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David SwigonView profile

Professor

• Mathematical Biology
• Nonlinear Dynamics
• DNA Mechanics
• +3 more

David Swigon is a Professor of Mathematics at the University of Pittsburgh's Dietrich School of Arts and Sciences. His research focuses on applying mathematical models to biological systems, including DNA mechanics, protein-DNA interactions, cell migration, and immune system dynamics. He holds a PhD from Rutgers University and maintains a dedicated research website. Research Interests: Swigon's work bridges mathematical theory with biological complexity, with key areas including stochastic modeling of viral infections, nonlinear dynamics of biochemical systems, and biomechanical simulations of cellular processes. His recent projects explore sepsis pathophysiology, cytokine networks, and surgical tissue mechanics. Notable Contributions: His 2025 studies on viral reproductive cycles and DNA topology reveal novel insights into stochastic mechanisms and structural motifs. Earlier work, such as the 2014 model of pneumococcal infection dynamics, demonstrates interdisciplinary integration of immunology and mathematics. Professional Affiliations: Swigon is affiliated with the University of Pittsburgh's Mathematics Research Center and contributes to the Department of Mathematics' academic leadership. His work frequently addresses translational challenges in medicine and ecology.

• Computational Neuroscience
• Theoretical Neuroscience
• Decision-Making Models
• +4 more

Ruben Moreno Bote is a Serra Húnter Professor in the Department of Information and Communication Technologies at Universitat Pompeu Fabra. His research focuses on theoretical and computational neuroscience, with an emphasis on decision-making processes, neural coding, and computational models of cognitive dynamics. His work bridges disciplines such as neuroeconomics, systems neuroscience, and artificial intelligence, addressing topics like uncertainty management, value encoding, and neural network dynamics. Key research areas include: Computational principles of decision-making under uncertainty Neural mechanisms of economic choice and value representation Stochastic control in recurrent neural networks Cognitive dynamics of attention and confidence Integration of astrocytes in systems neuroscience His recent articles explore complex behaviors arising from intrinsic motivation, optimal sampling strategies in decision-making, and the role of neural oscillations in value-based decisions. The breadth of his work reflects a commitment to understanding how neural systems process information and guide adaptive behavior. Dr. Bote collaborates across disciplines, contributing to theoretical frameworks that connect neuronal activity with higher-order cognitive functions. His research has implications for understanding both healthy cognition and potential dysfunctions in neurological disorders.

• Radiation Hardening
• Fault-Tolerant Systems
• Microelectronics
• +5 more

Mario Garcia Valderas is an Associate Professor at the Department of Electronic Technology, Universidad Carlos III de Madrid. He holds roles as Coordinator of the Electronic Technology program and Deputy Director of Infrastructure and Laboratories. His research focuses on radiation-hardened systems, fault-tolerant architectures, and embedded systems for aerospace and space applications. His work emphasizes reliability in microelectronics and FPGA-based solutions, addressing challenges like single-event effects in space and high-reliability computing. Key areas include RISC-V processors, heterogeneous computing, and hardware-software co-design for planetary exploration instruments. Publications highlight advancements in radiation-hardening techniques, error detection/recovery architectures, and fault-injection methodologies. Over 100 peer-reviewed articles and book chapters evidence his contributions to microelectronics and embedded systems reliability. He leads the Microelectronic Design and Applications (DMA) research group and collaborates with institutions like CNA for combined irradiation testing. His research spans from theoretical design hardening to practical implementations in aerospace-grade electronics.

• Energy Harvesting
• Low-Power Integrated Circuit Design
• Miniature System Design
• +3 more

Hee Lee is an Assistant Professor at the University of Pittsburgh's Department of Electrical and Computer Engineering. His research focuses on energy harvesting, low-power integrated circuits, miniature system design, and battery management. He holds a Ph.D. from the University of Michigan (2014) and M.S./B.S. degrees from Yonsei University (2007/2005). Research Interests: Design of ultra-low-power analog/mixed-signal circuits for IoT and biomedical applications Energy-autonomous systems using photovoltaic, piezoelectric, and biofuel-based harvesting Miniaturized sensor nodes with embedded neural network accelerators Recent work emphasizes hybrid timestamping circuits (2025), subthreshold voltage references (2024-2025), and mm-scale neural accelerators (2023). His publications span IEEE journals and conferences like ISSCC, JSSC, and VLSI Symposia. Patents include innovations in voltage references and energy harvesting interfaces. He has developed systems like the mSAIL platform for monarch butterfly tracking and implantable IOP monitors. Lab activities involve collaboration with Michigan's Blaauw/Sylvester group on projects like System-on-Mud oceanic sensors and millimeter-scale biofuel-powered devices. Current research explores high-temperature IoT systems and neuromorphic hardware accelerators.

