Dr Luca Manneschi is a Lecturer in Machine Learning at the School of Computer Science, University of Sheffield, with an IBM Liaison role. He holds a PhD in Physics from the University of Sheffield (2021) and completed a PostDoc there before his current position since March 2022. His research focuses on designing learning algorithms inspired by biological networks for physically defined systems, emphasizing neuromorphic computing, reservoir computing, and stochastic environments. Education: Bachelor's in Physics: University of Padua (Italy) Master's in Physics: Sapienza University of Rome (Italy) PhD in Physics: University of Sheffield (2021) Research Interests: Dr. Manneschi explores algorithms for physically defined networks, leveraging biological network principles to enhance computation in dynamic environments. His work bridges machine learning with neuromorphic hardware, emphasizing adaptability and energy efficiency. Key areas include reservoir computing, magnetic metamaterials, and multi-timescale learning strategies. Grants & Funding: "Real-time Reservoir Computing on Prosthetic Devices" (2023–2025, Royal Society, PI) "MARCH: Magnetic Architectures for Reservoir Computing Hardware" (2021–2025, EPSRC, Co-PI) "CausalXRL: Causal Explanations in Reinforcement Learning" (2021–2024, EPSRC, Co-PI) "ActiveAI" (2019–2024, EPSRC, Co-PI) Lab/Team Affiliation: Machine Learning Research Group, School of Computer Science.
Gustavo Deco is a Research Professor at the Institució Catalana de Recerca i Estudis Avançats (ICREA) and holds a Professorship (Catedrático) at Pompeu Fabra University (UPF). He leads the Computational Neuroscience Group and directs the Center of Brain and Cognition at UPF. His research focuses on computational models of brain dynamics, integrating biophysics, neuroimaging, and complex systems principles. Deco’s academic journey includes a PhD in Physics (1987, thesis on Relativistic Atomic Collisions), postdoctoral work at the University of Bordeaux (France) and University of Giessen (Germany), and a Habilitation in Computer Science (1997, Technical University of Munich). He has led computational neuroscience research at Siemens Corporate Research Center (1990–2003) and pioneered whole-brain modeling frameworks like The Virtual Brain (TVB). His research interests span critical brain dynamics, non-equilibrium thermodynamics in neural systems, psychedelics’ effects on brain hierarchy, and clinical applications of computational models in disorders such as Alzheimer’s and depression. Recent work emphasizes turbulence-like dynamics in healthy and diseased brains, and biomarker discovery using AI-driven simulations. Deco’s articles (2024–2025) explore topics like entropy production in brain networks, psychedelics-induced flattening of functional hierarchies, sleep-like dynamics post-stroke, and the role of long-range connections in global brain communication. His work bridges theoretical models with clinical insights, aiming to advance personalized neurology and digital brain research.
Bowei Xi is an Associate Professor at the Department of Statistics at Purdue University. He holds a Ph.D. in Statistics from the University of Michigan (2004) and has made significant contributions to adversarial machine learning, cybersecurity, and metabolomics. Education: Ph.D. in Statistics, University of Michigan (2004) His research focuses on: Adversarial Machine Learning – Developing defenses against attacks on AI systems Cybersecurity – Integrating statistical methods with network security Metabolomics – Applying statistical analysis to biological data Big Data – Creating scalable analytical frameworks Differential Privacy – Balancing data utility with privacy preservation Recent publications highlight: Advancements in causal inference under privacy constraints Novel cyber deception strategies for battlefield IoT Applications of graph neural networks with dropout techniques Statistical defense mechanisms against adversarial examples Awards include: Faculty of 1000 Evaluation (2011) US Patents #7272707 and #7490234 for application server optimization He teaches STAT 514: Design of Experiments with a focus on hands-on learning and statistical software (SAS) integration. His office hours and contact details are publicly listed for student engagement.
