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Christopher P. KempesView profile

Professor

• Complexity Science
• Physical Biology
• Astrobiology
• +7 more

Christopher P. Kempes is a Professor at the Santa Fe Institute and an External Professor at the Complexity Science Hub Vienna, where he conducts interdisciplinary research at the intersection of biology, physics, and complex systems. His work is focused on uncovering universal principles governing life across scales—from cells to cities. Research Interests: Dr. Kempes explores the fundamental laws underlying biological organization, the origins of life, ecological dynamics, and the complexity of human systems. His approach integrates physical constraints, mathematical modeling, and data-driven analysis to predict large-scale patterns in nature and society. Key themes include scaling laws, metabolic theory, astrobiology, urban systems, and evolutionary transitions. Publication Trends: Over the past 15 years, his research has consistently advanced theoretical frameworks in complexity science. His publications reveal a strong focus on universal patterns in biology and society, with recurring themes in scaling, thermodynamics, and system-level organization. He applies these principles to diverse domains including climate change, inequality, biosignatures, and organizational growth. Scientific Recognition: Work featured in BBC, Wired, and other major media outlets. Contributions to foundational theories in complexity and biological organization. Advising and Grants: While specific students and grants are not listed, his senior faculty role at leading complexity research institutes suggests active mentorship and leadership in funded interdisciplinary projects. He likely advises postdoctoral researchers and collaborates on large-scale initiatives in complexity science, astrobiology, and urban sustainability. Labs and Research Groups: Dr. Kempes is affiliated with the Santa Fe Institute’s core faculty, participating in its collaborative, theory-driven research environment. He also contributes to the Complexity Science Hub Vienna, engaging in cross-institutional projects on complex systems in natural and social domains.

• Wind Energy Systems
• Renewable Energy
• Energy Systems Engineering
• +4 more

Matti Juhani Koivisto is a Senior Researcher at the Department of Wind and Energy Systems at the Technical University of Denmark (DTU). His work focuses on renewable energy systems integration, particularly wind power modeling and analysis within large-scale energy systems. His research expertise spans several critical domains in renewable energy: Wind Energy Systems Engineering (100%) Wind Power Engineering (89%) Variable Renewable Energy Integration (80%) Power Engineering (56%) Time Series Analysis in Energy Systems (37%) Energy Engineering (35%) Dr. Koivisto's research addresses fundamental challenges in renewable energy integration, with particular emphasis on wind power forecasting, variability analysis, and the impacts of climate change on renewable resources. His work bridges theoretical modeling with practical energy system applications, contributing to more reliable and efficient integration of renewable energy sources into power grids. His recent publications demonstrate a strong focus on offshore wind energy, hybrid power plants, and advanced modeling techniques for wind farm generation. His work on increasing the resolution of solar and wind time series has been particularly influential in energy system modeling, addressing critical data requirements for high renewable penetration scenarios. Dr. Koivisto contributes significantly to UN Sustainable Development Goals related to affordable and clean energy (SDG 7) and climate action (SDG 13), with research directly addressing the technical challenges of transitioning to sustainable energy systems. He is actively involved in several major international research projects including AWETRAIN (Airborne Wind Energy Training), DTWO (Federated Digital Twins for Wind-Offshore), and GREAT (GRid Enhancement for Ancillaries in Tomorrow's power systems), where he serves as both project participant and PhD supervisor.

Christian HeckView profile

Assistant Professor

• Peace Research
• Aesthetic Practice
• AI Critique
• +10 more

Christian Heck is an Assistant Professor for Aesthetics and New Technologies at the Academy of Media Arts Cologne (KHM), where he has worked since 2017. His academic background includes a Master of Arts from the Berlin University of the Arts (2015) and a Diploma in Media Arts from the Academy of Fine Arts Munich (2012). Research Interests: Peace research, AI ethics, adversarial hacking, code literature, natural language processing, generative systems, and the militarization of digital technologies. Key Themes: Focus on algorithmic critique (especially neural networks), aesthetic research in cultural/peace work, and the societal impacts of technologies like IT security, weaponized drones, and autonomous weapon systems. His recent publications and talks address: AI-driven warfare and pre-emptive security policies Civil clauses in academia to prevent dual-use exploitation Cyberpeace strategies and artistic resistance Ethical implications of language models in military contexts He collaborates with organizations like the Forum InformatikerInnen für Frieden (FIfF), Science4Peace , and Arbeitskreis gegen bewaffnete Drohnen . His work connects artistic methods with peace activism, emphasizing civil society's role in reclaiming technological sovereignty.