William DittoView profile

Professor

• Nonlinear Dynamics
• Chaos Computing
• Machine Learning
• +3 more

William Ditto is a Professor at North Carolina State University's Department of Physics within the College of Sciences. His research focuses on nonlinear dynamics, chaos computing, and machine learning applications in physics and engineering. He explores reconfigurable logic systems, noise-assisted computing, and quantum physics problems using physics-informed neural networks. His work bridges theoretical physics with practical applications, including developing novel computing architectures and analyzing complex systems like epidemic spreading. Collaborations involve interdisciplinary projects in computational neuroscience and quantum computing. Key Research Areas: Chaos-based computing, nonlinear circuits, quantum algorithms, and AI-driven physics problems. Notable Achievements: Pioneered reconfigurable logic gates using chaos and developed machine learning tools for quantum eigenvalue problems.

Claes EskilssonView profile

Associate Professor

• Wave Energy Engineering
• Computational Fluid Dynamics
• Floating Offshore Wind Turbines
• +7 more

Claes Eskilsson is an Associate Professor in the Department of Construction, Urban and Environmental Engineering at Aalborg University's Faculty of Engineering and Science. He is a key member of the AAU BLUE – Marine & Maritime Research initiative and leads the Research Group for Marine and Coastal Construction. He also holds an external position as Senior Researcher at RISE Research Institutes of Sweden since July 2017. His research focuses on marine renewable energy systems, particularly wave energy converters (WECs) and floating offshore wind turbines . He specializes in computational fluid dynamics (CFD) , numerical modeling , mooring engineering , and hybrid simulation techniques that combine physics-based models with machine learning. His work aims to improve the efficiency, survivability, and scalability of offshore energy platforms through advanced simulation and experimental validation. The recent articles reflect a strong trend toward high-fidelity hydrodynamic modeling, comparative numerical studies, and the integration of machine learning for performance prediction in marine energy systems. His publications span topics from slack-moored platforms to multi-cylindrical breakwaters and spectral element simulations, indicating a broad and deep engagement with offshore renewable technologies. Scientific Projects and Leadership: RESCUER (2024–2028): Principal Investigator – Resilient Solutions for Coastal, Urban, Estuarine and Riverine Environments. CHiS-WeC (2023–2026): Principal Investigator – COmposites, Hybrid testing and SImulations for a disruptive Wave Energy Converter. VALID (2020–2023): Co-Investigator – Verification through Accelerated testing Leading to Improved wave energy Designs. ISLINGTON (2022–2024): Supervisor – Improved Soil-cable Interaction mooring simulations. Competitive Renewable Energy Platforms (2023–2026): Project Participant – Based on Shipbuilding Methods. He has advised PhD students, including Bárður Joensen, and has secured significant research funding, such as a EUR 2 million grant for the Wavepiston wave energy project. He is actively involved in professional events, including the European Wave and Tidal Energy Conference, and has contributed to media coverage on ocean energy innovations. His research is supported by a strong publication record of 95 works, including journal articles, conference papers, and reports. Laboratories and Research Groups: He is affiliated with the Research Group for Marine and Coastal Construction and contributes to the AAU BLUE marine and maritime research environment, which fosters interdisciplinary collaboration on sustainable offshore solutions.