Ben Duan is an Adjunct Lecturer at the Department of Data Science & AI, Faculty of Information Technology, Monash University. He holds a PhD in Big Data and Electronics from the University of Technology Sydney. Previously, he was a Research Fellow at the Hong Kong University of Science and Technology. His research focuses on reinforcement learning, graph neural networks, intelligent transportation systems (ITS), and fintech. He teaches courses such as FIT5221: Intelligent Image and Video Analysis, FIT5215: Deep Learning, and FIT5217: Statistical Data Modeling at Monash’s Suzhou campus. Dr. Duan’s work aligns with UN Sustainable Development Goals related to sustainable cities and communities (SDG 11) and industry innovation (SDG 9). His recent research explores AI-driven solutions for traffic congestion, stock trend prediction using graph neural networks, and spiking neural networks for neural activation control. He has contributed to over 28 publications and a patent with the Monash Suzhou Research Institute. He supervises PhD students interested in reinforcement learning, graph neural networks, or ITS, offering full scholarships at Monash Suzhou. Key collaborations span transportation systems, fintech, and data-driven industrial processes.
Professor David Halliday holds a position at the University of York as a Professor and Chair of the Research Committee. His research focuses on interdisciplinary fields of Computational Neuroscience and Neural Computing, with a particular emphasis on neural networks and neurophysiological signal analysis. He leads the Intelligent Systems and Nano-science Group and maintains an active research profile with over 100 publications. His work integrates theoretical modeling with experimental techniques to investigate topics such as motor control, neural oscillations, and clinical applications of machine learning. David Halliday's academic training includes a BSc and PhD, though specific institutions are not detailed in the provided text. His research interests span computational modeling of neural circuits, development of neuro-inspired algorithms, and application of signal processing techniques to neurological disorders. His homepage at http://www-users.york.ac.uk/~dh20 provides further details, including access to his ResearcherID profile (RID: A-3848-2009). Key research contributions include studies on cortico-muscular coherence in movement disorders, astrocyte-neuron interactions, and fault-tolerant spiking neural networks. His publications demonstrate interdisciplinary collaboration across computational neuroscience, biomedical engineering, and clinical diagnostics. Notably, his work combines experimental neurophysiology with advanced computational methods, such as non-parametric directionality analysis and wavelet-based coherence estimation. Recent studies have addressed clinically relevant topics like gait disturbance in spinal cord injury and biomarker identification in bladder cancer using machine learning approaches.
Dhanya Sridhar is an Assistant Professor at the Department of Computer Science and Operations Research (DIRO) of the University of Montreal and a core academic member of Mila - Quebec Artificial Intelligence Institute. She holds a Canada CIFAR AI Chair and co-leads the IVADO R3AI working group on safe and aligned AI. PhD from University of California Santa Cruz Postdoctoral research at Columbia University Data Science Institute Her research focuses on integrating causality and machine learning to build AI systems that are robust to distribution shifts, capable of efficient task adaptation, and aligned with human knowledge. Recent work includes causal effects of social interactions on US election participation and causal modeling in geriatric-oncology patient outcomes. Current research themes: Causal representation learning Temporal causal inference Counterfactual modeling Causal abstraction in large models Responsible AI development Scientific awards: Canada CIFAR AI Chair Major grants: NSERC Discovery Grant (PVX20965-RGP) 2023-2029 NSERC DGECR Grant 2023-2025 Multiple MITACS Acceleration Quebec grants Advising: PhD students: Philippe Brouillard, Shruti Joshi, Mizu Nishikawa-Toomey, Tom Marty, Cristian Manta
Matthew Fricke is a Research Associate Professor in the Department of Computer Science at the University of New Mexico, School of Engineering. He is affiliated with the Center for Advanced Research Computing (CARC), the Moses Biological Computation Lab, and the Lab for Agnostic Biosignatures, and contributes to the Interdisciplinary Working Group on Algorithmic Justice. His work bridges computer science, biology, and environmental science through computational modeling and robotics. His educational background includes a PhD and MS in Computer Science, a BS in Mathematics, all from the University of New Mexico, and a BA in Anthropology from Appalachian State University. Fricke's research centers on distributed complex systems, including computational biology (e.g., T cell and ant foraging dynamics), swarm robotics for planetary exploration and volcano surveys, high-performance computing, machine learning for climate modeling, and biosignature detection. He applies principles from biological systems to design efficient algorithms for robotic swarms and supercomputing environments. His recent publications (2020–2025) reveal a strong trend in applying machine learning and swarm robotics to Earth and space sciences, particularly in autonomous UAV-based environmental monitoring of volcanoes and climate systems. His work increasingly integrates causal discovery, field robotics, and interdisciplinary collaboration. He has mentored multiple PhD students, including Jannatul Ferdous, John Ericksen, Jake Nichol, and Humayra Tasnim, whose dissertations reflect his research themes. He has also secured research support through student-led projects in NASA robotics challenges and HPC competitions. Fricke teaches courses such as High Performance Computing, Swarm Robotics, and Complex Adaptive Systems, and leads workshops on SLURM, MPI, and scientific computing. He is actively involved in student mentorship and interdisciplinary research collaboration. His research labs include the Moses Biological Computation Lab and the Lab for Agnostic Biosignatures, where teams work on bio-inspired robotics, immune modeling, and planetary exploration technologies.
