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Rakib AbdurView profile

Associate Professor

• Formal Verification
• Model-based Testing
• Automotive Security
• +2 more

Dr. Rakib Abdur is Associate Professor and Systems Security Group member at Coventry University's Institute for Future Transport and Cities, specializing in formal verification of autonomous systems. Research focuses on modeling and verifying safety/security properties in resource-constrained environments using temporal logics and model-based testing. Holds PhD in Computer Science from University of Nottingham (2011) on verifying resource-bounded agents, with prior positions at University of West of England and University of Nottingham Malaysia Campus. Current work includes developing simulation frameworks for automotive cybersecurity risk assessment and context-aware security models. Leads projects on enhanced intrusion detection systems using meta-heuristic optimization and cybersecurity roadmaps for connected infrastructure. Contributions include formal methods for analyzing qualitative/quantitative properties in safety-critical autonomous systems.

Shash VirmaniView profile

Senior Lecturer

• Quantum Information Theory
• Entanglement Theory
• Quantum Computing
• +4 more

Dr. Shash Virmani is a Senior Lecturer in Mathematics at Brunel University of London, within the College of Engineering, Design and Physical Sciences. He holds an MSci in Physics and Theoretical Physics from the University of Cambridge and a PhD in Quantum Information Theory from Imperial College London. His research focuses on quantum information theory, including entanglement theory, quantum computing architectures, and classical simulation of quantum systems. He currently directs Brunel's foundation programmes in Mathematics and Computing and Economics and Mathematics. His work explores foundational questions such as robust quantum computation via singlet/triplet measurements, classical simulatability of quantum systems, and nonlocality in fault-tolerant schemes. He has contributed to understanding entanglement measures, quantum channel capacities, and local hidden variable models. His research bridges theoretical physics and practical quantum computing challenges. Education MSci in Physics and Theoretical Physics, University of Cambridge PhD in Quantum Information Theory, Imperial College London Research Interests Virmani's work centers on quantum information theory with an emphasis on: Entanglement theory and measures Quantum computation architectures (e.g., singlet-triplet-based models) Classical simulation of quantum systems under noise or structural constraints Foundational aspects like quantum reference frames and nonlocality Quantum channel capacities with memory effects Publications Trends His publications span theoretical frameworks for entanglement quantification, efficient classical simulations, and fault-tolerant quantum computing. Recent work emphasizes scalable methods for quantum state estimation and universality in measurement-based models. He frequently collaborates with experts in foundational physics and quantum computing. Grants & Future Work Virmani has secured funding including an EPSRC "Bright Ideas" grant for classical simulation projects. Current interests include extending simulation techniques to broader quantum systems and exploring the computational limits imposed by noise thresholds. He supervises PhD students in quantum information theory and computation. Labs/Teams While no specific lab is highlighted, his research aligns with Brunel's quantum information initiatives and collaborations with institutions like Ulm University (via Martin Plenio) and Pavia University (Chiara Macchiavello).

Mark YampolskiyView profile

Associate Professor

• Cybersecurity
• Additive Manufacturing Security
• Internet of Things (IoT)
• +4 more

Mark Yampolskiy is an Associate Professor in the Department of Computer Science and Software Engineering at Auburn University's Samuel Ginn College of Engineering. His research focuses on cybersecurity in additive manufacturing (AM), internet of things (IoT), and computer networks. Yampolskiy leads efforts to secure AM processes against cyber-physical attacks, including sabotage and data theft. He has received prestigious awards such as the ASTM International Additive Manufacturing Young Professor Award and led high-profile grants totaling over $685,000. Education: Ph.D. in Computer Science, Ludwig Maximilian University M.S. in Computer Science, Technical University Munich B.S. in Applied Mathematics, Moscow State Institute of Radiotechnics, Electronics and Automation Research Interests: Cybersecurity of digital & additive manufacturing IoT security and network defense Cyber-physical system vulnerabilities 3D printing integrity and sabotage detection Key Contributions: Developed frameworks for AM security awareness and threat modeling Pioneered studies on side-channel attacks in manufacturing Co-organized ASTM AM Security Working Groups and AMSec workshops Advocated for legal standards in AM intellectual property protection Awards: ASTM International Additive Manufacturing Young Professor Award America Makes Award ($260k+ for cybersecurity training) NSF Grant ($425k for sabotage prevention research) Labs/Initiatives: Active in Auburn's National Center for Additive Manufacturing Excellence (NCAME), McCrary Institute for Cyber and Critical Infrastructure Security.

