Kyriaki Corinna Datsiou is a Senior Lecturer in Structural Design and Future Infrastructure at the University of Hertfordshire's School of Physics, Engineering & Computer Science, Department of Engineering and Technology. She holds a PhD from the University of Cambridge (2017) and an MSc in Civil Engineering from Aristotle University of Thessaloniki (2012). Her research focuses on structural glass, façade engineering, additive manufacturing, and sustainability in construction, with a particular emphasis on laser powder bed fusion and cold-bent glass applications. She has held roles at the University of Nottingham's Centre for Additive Manufacturing (Research Fellow), Eckersley O'Callaghan (industrial design consultancy), and provided forensic engineering services during her Cambridge tenure. Her work is funded by EPSRC, Innovate UK, EU Research Fund for Coal and Steel, and industry partners. Recent projects include 3DGLaSS 2 (3D glass laser sintering) and ReStruct Glass (structural glass repair methods). Research interests include forensic structural investigation, additive manufacturing for building materials, and circular economy practices in construction. Awards include a Fellowship from Advance HE (2023).
Prof. Dr.-Ing. Fabian Duddeck is a Professor of Computational Mechanics at the Technical University of Munich (TUM), affiliated with the TUM School of Engineering and Design. His research focuses on numerical methods for structural simulation and optimization, including topology optimization for crashworthiness, material modeling for composites/biomaterials, and uncertainty quantification in multi-physics systems. He holds a Dr.-Ing. and Habilitation from TUM, with prior roles at BMW Group, Queen Mary University of London, and École des Ponts ParisTech. His work bridges academia and industry, addressing challenges in automotive and aerospace design. Education: Diplom (Civil Engineering, TUM 1990), Dr.-Ing. (Mechanics, TUM 1997), Habilitation (TUM 2002) Research Interests: Crash simulation optimization, composite materials, nonlinear dynamics, and uncertainty-aware design methodologies Scientific awards include the Spindler Award (1991). Over 50+ students have graduated under his supervision, with notable works in crashworthiness, composite structures, and multi-fidelity optimization. His lab collaborates internationally, integrating advanced algorithms with industrial applications.
Dr. Lin Li is an Associate Professor in Marine/Ocean Technology at the Faculty of Science and Technology , University of Stavanger . With a PhD from NTNU and degrees from Shanghai Jiao Tong University , she leads research in marine structural dynamics, aquaculture-hydrodynamics, and offshore wind integration. PhD Marine Technology (NTNU) MSc Design and Construction of Ships/Ocean Structures (Shanghai Jiao Tong University) BSc Naval Architecture and Ocean Engineering (Shanghai Jiao Tong University) Her research focuses on: Dynamic analysis of marine structures Design of aquaculture systems Hydrodynamic modeling for offshore wind Statistical wave analysis Recent publications highlight advancements in: Hybrid offshore fish cage-wind turbine systems Metocean condition modeling Subsea spool deployment methods Extreme response prediction techniques She actively contributes to international marine technology conferences and applies open-source tools for hydrodynamic validation.
Marco Domaneschi is an Assistant Professor in the Department of Structural, Building and Geotechnical Engineering (DISEG) at Politecnico di Torino, Italy. He teaches courses in Earthquake Engineering, Structural Design, and Seismic Protection Systems. He previously served as a Research Associate and Contract Professor at Politecnico di Milano and has extensive experience as a structural engineer and R&D consultant. His research focuses on bridge engineering, earthquake engineering, structural monitoring, disaster resilience, and sustainability . He employs advanced numerical simulations and experimental techniques to assess and enhance the safety and performance of civil infrastructure. His work integrates digital technologies, machine learning, and smart materials for structural control and health monitoring. The recent publications highlight a strong trend in resilience assessment, seismic protection systems, sustainable retrofitting using exoskeletons, structural health monitoring (SHM), and digital innovation in infrastructure management . His research increasingly combines computational modeling with real-world applications, including blockchain for asset management and AI for dynamic identification. Top 2% most-cited scientist (Stanford/Elsevier, 2022) Takuji Kobori Prize (2014) Best Presentation Award, ASEA SEC 5 (2020) Best Presentation Award, ISHMII 2017 Multiple MSC Research Assist Program Awards (2012–2014) Marco Domaneschi has supervised several PhD students and has scientific responsibility for multiple EU and national research projects, including Horizon Europe’s RESUME, BIO-RESTORE, and VIBRATIONCLEAR. He supports PIs in coordinating ERC projects and serves as a scientific advisor for international PhD researchers. He has led numerous commercial and departmental research contracts focused on structural monitoring, BIM implementation, and sensor integration. He is actively involved in the scientific community, serving as Associate Editor for journals including Journal of Vibration and Control and Frontiers in Built Environment , and as a member of editorial boards for Structure and Infrastructure Engineering and Bridge Engineering (ICE) . He has chaired and organized major international conferences such as WCEE2024 and IABMAS 2024, and has led over 15 conference sessions.