Vasanth Sarathy is a Research Assistant Professor of Computer Science at Tufts University, specializing in the intersection of Artificial Intelligence and Natural Language Processing. His work combines neuro-symbolic machine learning techniques with social and cognitive sciences to develop socially-competent and safe AI systems. Education: Ph.D. in Computer Science and Cognitive Science, Tufts University (2020) Juris Doctor (J.D.), Boston University School of Law (2010) M.S. in Electrical Engineering and Computer Science, MIT (2005) B.S. in Electrical Engineering, University of Arkansas (2003) His research spans three interconnected themes: Social NLP, Reasoning with Social Norms, and Sense-making and Problem-Solving. Through Social NLP, he develops computational methods to uncover patterns in human social behavior using qualitative texts. His work on Reasoning with Social Norms focuses on building AI architectures that can understand and apply social norms in reasoning and language interpretation. His Sense-making research explores how humans and AI systems simplify complex worlds for efficient problem-solving. His recent publications demonstrate a growing trend toward integrating Large Language Models with symbolic reasoning for more trustworthy AI. His work spans applications including automated writing assistance, intelligent tutoring, fact-checking, social media content moderation, and embodied social robots. His research shows particular strength in developing methods that combine neural and symbolic approaches to address complex social reasoning tasks that neither approach could solve alone. Previously, Dr. Sarathy worked as a Senior Researcher of AI at Smart Information Flow Technologies, collaborating with the U.S. Department of Defense and Intelligence Community. Before his AI career, he practiced intellectual property law at Ropes and Gray for nearly a decade. His unique interdisciplinary background bridges technology, law, and cognitive science. He advises students in AI, NLP, and cognitive systems, with research supported by grants from DARPA and other government agencies. His work has applications in human-robot interaction, social media analysis, and educational technologies. Outside academia, he creates single-panel cartoons, practices martial arts, plays chess, and designs puzzle video games. His interdisciplinary journey from law to AI has been featured in a BU Law article.
Qingjin Peng is a Professor in the Department of Mechanical Engineering at the University of Manitoba’s Price Faculty of Engineering. His work focuses on Product Design and Manufacturing, with expertise in Digital Manufacturing, Design Methodology, and Virtual Reality applications. He holds a PhD from the University of Birmingham (1998) and master’s and bachelor’s degrees from Xi’an Jiaotong University (1988, 1982). Education: PhD in Mechanical and Manufacturing Engineering, University of Birmingham (1998) M.Sc. Mechanical and Manufacturing Engineering, Xi’an Jiaotong University (1988) B.Sc. Mechanical and Manufacturing Engineering, Xi’an Jiaotong University (1982) Research Interests: His research spans Digital Manufacturing, Product Assembly/Disassembly Planning, CAD/CAM systems, and System Modeling. He pioneered methods integrating AI (e.g., Reinforcement Learning, Machine Learning) into product design and manufacturing processes. Notable contributions include optimizing Additive Manufacturing parameters using Taguchi methods and developing VR-based training systems for Coordinate Measuring Machines. Publications: Recent work emphasizes AI-driven innovation in manufacturing, with focus areas including sustainability in Additive Manufacturing, fault diagnosis of rotating machinery, and multi-agent systems for concept evaluation. His 2024 studies highlight advancements in product design through radical problem-solving frameworks, causal chain analysis, and genetic algorithm optimization. Advising & Grants: Currently no graduate student opportunities are listed, but his past collaborations include projects funded by national grants. Research teams often involve interdisciplinary partnerships, leveraging AI and data-driven approaches. Labs/Teams: His lab focuses on Digital Manufacturing and AI applications, though specific team names are not explicitly mentioned in the provided text.