• Computer Vision
• Machine Learning
• Probabilistic Graphical Models
• +7 more

Jacobus J Barnard is a Professor in the Department of Computer Science at the University of Arizona, College of Engineering. He is actively involved in teaching and research, with a focus on computer vision, machine learning, and interdisciplinary applications in emotion dynamics, biology, and planetary science. Department: Computer Science School: College of Engineering University: University of Arizona Academic Rank: Professor His research interests are centered on Computer Vision , Probabilistic Modeling , and Machine Learning , with applications in emotion regulation, biological structure analysis, and remote sensing. He develops statistical models for understanding indoor scenes, tracking biological motion, and analyzing social and emotional dynamics. His work integrates vision and language, with generative models linking image regions to semantic concepts. The recent publications reflect a strong trend in deep learning for image restoration (e.g., fiber bundle imaging), Bayesian modeling in planetary science (crater shape analysis), and interdisciplinary AI (emotion dynamics, gene expression). His work spans computer science, psychology, biology, and engineering, often combining statistical inference with real-world data from microscopy, aerial imaging, and human behavior studies. Scientific Awards: No explicit awards listed in the provided text. Barnard has advised numerous students, including PhD candidates and honors undergraduates, working on topics such as gesture authentication, pollen tube tracking, and emotion modeling. He has not received mention of major grants, but his sustained publication record and interdisciplinary collaborations suggest active funding. He teaches core courses like Intro to Computer Vision and Probabilistic Graphical Models , and supervises dissertation and independent study research. He is involved in developing computational models for understanding biological structures (e.g., Alternaria fungus, Drosophila neurons), emotion regulation in couples, and automated trail detection in aerial imagery. His lab appears to focus on statistical vision, with strong ties to cognitive science and bioinformatics.

Alberto MarzoView profile

Senior Lecturer

• Computational Fluid Dynamics
• Cardiovascular Biomechanics
• Medical Device Optimization
• +5 more

Dr. Alberto Marzo is a Senior Lecturer in the Department of Mechanical Engineering at the University of Sheffield, serving as Director of the MSc Bioengineering program. His research focuses on computational fluid mechanics, cardiovascular biomechanics, and clinical translation of engineering technologies. He holds a five-year Mechanical Engineering degree from the University of Cagliari, Italy, and a PhD from the University of Sheffield. Early in his PhD, he received the David Crighton Fellowship, enabling collaborative research at the University of Cambridge on flow-induced oscillations in elastic vessels. Education: Bachelor's/Master's (5-year): Mechanical Engineering, University of Cagliari, Italy PhD: University of Sheffield Research Interests: Image-based 3D cardiovascular modeling 1D whole-circulation blood flow modeling Medical device optimization (e.g., respiratory treatments) Hemodynamic influences on cardiovascular/biofilm dynamics High-performance computing (HPC) and statistical emulators for validation Recent Work Trends: His publications emphasize clinical translation, such as developing open-source tools (e.g., openBF solver), optimizing stroke treatment protocols, and integrating machine learning with biomechanics for diagnostic biomarkers. He also focuses on musculoskeletal modeling and renal artery hemodynamics. Awards: David Crighton Fellowship (2011) Grants & Leadership: Co-investigator in VPH-DARE@IT (EU FP7) for dementia modeling Principal Investigator for NHS/EPSRC urinary drainage system project Lead of CompBioMed EU H2020 Center of Excellence Teaching & Labs: Teaches courses like MEC320 (Computational Fluid Dynamics) and oversees labs focused on cardiovascular biomechanics and computational modeling.

• Machine Learning
• Computational Neuroscience
• Data Mining
• +3 more

Lena Bauer is a researcher affiliated with the Faculty of Computer Science at the University of Vienna, specifically within the Research Group Data Mining and Machine Learning . Her work bridges computational methods with neuroscience, focusing on applications in mental health. She holds degrees including DI Dr.techn. (PhD in Technical Sciences) and BSc. Research Interests: Bauer’s research integrates machine learning and data mining techniques to analyze complex biological systems, particularly brain networks. She explores multimodal data integration, clustering algorithms, and their application to neuroimaging (e.g., fMRI) for understanding disorders like Major Depressive Disorder. Key themes include quantifying neural synchronization, developing autoencoder-based models, and improving clustering methodologies. Collaborations: Bauer collaborates internationally on projects involving computational neuroscience and mental health. Recent activities include presenting work on Kuramoto model applications to fMRI data at conferences. Labs & Teams: She contributes to the University of Vienna’s Data Mining Group, which focuses on clustering, causality analysis, and big data applications.