• Information Visualization
• Software Systems
• Cybersecurity
• +3 more

Bill Buchanan is a Professor at the School of Computing Engineering and the Built Environment, Edinburgh Napier University, and a member of the Centre for Algorithms, Visualisation and Evolving Systems. His work spans Information Visualization , Software Systems , Cybersecurity , and AI , focusing on innovative solutions for complex data challenges. Research Interests: Information visualization for hierarchical data, cybersecurity frameworks, machine learning in industrial IoT, and post-quantum cryptographic systems. Projects: Supervised PhD projects on visualizing overlapping classification hierarchies and biological network inference. Currently leads research in AI-driven human-robot interaction and secure Dockerized architectures. Publications: 15+ recent works on quantum agents, GANs for anomaly detection, homomorphic encryption, and advanced cryptographic techniques. Collaborations: Engages with biologists, AI researchers, and cybersecurity experts across institutions.

• Systems Neuroscience
• Sensorimotor Control
• Neuroethology
• +5 more

Professor Holger G Krapp is a Professor of Systems Neuroscience in the Department of Bioengineering at Imperial College London's Faculty of Engineering. He holds affiliations with the Centre for Neurotechnology, Neuromuscular Rehabilitation Technology Network, and Robotics Forum. His research focuses on sensorimotor control mechanisms in insects, particularly blowflies, integrating neurobiology with engineering principles. Key areas include visual processing, flight dynamics, and biohybrid robotics. Education: Earned a Diploma in Biology (Neurobiology) from the University of Tübingen (1992) and a Dr. rer. nat. (PhD) from the Max-Planck Institute for Biological Cybernetics (1995). Postdoctoral research included work at Caltech and Bielefeld University before joining Imperial College as a Senior Lecturer in 2005. Research interests emphasize how insects process visual and sensory information to control movement, with applications in robotics and neurotechnology. Recent work explores optic flow processing, closed-loop control systems, and biohybrid interfaces for real-time behavioral analysis. His publications highlight innovative methodologies like high-speed X-ray imaging and neural recording platforms to study flight motor mechanics and neurophysiological responses. Publications span 25+ years, demonstrating sustained contribution to understanding multisensory integration, neuronal adaptation, and energy-efficient neural coding. Active in interdisciplinary collaboration, his work bridges neuroscience, engineering, and robotics to advance biomimetic technologies and biological system analysis.

• Translational Genomics
• Statistical Genetics
• Computational Genomics
• +4 more

