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Chris J BuddView profile

Professor

• Nonlinear Mathematics
• Industrial Mathematics
• Numerical Analysis
• +7 more

Professor Chris J Budd OBE is a distinguished Professor of Applied Mathematics at the University of Bath's Department of Mathematical Sciences, where he serves as Director of Knowledge Exchange for the Bath Institute for Mathematical Innovation (IMI). He is also Professor of Mathematics at the Royal Institution of Great Britain and a former Gresham Professor of Geometry. His leadership extends to directing the Centre for Nonlinear Mechanics and serving as Super Champion of the KE Hub. His educational background includes a gap year with Marconi that profoundly shaped his career, followed by undergraduate studies at Cambridge and a DPhil at Oxford. This industry experience during his formative years established his lifelong commitment to industrial mathematics and knowledge exchange. Budd's research focuses on nonlinear mathematical problems with industrial applications, particularly adaptive moving mesh methods for meteorology and climate modeling, data assimilation, non-smooth dynamical systems, and the mathematics of machine learning. He approaches linear problems as 'for cissies,' preferring the challenges of nonlinear systems that better represent real-world phenomena. His work bridges theoretical mathematics with practical applications across meteorology, environmental science, and engineering. His recent publications reveal a strong trend toward integrating machine learning with traditional numerical methods, particularly in climate modeling and solving partial differential equations. This includes Fourier Neural Operators, adaptive mesh methods enhanced by graph neural networks, and mathematical frameworks for understanding climate tipping points through non-smooth dynamics. OBE for services to mathematics National Teaching Fellowship (NTF) Knowledge Transfer Award for work with the Met Office Fellow of the Institute of Mathematics and its Applications (FIMA) Chartered Mathematician (C Math) British Science Association award for best science festival (2009) As principal investigator of the £3.5M EPSRC Programme Grant 'Maths4DL' on the Mathematics of Deep Learning, Budd leads a major collaborative effort between Bath, Cambridge, and UCL. He actively supervises numerous PhD students across diverse projects including climate modeling, machine learning applications, and industrial mathematics problems. His commitment to knowledge exchange is exemplified through V-KEMS (Virtual Forum for Knowledge Exchange in the Mathematical Sciences), which he co-founded to address challenges like the COVID-19 pandemic through mathematical approaches. Budd directs the Centre for Nonlinear Mechanics at Bath, fostering interdisciplinary research through mathematical modeling of complex systems. He also leads the Bath Institute for Mathematical Innovation's knowledge exchange activities, connecting academic mathematics with industrial and societal challenges. His work with V-KEMS has proven particularly effective during the pandemic, mobilizing teams of mathematicians to address urgent real-world problems.

Xiongcai CaiView profile

Adjunct Associate Professor

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +3 more

Dr. Xiongcai Cai is an Adjunct Associate Professor at the School of Computer Science and Engineering, University of New South Wales (UNSW). With expertise in Artificial Intelligence , Machine Learning , and Computer Vision , he contributes to advancing Recommender Systems , Natural Language Processing , and Health Informatics . His work bridges theoretical and applied research in technology for human-centric applications. Current roles: Adjunct Associate Professor, UNSW School of Computer Science and Engineering Key research areas: Machine Learning, Recommender Systems, Computer Vision, Generative AI Dr. Cai's research portfolio demonstrates a consistent focus on recommender systems and machine learning over the past decade. His technical contributions span graph convolutional networks , temporal bilinear models , and embedding techniques for collaborative filtering. Recent work in 2025 addresses knowledge distillation for GCNs-based recommenders, while earlier studies tackled cold-start transitions and matrix factorisation boosting. His publication history (2 book chapters, 7 journal articles, and 36 conference papers) reveals a strong emphasis on real-time applications in domains like gait recognition (2020), health data analytics (2016), and social network recommendation (2010-2015). The research applies mathematical rigor to practical challenges in online dating platforms , medical decision support , and object tracking systems . Contact details: Email: x.cai@unsw.edu.au Phone: +61 2 9385 8858