• Affective Neuropsychology
• Emotion Processing
• Clinical Neuroscience
• +12 more

Prof. Dr. Johanna Kißler is a full Professor of General Psychology and Affective Neuroscience at Bielefeld University, affiliated with the Faculty of Psychology and Sport Science and the Department of Psychology. She leads the Affective Neuropsychology research unit (Unit 02) and is a key member of the Center for Cognitive Interaction Technology (CITEC), where she serves on the Board of Directors and leads a participating research group. She is also involved in the Center for Interdisciplinary Research (ZiF) as a board member and co-leads a subproject in the Collaborative Research Center SFB 1646 on linguistic creativity. Her research focuses on the neural mechanisms underlying emotion and language processing, particularly in clinical populations such as patients with epilepsy and disorders of consciousness. Using EEG, fMRI, and behavioral methods, she investigates emotional face and word perception, memory, emotion regulation, and verbal fluency. Her work often examines the impact of brain lesions, especially in the temporal and frontal lobes, on cognitive and affective functions. She has led numerous third-party funded projects from the German Research Foundation (DFG), federal agencies, and foundations, including the NeuroCommTrainer project aimed at developing brain-computer interfaces for severely brain-damaged patients. Her recent publications reflect a strong trend in affective neuroscience, clinical neuroimaging, and neurolinguistics, with a focus on EEG/ERP markers, gamma-band activity, hemispheric specialization, and multimodal assessment of emotional and cognitive processing. Themes include emotion regulation in epilepsy, social feedback processing, and the neural basis of linguistic creativity. German Research Foundation (DFG) Federal Government (Bund) Foundations (Stiftungen) She has received research funding for projects on memory in epilepsy, emotion processing, and brain-computer interfaces. She has supervised students and contributed to academic governance, having served as Dean of the Faculty of Psychology and Sports Science (2016–2019) and Dean of Graduate Studies (2013–2016). She teaches courses in psychology and medicine, including clinical neuropsychology and communication in medical contexts. Prof. Kißler is actively involved in interdisciplinary research teams, including CITEC, SFB 1646, and ZiF, where she collaborates on advanced topics in cognitive interaction, linguistic creativity, and consciousness research. Her lab focuses on affective neuropsychology, utilizing electrophysiological and neuroimaging methods to explore emotion-language interactions and clinical applications in neurorehabilitation.

İlknur Kuşbeyzi AybarView profile

Associate Professor

• Dynamical Systems
• Bifurcation Analysis
• Nonlinear Differential Equations
• +7 more

İlknur Kuşbeyzi Aybar is an Associate Professor at the Department of Mathematics, Faculty of Arts and Sciences, Yeditepe University. She holds concurrent positions in the Department of Mathematics and Science Education within the Faculty of Education. Her academic journey includes roles from Research Assistant (2005) to Doctoral Lecturer and Associate Professor across multiple departments. Education: She earned a PhD in Mathematics (2010) from Gebze Technical University focusing on dynamical systems, an MSc in Mathematics (2006) from Yıldız Technical University on multi-objective optimization, and a BSc in Mathematics (2004) from Yıldız Technical University. Research Interests: Her work centers on nonlinear dynamical systems, bifurcation analysis, mathematical biology, and applications of chaos theory in physical and biochemical systems. Specific focus areas include predator-prey models, neuronal dynamics, and computational algebra methods in biochemical systems. She also explores educational technology applications in mathematics instruction. Publications: Over 15 peer-reviewed articles in journals like MATCH Communications in Mathematical and in Computer Chemistry , Nonlinear Dynamics , and Applied Mathematics and Computation . Recent work emphasizes bifurcation analysis in biochemical systems and neuronal networks, with growing focus on memristor-based models and educational software development. Grants & Projects: Led international projects like "Dynamical Analysis of Biochemical Systems Using Computational Algebra" (2019-2023) and contributed to EU-funded initiatives such as MAT-DYN-NET (2019-2024). Awards: Received Excellence in Dissertation Award and multiple TÜBİTAK publication grants. Labs/Teams: Active in interdisciplinary teams studying dynamical systems and reaction kinetics networks, collaborating with institutions in Slovenia and Austria on biochemical modeling.

