Find Academic Supervisors

• Life Cycle Engineering
• Ecodesign
• Sustainable Manufacturing
• +4 more

Wim Dewulf is a full professor at the Faculty of Industrial Engineering Sciences, KU Leuven, and serves as the dean of the faculty. He is a contact person for the Manufacturing Processes and Systems (MaPS) unit at Campus Group T Leuven and holds leadership roles such as division head and member of councils like the University Council and Academic Council. His research focuses on life cycle engineering, ecodesign, sustainable manufacturing, and computed tomography applications in industrial processes. His research interests span sustainable engineering, additive manufacturing (AM), dimensional quality control, and X-ray CT. Recent projects include using deep learning for CT reconstruction, improving AM surface quality via laser remelting, and enabling autonomous demanufacturing of battery-containing products. He actively supervises students in these areas, particularly in laser powder bed fusion and CT metrology. Wim Dewulf is a member of Leuven.AM (KU Leuven Institute for Additive Manufacturing) and SIM² (Institute for Sustainable Metals and Minerals). His work involves advising on circular economy strategies, process optimization, and advanced imaging techniques, with no explicit scientific awards listed in the provided data. He has contributed to education through courses like Applied Sustainability Assessment and Life Cycle Engineering , emphasizing sustainable design and manufacturing. His research teams focus on technology transfer, industrial collaboration, and developing data-driven models for AM and recycling.

• Uncertainty Quantification
• Bayesian Inference
• Scientific Machine Learning
• +7 more

Nathan Urban serves as Group Leader and Computational Scientist for the Applied Mathematics group at Brookhaven National Laboratory's Computational Science Initiative (CDS), a position held since 2020. Concurrently, he holds adjunct and guest appointments at Stony Brook University's Institute for Advanced Computational Science and Los Alamos National Laboratory's Statistical Sciences group. His interdisciplinary career bridges computational mathematics, statistics, and environmental science with emphasis on decision support under uncertainty. His educational foundation includes multiple degrees from Virginia Tech (1997): B.S. in Physics, Computer Science, and Mathematics, followed by advanced degrees from Penn State University (2006): Ph.D. and M.Ed. in Physics. Ph.D. in Physics, Penn State University (2006) M.Ed. in Physics, Penn State University (2006) B.S. in Physics, Virginia Tech (1997) B.S. in Computer Science, Virginia Tech (1997) B.S. in Mathematics, Virginia Tech (1997) Urban's research centers on uncertainty quantification and Bayesian inference methodologies applied to complex scientific systems. His work develops frameworks for probabilistic prediction, multi-model uncertainty analysis, reduced order modeling, and scientific machine learning. Key application domains include climate science, ice sheet dynamics, and molecular design, where he addresses challenges in decision making under uncertainty and optimal experimental design. His expertise spans computational statistics, hybrid physical-data driven modeling, and in-situ data analysis for large-scale simulations. Recent publications (2021-2024) reveal a pronounced shift toward integrating machine learning with physical modeling, particularly in climate and environmental systems. His work demonstrates innovative applications of active subspaces, Gaussian processes, and deep generative models to quantify epistemic uncertainties in ice sheet projections, molecular design, and extreme weather analysis. A consistent theme is developing scalable methods for uncertainty quantification in high-dimensional systems. Urban received significant recognition through the DOE Office of Science Early Career Research award (2013) in Biological and Environmental Research, supporting his foundational work in climate model uncertainty. Early Career Research award, DOE Office of Science (Biological and Environmental Research), 2013 As Applied Mathematics group leader at Brookhaven, Urban directs research in computational statistics and mathematical modeling for scientific applications. His team develops advanced methods for uncertainty quantification and decision support across multiple DOE mission areas, with strong collaborations in climate science and materials research.

