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Ton DiekerView profile

Associate Professor

• Stochastic Processes
• Operations Research
• Queueing Theory
• +3 more

Ton Dieker is an Associate Professor in the Department of Industrial Engineering and Operations Research at Columbia University's School of Engineering and Applied Science. He is a DSI Member and affiliated with the Center for Financial and Business Analytics. His research focuses on stochastic models, simulation techniques, and high-dimensional stochastic analysis. Dieker holds a Master’s in Operations Research from Vrije Universiteit Amsterdam (2002) and a PhD in Mathematics from the University of Amsterdam (2006). Dieker’s research explores stochastic processes, queueing theory, and rare-event simulation. He has contributed to methodologies like QPLEX for stochastic systems and advanced techniques in sequential analysis and exact simulation. His work bridges theoretical foundations with computational applications, addressing challenges in large-scale networks and high-dimensional problems. Education: PhD in Mathematics, University of Amsterdam, 2006 Master’s in Operations Research, Vrije Universiteit Amsterdam, 2002 Dieker’s articles emphasize computational modeling, stochastic calculus, and optimization, reflecting his focus on bridging theory and practice. His work often addresses efficiency in simulation, exactness in algorithms, and scalability in complex systems. Awards: Goldstine Fellowship (IBM Research) NSF CAREER Award Erlang Prize (INFORMS) Fouts Family Early Career Professorship (Georgia Tech) He serves on editorial boards for Operations Research and Mathematics of Operations Research . His research also addresses capacity management in stochastic networks and applications in cloud computing and commodity sourcing.

Joshua SocolarView profile

Professor

• Condensed Matter Physics
• Quasicrystals
• Granular Materials
• +2 more

Joshua Socolar is Professor of Physics at Duke University specializing in collective phenomena in condensed matter systems. His research explores exotic material structures including quasicrystals and granular systems. Education: Ph.D., University of Pennsylvania (1987) B.A., Haverford College (1980) Research Focus: Non-periodic structures, self-assembly of colloidal particles, shear jamming in granular matter, and topological properties of mechanical lattices. Leadership: Served as Chair of Duke's Academic Council (2013-2015) and Trinity College Arts & Sciences Council (2021-2024).

Tianhua XuView profile

Associate Professor

• Optical Communication Networks
• Optical Sensing Systems
• Optoelectronics
• +4 more

Dr. Tianhua Xu is a Reader in the School of Engineering at the University of Warwick and an Honorary Lecturer in the Department of Electronic and Electrical Engineering at University College London (UCL). He holds a Ph.D. in Optical Communications and Intelligent Signal Processing from KTH Royal Institute of Technology in Sweden, followed by postdoctoral and research roles at KTH, RISE Acreo, DTU, and UCL. His research focuses on optical communication systems, intelligent signal processing, machine learning, optical sensing, and advanced energy systems. He has secured major grants, including a €1.8M EU Horizon Europe project (SPAR) and a £285K UK National Grid initiative. Dr. Xu serves as an Associate Editor for IEEE Transactions on Communications and the Journal of the European Optical Society-RP, and chairs technical groups in the Optical Society of America. He has authored over 200 publications, including invited book chapters, with a Google Scholar h-index of 32. Current projects involve smart photonic sensing, energy storage systems, and deep learning in optical networks. His lab oversees six PhD students and multiple postdoctoral researchers. He actively recruits students through global scholarships and oversees vibrant research teams in optical communications, sensing, and energy systems.

Scott T. M. DawsonView profile

Assistant Professor

• Fluid Mechanics
• Dynamical Systems
• Control Theory
• +5 more

Scott T. M. Dawson is an Assistant Professor in the Mechanical, Materials, and Aerospace Engineering Department at Illinois Institute of Technology (Illinois Tech). He holds positions in the Armour College of Engineering and leads research at the intersection of fluid mechanics, dynamical systems, control theory, and data science. His work focuses on extracting dynamic models from large datasets to analyze and control turbulent flows and unsteady aerodynamic systems. Education includes a Ph.D. and M.A. from Princeton University (2017, 2013), and B.Eng. and B.S. degrees from Monash University (2010, 2009). Prior to Illinois Tech, he was a postdoctoral scholar at Caltech’s Graduate Aerospace Laboratories under Prof. Beverley McKeon. Research interests emphasize reduced-order modeling, data-driven techniques for fluid flows, and flow control applications. His group’s work is supported by NSF, AFOSR, and DOE grants. Recent projects include sparsity-promoting methods for flow analysis, wavelet-based resolvent analysis, and neural network-driven flow control systems. Publications span over 60 peer-reviewed articles, with a focus on turbulence modeling, transient flow dynamics, and machine learning integration in fluid mechanics. Key contributions include novel algorithms for isolating amplification mechanisms in wall-bounded flows and robust neural network frameworks for closed-loop flow stabilization. Grants and collaborations include multi-year NSF CAREER funding for automated distillation of coherent flow structures. Ongoing efforts explore time-localized spectral methods, nonlinear dimensionality reduction, and hydrogen decarbonization in vehicular systems.

