Gal Elidan is an associate professor in the Department of Statistics at the Hebrew University of Jerusalem, Faculty of Social Sciences. His work bridges statistical theory with practical applications in machine learning and artificial intelligence. Dr. Elidan's research focuses on Probabilistic Graphical Models , with emphasis on fundamental representations and methods for inference and learning in large-scale domains. His work addresses structure learning, discovery of hidden variables, transfer of knowledge between related tasks, and nonlinear high-dimensional representations of continuous or hybrid distributions. He also applies these techniques to real-life applications including computational biology, machine vision, and medical informatics, with recent focus on principled techniques for medical diagnosis. His publication record shows consistent contributions to top machine learning venues with a recent emphasis on copula models for high-dimensional data. His work demonstrates a progression from foundational graphical model research to specialized applications in medical informatics and time series analysis. Scientific recognition includes: Runner-up for Best Student Paper Award at UAI 2004 Best Paper Award at ISMB 2001 Dr. Elidan serves on the editorial board of the Journal of Artificial Intelligence Research (JAIR) and has contributed to the development of important software tools including FastInf, a library for large-scale inference of graphical models, and LibB, a package for Bayesian network inference and learning. He has organized significant academic events including the NIPS 2011 Workshop on Copulas in Machine Learning and co-organized the PASCAL2 Probabilistic Inference Challenge and UAI 2010 Approximate Inference Challenge.
Danica M Ommen is an Associate Professor at Iowa State University, specializing in forensic statistics and computational methodologies. Her research bridges machine learning, handwriting analysis, and source identification frameworks. Education: Ph.D. in Computational Science and Statistics (2017), M.S. in Mathematics (2014), B.S. in Mathematics (2012), all from South Dakota State University. Affiliations: Chair of the OSAC Statistics Task Group; Vice-Chair of the ASA Advisory Committee on Forensic Science. Her work focuses on statistical modeling for forensic evidence , particularly in handwriting identification, aluminum powder analysis, and digital device forensics. Recent publications explore interpretable deep learning, synthetic data anchoring, and ensemble methods for likelihood ratios. The 15 most recent articles (2023–2025) span forensic machine learning, handwriting kinematics, multi-camera smartphone identification, and Bayesian frameworks. Keywords include Forensic Science , Machine Learning , and Computational Statistics , with subfields like Score-Based Likelihood Ratios and Smartphone Forensics .
Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.
Erik Henning Thiede is an Assistant Professor of Chemistry at Cornell University, affiliated with the Department of Chemistry and Chemical Biology. He joined the faculty in Summer 2023 and leads the Thiede Lab, which focuses on understanding protein motion and function through computational tools that integrate machine learning, molecular simulation, and chemical physics. PhD in Chemistry from the University of Chicago (advised by Profs. Aaron Dinner and Jonathan Weare) Postdoctoral Research at Flatiron Institute CCM (collaborating with Prof. Risi Kondor, Dr. Pilar Cossio, and Dr. Sonya Hanson) The lab's research spans several key areas: Developing algorithms to extract free energies from cryo-EM data Creating permutation-equivariant machine learning models for chemical systems Improving error estimation in molecular simulation frameworks like MBAR Applying Wasserstein flows and probabilistic methods to cryo-EM analysis Expanding cryo-EM capabilities for disordered protein regions Integration of experimental data with computational simulations His lab's publications highlight trends in computational chemistry, including the application of graph neural networks, Bayesian inference, and advanced statistical methods to molecular dynamics. These works bridge chemical physics, machine learning, and structural biology. As an academic advisor, Thiede mentors a diverse group of graduate students and postdoctoral researchers. His lab actively collaborates with interdisciplinary teams across Cornell and other institutions, with current projects involving chemical engineering, applied mathematics, and biophysics. The Thiede Lab at Cornell is committed to open and inclusive science. Values include curiosity, openness to new ideas, mutual support in research, and active inclusion of diverse backgrounds. The lab occupies space at 214 Baker Lab and maintains a research website at thiedelab.github.io .
