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Paul S. RosenbloomView profile

Professor

• Cognitive Architecture
• Artificial General Intelligence
• Hybrid Symbol Systems
• +4 more

Paul S. Rosenbloom is a Professor in the Department of Computer Science at the University of Southern California , with affiliations at the Institute for Creative Technologies . His work focuses on cognitive architectures , particularly the development of the Sigma architecture and contributions to the Common Model of Cognition . He has pioneered the integration of symbolic, probabilistic, and neural systems in AI research. Research Interests: Dr. Rosenbloom's research spans hybrid symbol systems , neural-symbolic integration , and the evolution of computational models of cognition . His recent work rethinks the Physical Symbol Systems Hypothesis through hybrid systems that bridge symbolic AI and neural networks, addressing challenges in universality , compositional reasoning , and cognitive modeling . Scientific Awards: 2011 Kurzweil Award for Best AGI Idea 2012 Kurzweil Award for Best AGI Paper 2023 Springer Prize for Best Paper Publications and Contributions: He has authored over 150 publications and co-authored foundational works on the Common Model of Cognition with Laird, Lebiere, and Stocco. His research has been supported by grants from the U.S. Army RDECOM and USC's Institute for Creative Technologies. He has mentored numerous collaborators and co-authors, including researchers like V. Ustun , A. Demski , and H. Joshi , in projects spanning virtual humans , reinforcement learning , and distributed vector representations .

Andy SmithView profile

Researcher

• Computer Science Education
• Artificial Intelligence
• Data Mining
• +3 more

Andy Smith is a Research Scientist at the Department of Computer Science within the College of Engineering at North Carolina State University. He is affiliated with the Intellimedia Group and focuses on enhancing learning environments through data-mining and artificial intelligence techniques. Education: Ph.D. in Computer Science (2020), North Carolina State University M.S. in Computer Science (2016), North Carolina State University B.S.E. in Electrical/Computer Engineering and B.S. in Computer Science (2005), Duke University His research spans game-based learning environments, military training scenario generation, and automated analysis of student drawings in digital science notebooks. He applies machine learning and behavioral modeling to personalize educational experiences and assess learning outcomes. Scientific Awards: President’s Volunteer Service Award – Gold Level (2013) NCSU College of Engineering Fellowship (2011) NCSU Graduate Dean’s Fellowship (2010) Smith previously worked at SPAWAR Systems Center in San Diego, developing sensor integration systems for underwater gliders. He is part of the Intellimedia Group, which designs intelligent educational technologies and adaptive learning frameworks.

Benjamin W. DomingueView profile

Associate Professor

• Psychometrics
• Sociogenomics
• Quantitative Methods
• +3 more

Benjamin W. Domingue is an Associate Professor at the Graduate School of Education , Stanford University, with affiliations including the Stanford Center on Early Childhood and Policy Analysis for California Education (PACE) . His research bridges psychometrics and sociogenomics , focusing on statistical tools for measuring educational outcomes and integrating genetic data into social science research. Education: PhD in Education, University of Colorado Boulder (Advisor: Derek Briggs, 2012) MA in Mathematics, University of Texas at Austin (Advisor: Uri Treisman, 2006) BS in Mathematics, University of Texas at Austin (2001) His work examines test score properties , response time analysis , and genetic influences on educational attainment . Recent projects include developing the Item Response Warehouse (IRW) resource and analyzing oral reading fluency during the pandemic . Key methodological contributions involve the InterModel Vigorish (IMV) for predictive accuracy and speed-accuracy tradeoffs in real-world settings. Scientific Awards Jacobs Foundation Research Fellow (2022–2024) Outstanding Reviewer for AERA Open (2019, 2018) AERA Division D Quantitative Dissertation Award (2013) Faculty Advising Award (2018–2019) His research spans collaborations with institutions like the University of California, Berkeley, University of Colorado Boulder, and The University of Texas at Austin. He has contributed to genetic moderation studies , item response theory , and healthcare provider behavior through psychometric approaches.