Eleftheria Zeggini serves as Director of the Institute of Translational Genomics at Helmholtz Munich and holds the Liesel Beckmann Distinguished Professorship and Chair of Translational Genomics at the Technical University of Munich (TUM) School of Medicine. Her research leverages big data in genetics and genomics to translate genomic insights into disease mechanisms, advancing precision medicine through robust statistical and computational approaches integrating multi-omics data, electronic health records, and diverse population studies. Her educational background includes a PhD in Immunogenetics of Juvenile Arthritis from the University of Manchester School of Medicine (2003) and a Bachelor of Science (Hons) in Biochemistry from the University of Manchester Institute of Science and Technology (1999). Prior appointments include Group Leader at the Wellcome Sanger Institute (2008-2018) and Wellcome Research Career Development Fellow at the University of Oxford (2006-2008). Zeggini's research spans translational genomics, statistical genetics, and computational biology with emphasis on genetic architecture of complex diseases. Her team develops tools for multi-omics integration to dissect sequence variation in human health, focusing on osteoarthritis, type 2 diabetes, and cardiometabolic disorders. Recent work prioritizes health equity through tissue-specific multi-ancestry genomic studies and electronic health record integration, driving discovery of novel therapeutic targets and disease mechanisms. Analysis of her 15 most recent publications reveals dominant themes in osteoarthritis genomics (6 articles), type 2 diabetes heterogeneity (3 articles), and multi-ancestry genomic equity (3 articles). Her work consistently employs whole-genome sequencing, causal inference methods, and large-scale biobank analyses across diverse populations, with strong translational focus on precision medicine applications. Major scientific recognition includes: Elected Member of Academia Europaea (2024) Greek Excellence Award (2024) National Representative to the EMBL Council (2022) Elected Fellow of the Academy of Medical Sciences, UK (2020) Argo Award for Distinguished Greeks Abroad (2020) Honorary Senior Fellow, University of Cambridge (2018) Honorary Professor, University of Bristol (2018) Zeggini actively mentors early-career researchers while promoting diversity and inclusion in science. Her leadership in major consortia like UK Biobank analyses and ENABLE program demonstrates sustained grant acquisition capability. Current research direction focuses on developing computational frameworks for next-generation translational genomics, with particular emphasis on addressing health disparities through equitable genomic data representation. She directs Helmholtz Munich's Institute of Translational Genomics, which houses a multidisciplinary team of computational biologists, geneticists, and clinicians developing statistical tools for big data integration. The institute serves as a hub for precision medicine innovation, collaborating with TUM's medical faculty and international networks to accelerate genomic discoveries into clinical applications.

Gita AlaghbandView profile

Professor

• High-Performance Computing
• Artificial Intelligence
• Machine Learning
• +7 more

Professor Gita Alaghband is Chair and PhD Director at the Department of Computer Science and Engineering, University of Colorado Denver. Her work bridges high-performance computing with AI applications in computer vision, facial recognition, and deep learning optimization. Current research focuses on real-time multi-human tracking for autonomous systems Develops explainable AI solutions for medical and financial domains Leads the Parallel Distributed Systems (PDS) Lab Key publication trends include: 2025: Medical imaging AI for pediatric trauma analysis 2024: Financial forecasting with causal econometrics and adversarial defense systems 2023: Trajectory prediction models and HEVC compression frameworks Scientific recognition: IRC Best Paper Award (2020) for facial recognition research Students advised include industry leaders at Google, VMWare, and Nissan, with research spanning parallel computing, medical imaging AI, and autonomous systems.

Minghua ChenView profile

Professor

• Machine Learning with Performance Guarantee
• Online Optimization and Algorithms
• Energy Systems (Smart Grids, Data Centers)
• +5 more

Minghua Chen is a Presidential Chair Professor and Director of the Design and Analysis of Networked, Computing, and Energy (DANCE) Systems Lab at the School of Data Science, The Chinese University of Hong Kong (Shenzhen). He is also affiliated with the Department of Data Science at City University of Hong Kong (on leave). His research spans machine learning, optimization, energy systems, and networked systems, with a focus on performance guarantees and real-time applications. Education: PhD in Electrical Engineering and Computer Sciences, UC Berkeley (2006) Master in Electrical Engineering, Tsinghua University (2001) Research Interests: Minghua Chen investigates machine learning with performance guarantees, online optimization algorithms, energy systems (smart grids, energy-efficient data centers), intelligent transportation systems, distributed optimization, delay-constrained network coding, and data-driven prediction in system design. His work bridges theoretical rigor with practical implementations in wireless networks, power systems, and peer-to-peer communications. Publication Trends: His recent publications demonstrate a strong focus on machine learning applications in energy and transportation systems, particularly AC optimal power flow problems and emission-minimizing truck routing. He integrates neural networks with optimization theory, emphasizing feasibility guarantees and low-complexity algorithms. His work also explores age-of-information (AoI) optimization and multi-armed bandit approaches for model freshness. Scientific Awards: IEEE Transactions on Multimedia Prize Paper Award (2009) Eli Jury Award, EECS Department, UC Berkeley (2007) Best Paper Awards at ACM e-Energy 2023, IEEE INFOCOM 2022 (Poster), and ACM Multimedia 2012 Multiple Best Paper Finalists and Runner-ups Labs and Teams: He leads the DANCE Systems Lab, which develops theoretical frameworks and practical systems for networked, computing, and energy optimization. His team collaborates on projects like WISER (indoor white space networking) and DeepOPF series for power flow optimization.

