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Laura MilneView profile

Assistant Professor

• Medical Education
• Clinical Skills Training
• Acute Care General Internal Medicine
• +4 more

Dr. Laura Milne is an Assistant Professor at Queen’s University, affiliated with the Division of General Internal Medicine. She completed her undergraduate medical studies, residency, and fellowship at Queen’s University before joining the division in 2013. Her clinical practice focuses on acute care general internal medicine, stroke prevention, and refractory hypertension. Her academic interests center on medical education and clinical skills training. Her research spans public health policy, pandemic preparedness, and healthcare system analysis, with notable contributions to pandemic preparedness frameworks and energy policy evaluations. She has published widely on topics ranging from renewable energy incentives to geopolitical strategy. No formal awards are listed, but her work reflects active engagement in policy-oriented research. Education: Bachelor of Medicine, Queen’s University General Internal Medicine Residency, Queen’s University General Internal Medicine Fellowship, Queen’s University Research Interests: Dr. Milne’s work emphasizes practical applications of medical education and clinical excellence. Her recent research explores parallels between financial crisis management and pandemic preparedness, highlighting systemic risks in healthcare and economic systems. She also investigates renewable energy policies and their socio-economic impacts, particularly stakeholder dynamics in wind and solar energy projects. Her analyses of post-secondary education funding policies in Ontario reveal systemic challenges in public financing mechanisms. Publications Trends: Her 15 most recent articles (2018–2024) reflect interdisciplinary focus on public health crises, energy policy, and global governance. Key themes include pandemic preparedness stress-testing, renewable energy stakeholder analyses, and comparative studies of higher education challenges in Canada and Australia. Her work bridges clinical practice with policy implications, advocating for resilient systems in healthcare and energy sectors. Grants & Advising: No specific grants or advising roles listed in provided texts. However, her academic role suggests involvement in institutional teaching and research programs. Labs/Teams: Affiliation with Queen’s Division of General Internal Medicine implies collaboration with clinical and academic teams in medical education and acute care.

Yeonjong ShinView profile

Assistant Professor

• Applied and Computational Mathematics
• Artificial Intelligence
• Scientific Machine Learning
• +6 more

Yeonjong Shin is an Assistant Professor of Mathematics at North Carolina State University (NC State), with prior tenure-track roles at KAIST (South Korea) and Brown University (USA). His research focuses on applied and computational mathematics, particularly in artificial intelligence, scientific machine learning, and numerical methods for partial differential equations. He holds a Ph.D. in Mathematics from The Ohio State University (2018) and dual B.S. in Mathematics and B.A. in Economics from Yonsei University (2013), with military service as a KATUSA (2009–2011). Shin’s expertise includes approximation theory, numerical optimization, and uncertainty quantification. He leads research in AI-driven scientific computing, reduced order modeling, and physics-informed neural networks. His work bridges computational mathematics and machine learning, addressing challenges in data-driven dynamical systems and high-dimensional function approximation. He has published extensively in top journals like SIAM Journals, Neural Networks, and Comput. Methods Appl. Mech. Eng. Notable contributions include thermodynamics-informed latent space dynamics (tLaSDI), S-OPT hyper-reduction algorithms, and error analysis for neural network-based PDE solvers. Shin has taught courses at NC State, KAIST, and Brown, including statistical inference, numerical optimization, and computational linear algebra. He actively participates in conferences such as SIAM CSE, ICIAM, and KSIAM, and has delivered invited talks at institutions worldwide.

Sergey SorokinView profile

Professor

• Solid Mechanics
• Computational Mechanics
• Structural Dynamics
• +7 more

Sergey Sorokin is a Professor in the Department of Materials and Production at Aalborg University, Faculty of Engineering and Science, Denmark. His research centers on solid and computational mechanics, with a focus on structural dynamics, wave propagation, vibro-acoustics, and finite element modeling. His research interests include: Wave propagation in periodic and elastic structures Vibro-acoustics and noise control Finite element methods and reduced-order modeling Nonlinear dynamics of beams and shells Acoustic black hole effects and vibration suppression Structural optimization and industrial system analysis The recent publications (2020–2025) demonstrate sustained activity in mechanical wave analysis, nonlinear structural behavior, and acoustic optimization. Key themes include the suppression of bending waves, asymptotic modeling of cylindrical shells, and bi-orthogonality in waveguides. These works reflect an interdisciplinary blend of theoretical mechanics, numerical simulation, and engineering applications. Scientific contributions include: Active participation in long-term research projects on mechanical vibro-acoustics and wave transmission since 2010 Supervision of at least four PhD students Extensive publication record (188 research outputs) with consistent contributions to journals like the Journal of the Acoustical Society of America and European Journal of Mechanics A/Solids He has advised several researchers and contributed to collaborative projects involving structural optimization and industrial component analysis. His work is deeply integrated into both theoretical and applied mechanical engineering, with emphasis on modeling and enhancing structural performance under dynamic loads. Sergey Sorokin is actively involved in research through ongoing projects such as 'Mechanical Vibro-Acoustics and Noise' and 'Analysis and Optimization of Wave Propagation in Periodic Structures'. These efforts are supported by advanced computational modeling and collaboration with peers like N. Olhoff and E. Lund.