David MurrugarraView profile

Associate Professor

• Mathematical Biology
• Systems Biology
• Computational Biology
• +3 more

David Murrugarra is an Associate Professor in the Department of Mathematics at the University of Kentucky, within the College of Arts & Sciences. His research focuses on Mathematical Biology, with emphasis on Systems Biology and Computational Biology. He holds a PhD in Mathematics from Virginia Tech and completed a postdoctoral fellowship at the School of Mathematics at Georgia Tech before joining UK in 2014. His contact information includes murrugarra@uky.edu and an office at 771 Patterson Office Tower. Education: PhD in Mathematics, Virginia Tech Postdoctoral Research, School of Mathematics, Georgia Tech Research Interests: Dr. Murrugarra develops theoretical and computational tools for modeling gene regulatory networks, optimal control of probabilistic models using Markov decision processes, and RNA secondary structure prediction via machine learning. His funded projects include an NSF grant on modularity in biological systems and a UK Pilot Grant on RNA structure predictability. Recent Research Trends: His work bridges abstract mathematical frameworks with biological applications, emphasizing modularity, network control, and systems-level analysis. Key themes include modular decomposition of biological networks, intervention strategies for cancer systems, and the integration of machine learning into biological modeling. Grants & Projects: NSF grant: Mathematical Theory of Biological Modularity UK Pilot Grant: RNA Secondary Structure Prediction Labs & Collaborations: He leads a weekly Applied and Computational Mathematics seminar and co-organizes the Mathematics Community and Ethics (MCE) Working Group at UK. His research group actively engages in interdisciplinary projects combining mathematics, biology, and computational science.

• Geotechnical Engineering
• Soil Stabilization
• Materials Science
• +4 more

Dr. Arya Assadi Assadi Langroudi is an Associate Professor in the Department of Engineering at the University of East London, affiliated with the Architecture, Computing & Engineering research institute. His research focuses on geotechnical engineering, sustainable materials, and construction management, with a particular emphasis on soil stabilization, nanotechnology applications, and environmental adaptation of construction materials. He has contributed to advancements in nature-inspired solutions for ground engineering (NiSE) and the optimization of materials for small and medium enterprises (MSMEs) in the construction sector. His work integrates fractal geometry principles for sustainable design and explores the use of colloidal silica, nanobiosilica, and recycled materials to enhance soil properties. Notable projects include studies on pore-pressure effects in rocking foundations, liquefaction resistance of reinforced soils, and the formation of loess in UK environments. Dr. Langroudi's interdisciplinary approach bridges civil engineering, environmental science, and materials science to address challenges in sustainable infrastructure development. Research Interests : Geotechnical engineering, soil stabilization, biomimetic materials, construction supply chains, sustainable infrastructure, nanotechnology applications in geomechanics. Recent Trends in Articles : His recent work emphasizes sustainable material innovation, with a focus on colloidal nano-silica hydrosols for sand stabilization, fiber-reinforced soils for liquefaction resistance, and conceptual frameworks for MSME material procurement. He has also explored the environmental significance of loess deposits and the probabilistic modeling of ground properties for urban excavation projects.

Hongjing LuView profile

Professor

• Cognitive Psychology
• Machine Learning
• Computer Vision
• +3 more

Professor Hongjing Lu is a faculty member in the Department of Psychology at the University of California, Los Angeles (UCLA). Her research explores how humans learn and reason under conditions of sparse, ambiguous, and noisy data, with applications to understanding intelligent systems in both biological and artificial contexts. Her work integrates theories and methods from Psychology , Statistics , Computer Vision , Machine Learning , and Computational Neuroscience . Key research themes include: Exploiting generic priors for learning/inference Manipulating structured representations in perception and cognition Interdisciplinary approaches to motion perception, action recognition, and object recognition Connections between human cognition and AI systems Her publications span journals like Nature Communications , Nature Human Behavior , Psychological Review , and PLoS Computational Biology , focusing on analogical reasoning, causal learning, and neural mechanisms of perception. She leads the Computational Vision and Learning Lab at UCLA.

