Professor Robert Hewson leads the Multidisciplinary Design Optimization research group at Imperial College London's Department of Aeronautics. His work develops computational methods for designing optimized structures across multiple scales. Hewson's research integrates topology optimization, additive manufacturing constraints, and multiphysics modeling to create novel metamaterials and aerospace components. Recent work focuses on multiscale lattice structures with tailored mechanical, thermal, and dynamic properties for aerospace applications. His publications demonstrate expertise in computational mechanics, tribology modeling, and design automation. Hewson collaborates with aerospace industry partners to translate optimization methods into practical engineering solutions.
Dipankar Dasgupta is the William Hill Professor in Cybersecurity and Director of the Center for Information Assurance at the University of Memphis. He holds a PhD in Computer Science from the University of Strathclyde (1994) and has been a faculty member since 1997, advancing to Full Professor in 2004. His research focuses on cybersecurity, artificial intelligence, and bio-inspired computing, particularly in artificial immune systems and evolutionary computation. He has authored/co-authored over 300 publications, including influential books like *Immunological Computation* and *Advances in User Authentication*. Dr. Dasgupta is an IEEE Fellow, ACM Distinguished Speaker, and recipient of the 2014 ACM SIGEVO Impact Award and 2011-2012 Willard R. Sparks Eminent Faculty Award. He leads the Center for Information Assurance, a nationally recognized academic excellence hub in cybersecurity education and research. His work includes pioneering contributions to negative authentication systems, cybersecurity frameworks, and AI-driven threat detection. His research spans adversarial machine learning, federated learning security, and generative AI. He has collaborated widely, with an h-index of over 58 and collaborations with 106 co-authors. He also contributes to educational initiatives, such as cybersecurity summer camps for high school students, and chairs like the MIT Geospatial Data Center Advisory Board.
Shan Zhong is a Professor in the School of Mechanical, Aerospace and Civil Engineering at The University of Manchester. She holds a BEng and MEng from Tsinghua University and a PhD from the University of Cambridge. As the Head of the Aerodynamics Research Group, her work focuses on experimental fluid mechanics, flow control, and biofluid dynamics. Her research spans aerodynamics, turbulence, and low-Reynolds-number propulsion, aiming to enhance aerodynamic efficiency in turbomachinery and transportation systems. Supported by grants from EPSRC, Royal Society, and industry partners like Airbus and BAE Systems, her contributions include over 140 publications and the supervision of 23 PhD students. Research interests include boundary layer transition, flow separation control, fluid mixing enhancement, and bio-inspired surface patterns. Prof. Zhong is a Fellow of the Royal Aeronautical Society and leads the Laser Processing Research Centre. Her work aligns with UN Sustainable Development Goals related to clean energy and industrial innovation. Education: BEng/MEng (Tsinghua University), PhD (University of Cambridge) Affiliations: Aerodynamics Research Group, Laser Processing Research Centre Grants: EPSRC, Royal Society, Leverhulme Trust, Airbus, BAE Systems Her research employs advanced techniques like PIV and synthetic jet actuators to study complex fluid phenomena. Current projects explore flow control strategies for reducing drag and improving propulsion efficiency in aerospace and land-based systems.
Daniel Jarne Ornia is a Researcher at the University of Oxford's Department of Computer Science, located in Room 213 of the Wolfson Building on Parks Road, Oxford. His work focuses on artificial intelligence, multi-agent systems, and reinforcement learning, with particular emphasis on risk-aware decision-making, robust learning algorithms, and autonomous agent coordination. Research interests include developing methods for efficient and reliable coordination in multi-agent environments, exploring bounded rationality in resource-constrained systems, and advancing techniques for predictable reinforcement learning dynamics. His recent publications address topics such as contingency planning with multi-modal predictions, entropy rate minimization in learning systems, and navigation strategies for robotic swarms. No academic awards or grants are explicitly mentioned in the provided information. Daniel’s research often intersects with robotics, distributed control systems, and theoretical foundations of autonomous decision-making.