Joergen KornfeldView profile

Research Professor

• Connectomics
• Machine Learning
• Deep Learning
• +5 more

Dr. Joergen Kornfeld is a Research Group Leader at the MRC Laboratory of Molecular Biology in Cambridge, UK, where he leads the 'Connectomics of learned behaviour' group. He also maintains a connection with the Max Planck Institute for Biological Intelligence as a Guest Scientist. Previously, he was a Group Leader at the Max Planck Institute of Neurobiology in Martinsried, Germany (2021-2024). Dr. Kornfeld received his PhD in Biology (summa cum laude) from the Max Planck Institutes of Medical Research and Neurobiology, with research stays at NYU. He completed his Master's in Computational Biology and Bioinformatics at ETH Zurich and his Bachelor's in Molecular Cell Biology at Heidelberg University. Dr. Kornfeld's research focuses on understanding how animals store learned behaviors in their neuronal networks. His work centers on the zebra finch songbird as a model system, investigating how song memories are stored and retrieved from underlying brain circuits. His laboratory employs high-throughput 3D electron microscopy to map brain circuits at synaptic resolution, generating massive datasets that require advanced deep learning techniques for analysis. His team develops and applies state-of-the-art machine learning algorithms to reconstruct neural circuits and infer connectivity patterns. His recent publications demonstrate a clear trajectory toward increasingly sophisticated methods for neural circuit reconstruction and analysis, combining volume electron microscopy with deep learning approaches. The research spans multiple model organisms including zebra finches, mice, and zebrafish, with applications to understanding memory formation, behavioral control, and neural computation. Dr. Kornfeld leads a research team including Caitlin Gillespie, Nelson Medina, and Anastasia Sorokina. His laboratory is at the forefront of developing new methodologies for connectomics that bridge neuroscience, computer science, and engineering. He maintains active collaborations with multiple institutions including MIT, NYU, and the Max Planck Society.

Andreas HerzView profile

Professor

• Computational Neuroscience
• Theoretical Neuroscience
• Neural Coding
• +11 more

Andreas Herz is a Professor and Senior Scientist at the Division of Neurobiology, Faculty of Biology, Ludwig-Maximilians-Universität München (LMU). He is a Board Member and Speaker of the Bernstein Center Munich, a leading institution in computational neuroscience. His research integrates theoretical modeling with experimental data to explore neural dynamics, information processing, and spatial navigation. Institution: Ludwig-Maximilians-Universität München School: Faculty of Biology Department: Division of Neurobiology Leadership: Speaker, Bernstein Center Munich Email: herz@bio.lmu.de Herz’s research focuses on computational and theoretical neuroscience, particularly the neural basis of spatial navigation, population coding in grid cells, dendritic spine biophysics, and sensory information processing. He employs methods from dynamical systems, probability theory, and information theory to model single neurons and large-scale networks. His work spans model systems from grasshoppers to rodents and zebrafish, aiming to uncover general principles of neural computation. The recent publications highlight a strong trend in understanding grid cell dynamics, spatial memory, and dendritic computation. His work frequently involves decoding neural population activity, analyzing in vivo electrophysiological data, and developing theoretical frameworks for neural coding. Themes include burst firing, phase precession, remapping, and energy-efficient neural coding. He has mentored numerous PhD and MSc students through the Graduate School of Systemic Neurosciences (GSN), including Alexander Mathis, Johannes Nagele, and Michaela Poth. His group actively collaborates with experimental labs, contributing to interdisciplinary advances in neuroscience.