Emanuele BorgonovoView profile

Professor

• Sensitivity Analysis
• Decision Analysis
• Risk Analysis
• +3 more

Emanuele Borgonovo is a Full Professor and Director of the Department of Decision Sciences at Bocconi University. He serves as a Research Affiliate at MIT's Department of Nuclear Science and Engineering, co-editor-in-chief of the European Journal of Operational Research , and advisor for the Springer International Series in Management Science and Operations Research. His leadership roles include President of the Decision Analysis Society of INFORMS (2020-2022) and Co-Chair of the ESRA Technical Committee on Uncertainty Analysis. Affiliation: Bocconi University Collaboration: MIT, NASA, DARPA, Idaho National Laboratory, EDF His research focuses on Sensitivity Analysis, Decision Analysis, Risk Analysis, and Uncertainty Quantification. He pioneered techniques like the differential importance measure, now adopted in NASA's PRA Guide. Recent work explores explainable AI via Shapley values and optimal transport methods for sensitivity analysis. 2025: Explainable risk assessment modeling 2025: Sensitivity analysis of climate models 2024: AI impact on job markets Scientific honors include: Excellence in Research Award (2020) IBM Faculty Award (2015) Best Risk Analysis Paper Award (2014) Multiple Best Reviewer Awards (2006-2022) At SDA Bocconi, he leads the Artificial Intelligence and Data Science for Executives program and taught in MBA/DBA courses. His work has been supported by grants from US Department of Energy, NASA, and Electricité de France.

• Software Engineering
• Model Checking
• Formal Verification
• +4 more

Professor Tomoji Kishi is a distinguished faculty member at Waseda University's School of Creative Science and Engineering, where he has been serving since 2009. Previously, he held academic positions at Japan Advanced Institute of Science and Technology (2003-2009) following a 21-year career at NEC Corporation (1982-2003). He earned his Ph.D. in Information Science from Japan Advanced Institute of Science and Technology in 2002, building upon his earlier engineering graduate studies at Kyoto University. Professor Kishi's research focuses on software engineering, particularly in software product line development, model checking, formal verification, and aspect-oriented modeling. His work bridges theoretical formal methods with practical applications in embedded systems, automotive software, and IoT technologies. He has made significant contributions to scalability challenges in model checking for configurable systems and has pioneered approaches to variability management and approximate modeling techniques. His publication record demonstrates remarkable consistency and evolution, with 42 papers and 153 citations according to Scopus data (h-index: 7), spanning from foundational work in software architecture in the 1990s to cutting-edge research on AI-enhanced verification methods in 2025. His recent work shows increasing application of machine learning techniques to traditional formal methods problems, particularly in the context of highly configurable systems and IoT applications. ITS Standardization Activity Merit Prize (2022) from Society of Automotive Engineers of Japan IPSJ/ITSCJ Standardization Contribution Award (2017) IPSJ/ITSCJ Project Editor Award (2016 and 2013) Information Processing Society of Japan Society Activity Contribution Award (2010) IPA/SEC Journal Best Paper Award (2007) Information Processing Society of Japan Yamashita Memorial Research Award (1998) Professor Kishi has led multiple JSPS-funded research projects, including recent work on 'variability management methods prioritizing usability through variability mining' (2020-2023) and 'utility-first modeling method' (2017-2020). His industry collaborations, particularly with automotive systems developers, demonstrate the practical impact of his research. He maintains active membership in major professional societies including IEEE Computer Society, ACM, and the Information Processing Society of Japan.

• Machine learning
• Artificial intelligence
• Information systems
• +1 more

Brooks Paige serves as an Associate Professor in Machine Learning at University College London's Department of Computer Science, where he leads research at the intersection of artificial intelligence, computational biology, and environmental science. His work bridges theoretical machine learning with high-impact applications in drug discovery, genomics, and climate modeling. His research portfolio spans: Machine Learning (core methodology development) Artificial Intelligence (generative models and deep learning) Information Systems (data-intensive applications) Cognitive and Computational Psychology (human-AI interaction aspects) Analysis of his 56 publications (2021-2025) reveals a dominant focus on generative modeling for molecular design, particularly protein-ligand binding prediction and antibody-epitope analysis. His methodological innovations include Gibbs sampling variants, Gaussian processes on non-Euclidean domains, and active learning frameworks, applied across biomedical and environmental domains including Arctic sea ice forecasting and urban analytics. No scientific awards are documented in available sources. Similarly, student advisement records, research grant details, laboratory facilities, and collaborative team structures remain unspecified in the current dataset.