Young-Jin ChaView profile

Professor

• Deep Learning
• Structural Health Monitoring (SHM)
• Autonomous UAVs
• +5 more

Dr. Young-Jin Cha is a tenured full Professor in the Department of Civil Engineering at the University of Manitoba, affiliated with the Price Faculty of Engineering. He holds a PhD from Texas A&M University and has postdoctoral experience at MIT. His research focuses on deep learning-based structural health monitoring (SHM), autonomous UAVs for infrastructure inspection, and smart transportation systems, with over 100 peer-reviewed publications and $1.2M in grants. He is a Fellow of ASCE and has received notable awards including the 2021 Merit Award and 2022 International Association of Advanced Materials Scientist Award. His work has been cited over 9,200 times globally. Research interests include automated SHM with UAVs, nonlinear system identification, unsupervised deep learning for damage detection, and sustainable infrastructure design. He serves as an editor for journals like Structural Control & Health Monitoring and Engineering Reports . His lab, the Laboratory for Infrastructure Science and Technology (LIST), develops advanced technologies for infrastructure resilience. Key achievements include pioneering deep learning-based SHM with UAVs, top-cited papers in civil engineering journals, and leadership in organizing international conferences. He actively seeks graduate students for research in AI-driven infrastructure solutions.

Alicia L CarriquiryView profile

Professor

• Forensic Science
• Statistics
• Nutrition Epidemiology
• +3 more

Alicia L Carriquiry is a Distinguished Professor and President's Chair at Iowa State University, serving as Director of the Center for Statistics and Applications in Forensic Evidence (CSAFE). She holds a PhD in Statistics from Iowa State University (1989), an MS from the University of Illinois at Urbana-Champaign/ISU (1985/1986), and a BS from Universidad de la Republica in Uruguay (1981). Her research focuses on applying statistical methods to forensic science, nutrition epidemiology, and plant and animal breeding, with a particular emphasis on Bayesian frameworks. Recent research emphasizes forensic evidence analysis, including footwear impression algorithms, handwriting software development, and probabilistic evidence assessment tools. She also explores dietary intake patterns in populations across Latin America and Southeast Asia, addressing nutrient deficiencies and public health interventions. Her work bridges statistical rigor with practical applications in criminal justice, improving forensic methodologies through algorithmic innovation and interdisciplinary collaboration. As CSAFE Director, she leads initiatives to enhance statistical foundations in forensic disciplines, train practitioners, and develop open-source datasets. Notable contributions include database search methodologies, error rate analyses, and software tools like handwriter for handwriting analysis. Her research underscores the importance of probabilistic reasoning in legal contexts and addresses challenges in multi-camera source identification and nonlinear image distortion correction. Carriquiry’s leadership extends to editorial roles and professional service, advancing statistical standards in forensic science. She remains active in training programs and collaborative research projects, fostering reproducibility and relevance in scientific inquiry.