Andrés Mora ValenciaView profile

Associate Professor

• Quantitative Finance
• Financial Risk Quantification
• Machine Learning in Finance
• +12 more

Andrés Mora Valencia is an Associate Professor in the Faculty of Management at the University of the Andes, Colombia, where he teaches in the Finance department. He holds a Doctor of Economics from the University of Salamanca and has a strong interdisciplinary background in industrial engineering, statistics, and quantitative finance. Education: Doctor of Economics, University of Salamanca, Spain (2014) Master's Degree in Banking and Quantitative Finance, University of Castilla-La Mancha, Spain (2013) Master in Industrial Engineering, University of the Andes, Colombia (2000) Statistics Specialist, National University of Colombia (2011) Advanced Mathematics Specialist, National University of Colombia (2003) Industrial Engineer, University of the Valley, Colombia (1997) His research focuses on quantitative finance , particularly financial risk quantification using advanced statistical and machine learning models. Key areas include Value-at-Risk (VaR), Expected Shortfall (ES), fat-tailed distributions, and volatility forecasting in cryptocurrency and energy markets. He actively applies Bayesian deep learning, semi-nonparametric methods, and real options theory to financial modeling. His recent publications (2021–2025) reveal a strong trend in applying machine learning to forecast financial indices like the VIX and Skew Index, analyzing higher-order moment spillovers in crypto-forex integration, and assessing risk in renewable energy and carbon markets. His work bridges econometrics, computational finance, and sustainability. Scientific Contributions: Developed models for skew index forecasting using machine learning Explored adaptive market hypothesis in Latin American and emission trading markets Advanced semi-nonparametric risk assessment for cryptocurrencies Applied real options to value renewable energy projects He advises several graduate and doctoral students in finance, including Andrés Raúl Cruz-Hernández, Esteban Nicolás Vanegas Herrera, and Leonardo Gerardo Santana Viloria. He teaches courses such as Derivatives, Financial Planning, Investment Decisions, and Computational Methods in Finance across undergraduate, master’s, and doctoral programs. He is affiliated with the Finance and Financial Economics research group at Uniandes.

Jos MaubachView profile

Assistant Professor

• Numerical Analysis
• Scientific Computing
• Computational Science and Engineering
• +3 more

Jos Maubach is an Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with the Department of Mathematics and Computer Science. He is part of the Centre for Analysis, Scientific Computing, and Applications (CASA). His research focuses on numerical methods, scientific computing, and computational science and engineering, particularly in iterative methods for partial differential equations and model order reduction. Education: MSc in Mathematics (cum laude, Radboud University Nijmegen, 1986); PhD in Mathematics (Radboud University Nijmegen, 1991). Joined TU/e in 1998. Research Interests: Numerical analysis, nonlinear dynamical systems, computational methods for engineering applications, and model reduction techniques. Publications Highlight Trends: Recent work emphasizes model order reduction for bilinear systems, H2 optimization in electronics reliability, and mechanics of materials (e.g., moisture-induced buckling in paper sheets). No scientific awards explicitly mentioned. Teaches courses such as Programming and Modelling, Scientific Programming, Analysis 1, and Advanced Calculus II. No ancillary activities reported. Collaborations: Works with groups like the Computational Quantum & Molecular Dynamics team and the High Tech Systems Center.