• Renewable Energy
• Energy Storage
• GIS Analysis
• +3 more

Dr. Cheng Cheng is a Senior Research Officer at the Australian National University's School of Engineering. He holds a Bachelor of Engineering (Honours) and PhD from ANU. His research focuses on renewable energy integration, decarbonization pathways, energy system modeling, and GIS analysis. He specializes in optimizing off-river pumped hydro energy storage systems. Cheng leads multiple renewable energy projects funded by government and industry grants, and has developed methodologies for energy demand forecasting and least-cost optimization. His work bridges theoretical energy modeling with practical implementation through collaborative outreach activities. Education: B.Eng(Hons) & PhD (Australian National University) Research interests emphasize sustainable energy transitions, with particular attention to geospatial analysis for energy infrastructure planning. His recent projects include developing frameworks for integrating renewable generation with storage solutions. Over 150 publications span machine learning applications in energy systems, astrophysics, and bioinformatics. Awards include academic fellowships supporting his interdisciplinary work. Cheng collaborates with industry partners to translate research into policy and technology. He directs the ANU's Renewable Energy Integration Lab (RE100), advancing grid stability solutions through innovative storage technologies. His work has been recognized in special issues of journals focusing on robust machine learning applications.

Cédric FoucaultView profile

Research Fellow

• Computational Neuroscience
• Cognitive Modeling
• Decision-Making
• +4 more

Cédric Foucault is a Junior Research Fellow at Christ Church College, University of Oxford, affiliated with the Department of Experimental Psychology. His research focuses on computational models of human cognition and decision-making in dynamic environments, leveraging tools from computer science, neuroscience, and machine learning. Prior to academia, he worked as a software engineer at Apple in Silicon Valley, contributing to iOS development. Education: PhD in Cognitive Computational Neuroscience (2023), University of Paris-Saclay/Sorbonne University MSc in Cognitive Neuroscience (2020), École Normale Supérieure de Paris MSc in Computer Science (2014), École Normale Supérieure Paris-Saclay His research integrates fMRI , M/EEG , and Bayesian modeling to study learning under uncertainty, probabilistic inference, and decision-making processes. Collaborations include work with Laurence Hunt’s research group at Oxford. He maintains an active GitHub profile with repositories on neural networks and computational cognitive science. Recent work emphasizes expanding research into broader decision-making domains while continuing to develop computational tools for neuroscience applications.

Ece ErdogmusView profile

Adjunct Professor

• Masonry Design and Analysis
• Sustainable and Resilient Construction
• Nondestructive Testing
• +4 more

Ece Erdogmus is an Adjunct Professor at the School of Building Construction at Georgia Institute of Technology. She holds a PhD and MS in Architectural Engineering from Penn State University and a bachelor’s in Architecture from Middle East Technical University, Turkey. A licensed civil/structural engineer in Virginia, her interdisciplinary work focuses on improving the built environment through sustainable design, masonry innovation, and XR applications in education. She has authored over 100 peer-reviewed articles and led projects funded by NSF, NCMA Foundation, and others. Education: PhD in Architectural Engineering, Pennsylvania State University MS in Architectural Engineering, Pennsylvania State University Bachelor’s in Architecture, Middle East Technical University Research Interests: Erdogmus leads research in masonry design, nondestructive testing of historical structures, and AR/VR in STEM education. Her work bridges engineering and education, emphasizing sustainability, resilience, and inclusive design. Recent studies include sleep health in aging populations and gender equity in construction management. Key Contributions: Founder of the Women in Masonry Fund Chair of TMS 402/602 Structural Members Subcommittee Journal of Architectural Engineering Chief Editor (ASCE/AEI) Grants & Awards: NSF, NCMA Foundation, Nebraska DOT, NPS/NCPTT grants 2022 Leading Women@Tech Cohort AEI Outstanding Educator Award (2019) Labs & Teams: Collaborates with interdisciplinary teams on projects like the ARCHES group for historical structure assessment and VADERs (VR-based discipline exploration rotations).

Yuval KlugerView profile

Professor

• Immunology
• Bioinformatics
• Single-cell Omics Analysis
• +4 more

Yuval Kluger is a Professor of Pathology at the Yale School of Medicine, Yale University. His research focuses on integrating computational methods with biomedical data to address challenges in immunology, cancer biology, and virology. He specializes in single-cell omics analysis, machine learning applications in healthcare, and the development of bioinformatics tools for genomic and proteomic data interpretation. Key research interests include HIV pathogenesis mechanisms in the central nervous system, tumor immune microenvironment dynamics, and the development of algorithms for analyzing spatial transcriptomics and proteomics. He leads collaborative efforts such as the SCORCH consortium to study opioid responses in HIV contexts. His work spans methodological advancements in optimal transport for single-cell trajectory inference and noise reduction techniques in omics datasets. Publications emphasize cutting-edge tools like AMULETY for adaptive immune receptor analysis and SIMVI for spatial omics disentanglement. Kluger’s team also explores therapeutic implications of tumor-reactive T-cell suppression in melanoma and cytokine signaling pathways in inflammation regulation. No scientific awards are explicitly listed in the provided texts. His work is supported by grants enabling humanized mouse models for studying immune-oncology and viral persistence. Collaborations include multi-institutional projects on HIV reservoirs and cancer immunotherapy mechanisms.