Michael LandisView profile

Assistant Professor

• Phylogenetic Biogeography
• Evolutionary Modeling
• Comparative Methods
• +2 more

Michael Landis is Assistant Professor of Biology at Washington University in St. Louis, developing statistical models and computational tools to reconstruct evolutionary patterns. Research integrates phylogenetics, biogeography, and trait evolution to study historical biodiversity dynamics across deep timescales. Key research areas: Developing Bayesian methods for phylogenetic biogeography Modeling biome shifts and diversification patterns Creating open-source software for evolutionary analysis (RevBayes, phyddle) Reconstructing ancestral networks for pierid butterflies Recent work includes novel approaches for state-dependent diversification modeling, deep learning applications in phylogenetics, and global-scale analyses of butterfly evolution. The lab emphasizes collaborative software development and evolutionary hypothesis testing.

• Computational Statistics
• Probabilistic Machine Learning
• Markov Chain Monte Carlo
• +7 more

Professor Christopher Nemeth of Lancaster University's School Of Mathematical Sciences is a leading researcher in computational statistics and probabilistic machine learning. His work focuses on Markov chain Monte Carlo (MCMC), sequential Monte Carlo (SMC), Gaussian processes, and approximate Bayesian computation, with applications in environmental science, target tracking, and econometrics. He currently holds a UKRI Turing AI Acceleration Fellowship and leads the ProbAI research hub. Research Interests: Development of probabilistic AI algorithms for large-scale learning, state-space modeling, and intersections between sampling and optimization algorithms. Grants: £9M UKRI-EPSRC ProbAI hub (2024-2029), £1.1M Turing AI Acceleration Fellowship (2021-2026), and multiple NERC grants. Academic Roles: Turing University Academic Liaison (2023-present), Associate Editor for ACM Transactions on Probabilistic Machine Learning (2023-present), and leadership roles in the Royal Statistical Society. Supervision: Completed supervision of 7 PhD students with projects on scalable Gaussian processes, Monte Carlo methods, and network modeling.

Mateja NovakView profile

Assistant Professor

• Model Predictive Control
• Power Electronics
• Multilevel Converters
• +6 more

Mateja Novak is an Assistant Professor at AAU Energy, Aalborg University, Denmark, within the Department of Applied Power Electronic Systems under the Faculty of Engineering and Science. Her research focuses on model predictive control, multilevel converters, machine learning, and reliability of power electronic systems, contributing to sustainable energy systems and renewable energy integration. She holds a Ph.D. from Aalborg University (2020) and an M.Sc. from Zagreb University (2014). Previously, she was a Postdoc at AAU Energy (2020-2023) and a visiting researcher at Kiel University (2018) and Danfoss (2023). Notable achievements include the EPE Outstanding Young EPE Member Award (2019) and 2nd place in the 2021 IEEE-IES Student and YP Competition. Her work spans projects like ALL2GaN (2023-2026) and AI-Power (2022-2027), addressing GaN IC solutions and AI-driven power electronics advancements. She is actively involved with IEEE societies including the Power Electronics Society and IEEE Women in Engineering. Her research outputs emphasize control strategies for power electronics, reliability analysis, and optimization techniques. Key areas of exploration include thermal stress balancing in converters, statistical model checking, and multiobjective control algorithms. Collaborations with industry partners like Danfoss and academic institutions like Kiel University underscore her interdisciplinary approach to advancing power electronics technology.

Lisa LeeView profile

Researcher

• Artificial Intelligence
• Reinforcement Learning
• Continual Learning
• +5 more

Lisa Lee is a Research Scientist at Google DeepMind, focusing on creating AI agents that emulate biological learning and adaptability. She previously taught at Princeton University and received TA awards for Deep Reinforcement Learning and Probabilistic Graphical Models. Education: PhD in Machine Learning from Carnegie Mellon University (advised by Ruslan Salakhutdinov and Eric Xing); A.B. in Mathematics from Princeton University (advised by Sanjeev Arora). Her research centers on AI embodiment, intrinsic motivation, and hierarchical planning. She explores how evolutionary-inspired inductive biases and memory mechanisms can enable agents to generalize across physical and conceptual domains, as demonstrated in her work on robotic agility benchmarks and multimodal transformers. Notable scientific contributions include the Barkour quadruped robot benchmark, Gemini multimodal models, and theoretical work on causal language models. She co-organized key AI workshops at NeurIPS and ICML, and her awards include Princeton's TA of the Year for technical courses. Leadership: ICML Workflow Chair (2019), NeurIPS workshop co-organizer (2019, 2021), peer reviewer for top AI conferences.