• Nuclear Engineering
• Thermal-Hydraulics
• Reactor Safety
• +3 more

Nikola Popov is an Adjunct Professor in the Department of Engineering Physics at McMaster University . With a focus on Nuclear Engineering and Thermal-Hydraulics , he has contributed extensively to reactor safety, licensing, and computational modeling. Contact : npopov Teaching : Courses like Nuclear Reactor Physics , Advanced Nuclear Engineering , and Reactor Safety Design since 2017. His research explores nuclear reactor systems , emphasizing thermal-hydraulic behavior , severe accident mitigation , and regulatory compliance . Key projects include the ACR-1000 and CANDU reactors , with publications on Phenomena Identification and Ranking Tables (PIRTs) and computer code validation . Recent publications highlight trends in Nuclear Engineering and Thermal-Hydraulics , including reactor design comparisons, safety protocols, and computational modeling techniques. Themes span molten salt reactors , LOCA analysis , and code validation .

Andre FiliatraultView profile

Adjunct Professor

• Earthquake Engineering
• Structural Dynamics
• Seismic Isolation
• +4 more

Andre Filiatrault is an Adjunct Professor at the Department of Civil Engineering within McMaster University . His research spans seismic engineering, structural dynamics, and performance-based design, focusing on nonstructural components, damping systems, and wood/steel structures. Key contributions include seismic isolation, passive energy dissipation, and experimental validation of building systems. Research Interests: Earthquake Engineering, Structural Dynamics, Performance-Based Seismic Design, Nonstructural Components, Steel and Wood Structures, Seismic Retrofit Recent Trends: Advanced studies on viscous dampers, seismic loss estimation for acceleration-sensitive elements, and integration of building information modeling with seismic analysis.

Arnd HartmannsView profile

Associate Professor

• Stochastic Timed Systems
• Hybrid Systems
• Probabilistic Model Checking
• +5 more

Arnd Hartmanns is an associate professor in the Formal Methods and Tools group at the University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS). Previously, he was a postdoc at both the University of Twente and Saarland University's Dependable Systems and Software group, where he also completed his Ph.D. in computer science in 2015. He has led multiple research projects including the NWO VIDI project 'Trustworthy Analysis of Stochastic Timed Systems (TruSTy)', the Interreg North Sea project 'STORM_SAFE', and coordinates the MSCA RISE project 'MISSION'. His primary research interests focus on modeling tools and formalisms for stochastic timed and hybrid systems, particularly the Modest framework. Hartmanns has been a strong advocate for reproducibility in Computer Science research through artifact evaluation initiatives, tool competitions like QComp, and standardized benchmark sets. His work bridges theoretical foundations with practical applications across various domains including network-on-chip systems, power grids, and space communication infrastructure. He has made significant contributions to probabilistic model checking, statistical verification techniques, and formal methods for system analysis. Hartmanns serves on numerous program committees, particularly focusing on artifact evaluation for conferences like TACAS, QEST, and FORMATS. His leadership in establishing reproducibility standards has influenced the formal methods community significantly. The trends in his recent publications show a strong emphasis on verified implementations of verification algorithms, statistical model checking techniques that go beyond standard approaches, and applications of formal methods to increasingly complex real-world systems. Scientific Recognition 2016 Best Dissertation Award by the GI/ITG Technical Committee for 'Measurement, Modelling and Evaluation of Computing Systems' Hartmanns has supervised numerous Master's and PhD students through his research projects, though specific names aren't listed in the provided information. His grant portfolio includes several major projects funded by NWO (VENI, VIDI, Open Competition) and European programs (MSCA RISE, Interreg North Sea). He is actively involved in developing educational materials for formal methods and has contributed to establishing benchmark sets that are now standard in the field. His work on the Modest modeling language and toolset has created an important infrastructure for quantitative verification research. The integration of formal verification with machine learning techniques, particularly in strategy learning and decision tree generation, represents one of his more recent research directions that connects formal methods with emerging AI approaches.