Michael M. Zavlanos is the Yoh Family Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University's Pratt School of Engineering. He also holds secondary appointments in the Department of Computer Science and the Department of Electrical and Computer Engineering. Currently serving as the Director of the Healthcare Systems Optimization program with Duke AI Health and as an Amazon Scholar with Amazon Robotics, his academic career spans control theory, optimization, and artificial intelligence with applications across multiple domains. Dr. Zavlanos received his educational foundation from prestigious institutions: Diploma in Mechanical Engineering from the National Technical University of Athens (NTUA), Greece (2002) M.S.E. in Electrical and Systems Engineering from the University of Pennsylvania (2005) Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania (2008) His research program spans multiple interconnected domains, with a strong foundation in control theory, optimization, and learning methodologies . This theoretical work directly enables applications in robotics and autonomous systems , where his team develops algorithms for multi-robot coordination, motion planning under complex constraints, and network connectivity maintenance. A significant portion of his work addresses networked and distributed control systems , focusing on how multiple agents can coordinate effectively with limited communication. More recently, he has expanded his research into cyber-physical systems with healthcare applications, leveraging his expertise to optimize healthcare delivery systems through the Duke AI Health initiative. Dr. Zavlanos' work demonstrates a consistent trajectory from theoretical foundations to real-world applications. His early work established fundamental principles for maintaining connectivity in mobile robot networks, which evolved into more sophisticated approaches for temporal task planning and risk-averse decision making in uncertain environments. The most recent phase of his research integrates machine learning with traditional control theory to address complex healthcare system optimization problems. His significant contributions to the field have been recognized through prestigious awards: Office of Naval Research Young Investigator Program (YIP) Award (2014) National Science Foundation Faculty Early Career Development (CAREER) Award (2012) National Science Foundation Faculty Early Career Development (CAREER) Award (2011) Duke University Distinguished Faculty Rank (2019) Duke University Distinguished Professor designation (2018) As an educator, Dr. Zavlanos has taught courses including ME 627: Linear System Theory, ME 592: Research Independent Study, ECE 391/291: Projects in Electrical and Computer Engineering, and CEE 627: Linear System Theory. His research program has been supported by multiple grants from the National Science Foundation and the Office of Naval Research, enabling him to mentor numerous graduate students and postdoctoral researchers in the development of cutting-edge control and optimization algorithms. Dr. Zavlanos leads research efforts at the intersection of control theory, optimization, and artificial intelligence, with particular focus on translating theoretical advances into practical applications. His recent work with Duke AI Health represents a strategic expansion of his research portfolio into healthcare systems optimization, where he applies his expertise in algorithmic decision making to improve patient scheduling, resource allocation, and operational efficiency in medical settings. Through his Amazon Scholar role, he also contributes to advancing robotics technologies for real-world applications.
Irène Marcovici is a Professor in the Department of Mathematics at the University of Rouen Normandy, affiliated with the Raphaël Salem Mathematics Laboratory (LMRS). She leads the Probability and Dynamical Systems team within LMRS and participates in the ALEA and SDA2 working groups of the GDR Informatique Fondamentale et ses Mathématiques. PhD in Mathematics (2013, University of Paris Diderot) Habilitation à Diriger des Recherches (2021, University of Lorraine) Her research focuses on probability theory , dynamical systems , and cellular automata , with significant contributions to percolation theory, self-organization phenomena, and combinatorics on words. She investigates how randomness influences complex systems, particularly through probabilistic cellular automata and their ergodic properties. Analysis of her recent publications (2021-2025) reveals a strong emphasis on percolation dynamics (e.g., corner percolation with directional bias), self-stabilization mechanisms in cellular systems, and combinatorial structures like Kolakoski sequences. Her work bridges theoretical computer science and pure mathematics, often employing stochastic methods to solve problems in symbolic dynamics and discrete geometry. Supervision & Collaborations: Currently supervising Maxence Poutrel (with Jérôme Casse) Previously supervised Pierrick Siest (2021-2024), Jocelyn Begeot (now Associate Professor), and Pierre-Adrien Tahay (now PRAG at Telecom Nancy) Regular collaborations with Régine Marchand, Nazim Fatès, and Pascal Moyal Laboratory Context: As leader of the Probability and Dynamical Systems team at LMRS, she contributes to France's national research infrastructure in fundamental mathematics, with connections to CNRS and international working groups focused on automata theory and discrete probability.
Erik Johnson is a Professor of Civil Engineering at an unspecified university, affiliated with the Sonny Astani Department of Civil and Environmental Engineering. He has held leadership roles such as Associate Chair, Interim Chair, and currently serves as Vice Dean for Academic Programs. His research focuses on smart structures, structural vibration control, and computationally-efficient simulation algorithms for dynamical systems, with applications in controllable damping devices and seismic mitigation. Education: B.S., M.S., Ph.D. in Aeronautical and Astronautical Engineering (University of Illinois at Urbana-Champaign), Graduate Certificate in Biblical Studies (Trinity Evangelical Divinity School) Professional Affiliations: Senior Member of AIAA; Member of ASCE and ASME; Chair of ASCE technical committees; Associate Editor, ASCE Journal of Engineering Mechanics His work spans disciplines including control theory, structural engineering, and computational methods. Articles highlight Bayesian approaches, inverse problems, and sensor placement optimization under uncertainty. Erik contributes to advancing seismic resilience and mechatronic systems for civil infrastructure. Scientific Awards: 2001 NSF CAREER Award, 2005 International Association for Structural Safety and Reliability Medal, 2016 University of Illinois Distinguished AE Alumnus Award
Syed Ali Raza is a researcher affiliated with the University of Technology, Sydney (PhD 2018), with a former affiliation at the Institute of Business Administration, Karachi. His work focuses on reinforcement learning, robotics, and human-robot interaction. Key contributions include studies on social robots' question-answering services, human feedback integration in AI systems, and optimization of robotic movements in multi-agent scenarios. He has collaborated extensively with researchers like Mary-Anne Williams and Sajjad Haider. His research spans theoretical advancements in machine learning algorithms and practical applications in robotics competitions (e.g., RoboCup). Research interests emphasize computational reinforcement learning, reward shaping techniques, and ethical design of autonomous systems. Notable projects include privacy-first approaches for social robots and hybrid methods for humanoid robot locomotion. His work bridges theoretical AI advancements with real-world robotic implementations.