Dani S. BassettView profile

Professor

• Network Science
• Complex Systems
• Brain Connectivity
• +5 more

Dani S. Bassett is the J. Peter Skirkanich Professor at the University of Pennsylvania with primary appointment in the Department of Bioengineering (School of Engineering and Applied Science) and secondary appointments in Physics & Astronomy, Electrical & Systems Engineering, Neurology, and Psychiatry. They serve as an external professor at the Santa Fe Institute and lead a research group focused on complex systems and network science. B.S. in Physics, Penn State University (2004) Ph.D. in Physics, University of Cambridge as Churchill Scholar and NIH Health Sciences Scholar (2009) Postdoctoral position at UC Santa Barbara and Junior Research Fellow at Sage Center for the Study of the Mind Their research integrates complex systems science, statistical mechanics, and applied mathematics to study network dynamics in physical and biological systems. Key areas include brain connectivity mechanisms, cognitive processes, neurological disease modeling, granular matter physics, and collective human curiosity. Bassett employs advanced methodologies including algebraic topology, network control theory, and multilayer network analysis to investigate how network architecture influences system function across diverse domains. Recent publications reveal a strong trend toward interdisciplinary network science applications, particularly in modeling human curiosity through Wikipedia navigation patterns and analyzing brain network reconfiguration during cognitive development. Their work bridges physics, neuroscience, and behavioral science with emphasis on topological network properties and dynamical processes. American Psychological Association's Rising Star (2012) MacArthur Fellow Genius Grant (2014) Lagrange Prize in Complex Systems Science (2017) Erdos-Renyi Prize in Network Science (2018) American Physical Society Fellow (2021) Web of Science Highly Cited Researcher (3 consecutive years) Bassett's research is supported by major agencies including NSF, NIH, DoD, ONR, and private foundations (MacArthur, Sloan, Paul Allen). Their lab actively recruits students from physics, engineering, neuroscience, and computer science backgrounds, emphasizing diversity in academic perspectives. Current projects include the 'Curious Minds' initiative exploring collective knowledge building and network-based models of neurological disorders. Bassett co-authored the MIT Press book 'Curious Minds: The Power of Connection' with philosopher Perry Zurn.

• Numerical Analysis
• Differential Equations
• Geometric Numerical Integration
• +5 more

Elena Celledoni is a Professor in the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU). She has been employed at NTNU since 2004 and has held the position of professor since 2009. She is a member of the Differential Equations and Numerical Analysis Group at the Department of Mathematical Sciences and serves as its leader. Her educational background includes: Master's degree in Mathematics from the University of Trieste (1993) Ph.D. in Computational Mathematics from the University of Padua, Italy (1997) Elena Celledoni's research focuses on numerical analysis, particularly structure preserving algorithms for differential equations and geometric numerical integration. Her work bridges theoretical mathematics with practical computational methods, developing algorithms that maintain the geometric properties of the systems they approximate. She has made significant contributions to Lie group integrators, energy-preserving methods, and the application of these techniques to mechanical systems and shape analysis. In recent years, her research has expanded to include the intersection of numerical methods with machine learning, exploring how structure-preserving approaches can enhance neural networks and data-driven modeling. Her publications demonstrate a clear trend toward integrating traditional numerical analysis with modern machine learning techniques while maintaining a strong foundation in geometric integration and structure preservation. This interdisciplinary approach has led to innovations in neural ODEs, structure-preserving neural networks, and physics-informed machine learning models that respect the underlying mathematical structures of the systems they model. Elena Celledoni has received recognition for her work through the following honors: Member of the Royal Norwegian Society of Sciences and Letters Member of the European Consortium of Mathematics in Industry Council Member of the board of the International Council of Mathematics in Industry and Applications Editorial board member for SIAM Review, Journal of Computational Dynamics, Journal of Geometric Mechanics, Calcolo, and Networks and Heterogeneous Media As an advisor, she has mentored several students including Torbjørn Ringholm who completed his doctoral dissertation on 'Discrete gradient methods in image processing and partial differential equations on moving meshes.' Her research has been supported by various grants enabling her to lead projects on geometric numerical integration, collaborate internationally, and organize significant academic events such as the special semester at Isaac Newton Institute of MS in 2019 on 'Geometry, compatibility and structure preservation.' She leads the Differential Equations and Numerical Analysis Group at NTNU, which focuses on developing and analyzing numerical methods that preserve the geometric structure of differential equations. The group maintains active collaborations with researchers worldwide and has made substantial contributions to advancing the field of geometric numerical integration and its applications to real-world problems.

Chengzong PangView profile

Associate Professor

• Power Systems
• Electrical Engineering
• Control Systems
• +7 more

Chengzong Pang is an Associate Professor and MSECE Graduate Coordinator at the Department of Electrical and Computer Engineering, College of Engineering, Wichita State University. His work focuses on power systems, electrical engineering innovations, and renewable energy integration. He specializes in transient stability analysis, control systems, and smart grid technologies. Research Interests: Dr. Pang's expertise includes advanced control strategies for power electronics (e.g., PMSM, UPQC), machine learning applications for grid stability (LSTM/SVM), and energy storage solutions for renewable integration. His research also addresses challenges in microgrid operation, subsynchronous oscillation mitigation, and battery storage systems. Key Trends in Publications: Over 20 years of publications (2002–2022) emphasize: (1) Machine learning for power system analysis, (2) Control system design for renewable integration, (3) Grid stability enhancement via advanced algorithms, and (4) Smart grid infrastructure optimization. Recent works (2021–2022) highlight transient stability prediction and ANFIS-based power quality solutions. Labs/Teams: Active in energy systems research groups focusing on renewable integration and grid modernization, though specific lab names are not explicitly stated in the provided texts.