• Reservoir Characterization
• Sedimentary Geology
• Petroleum Geosciences
• +4 more

Matthew J. Pranter is the Director of the School of Geosciences and Eberly Family Chair Professor of Geosciences at the University of Oklahoma. He holds affiliate faculty status in the Mewbourne School of Petroleum and Geological Engineering. His research bridges fundamental and applied sedimentary geology, focusing on reservoir characterization, stratigraphic architecture, and heterogeneity in conventional/unconventional reservoirs using multidisciplinary approaches including 3D reservoir modeling, seismic interpretation, and machine learning. Education: Ph.D. in Geology from Colorado School of Mines M.S. in Geology from Baylor University B.S. in Geological/Petroleum Engineering (High Honors) from Colorado School of Mines B.S. in Geology from Oklahoma State University His research explores stratigraphic controls on reservoir quality through integrated analysis of outcrop and subsurface data. Primary interests include: Reservoir characterization for carbon sequestration and energy resources Sedimentary architecture of mixed carbonate-siliciclastic systems Geostatistical modeling of petrophysical heterogeneity Machine learning applications in seismic facies classification and petrofacies prediction Geothermal reservoir characterization His recent publications demonstrate a strong focus on CO₂ sequestration potential assessment in Paleozoic strata, seismic-constrained reservoir modeling, and machine learning applications for subsurface characterization. Dominant themes include carbon storage capacity evaluation, seismic attribute analysis, and diagenetic controls on reservoir quality in mixed sedimentary systems. Dr. Pranter advises a large cohort of graduate students and leads the Reservoir Characterization and Modeling Laboratory (RCML), conducting applied research in reservoir geosciences with emphasis on stratigraphic controls on reservoir architecture. Current research projects include statewide CO₂ storage assessments of Oklahoma's Paleozoic strata and integrated characterization of fluvial-deltaic systems.

• Accelerator Physics
• Machine Learning
• Reinforcement Learning
• +4 more

Dr Andrea Santamaria Garcia is a Lecturer in Artificial Intelligence for Particle Accelerators at the University of Liverpool and a member of the Cockcroft Institute. Her work bridges accelerator physics with machine learning, focusing on reinforcement learning and Bayesian optimization for real-time particle accelerator control. Current Research: Reinforcement learning in autonomous accelerators, Bayesian optimization, differentiable simulations Key Projects: RL4AA collaboration, Cheetah simulation code, microbunching instability control Education: PhD in Accelerator Physics (CERN), postdoctoral work on electron light sources Her research explores the synergy between accelerator physics and machine learning, particularly through reinforcement learning and Bayesian optimization . Recent work focuses on zero-shot reinforcement learning, meta-algorithms, and deploying machine learning on hardware for low-latency beam control. She leads collaborations with KIT, DESY, and CERN, including the development of the first differentiable beam physics simulation code. Scientific highlights include the 2024 Physical Review Accelerators and Beams Editors' Choice for her work on online reinforcement learning in accelerators. Her publications demonstrate cross-disciplinary impact in Scientific Reports and Physical Review Accelerators and Beams . PhD Supervision: Chenran Xu (KIT/DESY), Luca Scomparin (real-time RL platform) Teaching Philosophy: Emphasizes precision, ethics, and interdisciplinary collaboration She collaborates with international institutions and co-founded the Reinforcement Learning for Autonomous Accelerators (RL4AA) initiative, which organizes annual workshops on cutting-edge control techniques.

Patrick BrewickView profile

Assistant Professor

• Data Analytics
• System Identification
• Structural Health Monitoring
• +10 more

Patrick Brewick is an Assistant Professor in the Department of Civil and Environmental Engineering and Earth Sciences at the University of Notre Dame. His research bridges data analytics, mechanics, and computational modeling to address challenges in structural resilience and material behavior under extreme conditions. Ph.D., Columbia University (2014) M.S., Civil Engineering and Engineering Mechanics, Columbia University (2010) B.S., Civil Engineering, University of Notre Dame (2009) Professor Brewick’s work focuses on transforming noisy or incomplete data into accurate predictive models. Key research areas include: System Identification : Uncovering physics of seismic protection systems. Uncertainty Quantification : Developing Bayesian frameworks for model calibration. Damage Detection : Identifying nonlinear structural transitions. Microstructural Modeling : Investigating corrosion and material heterogeneity. Hybrid Modeling : Balancing high- and low-fidelity simulations. Wind Energy Applications : Using distributed fiber optics for turbine monitoring. His publications span disciplines like structural mechanics, materials science, and biomedical engineering, emphasizing multi-fidelity approaches and probabilistic methods. Current projects address infrastructure resilience against climate change, naval structural reliability, and microstructural impacts on corrosion. The Brewick Group collaborates extensively, offering open-source tools and inviting partnerships. Students in his lab, such as Ph.D. candidates Reza Farzad and Jichuan Tang , explore cutting-edge applications in structural dynamics and material modeling. Before Notre Dame, Prof. Brewick conducted research at the U.S. Naval Research Laboratory, focusing on predictive modeling for heterogeneous systems, including marine structures and brain tissue mechanics.