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +4 more

Dr. Lea Frermann is an active researcher in computational linguistics and natural language processing, with a focus on media framing analysis, fairness in AI, and Bayesian modeling of semantic categories. Her work bridges machine learning with social science applications, particularly in political discourse and climate communication contexts. PhD in Bayesian models of category acquisition (University of Edinburgh, 2017) Key collaborations with University of Melbourne, ACL, EMNLP, and CoNLL communities Research emphasizes: Media framing analysis across cultures Explainable AI for bias detection Bayesian approaches to language understanding Word association modeling for value systems Computational methods for political discourse analysis Applications in mental health support forums Recent publications focus on large language models' limitations in hierarchical reasoning, multimodal categorization, and systematic fairness evaluation frameworks. Collaborations show strong connections with researchers like Timothy Baldwin, Trevor Cohn, and Omri Abend.

• Medical Data Science
• Anomaly Detection
• Generative AI
• +3 more

Dr. Thomas Sutter is a Researcher affiliated with the Department of Computer Science at ETH Zurich , specifically part of the Professorship for Medical Data Science. His work focuses on advanced machine learning techniques applied to medical data, including anomaly detection, generative AI, and multimodal learning. He contributes to healthcare innovation through projects like improving radiology diagnostics via Vision-Language Models (RadVLM) and developing denoising techniques for physiological signals in cardiology. Research Interests: Medical Data Science, Anomaly Detection in Healthcare, Generative AI for Biomedical Signals, Multimodal Representation Learning, and Cardiac Function Prediction using Echocardiograms. His methods often combine deep learning with domain-specific medical challenges. Key Publications Trends: Recent work emphasizes medical imaging analysis (e.g., MIMIC-CXR studies), generative models for signal denoising, and contrastive learning for anomaly detection. His research bridges computer science theory with clinical applications, particularly in cardiology and radiology. Awards & Grants: No specific awards or grants mentioned in the provided texts. Advising & Teams: While no advisees are listed, he collaborates within the Medical Data Science research group at ETH Zurich, contributing to interdisciplinary projects in healthcare AI.

• Machine Learning
• Climate Modeling
• Environmental Science
• +3 more

Dr. Shuolin (Shawn) Li is a Postdoctoral Research Scientist at Columbia University's Data Science Institute, collaborating with Professors Pierre Gentine, Upmanu Lall, and Tian Zheng. He holds a Ph.D. in Fluid Dynamics and Hydrology and an M.S. in Computer Science from Duke University. His research focuses on developing machine learning algorithms for Earth observations, particularly in climate model parameterization using Bayesian inference, neural networks, and physical parameterizations. He collaborates with the Learning the Earth with Artificial Intelligence and Physics (LEAP) initiative and scientists at the National Center for Atmospheric Research (NCAR). Research interests include data assimilation, environmental fluid mechanics, and interdisciplinary applications of machine learning in hydrology, turbulence, and climate science. His work bridges computational methods with physical processes, addressing challenges in sediment transport, vegetation dynamics, and turbulent flow modeling. Recent publications highlight advancements in generative data assimilation, sediment flux parameterization, and turbulence modeling. His contributions span environmental engineering, climate science, and mathematical modeling, emphasizing scalable solutions for complex Earth systems. Collaboration with LEAP and NCAR underscores his commitment to advancing AI-driven Earth science. No formal awards are listed, but his work reflects significant contributions to interdisciplinary environmental research.