George MagoulasView profile

Professor

• Machine Learning
• Artificial Intelligence
• Neural Networks
• +7 more

Prof. George Magoulas is a Professor of Computer Science at the University of London's School of Computing and Mathematical Sciences and Director of the Birkbeck Knowledge Lab. He specializes in machine intelligence, machine learning algorithms, and AI system architectures, with applications in healthcare (e.g., neurodegenerative disease diagnosis) and educational technologies. His research has received awards from IEEE, ACM, and others. He holds a PhD in Nonlinear Optimization for Neural Networks and a PGCE in Higher Education. Education: BEng/MEng (Integrated Master's in Systems & Control Engineering), University of Patras, Greece PhD in Nonlinear Optimization for Neural Networks Learning, University of Patras, Greece PGCE in Teaching and Learning (Higher Education) Research & Leadership: He leads the Birkbeck Knowledge Lab, focusing on AI's impact on learning and communication. His work includes designing learning algorithms for psychophysiological data modeling and developing the cloudUPDRS app for Parkinson's disease assessment. He has supervised over 12 PhD students and contributed to 200+ publications. Awards & Recognition: Stanford’s “World’s top 2% of Scientists” (2024) Best Paper Awards at IEEE, ACM, and EUNITE Keynote speaker at major AI and e-learning conferences Honorary membership in the Hellenic Artificial Intelligence Society Administrative Roles: Director of Teaching & Learning Quality (2016–2023) Chair of Postgraduate Programmes Exam Board (2010–2022) Editor-in-Chief, International Journal on Artificial Intelligence Tools Teaching: He teaches courses on Artificial Intelligence, Neural Networks, and Project Management at both undergraduate and postgraduate levels. Labs & Collaborations: He directs the Birkbeck Knowledge Lab and is a member of the Data Science and AI Research Group. His projects include analyzing violent cycles using AI and collaborating on EU-funded initiatives.

Bauyrzhan PrimkulovView profile

Assistant Professor

• Interfacial Fluid Dynamics
• Soft Matter Physics
• Porous Media Flow
• +3 more

Bauyrzhan Primkulov is an Assistant Professor of Mechanical Engineering at Yale University. His research focuses on interfacial fluid dynamics and soft matter physics, with emphasis on fluid-fluid displacement in disordered environments and hydrodynamic pilot-wave theory. He holds a Ph.D. from MIT (2022) and B.Sc./M.Sc. from the University of Alberta. Primkulov's work bridges theoretical and experimental approaches to address energy and environmental challenges. His team investigates phenomena such as capillary flow dynamics in porous media, wettability effects on displacement patterns, and pilot-wave systems that mimic quantum behaviors. Key contributions include advancing Lenormand's phase diagram for multiphase flows and studying avalanches in imbibition processes. Recipient of InterPore PoreLab Award (2024) and MIT's CEE Best Doctoral Thesis (2022) Expertise spans experimental hydrodynamics, multiphase flow modeling, and granular media mechanics Active in developing novel methods like photoporomechanics to visualize stress fields in fluid-filled granular systems His recent studies explore crossover dynamics between stick-slip and steady sliding regimes in viscous slugs, as well as confinement effects in pilot-wave hydrodynamics. Primkulov collaborates across disciplines to translate fundamental fluid mechanics insights into practical solutions for energy storage and environmental systems.

• Topological Quantum Computing
• Quantum Many-Body Physics
• Condensed Matter Physics
• +4 more

Jay D. Sau is a Professor of Physics at the University of Maryland, College Park, and Co-Director of the Joint Quantum Institute (JQI). His research focuses on theoretical condensed matter physics, particularly topological quantum computing, quantum many-body systems, and Majorana fermions. He holds affiliations with the Condensed Matter Theory Center (CMTC) and JQI. Sau received his Ph.D. from UC Berkeley in 2008. His work bridges theoretical concepts in topological materials, superconductivity, and quantum information processing. Research Interests: Sau's primary interests include applying topological principles to solid-state and cold-atomic systems for quantum computation. Key areas include topological superconductivity, Majorana fermions, quantum Hall effects, and spin-orbit coupled systems. His group explores phenomena like topological degeneracy, Weyl semimetals, and cold atomic gases. Awards: He has been recognized with the National Science Foundation CAREER Award (2016) and the Sloan Research Fellowship (2016). His work has been published extensively in high-impact journals and covers topics ranging from Majorana physics to quantum phase transitions. Advising & Labs: Sau mentors graduate students including Tamoghna Barik, Stuart Thomas, Huan-Kuang Wu, and Shuyang Wang. His research group collaborates on projects at JQI and CMTC, focusing on experimental realizations of topological qubits and quantum devices.