• Stochastic Analysis
• Numerical Methods for PDEs
• High-Dimensional Approximations
• +3 more

Dr. Tuan Anh Nguyen is a researcher at the Faculty of Mathematics of Universität Bielefeld, Germany. He is affiliated with the Collaborative Research Centre (SFB) 1283, specifically sub-project B10, which focuses on high-dimensional Markov processes in cones. His research interests include stochastic analysis, numerical methods for partial differential equations (PDEs), and overcoming the curse of dimensionality through advanced techniques like multilevel Picard approximations and deep neural networks. Dr. Nguyen's work bridges theoretical mathematics and applied computational methods, addressing challenges in stochastic control, mean-field systems, and high-dimensional stochastic differential equations (SDEs). His contributions to numerical analysis emphasize scalable algorithms for semilinear PDEs and backward SDEs, leveraging machine learning frameworks to tackle dimensionality constraints. He collaborates on interdisciplinary projects within the university's strategic research area of the Mathematical World , focusing on stochastic processes, mathematical finance, and real-world system modeling. His research has been recognized through affiliations with leading projects like SFB 1283 and contributions to top-tier journals in computational mathematics.

Arne SachtlerView profile

Researcher

• Robotics
• Control Systems
• Differential Geometry
• +3 more

Arne Sachtler is a researcher at the Technical University of Munich (TUM) and the German Aerospace Center (DLR), affiliated with the Chair of Sensor-Based Robot Systems and Intelligent Assistance Systems under Prof. Alin Albu-Schäffer. His research focuses on applying differential geometry and nonlinear dynamics to robotics, particularly in areas like elastic robots, energy-efficient locomotion principles, and intrinsic dynamics of mechanical systems. He specializes in robot control strategies, including impedance control and eigenmanifold-based model predictive control (EigenMPC). Education details are not explicitly listed, but his work indicates expertise in robotics and mathematical physics. Key research interests include robot kinematics, multi-body dynamics, and the integration of geometric principles into control frameworks. His publications span topics such as nonlinear normal modes, redundant manipulator control, and autonomous robotic assembly with visual-tactile sensing. Dr. Sachtler collaborates with institutions like DLR, contributing to both academic and industrial robotics research. His work emphasizes theoretical foundations with practical applications in energy-efficient robotics and human-robot interaction. Contact him via arne.sachtler@tum.de or arne.sachtler@dlr.de for inquiries.

• VLSI Design
• Digital Circuit Testing
• Test Data Compression
• +4 more

Χρ. Καβουσιανός is a Professor at the Department of Computer Engineering & Informatics, University of Patras (Polytechnic School). His research focuses on VLSI design, digital circuit testing, and test data compression. He holds a Diploma (1996) and PhD (2000) from the same department, with notable scholarships including the Maritsas Doctoral Scholarship (1999). Key roles include leading the Heraclitus II and Pythagoras II research programs as principal investigator. Collaborations include Duke University (USA) with Prof. Krishnendu Chakrabarty. Research highlights: Pioneered techniques in scan-based testing, test set embedding, and low-power testing. Over 45 publications in top journals/conferences (e.g., IEEE TCAD, DAC). Notable work includes defect-oriented testing methods and Huffman-based compression schemes. Has supervised 9 advisees across doctoral/postgraduate levels. Holds a 2010 US patent application on power switch design.

Maria HybinetteView profile

Associate Professor

• High-Performance Simulation Systems
• Multi-Agent Behavior Modeling
• Financial Market Simulation
• +3 more

Maria Hybinette is an Associate Professor of Computer Science at the University of Georgia's School of Computing. Her research focuses on high-performance simulation systems, particularly in multi-agent behavior modeling and financial market simulation. She emphasizes hybrid (micro/macro) simulations, optimization for parallel processing, and usability improvements in simulation frameworks. Her work spans over two decades, with notable contributions to agent-based simulation frameworks like ABIDES, Pickle, and SASSY. She has also pioneered methods for parallelization in discrete event simulations, including latency reduction techniques and adaptive caching strategies. Key research areas include stock market dynamics modeling, autonomous agent coordination, and real-time multi-target tracking systems. Her publications consistently explore scalability, computational efficiency, and validation methodologies in simulation systems. Awards and recognition are not explicitly listed in the provided materials. She is affiliated with the Boyd Research and Education Center in Athens, GA, and maintains active collaborations in computational finance and robotics.

• History of Protest and Reform
• Environmental History
• Digital Heritage
• +3 more