Dr. Andrew Martin Pownuk is an Associate Professor at the Department of Mathematical Sciences, The University of Texas at El Paso. His work bridges computational science, uncertainty modeling, and fuzzy logic, with a focus on engineering applications. He holds dual PhDs in Computational Science (2017) and Computational Methods in Engineering (2001), and has taught at UTEP since 2006. Ph.D. Computational Science, The University of Texas at El Paso (2017) Ph.D. Computational Methods in Engineering, Silesian University of Technology, Poland (2001) M.S. Mathematics and Computational Science, The University of Texas at El Paso His research centers on combining interval and probabilistic uncertainty for engineering problems, unsupervised learning, and computational mechanics. Recent work explores quantum computing's potential for uncertainty propagation and HyFlex teaching methodologies. Dr. Pownuk has received multiple accolades, including the 2018 NAFIPS Best Thesis Award and multiple Academic and Research Excellence awards. He contributed to projects with Chevron Corporation, Army Research Laboratory, and international collaborations like the COCONUT Project. 2018 NAFIPS Best Thesis Award 2017/2014 UTEP Academic and Research Excellence 2000 Silesian University Scientific Achievement Award
Georgios P. Karatzas is a Professor at the Department of Environmental Hydraulics, Coastal Engineering and Geoenvironmental Engineering (IV) within the Technical University of Crete. His research focuses on Water Resources Management , Groundwater Flow and Pollutant Transport , and Geoenvironmental Engineering . He leads the Laboratory of Geoenvironmental Engineering and has developed innovative methodologies like the 'Outer Approximation Method' Optimization adopted by major US agencies. His academic credentials include a Ph.D. (1992), M.Sc. (1987), and B.Sc. (1985) from Rutgers University, with prior education in Forestry from Aristotle University of Thessaloniki (1982). He has received international recognition, including adoption of his optimization methods by the Federal Remediation Technologies Roundtable (2003-present) and EPA (1999). His research integrates Groundwater Simulation Models with Optimization Methods for sustainable aquifer management, addressing challenges like Coastal Aquifer Salinization , Karst Aquifer Dynamics , and Flood Risk Assessment . Recent projects include InTheMED (€220,000) and Sustain–COAST (€290,000) focused on Mediterranean groundwater sustainability. Notable scientific awards include the 2014 Outstanding Teaching Award at Technical University of Crete and multiple grants from PRIMA, Interreg, and LIFE+ programs. His work combines advanced computational methods with practical environmental risk mitigation strategies.
Dr. Yulia Alexandr is a Hedrick Assistant Adjunct Professor at UCLA and a Postdoctoral Fellow in Applied Mathematics at Harvard University . She completed her PhD in Mathematics at UC Berkeley in 2023 under the supervision of Bernd Sturmfels and Serkan Hoşten , with a thesis titled "From Voronoi Cells to Algebraic Statistics" . She holds a Bachelor's degree in Mathematics from Wesleyan University (2019), where she was advised by Karen Collins .
Qing Zhao is a Professor in the Department of Electrical and Computer Engineering at Cornell University's College of Engineering, where she holds the Joseph C. Ford Professor of Engineering title. She joined Cornell in 2015 after serving as a professor at UC Davis from 2004 to 2015. Her research bridges theoretical foundations in decision-making with practical applications in networked systems, infrastructure, and communications. B.S., Electrical Engineering, Sichuan University, 1994 M.S., Electrical Engineering, Fudan University, 1997 Ph.D., Electrical Engineering, Cornell University, 2001 Postdoc, Electrical Engineering, Cornell University, 2004 Her research interests lie at the intersection of sequential decision theory , stochastic optimization , machine learning , and algorithmic theory , with applications in communications, power systems, and socio-economic networks. She specializes in multi-armed bandits , anomaly detection , online learning , and Bayesian optimization , developing frameworks for intelligent decision-making under uncertainty. The recent 15 publications reflect a strong trend toward AI-driven solutions for infrastructure , particularly in grid monitoring, distributed energy systems, and federated learning. Her work emphasizes theoretical rigor (e.g., order-optimal regret, asymptotic efficiency) while addressing real-world constraints such as communication limits, privacy, and computational complexity in distributed and networked settings. Notable scientific awards include: Fellow of IEEE Marie Skłodowska-Curie Fellow, European Union Jubilee Chair Professor, Chalmers University (2018–2019) Distinguished Lecturer, IEEE Signal Processing Society IEEE Signal Processing Magazine Best Paper Award (2010) Young Author Best Paper Award, IEEE Signal Processing Society (2000) UC Davis Chancellor’s Fellow Michael Tien ’72 Excellence in Teaching Award, Cornell (2022) She has made significant contributions to research funding and leadership , particularly through her involvement in the AI Institute for Next Generation Food Systems (AIFS). Her work bridges academia and societal impact, with applications in smart grids, digital food safety, and resilient cyber-physical systems. She has advised numerous students and leads a research group focused on information, networks, and decision systems. Her lab and team activities center on AI for infrastructure , including projects on grid monitoring with AI foundation models , federated learning for energy systems , and anomaly detection in hierarchical networks . These efforts are supported by interdisciplinary collaborations and focus on deploying machine learning in safety-critical and resource-constrained environments.