• Statistical Machine Learning
• Signal Processing
• Data Fusion
• +17 more

Salvo Rossi is a Full Professor of Statistical Machine Learning for Signal Processing at the Department of Electronic Systems, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU). He holds leadership roles as Deputy Director for Research and Deputy Manager of the Centre for Green Shift in the Built Environment. Additionally, he serves as a part-time Senior Research Scientist at SINTEF Energy Research. His work bridges academia and industry, with significant contributions to signal processing, machine learning, and IoT systems. Research Interests: Statistical Machine Learning Data Fusion in Wireless Sensor Networks Digital Twins and Industrial IoT Anomaly Detection and Fault Diagnosis Federated and Distributed Learning Graph Signal Processing His recent publications reflect a strong trend toward intelligent, distributed systems for industrial monitoring, particularly in energy and safety-critical environments. The integration of machine learning with signal processing for decision fusion in sensor networks is a central theme. Scientific Awards and Recognition: Exemplary Senior Editor, IEEE Communications Letters (2018) Department Ambassador, NTNU (2016) IEEE Senior Member (since 2011) Editorial and Professional Service: Senior Area Editor, IEEE Transactions on Signal and Information Processing over Networks (since 2025) Topical Editor, IEEE Sensors Journal (since 2022) Area Editor, IEEE Open Journal of the Communications Society (2019–2023) Executive Editor, IEEE Communications Letters (2019–2021) General Chair, IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), Trondheim, 2022 He has advised numerous PhD and master’s students (not explicitly listed), led major research projects in IoT and green energy, and contributed to national and international research initiatives. His lab, SPIN (Signal Processing Group), focuses on intelligent signal processing for real-world applications in smart environments and Industry 4.0.

Saonli BasuView profile

Professor

• Statistical Genomics
• Genetic Mapping
• Rare Variant Association
• +5 more

Dr. Saonli Basu is a Professor in the Division of Biostatistics & Health Data Science at the University of Minnesota School of Public Health. She serves as Founding Director of the Genomic Data Commons and Co-Director of the Analytics Core at the Masonic Institute for the Developing Brain. Education: PhD in Statistics (University of Washington), MStat (Indian Statistical Institute), BS in Statistics (Presidency College) Her research focuses on developing statistical methodologies for genetic mapping of complex traits, particularly rare variant association and gene-environment interaction modeling. She specializes in computational statistics, nonparametric inference, and statistical genetics applications for diseases like Alzheimer's, type 2 diabetes, and substance abuse. Recent publications show trends in SNP heritability analysis , admixed population genetics , longitudinal pregnancy studies , and multi-variant association tests . Key collaborative projects include cerebral small vessel disease genomics and B. pertussis vaccination outcome prediction models. Scientific honors include: Chair, ASA Genomics and Genetics Section (2020) Fellow, American Statistical Association (2017) NIH BMRD study section member (2017-2021) Young Investigator, International Indian Statistical Association (2016) She has taught graduate-level courses in human genetics statistics and probability models for over 15 years. Current research receives NIH/NIDA R01 and NIDDK R21 grants, with co-investigator roles in epidemiology and psychology-led R01 projects.

Caitlin WardView profile

Assistant Professor

• Bayesian Statistics
• Infectious Disease Modeling
• Spatio-Temporal Modeling
• +4 more

Caitlin Ward, PhD is an Assistant Professor at the University of Minnesota's Division of Biostatistics & Health Data Science. Her work bridges methodological development and interdisciplinary collaboration. Education : PhD/MSc in Biostatistics (University of Iowa), BS in Statistics (Iowa State) Methodologically, she specializes in Bayesian modeling for complex systems, particularly infectious disease dynamics , spatio-temporal analysis , and multiplex imaging data . Collaboratively, she engages with: Nursing (dementia care communication) Neuroradiology (brain tumor classification) Veterinary medicine (Mycoplasma bovis transmission) Public health (vaccine hesitancy) Education (open educational resources) Her 15 most recent publications span epidemiological modeling , healthcare communication , and machine learning applications . Key contributions include: Bayesian SEIR models for behavioral change Network analysis of care coalitions Evaluation of elderspeak communication impacts Development of open-source BayesSEIR R package Statistical methods for imperfect diagnostics Recognitions include the CANSSI Distinguished Postdoctoral Fellowship and Outstanding Teaching Assistant Award . Current projects focus on making biostatistics education more accessible through open resources.

• Numerical Mathematics
• Data Assimilation
• Hamiltonian Dynamics
• +5 more

Sebastian Reich is a Professor of Numerical Analysis at the University of Potsdam and holds an honorary Visiting Professorship at Imperial College London . He leads the Chair of Numerical Mathematics and serves as Editor-in-Chief of the SIAM/ASA Journal on Uncertainty Quantification since 2021. Research Interests Numerical methods for Hamiltonian systems Data assimilation in geoscience Stochastic particle filters Bayesian inference algorithms Molecular dynamics simulation Multi-scale modeling Collaborative Projects : Principal Investigator and former Speaker (2017-2024) of SFB 1294 Data Assimilation , a DFG-funded Collaborative Research Center Active participant in SFB 1114 Scaling Cascades in Complex Systems at Freie Universität Berlin Books Authored : Probabilistic Forecasting and Bayesian Data Assimilation (Cambridge UP, 2015) Simulating Hamiltonian Mechanics (Cambridge UP, 2005) Technical Contributions : Development of symplectic integration methods Innovations in ensemble Kalman filtering Regularization approaches for geophysical models Stochastic algorithms for molecular simulations