Professor Keith Worden is a Professor of Mechanical Engineering at the University of Sheffield's School of Mechanical, Aerospace and Civil Engineering. His research focuses on applications of advanced signal processing and machine learning to structural dynamics, particularly in aerospace systems. He has held this position since 1995 and has contributed significantly to the field of structural health monitoring (SHM), including work on nonlinear system analysis and damage detection. His work emphasizes pragmatic engineering solutions and collaboration with industries like aerospace and offshore sectors. Education: Holds a degree from York University and a PhD in Mechanical Engineering from Heriot-Watt University. Career highlights include research at Manchester University before joining Sheffield. Research Interests: Specializes in structural dynamics, SHM using machine learning, nonlinear systems, and vibration analysis. Key themes include population-based SHM frameworks, damage prognosis, and environmental adaptation in monitoring systems. His group develops algorithms for automated inspection and diagnosis, leveraging neural networks, genetic algorithms, and other biological-inspired methods. Articles Trends: Recent publications focus on population-based SHM methodologies, transfer learning applications, and algorithm development for novelty detection, damage localization, and risk-informed decision frameworks. His work bridges theoretical advancements with practical engineering challenges. Grants & Advising: Extensive grants and collaborations in SHM, wind turbine monitoring, and aerospace structures. Advises on projects involving machine learning in structural dynamics and probabilistic modeling. Labs & Teams: Leads research groups exploring computational tools for SHM, including the application of Gaussian processes, Bayesian methods, and data-driven models. Collaborates internationally on projects such as the RAPTOR telescope system and offshore wind farm monitoring.
Professor Nikolaos Dervilis is a faculty member in the Department of Mechanical Engineering at the University of Sheffield, serving as Director of Research and Innovation for the School of Mechanical, Aerospace and Civil Engineering. He holds a BSc from the National and Kapodistrian University of Athens, an MSc in Sustainable and Renewable Energy Systems from the University of Edinburgh, and a PhD from the University of Sheffield in Mechanical Engineering with a focus on machine learning for Structural Health Monitoring (SHM). His research emphasizes SHM, renewable energy systems (particularly wind turbines), data analysis, nonlinear dynamics, and advanced signal processing. His work spans population-based SHM (PBSHM), machine learning applications in structural dynamics, and probabilistic modeling. Recent publications focus on active learning frameworks, Bayesian methods, and generative models for damage prognosis. He collaborates with industry on wind energy and has contributed to datasets for experimental bridges and aerospace components. Notably, he leads efforts in transfer learning and domain adaptation for heterogeneous structural populations. Research highlights include developing frameworks for risk-informed decision support, model selection via approximate Bayesian computation, and digital twin tools for engineering systems. His lab, part of the Dynamics Research Group, addresses challenges in energy systems, composite materials, and condition monitoring of critical infrastructure.
Professor Serge Guillas is a faculty member at the University College London (UCL) Department of Statistical Science. His research focuses on functional data analysis, uncertainty quantification, environmental statistics, and emulation of complex computer models. He leads the NERC consortium on Uncertainty Quantification of Natural Hazards and has held roles such as Work Package Leader for quantifying uncertainties in natural hazard models. His work integrates statistical methods with geophysical and climate modeling, emphasizing tsunami risk analysis, climate dynamics, and ozone exposure studies. Education: PhD in Statistics from Paris 6 University (2001), followed by roles at the University of Chicago, Georgia Institute of Technology, and UCL. Current roles include teaching STAT1006 and STAT7001 courses. Research interests span functional regression, spatial data analysis, and probabilistic hazard modeling. Recent work explores machine learning-driven climate models, ozone exposure health impacts, and real-time data assimilation software (ParticleDA.jl). He collaborates globally, including with institutions in Georgia, Italy, and Indonesia, to advance tsunami modeling and disaster risk reduction. Key awards include ESRC-DFID-NERC funding, MAPS Faculty Postgraduate Research Prize (for student Ah Yeon Park), and leadership roles in SIAM’s Uncertainty Quantification group. Active in mentoring PhD students and securing interdisciplinary grants. Labs/Teams: Involved with the UCL Institute for Risk & Disaster Reduction and leads statistical emulation efforts in climate and geophysical modeling. Collaborates on fusion reactor design (ExCALIBUR project) and global temperature uncertainty quantification (GETQUOCS initiative).