Farshad Arvin is a Professor in the Department of Computer Science at Durham University, with affiliations to the Wolfson Research Institute for Health and Wellbeing. He holds a BSc in Computer Engineering (2004), MSc in Computer Systems Engineering (2010), and PhD in Computer Science (2015). His research focuses on swarm robotics, bio-inspired systems, and multi-agent autonomous systems. Research interests include biohybrid robotics, swarm intelligence algorithms, and applications in extreme environments. His recent publications demonstrate strong focus on underwater robotics, UAV coordination, and federated learning for swarm navigation. He directs the Swarm & Computational Intelligence Laboratory (SwaCIL) and leads major EU projects including RoboRoyale (€3.27M), Sensorbees (€3.2M), and BioDiMoBot (€8M). His team includes 4 Post-Doctoral researchers and 6 PhD students.
Dr. Kheng Cher Yeo is a Senior Lecturer in Information Technology at the Faculty of Science and Technology. His research focuses on interdisciplinary areas including cybersecurity, networking, image processing, control systems, and machine learning. He explores innovative solutions for IoT security frameworks, healthcare technology, and privacy-preserving systems. His work integrates machine learning with healthcare diagnostics, cybersecurity forensics, and smart city accessibility. Key research interests include: IoT Security & SDN Integration Medical Imaging & AI-Driven Diagnostics Privacy in Healthcare Systems Malware Analysis & Forensic Techniques Bio-inspired Authentication Systems Recent publications highlight advancements in: Automated respiratory disease diagnosis via lung ultrasound Cybersecurity frameworks for IoT and DDoS mitigation Privacy-by-design healthcare record management Smart city accessibility through crowdsourcing His research demonstrates a strong focus on practical applications of AI and cybersecurity in healthcare, smart infrastructure, and data protection. No scientific awards are noted in the provided texts.
Piotr Łuczak is a Researcher at the Institute of Applied Computer Science, Faculty of Electrical, Electronic, Computer and Control Engineering, Lodz University of Technology. His work bridges computer engineering, machine learning, and biomedical applications. Research spans VLSI systems for radar-based health monitoring, thermal imaging for industrial control, hyperdimensional computing frameworks, and neuromorphic approaches. Recent publications demonstrate strong interdisciplinary focus on hardware-efficient AI implementations. Article analysis shows consistent evolution in: (1) Radar-based biomedical sensing with novel signal processing; (2) Thermal imaging for industrial automation; (3) Hybrid AI models combining neural networks with symbolic methods; (4) Neuromorphic computing for edge devices; (5) Optimization techniques for efficient neural architectures; (6) Human-machine interaction systems.
Dr. Jeremy Holleman is an Associate Professor and Program Coordinator of Electrical Engineering at the University of North Carolina at Charlotte. He directs the assessment efforts within the Electrical and Computer Engineering department. His research focuses on low-power analog/mixed-signal circuits, biomedical interface design, neuromorphic computation, and machine learning hardware for resource-constrained systems. He holds a Ph.D. (2009) and M.S. (2006) from the University of Washington and a B.S. (1997) from Georgia Institute of Technology. Dr. Holleman's work emphasizes energy-efficient computing architectures and hardware implementations of machine learning, including contributions to MLPerf Power benchmarks and TinyML standards. His academic background includes over 20 years of experience in analog circuit design, neuromorphic systems, and biomedical signal processing. Notable projects include a 1 Tera-OPS/Watt analog deep learning engine and ultra-low-power neural amplifiers for bio-potential recording. He has published extensively across IEEE journals and conferences, with over 50 peer-reviewed articles. His research has been applied in medical implants, wireless neural interfaces, and energy-harvesting systems. Current initiatives include advancing analog deep learning architectures and sustainable AI hardware optimization. Dr. Holleman’s lab collaborates on hardware-software co-design for embedded machine learning and neuromorphic systems.