Brittany Johnson-Matthews is an Assistant Professor in the Department of Computer Science at George Mason University, where she directs the INSPIRED Lab (INterdisciplinary Software Practice Improvement REsearch and Development). Her work bridges software engineering, human-computer interaction, and machine learning to address sociotechnical challenges in software development. Her educational background includes: Ph.D. in Computer Science from North Carolina State University (2017) B.A. in Computer Science from the College of Charleston (2011) Dr. Johnson-Matthews' research centers on sociotechnical problems in software development, with emphasis on developer productivity, tool support, work environments, ethics, and software for social good. She employs interdisciplinary approaches to study how developers interact with tools and environments, particularly in the context of emerging technologies like AI. Her work often involves empirical studies and tool development to promote fairness, inclusivity, and well-being in software engineering. Analysis of her recent publications (2023-2026) reveals a consistent focus on the human aspects of software engineering. Key themes include the impact of AI-assisted tools on developer well-being, fairness in machine learning toolkits, and ethical considerations in software development. Her research frequently involves building and evaluating tools (e.g., for detecting harmful terminology or causal testing) and conducting empirical studies across open source and industrial settings. She leads the INSPIRED Lab, which fosters interdisciplinary collaboration to improve software practices through research in human-centered computing, empirical software engineering, and ethical AI.
Enrico Blanzieri is an Associate Professor at the University of Trento's Department of Information Engineering and Computer Science. His research focuses on artificial intelligence, bioinformatics, quantum computing, machine learning, and computational biology. He actively contributes to interdisciplinary projects such as Vitis OneGenE for gene network analysis and quantum machine learning pipelines. His teaching includes courses like Algorithms for Bioinformatics, Quantum Machine Learning, and Data Mining. Blanzieri's work bridges theoretical foundations and practical applications, with notable contributions in quantum annealing-based algorithms, gene network expansion using distributed computing, and socially-competent artificial agents. His research integrates computational methods with biological systems, addressing challenges in precision agriculture and systems biology. His recent publications emphasize quantum machine learning advancements, causal role attribution in gene networks, and ethical AI frameworks for language models. Collaborative projects include the GENE@HOME volunteer computing initiative for bioinformatics tasks. Blanzieri also engages in educational activities, supervising courses that blend theoretical computer science with real-world applications.
Lilianne Mujica-Parodi is an Adjunct Professor in Physics and Astronomy at Stony Brook University and leads the Laboratory for Computational Neurodiagnostics (LCNeuro). Her research employs control systems engineering to study homeostatic regulation in brain circuits, with focus areas including neurometabolic interventions, brain aging, and multiscale neural modeling. Key findings demonstrate nonlinear transitions in brain aging where ketone-based metabolic interventions reverse age-related signaling deficits. Her work integrates neuroimaging (fMRI, MRS), computational modeling, and electrophysiology to explore how energy substrates influence network stability and ion channel regulation. Recent publications highlight applications in diabetes-related brain aging and optimization of computational psychiatry frameworks.
Dr. Poonam Yadav is a Senior Lecturer (equivalent to Associate Professor) in the Department of Computer Science at the University of York, UK, and a visiting research fellow at the University of Cambridge's Computer Laboratory. Her research focuses on resilient IoT and edge computing systems, addressing challenges in distributed systems, privacy, and interoperability. She leads the SYSTRON Lab, exploring distributed systems and network technologies. Her work integrates machine learning, wireless networking, and bio-inspired computing to enhance system reliability. Education: PhD in Computing Research (Distributed Systems & Networking), Imperial College London (2011) M.Tech in Intelligent Systems (AI/ML), IIIT, Allahabad, India Postdoctoral Research at Imperial College London, CERN, and the University of Cambridge Research Interests: Her interdisciplinary research spans IoT, wireless networks, TinyML, citizen science, and complex systems. She emphasizes collaborative trust mechanisms (M2M, M2H, H2D interactions) and system design under resource constraints. Recent work includes resilient 6G architectures, quantum key distribution, and energy-efficient edge computing frameworks. Awards & Recognition: UKIERI Fellowship (PhD funding) Advising & Grants: Dr. Yadav has collaborated on EPSRC, TSB, and NERC projects such as Databox, OpenShare, and FUSE. She leads the SYSTRON Lab and contributes to initiatives like MedPerf (medical AI benchmarking) and the Hyperledger Fabric platform for secure data sharing in autonomous vehicles. Labs & Teams: Founder and Director of the SYSTRON Lab , focusing on distributed systems, interoperability, and network innovation.