• Urban Analytics
• Urban Informatics
• Smart Cities
• +12 more

Jorge Gil is an Associate Professor in Urban Analytics and Informatics at Chalmers University of Technology's Department of Architecture and Civil Engineering. His research focuses on integrated urban models, Smart Cities, City Information Modelling (CIM), and Urban Digital Twins, with applications in sustainable mobility, social inclusion, energy transition, and circular economy. He develops GIS solutions and open science methodologies. Teaching includes GIS, sustainable mobility, and spatial data science courses. He supervises Bachelor, Master's, and PhD students. Current projects include LogiNets (logistics network flows analysis), ComCy (cycling safety), and FlowSense (traffic flow data). Key research outputs span agent-based modeling of waste sorting behavior, mobility equity analysis, and multimodal urban network frameworks. He co-authored over 50 publications and actively contributes to interdisciplinary urban planning initiatives.

Vito LatoraView profile

Professor

• Complex Systems
• Network Science
• Applied Mathematics
• +13 more

Vito Latora is a Professor of Applied Mathematics and Chair of Complex Systems at the School of Mathematical Sciences, Queen Mary University of London, and also holds the position of Professor of Theoretical Physics at the University of Catania. He leads the Complex Systems and Networks Group, driving cutting-edge research at the intersection of physics, mathematics, and interdisciplinary sciences. His research focuses on complex systems, particularly the structure and dynamics of networks, including multiplex, temporal, and higher-order networks such as simplicial complexes and hypergraphs. He explores applications in social, biological, financial, and cognitive systems, with recent work on creativity, innovation, and success through network analysis. The 15 most recent publications reveal a strong trend in advancing network theory beyond pairwise interactions, with a focus on higher-order structures, memory effects, synchronization, and epidemic spreading. His work combines rigorous mathematical modeling with real-world applications, often published in high-impact journals like Nature Communications , Physical Review Letters , and Science Advances . Dual communities in spatial and biological networks Modeling epidemics with limited detection resources Synchronization via higher-order and directed interactions AI-driven financial risk management Evolutionary games on hypergraphs Interdisciplinary success and funding dynamics Vito Latora has mentored several researchers who appear as co-authors, including Iacopini, Williams, Di Bona, and Lacasa. While specific grants are not listed, his collaborative projects with neuroscientists and anthropologists, along with frequent publications, suggest active funding. He is involved in major scientific events such as NetSci 2023, indicating leadership in the network science community. He leads the Complex Systems and Networks Group at Queen Mary, fostering a collaborative environment for studying complex systems through theoretical, computational, and data-driven approaches.

Håkan LarssonView profile

Professor

• Gender Studies
• Sports Didactics
• Physical Education
• +5 more

Professor Håkan Larsson holds the position of Professor and Head of the Department of Movement, Culture, and Society at the Swedish School of Sport and Health Sciences (GIH). He specializes in Educational Science with a focus on gender, sexuality, and sport didactics. His academic leadership includes directing research initiatives such as the PIF group (Pedagogical Sport Research) and the FIHD Graduate School, which trains teachers in physical education and health. Larsson supervises eight doctoral students and teaches in GIH’s master’s and doctoral programs, notably leading mandatory courses in sports science. Research Interests: Pedagogical processes in club and school sports Gender, sexuality, and embodiment in physical education Heteronormativity and LGBTQ+ inclusion in sports Collaborations: Norwegian School of Sport Sciences (part-time leave) Swedish National Agency for Education (Skolverket) Riksidrottsförbundet (Swedish Sports Confederation) His research spans projects like 'Heteronormativitet inom idrotten' (2012–2013) and the KUL project (2011–2013), funded by the Swedish Research Council. Larsson also chairs evaluation committees for academic programs and collaborates with interdisciplinary groups like Research in Education & Movement Culture. Publications emphasize critical pedagogy, transgender embodiment in education, and innovative teaching methods. His work bridges theory and practice, addressing contemporary challenges in physical education and sport equity.