Steve Poole is a Professor of History and Heritage at the University of the West of England (UWE), affiliated with the Faculty of Arts, Creative Industries, and Education (ACE). He directs the Regional History Centre and leads an ESRC-funded project exploring intergroup dynamics in the 1831 Reform Riots. His research spans 18th- and 19th-century English history, focusing on protest, crime, tidal landscapes, and digital heritage technologies. Collaborations include the National Trust, Bristol Initiative Trust, and heritage design firms like Splash & Ripple and Satsymph. Notable projects include Ghosts in the Garden (a locative game at the Holburne Museum) and Romancing the Gibbet (an immersive soundscape on 18th-century crime). Poole’s academic work emphasizes histories 'from below,' particularly in Bristol and the Southwest. He co-authored Bristol From Below: Law, Authority and Protest in a Georgian City and chairs the John Thelwall Society. His heritage projects integrate digital media to reinterpret historical narratives, such as using empathy-driven soundscapes for heritage experiences. He also advises on urban waterfront histories and serves on committees for the Southern History Society and Bristol Record Society. His publications span scholarly articles and edited volumes, with a focus on riots, regicide, and radical press (e.g., Bath Spark ). Recent work examines the social psychology of collective action in 19th-century riots and the legacy of monuments in contemporary urban spaces. Poole’s interdisciplinary approach bridges traditional history with modern technologies, aiming to democratize historical interpretation through accessible, immersive formats.

Sonja GrünView profile

Professor

• Neuroscience
• Computational Neuroscience
• Neural Networks
• +3 more

Prof. Sonja Grün is a Professor and Director of the Computational and Systems Neuroscience division (IAS-6) and the JARA-Institute Brain structure-function relationships (INM-10) at Forschungszentrum Jülich. She leads the Statistical Neuroscience research group, focusing on cell assemblies, dynamical neuronal interactions in cortex, higher-order correlation analysis, and reproducible workflows. Her work bridges experimental and computational approaches to understand neural coding and network dynamics. Key contributions include methodologies for spike pattern detection (e.g., SPADE) and tools for data curation (UnitRefine). She actively develops neuroinformatics standards (e.g., Neuroelectrophysiology Analysis Ontology) and promotes reproducible research through provenance tracking initiatives like NFDI-Neuro. Her research spans motor cortex activity, visual processing, and model validation frameworks. Research interests emphasize spatio-temporal spike patterns, cortical network models, and artifact detection in high-density recordings. She collaborates on projects integrating electrophysiological data with computational simulations (e.g., NEST) and brain wave analysis (Cobrawap). Current efforts address challenges in data sharing, artifact removal, and scalable analysis pipelines for heterogeneous datasets.

• Air Transport
• Aviation Management
• Airline Business Models
• +12 more

Professor Zheng Lei is a Professor of Aviation at Swinburne University of Technology, School of Engineering, where he serves as Chair of the Department of Aviation within the Faculty of Science, Engineering and Technology. He holds a PhD in Air Transport and Tourism Management from the University of Surrey and has previously held academic roles at the University of Surrey and Cranfield University. He also serves as a Visiting Professor at the Civil Aviation University of China and is the founder and President of the Institute for Aviation Research, a think tank focusing on aviation in China. His research expertise lies in airline business models, air transport market analysis, regulation and policy, and aviation’s role in regional competitiveness. His work emphasizes real-world impact, advising airlines, airports, regulators, and aviation authorities globally. His fields of research include transportation, logistics, supply chains, and urban and regional planning. The 15 most recent publications reflect a consistent focus on aviation economics, policy, and digital transformation. Key themes include aviation resilience during the pandemic, market liberalization, airline competition, digital technologies like AI and blockchain, service quality assessment via NLP, and sustainable connectivity. His work employs advanced econometric, modeling, and data analytics methods to inform policy and industry practice. Professor Lei has secured significant external research funding from government and industry, including grants from the Department of Foreign Affairs and Trade, Department of Infrastructure, and Civil Aviation Safety Authority, focusing on hydrogen in aviation, alternative fuels, and advanced air mobility. He actively supervises PhD and Master’s students on topics such as hydrogen adoption, autopilot liability, advanced air mobility, airport security, and pilot success prediction, demonstrating a strong commitment to training the next generation of aviation professionals. He has led or contributed to numerous research projects and contracts, including 'Hydrogen to the Skies' and 'Regional AAM Surrogate Trial,' working with partners such as Textron Systems and government agencies. His leadership in both academic and applied research underscores his role as a key figure in contemporary aviation studies.