Jonas Kuckling is a postdoctoral researcher at the Department of Computer and Information Science, University of Konstanz, and a fellow at the Zukunftskolleg. His research focuses on artificial life and swarm robotics, particularly open-endedness in embodied collectives, integrating disciplines including biology, chemistry, physics, computer science, philosophy, and art. Dr. Kuckling obtained his doctoral degree in April 2023 from the Artificial Intelligence research laboratory (IRIDIA) at the Université Libre de Bruxelles, Belgium. His doctoral work established foundations for his current investigations into emergent collective behaviors in robotic systems. His primary research interests center on artificial life and swarm robotics, with emphasis on open-ended evolution in embodied collectives. He explores how interdisciplinary approaches can generate autonomous systems capable of continuous novelty without external direction, bridging computational models with biological principles of self-organization and adaptation. This work addresses fundamental challenges in scalability, robustness, and emergent complexity in multi-robot systems. Analysis of his recent publications reveals a concentrated focus on automatic modular design of robot swarm control software, particularly through behavior trees and optimization techniques like simulated annealing. His work progresses from theoretical frameworks (e.g., manifestos on off-line design) to practical implementations (AutoMoDe tools), with increasing integration of learning, communication, and edge computing in large-scale swarm deployments. No scientific awards are documented in available sources. Dr. Kuckling currently leads an interdisciplinary research project at the Zukunftskolleg, collaborating with experts in complex systems, artificial life, AI, and robotics to advance open-endedness in embodied collectives. No information is available regarding student advising or specific grant funding beyond his fellowship appointment.
Mohammadmahdi Faraji is a Research Fellow at the International Iberian Nanotechnology Laboratory (INL), where he joined the Integrated Micro and Nanotechnologies Group in April 2021. His work focuses on designing and developing digital electronic systems for MEMS controllers, including precise temperature controllers, gas sensor setups using pyroelectric crystals, and customized drivers for inkjet print heads. Dr. Faraji received his PhD in Digital Systems from Sharif University of Technology, Tehran, Iran in 2020. His doctoral research proposed a distributed sound source localization system using acoustic sensor nodes and a fuzzy fusion algorithm for real-time outdoor applications. He also holds an MSc in Electrical Engineering from Amirkabir University of Technology (2013), where he designed a distributed acoustic source localizer based on wireless sensor networks. His research spans several key areas: MEMS and sensor systems design Digital signal processing and embedded systems FPGA implementation for real-time control Distributed sensor networks for acoustic localization Flexible printed electronics and nanomaterials for sensors Recent publications (2015-2022) demonstrate a progression from acoustic source localization using spiking neural networks to advanced materials for force sensing and printed electronics. His work bridges electrical engineering, materials science, and computational neuroscience. No scientific awards or fellowships are mentioned in the provided text. Dr. Faraji is a member of the Piteira Research Group at INL, contributing to projects in the Integrated Micro and Nanotechnologies Group. His current research involves developing MEMS controllers and sensor systems for diverse applications.
Dr Mathew Emmett is an Associate Professor in Architecture at the University of Plymouth's School of Art, Design and Architecture. He leads the Master of Architecture (ARB/RIBA Part 2) program and has held roles in MA Architectural Design and 3D Design. His work bridges architecture with interdisciplinary fields like cybernetics, AV installation, and situated cognition. Emmett's research emphasizes transcultural communication through spatial design, exemplified by projects like Sender/Receiver (Tate Modern, 2016) and St Sebastian: Plague Memory (Rome, 2022). Education: PhD in Situated Cognition and Architecture, Doctorate in Architecture, Fine Art Foundation at Central Saint Martins. Awards include the Gardener Theobald Scholarship and Sir Henry Herbert Bartlett Award. He has collaborated with figures like Charles Jencks and Eberhard Kranemann (Kraftwerk). Teaching spans institutions globally, including Eindhoven University of Technology and The Royal College of Art. Supervised PhDs include Leah Dinning and Zoe Latham. As Principal Investigator of the €4.1M EU Marie Curie Project 'Cognovo', he advanced interdisciplinary cognitive innovation. Exhibitions span installations in London, Rome, Tokyo, and Germany. Key projects explore public realm design, healthcare environments, and immersive digital experiences. His work is featured in venues like Tate Modern and the Museo dell'Arte Classica.