• Computer Architecture
• Vector Processors
• GPU Microarchitecture
• +9 more

Roger Espasa Sans is a senior academic and researcher at the Universitat Politècnica de Catalunya (UPC), affiliated with the Faculty of Informatics of Barcelona (FIB) and the Department of Computer Architecture. He has made significant contributions to computer architecture, particularly in vector processing, GPU design, and performance optimization. Universitat Politècnica de Catalunya (UPC) Faculty of Informatics of Barcelona (FIB) Department of Computer Architecture His research focuses on advanced computer architecture, including vector and superscalar processors, memory systems, instruction-level and data-level parallelism, GPU microarchitecture, and performance modeling. His work bridges theoretical design and practical implementation, especially in high-performance and embedded systems. The recent articles highlight a strong trend in GPU and shader optimization, memory redundancy elimination, and performance modeling. His publications span prestigious venues such as IEEE Microarchitecture Symposium, IEEE HPCA, and The Visual Computer, indicating sustained impact in the field of computer systems and architecture. Premiada (Awarded activity) Roger Espasa Sans has advised several doctoral students, including Manel Fernandez Gomez and Francisca Quintana Domínguez, contributing to the next generation of computer architects. His collaborative network is extensive, involving key figures like Mateo Valero and international institutions. While specific grants are not listed, his prolific output suggests sustained funding. He has contributed to major research groups such as CAP (High Performance Computing Group). He is a core member of the CAP - Grup de Computació d'Altes Prestacions (High Performance Computing Group), and has also been associated with PM - Programming Models and ARCO - Microarchitecture and Compilers. His work often involves simulation frameworks like JINKS and Asim, indicating a strong focus on performance evaluation and modeling.

Gary AllenView profile

Senior Lecturer

• Software Engineering
• Distributed Systems Development
• Aspect Oriented Programming
• +13 more

Gary Allen is a Senior Lecturer in the Department of Computer Science at the School of Computing and Engineering, University of Huddersfield. He is an active member of the Centre for Planning, Autonomy and Representation of Knowledge, the Centre for Sustainable Software Engineering, the Centre for Sustainable Computing, and an affiliate of the Centre for Autonomous and Intelligent Systems. He holds a PhD in Software Development Methods for Educational Use and has been with the university since 1990, first as a research assistant and then as a full-time lecturer since 1995. His research interests include: Software Engineering Distributed and Client-Server Systems Aspect-Oriented Programming Object-Oriented Analysis and Design Design Patterns Java Programming and JavaSpaces Software Engineering Education Integrated Project Support Environments His recent publications reflect a strong focus on software quality, empirical evaluation of programming paradigms, and educational applications of software engineering. He has contributed to studies on automated testing adoption, hybrid programming systems, and UML extensions, demonstrating a consistent thread in software design, modularity, and educational technology. His scientific contributions are aligned with sustainable computing and autonomous systems, supporting UN Sustainable Development Goals. He actively participates in peer-review for journals such as the Journal of Software: Evolution and Process and international conferences. He supervises PhD students and has been involved in multiple research supervision activities. His work integrates teaching and research, particularly in practical and pedagogical aspects of computer science. He is also engaged in activities related to knowledge representation and intelligent systems through his centre affiliations.

Hussam N. MahmoudView profile

Professor

• Sustainable and Resilient Infrastructure
• Community Resilience
• Multi-Hazard Structural Engineering
• +4 more