Professor Eleni Vasilaki holds the Chair in Bioinspired Machine Learning at the University of Sheffield's School of Computer Science, where she serves as Head of the Machine Learning research group and member of the Complex Systems Modelling research group. She joined as Lecturer in 2009 and became Professor in 2016. Education includes: Bachelor's in Informatics and Telecommunications from University of Athens Master's in Microelectronics from University of Athens DPhil in Computer Science and Artificial Intelligence from University of Sussex Research focuses on developing novel machine learning techniques inspired by biological principles, particularly in reinforcement learning and reservoir computing methods. Her team collaborates with material scientists and engineers to design neuromorphic computing hardware. Publications demonstrate strong emphasis on neuromorphic computing, machine learning applications in neuroscience, reservoir computing systems, and computational modeling of biological processes. Recent work addresses device-agnostic modeling, physical neural networks, and stochastic computing platforms. Significant grants include: MARCH: Magnetic Architectures for Reservoir Computing Hardware (£936,815, Co-PI) ActiveAI - active learning and selective attention for robust AI (£953,584, Co-PI) Modeling probabilistic reinforcement learning in Drosophila (Google, £50,769, PI) CausalXRL: Causal explanations in Reinforcement Learning (£309,915, PI) Brains on Board: Neuromorphic Control of Flying Robots (£2,128,934, Co-PI) Leads the Machine Learning research group and collaborates with the Complex Systems Modelling group, focusing on brain-inspired computing architectures and neuromorphic hardware design.
Dr. Esther Mondragón is a Senior Lecturer in the Department of Computer Science at City St George's, University of London, within the School of Science and Technology. She is a member of CitAI (the Artificial Intelligence Research Centre at City) and directs the virtual Centre for Computational and Animal Learning Research. Her academic leadership includes serving as Chair of the School of Science and Technology Research Degrees Committee and former Director of the MSc in Artificial Intelligence. PhD Psychology, University of the Basque Country, Spain MSc Psychology, University of the Basque Country, Spain BSc Psychology, University of the Basque Country, Spain Dr. Mondragón is a computational cognitive neuroscientist whose research lies at the intersection of artificial intelligence and cognitive neuroscience. Her primary focus is on bio-inspired AI, particularly reinforcement learning models of cognition grounded in associative learning principles. She develops computational frameworks that bridge natural and artificial cognition, including influential models like the Double Error Dynamic Asymptote (DDA) and the Rescorla-Wagner Drift-Diffusion Model (RWDDM). Her work extends to deep learning integration, Hebbian learning, representational learning, and episodic memory in AI. She has also contributed to understanding perceptual learning, rule acquisition in animals, and timing mechanisms in conditioning. The 15 most recent publications reflect a strong trend in computational modeling of learning, with increasing integration of deep learning and neuro-inspired architectures. Her work spans theoretical cognitive science, applied AI, and software development for simulation. Key themes include associative mechanisms in representation, temporal modeling, and biologically plausible neural networks. She frequently collaborates with researchers like Eduardo Alonso and others across disciplines. Member, Spanish Society for Comparative Psychology (SEPC) Member, Cognitive Science Society International Affiliate, Pavlovian Society Member, Division 6: Society for Behavioral Neuroscience and Comparative Psychology, American Psychological Association (APA) Senior member, The Society for the Study of Artificial Intelligence and the Simulation of Behaviour (AISB) Dr. Mondragón actively supervises PhD students including Corina Cătărău-Cotuțau, Alexander Dean, and Mpagi Kironde. She has supervised several to completion, such as Esther Mulwa, André Luzardo, and Niklas Kokkola. She has secured significant research funding as PI/Co-PI on projects like 'INDUSTRIAL PhD SCHOLARSHIP, BOSCH AASS' (£140,000, Innovate UK), 'DEEPSYNC: AUTOMATED VFX FOR VIDEO DUBBING' (£143,000, Innovate UK), and 'FREE ENERGY PRINCIPLE FOR ADAPTIVE COGNITIVE ARCHITECTURES' (£98,000, DSTL). She also serves as a reviewer for the US National Science Foundation, BBSRC, NCN Poland, and numerous high-impact journals. She directs the virtual Centre for Computational and Animal Learning Research, which fosters interdisciplinary collaboration among cognitive scientists, neuroscientists, biologists, mathematicians, and computer scientists. She also leads the Knowledge Graphs Interest Group at The Alan Turing Institute.