Maryam Parsa is a Tenure-Track Assistant Professor in the Department of Electrical and Computer Engineering at George Mason University. Her research focuses on neuromorphic computing, Bayesian optimization, and algorithm-hardware co-design, aiming to develop energy-efficient, secure, and resilient AI systems for edge computing applications. She holds a PhD from Purdue University, supported by an Intel/SRC fellowship, and previously worked at Oak Ridge National Lab. Education: PhD in Electrical and Computer Engineering, Purdue University (2020) MS in Civil Engineering, Purdue University (Year unknown) MS in Electrical and Computer Engineering, University of Ottawa (Year unknown) BS in Electrical and Computer Engineering, Khaje Nasir Toosi University of Technology (Year unknown) Research Interests: Neuromorphic learning and bio-inspired robotics Bayesian optimization for materials discovery Privacy-preserving spiking neural networks Edge AI and real-time embedded systems Major Achievements: Lead a $2.4M 3-year project on 3D chip creation (2023) Recipient of Intel/SRC PhD Fellowship Current Work: Developing neuromorphic architectures for smart healthcare and cyber-physical systems Pioneering causal machine learning for materials innovation Advancing privacy & security in neuromorphic systems Labs/Teams: Active in Mason's neuromorphic computing research group with collaborations in national labs and industry partners.
Dr. Mark Elshaw is a Lecturer in Computer Science at the School of Science, with expertise in robotics, neural networks, and biologically inspired computing systems. He has worked at the University of Sunderland, Sheffield University, and Coventry University, contributing to projects like the development of a bio-inspired robot that won the British Computer Society Intelligent Machine Prize. His research spans speech recognition, robot-human interaction, and emotion recognition in robots. Research Interests : Neural architectures, biomimetic robotics, unsupervised learning, experimental methodologies for social robots. Key Contributions : Edited computational neuroscience books, organized conferences, and contributed to robotics and deep learning advancements. Scientific Awards : British Computer Society Intelligent Machine Prize (team member). Recent Publications focus on emotion recognition, deep learning for autonomous vehicles, and neural architectures for social robots. He has also explored reinforcement learning, mirror neuron systems, and multimodal learning in robotics.
Robert L. Steward Jr. is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of Central Florida (UCF). He leads the Cellular Biomechanics Lab at UCF's Health Science Campus at Lake Nona, focusing on linking mechanics and medicine through interdisciplinary research. His work explores cellular responses to mechanical forces in contexts like cardiovascular disease, diabetes, and neuroscience. Education: Ph.D. in Mechanical Engineering, Carnegie Mellon University Bachelor of Science in Mechanical Engineering, Clark Atlanta University Research Interests: His research integrates biomechanics, mechanotransduction, and biomedical engineering to understand how cells respond to mechanical stimuli such as fluid shear stress and substrate stiffness. Key areas include endothelial cell mechanics, tumor microenvironment modeling, and bio-inspired medical devices. Recent work emphasizes predicting cellular mechanosensation using machine learning and developing lab-on-a-chip systems for cardiovascular studies. Publications: His articles highlight advancements in understanding fluid dynamics' effects on endothelial cells, 3D scaffold applications for cancer research, and open-source biomedical devices. Notable trends include mechanotransduction modeling, extracellular matrix interactions, and translational biomedical engineering solutions. Awards: NIH Mentored Quantitative Research Development Award (K25) Grants & Labs: Steward's lab at Lake Nona focuses on translational projects bridging engineering and medicine. Collaborations involve developing biomaterials for disease modeling and diagnostic tools. His NIH-funded research explores predictive models of cellular mechanical responses to chemical perturbations.
Hoang Nguyen is an Assistant Professor at the Department of Mechanical and Construction Engineering, Northumbria University. Previously, he was a Postdoctoral Research Associate at the University of Glasgow, focusing on structural integrity of nuclear power plant components. He holds a PhD from Northumbria University, where his research centered on generalized continua and isogeometric analysis for microstructure behaviors. His expertise spans data-driven material predictions, structural optimization, and high-performance computing. Education: PhD in Civil Engineering (Northumbria University). Research interests include structural engineering, finite element methods, and mechanics of microstructures. He employs advanced computational techniques to address challenges in materials science and structural analysis. Advising and Grants: No specific advisees or grants listed. Active in interdisciplinary research collaborations, particularly in computational mechanics and structural integrity.