Arlei SilvaView profile

Assistant Professor

• Data Science
• Network Science
• Machine Learning
• +5 more

Arlei Silva is an Assistant Professor of Computer Science at Rice University. He holds a Ph.D. from the University of California, Santa Barbara (UCSB), and M.Sc. and B.Sc. degrees from Universidade Federal de Minas Gerais, Brazil. His research focuses on data science, network science, machine learning, and graph algorithms, with applications in flood prediction, graph anomaly detection, and temporal graph analysis. He has held visiting roles at Rensselaer Polytechnic Institute and has contributed to projects like trajectory compression and network process discovery. Education: Ph.D. in Computer Science, University of California, Santa Barbara (2019) M.Sc. in Computer Science, Universidade Federal de Minas Gerais, Brazil (2011) B.Sc. in Computer Science, Universidade Federal de Minas Gerais, Brazil (2008) Research Interests: His work integrates machine learning and graph theory to address challenges in network dynamics, spatiotemporal modeling, and anomaly detection. Recent projects include FloodGNN-GRU for flood prediction, DOPPLER for dataflow optimization, and Pole for signed network embeddings. Awards: SNAKDD Best Paper Runner-up (2013) Best M.Sc. Thesis (Brazilian Computer Society, 2011) Best Undergraduate Research (top 6, Brazilian Computer Society, 2009) Labs/Teams: Engages in interdisciplinary projects at Rice, focusing on environmental informatics and network science. His GitHub repositories include work on trajectory compression, anomaly detection, and spectral algorithms for graph cuts.

• Category Theory
• Compositional Game Theory
• Categorical Cybernetics
• +7 more

Jules Hedges is a mathematics and computer science researcher affiliated with the Institute for Categorical Cybernetics and the Mathematically Structured Programming group at the University of Strathclyde. As of September 2024, he is working 90% for the CyberCat Institute and 10% for the University of Strathclyde, focusing on applying category theory across various interdisciplinary domains. Hedges completed his PhD at Queen Mary University of London in 2016 with a thesis titled "Towards compositional game theory," which established foundational work for his ongoing research program. His academic journey spans from early work on game theory to current developments in categorical cybernetics. His primary research involves applying category theory in economics, machine learning, and control theory, with a view towards what was historically called cybernetics. Hedges is particularly known for developing compositional game theory, which provides a categorical framework for understanding strategic interactions through concepts like "open games" and "selection functions." His other research interests include graphical calculi for visual representation of categorical structures, functional programming paradigms, proof theory, and the semantics of natural language. He has made significant contributions to understanding how complex systems can be built from simpler components while preserving mathematical properties. Hedges' publication record reveals a consistent trajectory toward developing compositional frameworks for complex systems, with recent work increasingly focused on categorical cybernetics—the application of category theory to systems with feedback loops and goal-directed behavior. His research often bridges abstract mathematical concepts with practical applications in economics and artificial intelligence, particularly in reinforcement learning where he has demonstrated connections between value iteration algorithms and categorical optic composition. Hedges is a signatory of the "No Free View? No Review!" pledge, indicating his strong commitment to open access publishing. He has publicly stated he will not peer review for closed access or hybrid venues, reflecting his principled stance on making academic research freely available. While specific details about his advising activities are not documented in the provided text, his extensive publication record with numerous co-authors suggests active research collaboration and likely mentorship of junior researchers. His work spans multiple institutions and involves collaborators across mathematics, computer science, and economics. Hedges is a founding member of the Institute for Categorical Cybernetics, formally incorporated in October 2023, which represents the primary focus of his current work (90% of his professional time as of September 2024). This institute serves as a hub for researchers working at the intersection of category theory, cybernetics, and their applications to complex adaptive systems, potentially supporting collaborative projects, workshops, and publications in this emerging field.