• Chemical Engineering
• Process Engineering
• Reaction Engineering
• +3 more

проф. др Никола Никачевић is a Full Professor at the Department of Chemical Engineering, Technical Faculty in Bor (TMF), University of Belgrade. He specializes in chemical engineering with a focus on process intensification, reaction engineering, and mathematical modeling of chemical processes. His research emphasizes optimizing fixed-bed reactors for Fischer-Tropsch synthesis, catalyst performance analysis, and multiphase flow dynamics. His work combines computational modeling (CFD, reaction kinetics) with experimental validation to enhance reactor efficiency and reduce environmental impacts. Key areas include: Fischer-Tropsch synthesis optimization, catalyst deactivation mechanisms, and process intensification via microreactor technologies. Prof. Nikčević has advised numerous PhD, master's, and undergraduate students on advanced topics like enzymatic reactor design, hydrodynamic studies of oscillating flow systems, and environmental impact assessments. His publications span high-impact journals like Chemical Engineering Science, Industrial & Engineering Chemistry Research, and Catalysis Today, focusing on reactor design, process modeling, and catalyst characterization.

Martin VinckView profile

Professor

• Neural Circuits
• Predictive Processing
• Neural Coding
• +3 more

Prof. Martin Vinck is a Group Leader at the Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with the Max Planck Society, based in Frankfurt am Main, Germany. His research focuses on understanding neural mechanisms underlying information processing, particularly through three core approaches: neural circuits, collective neuron activity, and predictive learning. His work integrates experimental and computational methods, including electrophysiology, machine learning, and information theory to study how the brain encodes and processes sensory inputs and predictions. Key contributions include investigations into gamma oscillations' role in predictive coding, cell-type-specific neural propagation, and the development of novel analysis tools like SpikeShip for decoding neural spiking patterns. His lab is supported by major grants such as the ERC Starting Grant and the German Federal Ministry of Education and Research (BMBF). He advises students and collaborates internationally, with alumni including Irene Onorato and Matteo Saponati. Research interests span neural circuits, predictive processing, and computational neuroscience, with applications in understanding how brains integrate sensory input and prior knowledge. Notable publications address topics like cortical interactions, attention effects, and the neurobiology of aesthetic perception.

• Biomaterials
• Biosensors
• Sustainable Architecture
• +4 more

Anna Nikolaidou is a Senior Lecturer in Architecture and Design at the Faculty of Environment and Technology (FET), University of the West of England (UWE Bristol). She holds a DPhil from UWE Bristol and an MArch from University College London's Bartlett School of Architecture. As a Senior Fellow of the Higher Education Academy (SFHEA), she specializes in interdisciplinary research at the intersection of architecture, biomaterials, and biotechnology. Her work focuses on biohybrid material systems using microbial communities for responsive, regenerative, and computing capabilities in architectural and wearable applications. Anna has taught as a Visiting Professor at IE University (Spain) and conducted research at the University of Applied Arts Vienna and BOKU University Vienna. She leads the BSc (Hons) Architecture programme and is part of UWE’s Unconventional Computing Laboratory. Her research themes include biomaterials, biosensing, sustainable design, and smart wearables, with a focus on fungal and bacterial composites for environmental sensing and adaptive systems. Anna’s industry experience includes roles at firms like Foster + Partners and KCA International, blending design practice with academic innovation. Her lab explores living materials such as kombucha-based biofilms and fungal networks for applications in responsive building systems, smart wearables, and bioelectric circuits. She actively collaborates across disciplines, including nanoscience and biomedical engineering.

• Electronics Engineering
• Semiconductor Devices
• Terahertz Technology
• +3 more

Dr. Abdullah Al-Khalidi is a Lecturer in Electronic & Nanoscale Engineering at the University of Glasgow’s College of Engineering. He holds BEng, MSc, and PhD degrees from the University of Glasgow (2010–2015). Previously a postdoctoral researcher at Glasgow (2015–2019), his work focuses on THz resonant tunnelling diodes (RTDs), GaN transistor technologies, and high-frequency electronic systems. Key research areas include terahertz wireless communications, neuromorphic computing, and semiconductor device optimization. His research spans RTD-based photodetectors, GaN HEMT thermal performance, and compact terahertz source development. Notable contributions include high-power J-band oscillators (1 mW output) and low-power nano-RTD photodetectors. Dr. Al-Khalidi collaborates on projects like the TERAPOD initiative for terahertz data center links and the iBROW program for ultra-broadband wireless systems. Publications highlight advancements in resonant tunneling diode modeling, antenna design for THz applications, and GaN device fabrication techniques. His work bridges semiconductor physics with practical applications in high-speed communication and neuromorphic hardware.