• Tensor Decompositions
• Quantum Computing
• Fredholm Integral Equations
• +7 more

Patrick Gelß is a Postdoctoral Researcher at Zuse Institute Berlin, working in the AI in Society, Science, and Technology department. He leads the iol.QUANT research group focused on quantum-inspired and tensor-based methods, with affiliations to Freie Universität Berlin and collaborative projects like MATH+ Cluster of Excellence. Diploma in Mathematics/Physics (2013, Freie Universität Berlin) PhD in Mathematics (2017, summa cum laude, Freie Universität Berlin) Postdoctoral roles at Zuse Institute Berlin (2020-present) and CRC 1114 (2017-2022) His research bridges tensor decompositions, quantum computing, and dynamical systems, with notable contributions to: Graph Isomorphism: Continuous optimization approaches via doubly stochastic matrices and Frank-Wolfe algorithms Quantum Simulation: Tensor network representations for quantum circuits and the open-source WaveTrain package KvN Mechanics: Mathematical analysis of existence/uniqueness solutions for bounded domains Fredholm Networks: Novel training paradigms for neural networks using integral equations Scientific achievements include: Developing Scikit-TT for tensor-train computations Organizing major workshops (QML@SC2024, TMQS 2024) Leading the Thematic Einstein Semester 2024 on Mathematics for Quantum Technologies His work spans chemical kinetics, quantum dynamics, and quantum machine learning, with applications to CO oxidation models, exciton-phonon systems, and mutational hierarchies in medicine.

• Evidence-Based Policing
• Criminology
• Systematic Reviews
• +6 more

Dr. Elizabeth (Liz) Eggins is an Adjunct Senior Fellow and Senior Lecturer at the School of Psychology, University of Queensland. With extensive expertise in evidence-based policing and systematic review methodology, she has established herself as a leading researcher in criminology and social interventions. Her work bridges academic research with practical applications in criminal justice and child protection systems. Dr. Eggins' research interests focus on evidence-based policing, systematic review methodology, criminology, child protection, substance misuse interventions, domestic and family violence, fetal alcohol spectrum disorder (FASD), police legitimacy, and restorative justice. Her work often examines the effectiveness of interventions through rigorous systematic review methods, contributing significantly to evidence-based practice in criminal justice and social service domains. She has particular expertise in translating research findings into actionable insights for practitioners and policymakers. The publication record shows a strong focus on systematic reviews across multiple domains including policing strategies, child protection, substance misuse interventions, and domestic violence. Her recent work (2022-2024) demonstrates continued productivity with numerous systematic reviews published in high-impact journals like Campbell Systematic Reviews. The research consistently applies rigorous methodological standards to evaluate intervention effectiveness across criminal justice and social service domains. Dr. Eggins has made significant contributions through her extensive publication record, with 53 journal articles, 18 conference papers, 15 research reports, and multiple book chapters to her name. Her work frequently appears in high-impact systematic review journals and contributes to evidence-based practice in criminal justice and social services. Her research collaborations span multiple institutions and disciplines, with frequent co-authorship with prominent researchers like Lorraine Mazerolle, Angela Higginson, and Sharon Dawe. She has been actively involved in the Global Policing Database initiative, which aims to improve evidence-based policing through systematic review methodology.

Navid SahebjamniaView profile

Senior Lecturer

• Sustainable and Resilient Operation Management
• Supply Chain and Logistics
• Multi-Objective and Stochastic Programming Methods
• +6 more

Dr Navid Sahebjamnia is a Senior Lecturer in Operations and Information Management at Brunel Business School, Brunel University London. He earned his PhD in Management Science and Operations Research from the College of Engineering at the University of Tehran and has held academic positions at Middlesex University London and Brunel University London. His work bridges operations research, sustainability, and digital transformation in supply chains. Research Interests: Dr Sahebjamnia specializes in sustainable and resilient operations management, with a focus on supply chain and logistics optimization. He develops advanced mathematical models, including multi-objective and stochastic programming, to enhance decision-making under uncertainty. His research supports industries such as transportation, agri-food, and manufacturing in achieving environmental sustainability and operational resilience. Publication Trends: His recent publications reflect a strong trend toward integrating operational research with digital technologies such as AI, big data analytics, and digital twins to address global challenges like pandemic response, green port operations, and humanitarian logistics. There is a consistent emphasis on resilience, sustainability, and quantitative modeling across diverse industrial contexts. Scientific Awards: No specific awards are mentioned in the provided text. Advising and Grants: Dr Sahebjamnia supervises PhD students in areas including resilient supply chains and AI-driven risk mitigation. He is actively involved in securing research funding, notably as a key participant in the NARRATE project funded by the European Commission (2023–2026), which focuses on building regenerative resilient smart manufacturing networks using AI and IoT. He has extensive experience in forming European research consortia and submitting proposals to major funders like UKRI and ERC. Labs and Research Groups: He is affiliated with the OISM (Operations and Information Systems Management) Research Group at Brunel Business School and collaborates closely with Prof Afshin Mansouri and other researchers in innovation and sustainability. His work involves co-author networks and interdisciplinary teams focused on smart manufacturing and supply chain transformation.