Giona CasiraghiView profile

Researcher

• Network Science
• Complex Systems
• Data Science
• +4 more

Giona Casiraghi is a Senior Researcher at ETH Zurich specializing in network science and complex systems, with a primary focus on resilience modeling in social organizations and data-driven network analysis. His work bridges theoretical advances in statistical network models with practical applications in supply chain management, open-source software ecosystems, and online social dynamics. His research interests center on developing quantitative methods for analyzing complex systems, particularly through the generalized hypergeometric ensemble of random graphs (gHypEG). Casiraghi's work spans multiple disciplines including network science, statistical physics, data science, and resilience theory, with particular expertise in temporal network analysis, multi-edge networks, and zero-inflation models for sparse networks. His research group develops the ghypernet R package , providing open-source tools for network regression and inference. Analysis of his recent publications reveals a strong trend toward applying network science to real-world resilience problems, particularly in pharmaceutical supply chains and social organizations. His 2025 Science paper on US tariffs threatening medicine supply chains exemplifies this practical turn, while his methodological work on zero-inflated network models (PNAS Nexus 2025) demonstrates continued theoretical innovation. Casiraghi frequently collaborates with Frank Schweitzer and others in the Systems Group at ETH Zurich, producing interdisciplinary work that bridges computer science, economics, and social science. Casiraghi's research has significant implications for understanding how social organizations withstand shocks and how supply chains can be made more resilient to disruptions. His work on the gHypEG framework provides foundational tools for network scientists across multiple disciplines, while his applied research offers concrete insights for policymakers and industry practitioners dealing with complex system failures. He has contributed to numerous projects examining online migration after community bans, developer productivity in open-source projects, and reconstruction of social relations from interaction data. His research methodology typically combines large-scale data analysis with advanced statistical modeling, often developing new network analysis techniques to address specific research questions.

Emily MitchellView profile

Lecturer

• Paleobiology
• Community Ecology
• Marine Ecology
• +6 more

Emily Mitchell is a faculty member in the Department of Zoology at the University of Cambridge, where she conducts research at the intersection of paleobiology and marine community ecology. She is an active supervisor in the Cambridge NERC Doctoral Training Partnerships (CREATES and C-CLEAR DTP), mentoring postgraduate students in quantitative approaches to ecological and evolutionary questions. Her research focuses on two major areas: the paleoecology of Ediacaran early animal communities and the dynamics of modern marine benthic ecosystems. She applies mathematical and statistical methods, including Bayesian network inference and spatial analyses, to understand how ecological processes shape biodiversity across deep time. Her work spans from the origins of animal life over 500 million years ago to contemporary Antarctic, deep-sea, and tropical reef systems. The most recent publications highlight a strong trend in applying advanced computational and network-based methods to both fossil and modern ecosystems. Her research integrates field data, digital modeling, and theoretical frameworks to investigate community structure, resilience, and evolutionary drivers. Key themes include the role of morphology, reproductive strategy, and environmental gradients in shaping ecological dynamics. Emily Mitchell has received no explicitly mentioned scientific awards in the provided text. She is actively involved in advising postgraduate research students and securing research funding through NERC and other sources. Her projects often involve fieldwork, video database analysis (e.g., AWI Pangea), and the development of 3D digital models for community analysis. She encourages student-led project development in areas such as Ediacaran taphonomy, eco-evolutionary modeling, and Antarctic benthic ecology. Her research group focuses on reconstructing community dynamics throughout ecological succession, particularly in extreme environments like the Antarctic and deep sea. She collaborates with institutions such as the British Antarctic Survey and uses extensive photographic and video datasets to model ecosystem interactions and predict future changes under environmental perturbation.

CLINTView profile

Research Fellow

• Neuroscience
• Neurology
• Psychiatry
• +3 more

CLINT leads the Lab for Clinical & Integrative Neuroscience (Lab CLINT) at Trinity College Dublin , focusing on brain disorders through multimodal imaging and neuromodulation. The lab investigates predictive coding theory in phantom perceptions like tinnitus, Parkinson's, and chronic pain, while developing treatments through peripheral nerve stimulation. Research Pillars : Predictive coding, thalamocortical dysrhythmia, locus coeruleus noradrenergic pathways Key Disorders : Tinnitus, Alzheimer's, chronic pain, depression Scientific Contributions include: Developing TIDE consortium for tinnitus biomarkers Implementing BRAI3N clinic for neuromodulation therapies Creating Tinnitus Éire community for peer support Recent publications analyze: Deep learning applications for tinnitus prognosis HD-tACS effects on attention networks Bayesian models of chronic pain comorbidities CLINT's work bridges laboratory research with clinical translation, emphasizing patient-centered approaches and interdisciplinary collaborations across Europe and North America.