Hussam N. Mahmoud is the George T. Abell Professor in Infrastructure and Director of the Structural Laboratory at Colorado State University's Department of Civil and Environmental Engineering. He leads the SoPHIE Research Group , focusing on Sustainable and Resilient Infrastructure and Communities across three major research thrusts: community resilience quantification, multi-hazard structural performance, and deteriorated infrastructure lifecycle assessment. Key research areas include climate change adaptation , multi-hazard design , and life-cycle cost analysis for infrastructure subjected to earthquakes, wildfires, hurricanes, and climate-related deterioration. His work integrates machine learning (e.g., neural networks for wind damage prediction), hybrid simulation , and multi-resolution modeling of structural systems. Recent publications highlight interdependent recovery of healthcare/school networks , coastal community resilience to cyclones, and fatigue repair methodologies using fiber composites. Scientific Recognition Keynote speaker at international conferences (STESSA 2015, NEES Annual Meeting 2013) Invited to prestigious events: G7 One Health Summit , NASEM New Voices program Recipient of the George T. Abell Professorship, a distinguished academic honor Professional Leadership Chaired NASEM workshop on Natural Infrastructure Active in global collaborative frameworks through the InterAcademy Partnership Organizing committee member for triennial international academic conferences

• Action recognition
• Representational dynamics
• Conceptual knowledge
• +3 more

Moritz Franz Nikolaus Wurm is a Researcher at the Interdepartmental Center for Mind/Brain Sciences (CIMEC) at the University of Trento, Italy , where he leads the Action Concepts and Recognition Group since 2022. He completed his PhD in Psychology (magna cum laude) at the Max Planck Institute for Neurological Research and the University of Münster in 2011, followed by postdoctoral work at Harvard University and the University of Trento. Research Interests : Dr. Wurm specializes in action recognition , conceptual knowledge representation , and neural decoding using fMRI , MEG , and MVPA/RSA techniques. His work explores how contextual, social, and physical cues shape neural processing of actions and objects, with recent projects on dynamic RSA for temporal neural analysis. Scientific Awards : Fondo Italiano della Ricerca (FIS) Starting grant (2024-2029) Piano Nazionale di Ripresa e Resilienza (PNRR) PhD Grant (2023-2027) University of Trento Starting Grant Giovani Ricercatori (2021) DFG Research Fellowship (2014–2017) Best Abstract Award at CAOs Workshop (2014) Other Activities : He serves as a scientific advisor for the MEG Laboratory at CIMEC, organizes the Concepts, Actions, and Objects workshop series, and leads the CIMeC Colloquia Series. He is an ad hoc reviewer for journals like Nature Communications , NeuroImage , and Journal of Neuroscience .

Shanghong XieView profile

Assistant Professor

• Machine Learning
• Network Analysis
• Precision Medicine
• +12 more

Shanghong Xie is an Assistant Professor in the Department of Statistics at the University of South Carolina, College of Arts and Sciences. She is also a member of the Carolina Autism and Neurodevelopment (CAN) Research Center and the Institute for Mind and Brain (IMB), reflecting her interdisciplinary focus on statistical methods for neuroscience and public health. Dr. Xie earned her PhD in Biostatistics from Columbia University in 2019 and completed her postdoctoral training there. Her research centers on developing machine learning and generative models to tackle complex challenges in functional data analysis, network modeling, causal inference, and precision medicine. She applies these methods to neuroimaging (fMRI, DTI, EEG), genetic biomarkers, and public health data, particularly in mental health and neurological disorders. Her recent publications demonstrate a strong trajectory in methodological innovation with high-impact applications, including COVID-19 forecasting models adopted by the CDC, mediation analysis with neuroimaging, and personalized treatment regime estimation. The research trends highlight expertise in high-dimensional data, graphical models, and integrative learning across multi-modal biomarkers. American Statistical Association (ASA) Statistics in Imaging Section First Prize Student Paper Award American Statistical Association (ASA) Mental Health Statistics Section Best Student Paper Award International Conference on Health Policy Statistics (ICHPS) Student Travel Award Dr. Xie advises several graduate students and co-founded the Functional and Complex Data Analysis (FUNCODA) Working Group, fostering collaboration on advanced data modeling. She has been involved in significant grant-supported work, notably contributing to the CDC’s ensemble forecasting effort during the pandemic through her survival-convolution model. Her lab emphasizes computational statistics and open science, with code available on GitHub.