Edmond ChowView profile

Professor

• High-Performance Computing
• Scientific Computing
• Numerical Linear Algebra
• +7 more

Edmond Chow is a Professor and Associate Chair in the School of Computational Science and Engineering at Georgia Institute of Technology. His research focuses on numerical methods, high-performance computing, and scientific computing applications in quantum chemistry, molecular dynamics, and machine learning. He holds awards including the 2009 ACM Gordon Bell Prize and 2002 PECASE. He leads the Intel Parallel Computing Center, advancing computational chemistry algorithms for Intel architectures. Education: Ph.D., Computer Science (minor in Aerospace Engineering), University of Minnesota, 1997 Honors B.A.Sc., Systems Design Engineering, University of Waterloo, 1993 Research Interests: Parallel algorithms for sparse linear systems and preconditioning High-performance quantum chemistry simulations Asynchronous iterative methods for extreme-scale computing Kernel-based methods and hierarchical matrices for Gaussian processes Applications in biological macromolecule dynamics and machine learning Awards & Recognition: ACM Gordon Bell Prize (2009) PECASE (2002) SIAM Fellow (2021) SC23 Test of Time Award Advising & Grants: Advised 11+ PhD/MSc students in numerical methods and HPC Funded by NSF, DOE, DARPA, and Intel Developed software tools like H2Pack (hierarchical matrices), GTFock (quantum chemistry), and Simint (electron integrals) Labs & Teams: Leads the Intel Parallel Computing Center and collaborates with institutions like LLNL, Sandia, and Temple University on asynchronous computing and quantum chemistry projects.

• Cognitive Neuroscience
• Decision Making
• Attention & Cognitive Control
• +4 more

Professor Nick Yeung is a Professor of Cognitive Neuroscience at the University of Oxford and Tutorial Fellow at University College, Oxford. His primary affiliation is with the Department of Experimental Psychology. His research focuses on the cognitive and neural mechanisms underlying human learning, memory, attention, and decision-making. He explores how subjective confidence in choices influences information-seeking behavior and error detection, with applications in mental health (e.g., depression) and human-machine collaboration. Yeung holds a BA (Hons) and PhD in Psychology. His work integrates computational modeling, neuroimaging (EEG/fMRI), and experimental psychology to study metacognition, task switching, and attentional control. Recent studies investigate domain-general error signals, advice utilization dynamics, and neural correlates of confidence. Research highlights include EEG-based attention monitoring for safety-critical tasks (e.g., driving) and exploring how confidence biases affect decision-making in healthcare and collaborative systems. His lab also develops practical applications of cognitive neuroscience findings, such as attention-stabilizing neurostimulation devices. Key contributions span over 80 publications, including high-impact work on decision confidence, error monitoring, and cognitive control. His 2024 perspective paper outlines foundational advancements in cognitive control research, while 2025 studies reveal shared neural error signals across sensory modalities.

Michael BoehnkeView profile

Professor

• Biostatistics
• Genetics
• Genomics
• +3 more

Michael Boehnke is the Richard G. Cornell Distinguished University Professor of Biostatistics at the University of Michigan School of Public Health. He has been on the faculty since 1984 and directs the University of Michigan Center for Statistical Genetics and the Genome Science Training Program. His research focuses on statistical methods for human genetics, particularly in type 2 diabetes and genome-wide association studies (GWAS). He leads major consortia such as DIAGRAM and MAGIC, and has published over 400 peer-reviewed articles. He has trained 36 doctoral students and post-doctoral fellows, many of whom hold academic positions at major research institutions. Education PhD in Biomathematics, University of California, Los Angeles (1983) BA in Mathematics, University of Oregon (1977) Research Boehnke develops statistical tools for analyzing genetic data and investigates genetic contributors to type 2 diabetes, lipid traits, and anthropometric measures. His work emphasizes collaborative consortia to aggregate large-scale genetic datasets. Recent projects include the Accelerating Medicines Partnership (AMP) to integrate genomic data for metabolic disease research. Publications His most recent work addresses pleiotropy in genetic variants, sex-specific gene expression in skeletal muscle, and causal mechanisms of complex traits using multi-omics approaches. These studies highlight the utility of integrative genomics and population-scale data. Awards Member, National Academy of Medicine Fellow, American Statistical Association Fellow, American Association for the Advancement of Science Grants & Advising He oversees NIH-funded projects (e.g., R01 HG009976, U01 DK062370) and has trained 24 doctoral students and 12 postdocs. His lab collaborates globally through initiatives like the Finland-United States Investigation of NIDDM (FUSION) study. Labs & Teams He leads the Center for Statistical Genetics, a hub for interdisciplinary research in computational genetics and statistical methodology.