiliated with the Faculty of Natural, Mathematical & Engineering Sciences. His research focuses on theoretical physics, particularly in quantum field theory (QFT) and conformal field theories (CFTs). Education: B.Sc. in Physics, National and Kapodistrian University of Athens M.Sc. in Theoretical Physics, University of Amsterdam Ph.D. in Physics, University of California, San Diego His work explores renormalization group (RG) flows, conformal and superconformal field theories, and their applications to critical phenomena, condensed matter, particle physics, and string theory. He has contributed to understanding the relationship between scale invariance and conformal symmetry in higher-derivative theories. Recent research includes advancements in the conformal bootstrap program, multiscalar RG flows, and axion superfluidity in Weyl semimetals. He employs analytical and numerical methods to study fixed points and emergent properties in QFTs. Scientific Awards: Royal Society University Research Fellowship (2021) He has secured multiple grants from the Royal Society, including projects on conformal defects and enhanced research expenses (2023–2026). His collaborations span institutions like Yale University, CERN, and Los Alamos National Laboratory.

• Computational Materials Science
• Ab Initio Methods
• Thermodynamics
• +7 more

Dr. Li-Fang Zhu is a DFG Project Leader at the Institute of Materials Science, University of Stuttgart, Germany, and a Guest Scientist at the Max-Planck-Institut für Eisenforschung, Düsseldorf, Germany. She previously worked as a Research Scientist at the Max-Planck-Institut für Eisenforschung from 2009 to 2022. Education: PhD in Physics, Institute of Physics, Chinese Academy of Sciences (2005-2009) M.Sc. in Physical Science and Technology, Lanzhou University (2002-2005) B.Sc. in Physics, Hebei Normal University (1998-2002) Her research focuses on ab-initio-based thermodynamics , finite temperature properties of materials from first principles , and method development for melting property studies . She specializes in multi-scale modeling of mechanical properties in alloys , magnetism in magnetic materials , and integrating machine learning with statistical mechanics to solve materials science challenges. Her work bridges fundamental computational techniques with practical applications in structural materials design. Dr. Zhu leads the Adaptive Structural Materials group and contributes to computational materials design. Her affiliations include the Max-Planck-Institut für Eisenforschung and the University of Stuttgart, where she applies advanced modeling techniques to understand material behavior under extreme conditions.

Dan CasasView profile

Associate Professor

• Computer Graphics
• Computer Vision
• Machine Learning
• +5 more

Dan Casas is a Senior Applied Scientist at Amazon in Seattle and an Associate Professor (Profesor Titular) on leave from King Juan Carlos University in Spain. His research spans the intersection of Computer Graphics, Computer Vision, and Machine Learning with a focus on 3D reconstruction, modeling, and animation of virtual humans and clothing. He has authored over 40 high-impact publications in top venues including SIGGRAPH, CVPR, and NeurIPS, and holds 3 international patents. Dr. Casas received his M.Sc. degree (2009) from Universitat Autònoma de Barcelona (Spain), including a research visit at Carnegie Mellon University. He earned his Ph.D. in Computer Graphics (2014) from the University of Surrey (UK), supervised by Prof. Adrian Hilton. He completed postdoctoral research at the University of Southern California's Institute for Creative Technology (2014-2015) and the Max Planck Institute in Saarbrücken (2015-2016). His research interests center on creating realistic virtual humans and digital clothing through advanced techniques in computer vision and machine learning. Casas has pioneered methods for 3D reconstruction of humans and garments from video input, physics-based simulation of soft-tissue deformations, and data-driven approaches to character animation. His work bridges the gap between theoretical computer graphics and practical applications in virtual reality, digital fashion, and immersive communication. Analysis of his recent publications reveals a consistent focus on human digitization, with increasing emphasis on machine learning approaches. His work has evolved from traditional computer graphics techniques toward neural representations and diffusion models, particularly in the areas of 3D garment simulation and human avatar creation. The trend shows growing integration of physics-based modeling with data-driven approaches to achieve both realism and computational efficiency. Marie Skłodowska-Curie Individual Fellowship (2015) FBBVA Leonardo Fellowship (2021) Medal from the Royal Academy of Engineering of Spain for Young Researcher Award (2023) i3 certification (outstanding researcher) from Spanish Ministry of Universities (2022) Winner of 2021 IEEE Retail Digital Transformation Grand Challenge Multiple Outstanding Reviewer Awards at top conferences (CVPR, BMVC, 3DV) Dan Casas has successfully advised multiple PhD students including Suzanne Sorli, Cristian Romero, Raquel Vidaurre, and Igor Santesteban (now at Meta Reality Labs), with several ongoing students including Melania Prieto-Martin, Gonzalo Gómez-Nogales, and Andrés Casado-Elvira. He has secured significant research funding as Principal Investigator, totaling over €1.2 million from Spanish Ministry of Science projects, EU H2020 programs, and industry fellowships including the FBBVA Leonardo Fellowship. His leadership extends to conference organization as Area Chair for ICCV 2023 and General Chair for ACM i3D 2020. Dr. Casas leads research in digital human modeling with applications in virtual reality, fashion technology, and immersive communication. His team develops advanced techniques for creating personalized 3D avatars from minimal input (like smartphone videos), addressing challenges in geometry, appearance, and physical simulation of virtual humans and their clothing.

• Crowd Simulation
• Path Planning
• Real-time Simulation Software
• +3 more

Wouter van Toll is a Lecturer at the Academy for AI, Games & Media, specializing in crowd simulation and real-time systems. His research focuses on path planning, crowd behavior modeling, and fluid dynamics in agent-based simulations. He has contributed to advancing algorithms for microscopic crowd simulation and integrating techniques like Smoothed Particle Hydrodynamics (SPH) to handle extreme crowd densities. Key research interests include sketch-based interaction design for steering behaviors, navigation mesh optimization, and topological strategies for agent coordination. His work bridges computational methods with creative applications in game development and artificial intelligence. Received Best Paper Award Honorable Mention (2022) for his work on sketch-based steering behaviors in crowd simulation. Active collaborations in Europe and North America, particularly in crowd simulation software development. Publications span algorithmic advancements in crowd simulation, navigation systems, and interdisciplinary applications combining physics-based methods with agent-based models. Current research emphasizes real-time simulation efficiency and human-centered design tools for behavior specification.