B. F. Spencer Jr. is the Nathan M. and Anne M. Newmark Endowed Chair in Civil Engineering at the University of Illinois at Urbana-Champaign, where he directs the Multi-Axial Full-Scale Sub-Structured Testing & Simulation Facility and the Smart Structures Technology Laboratory. He joined the university in 2002 after serving as Leo E. and Patti Ruth Linbeck Professor of Engineering at the University of Notre Dame (1985-2002). Education includes: Ph.D. in Theoretical and Applied Mechanics, University of Illinois at Urbana-Champaign (1985) M.S. in Theoretical and Applied Mechanics, University of Illinois at Urbana-Champaign (1983) B.S. in Mechanical Engineering, University of Missouri-Rolla (1981) His research focuses on pioneering innovations in structural health monitoring, stochastic mechanics, and smart sensor technologies. Key areas include development of wireless sensor networks for real-time infrastructure assessment, seismic hazard mitigation strategies, and AI-driven damage detection systems. His work bridges theoretical computational mechanics with practical civil engineering applications to enhance resilience against natural disasters. Recent publications emphasize digital twins, UAV-based structural inspection, machine learning for damage identification, and advanced sensor networks. Trends show strong integration of AI, 3D visualization, and edge computing for rapid post-disaster evaluation and predictive maintenance of critical infrastructure. Major scientific honors: ASCE Housner Medal (2015) J.M. Ko Medal (2014) Foreign Member of Polish Academy of Sciences (2005) Structural Health Monitoring Person of the Year (2011) JSPS Fellowships (1999, 2000) He leads significant infrastructure projects including NSF-funded facilities and industry collaborations. Laboratory initiatives involve full-scale testing of bridges, gates, and seismic mitigation systems. Educational outreach includes K-12 STEM programs like 'Shakes and Quakes' to inspire future engineers.
Joshua Hunte is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. His research focuses on applying Bayesian network methodologies to product safety, risk assessment, and medical device risk management. He specializes in causal modeling for safety protocols and consumer risk communication. Key research interests include developing causal Bayesian network frameworks for evaluating risks in smart technologies, medical devices, and consumer products. His work bridges artificial intelligence with practical safety standards, aiming to enhance decision-making processes in risk management. Recent publications highlight advancements in hybrid Bayesian networks for medical device safety and methodologies for building causal idioms in product safety analysis. His research emphasizes both theoretical causal modeling and real-world applications in industry and healthcare. No academic awards or grants are explicitly listed in the provided materials. He contributes to the School's research initiatives in electronic engineering and computer science, particularly in risk-related computational methods.
Dr. Ken Kitson is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. His research focuses on causal discovery, Bayesian networks, and machine learning methodologies. He specializes in developing algorithms that infer causal relationships from data, particularly through active learning frameworks that integrate human expert knowledge. Kitson's work emphasizes probabilistic graphical models and their applications in diverse fields such as healthcare, sports analytics, and epidemiology. He has contributed to improving the stability and accuracy of Bayesian network structure learning algorithms, addressing challenges like variable ordering instability and noisy data. His recent research trends highlight advancements in causal machine learning, including the use of large language models like GPT-4 to enhance causal discovery processes. His studies on sepsis and COVID-19 demonstrate practical applications of causal modeling in health informatics. Kitson's publications span algorithmic improvements, surveys of Bayesian network learning techniques, and empirical validations of methods in real-world scenarios. He holds a focus on bridging theoretical advancements with practical implementations in data-driven decision-making contexts.
Divakar Viswanath is a Professor of Mathematics at the University of Michigan , with research expertise in numerical analysis, nonlinear dynamics, and mathematical genetics. His work bridges computational mathematics and scientific programming, focusing on problems like coalescent theory in population genetics and fluid dynamics. Education: BTech in Computer Science and Engineering, IIT Bombay (1992) PhD in Computer Science, Cornell University (1998) Research interests include: Numerical analysis of differential equations Nonlinear dynamics and chaotic systems Coalescent theory in population genetics with applications to mutation rates and inference algorithms Connections between dynamical systems and computational mathematics Recent publications span computational fluid dynamics, numerical methods, population biology, and symbolic dynamics, reflecting interdisciplinary applications of mathematics. He authored the book Scientific Programming and Computer Architecture (MIT Press, 2017), which explores program performance, memory optimization, and parallel computing models.