Giovanni SilenoView profile

Lecturer

• Artificial Intelligence
• Logic Programming
• Knowledge Representation
• +5 more

Giovanni Sileno is an academic specializing in Artificial Intelligence, Logic Programming, and Knowledge Representation, with teaching appointments at the University of Amsterdam, EPITA Paris, and Université Pierre et Marie Curie. His primary affiliation is with the Informatics Institute at the University of Amsterdam where he serves as a Lecturer. His research interests span multiple domains within computer science, focusing particularly on formal methods for normative systems, agent-based programming, and logic-based knowledge representation. His work bridges theoretical computer science with practical applications in policy modeling, forensic science, and business information systems. Dr. Sileno has developed several notable software projects including AgentScriptCC for single-threaded intentional agents, DCPLschema for normative policy specifications, and libraries for normative primitives in Answer Set Programming. His GitHub activity shows consistent contributions from 2012 through 2025, indicating active engagement in both research and development. His publications reflect a strong focus on formal methods applied to practical problems, with particular emphasis on normative systems, agent architectures, and logic programming applications. The research trajectory shows evolution from foundational topics in logic and numeral systems toward increasingly sophisticated applications in policy modeling and agent-based systems. As an educator, he has taught across multiple institutions and disciplines, covering topics from basic programming paradigms to advanced knowledge representation and formal modeling techniques. His teaching spans undergraduate to master's level courses in information studies, cognitive science, data science, and forensic science.

Xiaolin HuView profile

Professor

• Modeling and Simulation
• Complex Systems Science
• Agent-Based Systems
• +3 more

Xiaolin Hu is a Professor in the Department of Computer Science at Georgia State University within the College of Arts & Sciences. His research focuses on modeling and simulation theory with applications in complex systems science, agent-based systems, and advanced computing environments. Ph.D. in Electrical and Computer Engineering, University of Arizona (2004) NSF CAREER Award recipient Senior member of IEEE and Society for Modeling and Simulation International (SCS) Associate editor for ACM Transactions on Modeling and Computer Simulation (TOMACS) and other journals SIMULATION: Transactions journal and International Journal of Modeling, Simulation, and Scientific Computing (IJMSSC) Dr. Hu's research spans three major areas: Dynamic Data-Driven Simulation (DDDS): Development of simulation frameworks that assimilate real-time data, particularly applied to wildfire spread prediction and autonomous systems. Multi-Agent Systems: Study of adaptive behaviors and coordination mechanisms for unmanned aircraft and social/public health systems. Advanced Computing: Implementation of parallel and cloud computing solutions for large-scale simulations. His recent publications (2025-2023) demonstrate a strong focus on integrating real-time data from UAVs and satellite systems into wildfire simulations, utilizing Bayesian methods for sequential data assimilation, and developing cloud-based simulation services. These works bridge computational modeling with environmental science applications. Scientific recognitions include: National Science Foundation (NSF) CAREER Award Dr. Hu has chaired multiple international conferences in modeling and simulation and leads the SIMS Lab at Georgia State University, which specializes in simulation technologies and their applications in diverse domains.

Andrei VolodinView profile

Professor

• Statistical Inference
• Limit Theorems
• Distributions on Abstract Spaces
• +12 more

Andrei Volodin is a Professor in the Department of Mathematics and Statistics at the University of Regina, Canada. He serves as the Co-op Work/Study Coordinator and has an extensive publication record spanning probability theory, statistical inference, and applied statistics. His research focuses on limit theorems, bootstrap methods, and distributional analysis with applications to quality control and healthcare economics.