Alvaro TorralbaView profile

Associate Professor

• Symbolic Search
• Artificial Intelligence
• Machine Learning
• +3 more

Alvaro Torralba is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark, affiliated with the Technical Faculty of IT and Design. His research focuses on symbolic search, heuristic functions, and planning algorithms within artificial intelligence and machine learning. Notable projects include the ConAn initiative exploring contrastive analysis for state-space exploration. He has contributed extensively to classical planning, probabilistic planning, and automated planning competitions, earning awards such as the First Prize in the Agile Track of the 10th International Planning Competition (IPC’23). His work often bridges theoretical advancements with practical applications, including game-based network update synthesis and believable non-player character development. Research outputs include over 60 publications since 2011, with a focus on optimizing search algorithms and enhancing planning efficiency through techniques like operator-potential heuristics and bidirectional search strategies. His scientific contributions span algorithmic innovation, verification methodologies, and large-scale abstraction evaluation. Collaborations and datasets include foundational work on PDDL generators and pattern databases, with open-access resources available via Zenodo. As a program committee member and award-winning researcher, Torralba actively contributes to advancing the frontiers of AI planning and decision-making systems.

Jilles VreekenView profile

Professor

• Causal Inference
• Machine Learning
• Data Mining
• +5 more

Prof. Dr. Jilles Vreeken is tenured faculty at the CISPA Helmholtz Center for Information Security , where he leads the Exploratory Data Analysis group. He also serves as an Honorary Professor at Saarland University . Research focuses on causal inference, machine learning, and data mining Develops unsupervised methods for robust, interpretable models PI on grants like HAICU's Neuro-Explicit Models and Crushing Antimicrobial Resistance His recent work spans causal discovery in non-stationary time series ( SPACETIME ), federated binary matrix factorization, interpretable neural search patterns, and data modification rule mining from event logs. He applies information-theoretic approaches to address hidden confounding, selection bias, and multi-environment causal modeling. Key trends in his publications include: Integrating causal inference with machine learning via algorithmic Markov conditions Advancing federated learning for privacy-preserving causal discovery Creating interpretable pattern mining frameworks for graphs, sequences, and high-dimensional data Developing MDL-based methods for reliable dependency and rule discovery Scientific Recognition: 2018 - IEEE ICDM Tao Li Award 2018 - IEEE ICDM Best Paper 2015 - UdS-CS Busy Beaver Teaching Award 2011 - ACM SIGKDD Best Student Paper 2010 - ACM SIGKDD Doctoral Dissertation Runner-Up 2009 - ECML PKDD Best Student Paper As an educator, he has supervised 15+ PhD/MSc students and taught courses like Topics in Algorithmic Data Analysis and Information-Theoretic Machine Learning . His research group pioneers methods for trustworthy information processing and causal anomaly detection , with applications in materials science, epidemiology, and cybersecurity.

Anastasios VassilopoulosView profile

Adjunct Professor

• Composite Materials
• Wind Energy Structures
• Fatigue Analysis
• +5 more

Anastasios Vassilopoulos serves as Head of the Composite Mechanics Group (GR-MeC) and Adjunct Professor at École Polytechnique Fédérale de Lausanne (EPFL), within the School of Architecture, Civil and Environmental Engineering. He directs the Doctoral Program in Civil and Environmental Engineering while maintaining active roles in the Structural Engineering Group and School Council. His research focuses on composite materials for renewable energy infrastructure , particularly wind turbine rotor blades. Key areas include fatigue analysis of adhesively bonded joints, experimental methods for FRP composites under complex loading, and design methodologies for composite structures. His work bridges fundamental mechanics with industrial applications through extensive collaboration with wind energy stakeholders. Analysis of his 15 most recent publications reveals dominant themes in thick adhesive joint mechanics (73% of articles), fatigue/fracture characterization (67%), and machine learning applications (40%). The research consistently targets wind turbine blade challenges, with 87% of articles addressing specific aspects of renewable energy infrastructure. Methodological trends show increasing integration of computational-experimental approaches and AI-driven predictive modeling. Dr. Vassilopoulos has secured 18 major research projects since 2000, primarily funded by Swiss National Science Foundation and international collaborations. Current projects include NSF-funded work on wind turbine blade adhesive joints (2020-2024) and fire-resistant composite bridge decks. His teaching portfolio includes advanced courses on composites design, structural mechanics, and floating offshore renewables. As Doctoral Program Director, he oversees PhD training while personally supervising 17 doctoral students to completion.