Nicholas Harvey is a Professor in the Department of Computer Science at the University of British Columbia (UBC), part of the Faculty of Science. He holds affiliations as an Associate Member in the Department of Mathematics and serves as Director of the Mathematics of Information, Learning, and Data (MILD) research cluster. His research focuses on randomized algorithms, convex optimization, and machine learning theory, with notable contributions to online learning, submodular functions, and algorithmic aspects of discrepancy theory. Harvey's academic journey includes significant work on problems such as prediction with expert advice, learning mixtures of Gaussians, and graph algorithms. He has supervised numerous graduate students, including notable researchers like Huang Fang and Victor Sanches Portella. His work has been recognized with awards such as the Best Paper Award at NeurIPS 2018 and the Excellence in Teaching Award in 2024. His research outputs span theoretical computer science, optimization, and machine learning, with key contributions in algorithms for submodular functions, online convex optimization, and probabilistic methods. He has also contributed to practical applications, such as algorithms for network coding and efficient failure notification systems in distributed networks. Harvey is actively involved in teaching, including pioneering courses on randomized algorithms and machine learning theory. His research group collaborates on projects related to the MILD cluster, focusing on interdisciplinary applications of data science and mathematics.
Xavier Rival is a Research Director at INRIA Paris and Adjunct Professor at École Normale Supérieure (ENS) PSL. He serves as Director of the Computer Science Department at ENS, leading a CNRS/ENS/PSL/INRIA laboratory. Previously, he was head of the ANTIQUE (ANalyse staTIQUE) research group at INRIA Paris/DIENS from 2014 to 2024. His academic roles include: Adjunct Professor in Computer Science (ENS/PSL) Director of Computer Science Department at ENS Principal Investigator (PI) of the VeriAMOS ANR Project Education & Academic Background While specific education details are not explicitly stated, his long-term involvement in academic research and leadership roles at ENS/PSL and INRIA indicates a strong academic foundation in computer science and formal methods. Research Interests Rival's work focuses on static analysis and abstract interpretation , particularly in: Verification of semantic program properties Probabilistic program analysis Safety-critical embedded software verification (via the Astrée project) Static analysis of spreadsheet and JavaScript applications Software reliability in safety-critical systems Teaching He contributes to: Semantics and Applications to Verification course at ENS (taught with Jérôme Feret and Josselin Giet) Master Parisien de Recherche en Informatique (MPRI) course on Abstract Interpretation Projects & Labs Key initiatives include: VeriAMOS (ANR Project): Focuses on verified OS services via Domain-Specific Languages (DSLs) Astrée project: Static analysis for safety-critical embedded software MemCAD (ERC Project): Static analysis of memory-constrained systems
Paolo Ballarini is a Professor specializing in stochastic modeling, formal verification, and computational biology. He leads the Paolo Ballarini Laboratory for Mathematics and Computer Science for Complexity and Systems , focusing on interdisciplinary research at the intersection of computer science and life sciences. His work spans probabilistic systems analysis, statistical model checking, and algorithm design for complex networks. Research interests include stochastic process discovery, parameter estimation in biological systems, and performance evaluation of wireless networks. His contributions to formal methods have advanced applications in healthcare systems, manufacturing processes, and genetic network analysis. Key technical developments include the COSMOS platform for statistical model checking and the HASL formal language for specifying verification properties. His work bridges theoretical foundations with practical tools for analyzing real-world systems under uncertainty. Ballarini’s recent studies emphasize Bayesian methods for biological pathway inference and optimization-based approaches to stochastic process discovery. His collaborations span academia and industry, addressing challenges in network security, distributed systems, and systems biology.