Seongim ChoiView profile

Assistant Professor

• Computational Design Optimization
• CFD (Computational Fluid Dynamics)
• Multidisciplinary Analysis and Design Optimization (MADO)
• +5 more

Seongim Choi is an Assistant Professor in the Kevin T. Crofton Department of Aerospace and Ocean Engineering at Virginia Tech and an Adjunct Professor at KAIST's Graduate School of Transportation. His research focuses on computational design optimization, advanced CFD methods, and multidisciplinary analysis for aerospace systems. He holds a Ph.D. from Stanford University (2006) and degrees from Seoul National University (B.S. 1997, M.S. 1999). His expertise includes unsteady flow analysis, fluid-structure interaction, and rotorcraft design. Notable awards include the Young Investigator Award from the Asian/Australian Rotorcraft Forum (2012) and Best Paper Awards from AIAA conferences (2004). Professional roles span NASA Ames, the U.S. Army Aeroflightdynamics Directorate, and KAIST. Research interests emphasize adjoint-based sensitivity analysis, time-spectral methods for CFD, and machine learning integration in design optimization. Current work addresses low-boom supersonic jets, helicopter rotors, and autonomous UAV systems. His affiliations include the Virginia Tech College of Engineering and collaborations with KAIST's aerospace programs.

• Geotechnical Engineering
• Civil Engineering
• Construction Engineering
• +6 more

Dr. Brian Sheil is the Laing O'Rourke Associate Professor of Construction Engineering at the University of Cambridge and holds a Visiting Fellowship at the University of Oxford. He holds a BE from the National University of Ireland, Galway (NUIG) and a PhD from a collaborative program between NUIG and the University of California, Berkeley. His research focuses on soil-structure interaction, underground construction monitoring, and machine learning applications in geotechnical engineering. He leads major projects such as the EPSRC-funded FOCUS initiative for sensor development and the iDRIVE project for offshore monopile forecasting. His research interests include geotechnical BIM, digital twins, and the integration of fiber optic sensors and Bayesian algorithms for real-time construction monitoring. He is an editor for journals like Data-Centric Engineering and sits on technical committees such as ISSMGE TC222. He has received awards including the 2022 Young Researcher Award and the Bright Spark Lecture Award. As a Principal Investigator, he oversees projects commercializing fiber optic force sensors and collaborates with industry partners. His work aims to bridge geotechnical science with advanced data analytics, addressing challenges in underground construction and infrastructure resilience.

Federico CiardoView profile

Assistant Professor

• Geomechanics
• Subsurface Engineering
• Fault Dynamics
• +5 more

Federico Ciardo is an Assistant Professor of Civil and Environmental Engineering at Northwestern University, specializing in geomechanics and subsurface engineering. His research focuses on understanding deformation mechanisms in brittle rocks and fault zones under fluid injection, aiming to improve predictive models for geo-engineering applications like geothermal energy and CO2 sequestration. He leads the GEOFORCE Group, which develops advanced computational and theoretical approaches to study subsurface processes across multiple scales. Education: Ph.D. in Mechanics from École Polytechnique Fédérale de Lausanne (EPFL, Switzerland), M.Sc. and B.Sc. in Civil/Structural Engineering from the University of Bologna, Italy. Research Interests: Investigating fluid-driven deformation instabilities in fractured rocks, seismic hazard mitigation, and the interplay between in-situ stresses and external forces. Key topics include aseismic/a-seismic slip dynamics, hydraulic stimulation effects, and fault zone heterogeneity. His work integrates fracture mechanics, numerical modeling (e.g., boundary element methods), and scaling arguments to achieve mechanistic insights. Scientific Contributions: Published extensively on induced seismicity forecasting, fault slip nucleation, and hybrid hydromechanical models. Notable recognition includes the EPFL Doctoral Program Thesis distinction award (2020). Labs/Teams: Principal investigator of the GEOFORCE Group, affiliated with the Paula M. Trienens Institute for Sustainability and Energy. Collaborates on projects involving underground laboratories (e.g., Bedretto Lab) and geothermal energy systems.