Rudy Setiono is an Associate Professor and Assistant Dean of Graduate Studies at the School of Computing, National University of Singapore (NUS). He has been with NUS since August 1990, following the completion of his Ph.D. in Computer Science from the University of Wisconsin-Madison. Previously, he served as Vice Dean (Undergraduate Affairs) from November 2001 to July 2005 at the School of Computing. B.Sc. in Computer Science from Eastern Michigan University (1984) M.Sc. in Computer Science from University of Wisconsin-Madison (1986) Ph.D. in Computer Science from University of Wisconsin-Madison (1990) Professor Setiono's research focuses on neural networks, particularly in rule extraction, neural network construction and pruning, and applications in optimization. His work spans theoretical foundations to practical implementations in credit scoring, poverty analysis, and business intelligence. He has made significant contributions to making neural networks more interpretable through rule extraction techniques, bridging the gap between black-box models and transparent decision systems. His recent publications show a strong trend toward practical applications of neural network rule extraction in credit scoring, poverty analysis, and document processing. The research demonstrates consistent evolution from theoretical neural network construction to real-world applications across finance, social sciences, and business analytics, with an emphasis on model interpretability and practical implementation. Senior Member of IEEE Associate Editor of IEEE Transactions on Neural Networks (2000-2005) Professor Setiono has supervised numerous research projects and taught courses including BT4103 Business Analytics Capstone Project, BT4240 Machine Learning for Predictive Data Analytics, IS5152 Data-Driven Decision Making, and IS4240 Business Intelligence Systems. His work has been published in reputable journals including IEEE Transactions on Neural Networks, IEEE Transactions on Data and Knowledge Engineering, and Neurocomputing.
Jacob Laurel is an Assistant Professor in the School of Computer Science at Georgia Institute of Technology. His research focuses on static analysis of programming languages, particularly in probabilistic and differentiable programming. He actively recruits PhD students starting Fall 2025 for work in these areas. Research interests include static analysis techniques for probabilistic/differentiable languages, compiler optimizations, and formal methods for ensuring correctness in AI systems. His work spans topics like abstract interpretation, automatic differentiation, and uncertainty quantification in distributed systems. Notable contributions include the Diamont framework for uncertainty monitoring in distributed programs, the Statheros compiler for low-precision probabilistic inference, and foundational work on higher-order automatic differentiation analysis. He has served on program committees for OOPSLA, SAS, ECCV, and WFVML. Teaching includes a special topics course on probabilistic/differentiable programming in Spring 2025.
Noah Goodman is a Professor of Psychology and Computer Science at Stanford University. He holds dual appointments in the Department of Psychology and the Department of Computer Science, reflecting his interdisciplinary work at the intersection of cognitive science and artificial intelligence. Education: B.A. in Mathematics (1997), B.S. in Physics (1997) from the University of Arizona, and a Ph.D. in Mathematics (2003) from the University of Texas at Austin. Research focuses on cognitive modeling, probabilistic programming, AI alignment, and the computational foundations of human-like reasoning. Recent work explores topics such as in-context learning strategies, causal abstraction mechanisms, and the ethical implications of advanced AI systems. His lab, the Computation & Cognition Lab, develops tools like NumPyro and frameworks like BoxingGym to advance AI research. Articles from 2025 highlight trends in AI ethics, cognitive modeling, and symbolic reasoning in neural networks. Notable collaborations include work on self-improving reasoners and value profiles for encoding human variation. No awards explicitly listed, but contributions to foundational AI research are widely recognized. No student advisees listed in available texts. Labs/Teams: Computation & Cognition Lab at Stanford University.
Saeed Amizadeh is a prominent researcher specializing in artificial intelligence and machine learning, currently affiliated with Microsoft's research division. His work spans multiple domains of AI including speech processing, natural language understanding, computer vision, and time series analysis, with a particular focus on developing novel frameworks that bridge symbolic reasoning with neural approaches. Over the past decade, he has established himself as a significant contributor to the field through publications in top-tier conferences including ICASSP, ICLR, AAAI, KDD, and IJCAI. Dr. Amizadeh's research interests primarily center around advancing the theoretical foundations and practical applications of machine learning systems. His work on neuro-symbolic visual reasoning has contributed to understanding how to effectively disentangle visual perception from logical reasoning in AI systems. He has made significant contributions to differentiable programming, particularly in making classical machine learning pipelines fully differentiable, which enables end-to-end optimization of complex ML workflows. His research on speech enhancement using GANs represents cutting-edge work in audio processing, while his time series anomaly detection frameworks have practical applications in numerous industry settings. Analysis of his publication trends reveals a consistent trajectory from theoretical machine learning foundations toward increasingly applied research with practical industrial relevance. His early work (2010-2015) focused on fundamental algorithms and probabilistic modeling approaches, while his more recent publications (2019-2025) demonstrate a shift toward practical AI systems with direct applications in speech processing, audio separation, and enterprise machine learning. A notable theme throughout his career is the development of frameworks that enable more efficient, scalable, and interpretable AI systems. As a key contributor to Microsoft's ML.NET framework, Dr. Amizadeh has played an important role in developing tools that make machine learning more accessible to enterprise developers. His collaborative work spans academia and industry, with notable partnerships with researchers from various institutions as well as within Microsoft Research.