Julius GoesView profile

Researcher

• Bayesian Statistics
• Actuarial Science
• Epidemiological Modeling
• +3 more

Julius Goes, M.Sc., is a Researcher at the Professorship for Mathematics in Economics within the Faculty of Social and Economic Sciences at Otto-Friedrich-University Bamberg. Since October 2020, he has contributed to academic research in statistical modeling and demographic forecasting. Education 2012: Abitur, Albert Einstein Gymnasium Böblingen 2017: Bachelor of Science in International Business Administration, University of Bamberg 2020: Master of Science in Survey Statistics, University of Bamberg His research focuses on Bayesian mortality modeling, particularly in small-area forecasting and pandemic-related demographic impacts. He has presented his work at conferences including Statistische Woche 2022, Longevity 18, and SurvConf 2025. Recent publications include methodological advancements in spatiotemporal Bayesian models for mortality rate prediction, appearing in Demography and the Journal of the Royal Statistical Society Series C: Applied Statistics . His work bridges statistical innovation with real-world demographic challenges. Contact information: Room F21/00.82, Feldkirchenstraße 21, 96052 Bamberg. Phone: 0951/863 2632. Consultation hours: Wednesdays 10:00-11:30 AM by appointment via email.

• Probabilistic Machine Learning
• Probabilistic Circuits
• Probabilistic Programming
• +2 more

Martin Trapp is an Academy Postdoctoral Researcher in the Department of Computer Science at Aalto University, specializing in probabilistic machine learning. He is affiliated with Professor Arno Solin's research group, focusing on advancing tractable probabilistic models for real-world applications. His research centers on Probabilistic Circuits , Probabilistic Programming , and Bayesian Nonparametrics , with emphasis on hardware-efficient implementations for edge devices and multimodal systems. Key interests include uncertainty quantification in deep learning, neurosymbolic AI integration, and medical imaging applications. His work bridges theoretical foundations with practical deployment constraints, particularly in resource-limited environments. Analysis of his 15 most recent publications (2022-2025) reveals three dominant trends: (1) hardware-aware probabilistic inference for TinyML applications, (2) scalable Bayesian methods using bitstring representations and probabilistic programming, and (3) multimodal robustness in vision-language systems and medical imaging. His contributions span from theoretical circuit representations to real-world implementations in mammography analysis and vision-language models. Trapp secured a HIIT short-term project grant (November 2022) for "Positive Semi-Definite Circuits" under the Department of Computer Science. No formal advising relationships are documented in available sources. He actively collaborates with researchers including Arno Solin, Rui Li, and Marcus Klasson across institutions like Aalto University and the Helsinki Institute for Information Technology. As a core member of Aalto's Probabilistic Machine Learning group, he contributes to advancing probabilistic AI methodologies with applications in healthcare, edge computing, and multimodal reasoning. His current work emphasizes deployable probabilistic systems that maintain rigorous uncertainty quantification while meeting hardware constraints.

Sigrunn Holbek SørbyeView profile

Professor

• Bayesian Statistics
• Time Series Analysis
• Spatial Data Modeling
• +3 more

Sigrunn Holbek Sørbye is a Professor at the Department of Mathematics and Statistics, UiT The Arctic University of Norway. Her research focuses on Bayesian statistics, time series analysis, and spatial data modeling with applications in climatology, ecological statistics, and computational statistics. Institution: UiT The Arctic University of Norway Department: Department of Mathematics and Statistics Research Interests: Dr. Sørbye specializes in Bayesian computation using integrated nested Laplace approximation (INLA), statistical modeling of long-range dependent processes, and applications to climate systems. Her work includes modeling cosmic dust detection rates, analyzing metabolic risk factors, and studying population dynamics through capture-recapture data. Selected Publications Trends: Recent research spans dietary pattern analysis, solar dust modeling, climate sensitivity studies, and ecological monitoring. Key methodologies involve Bayesian hierarchical modeling (2025, 2023), INLA applications (2022, 2020), and long-memory stochastic processes (2020-2019). Collaborative Networks: Dr. Sørbye collaborates on interdisciplinary projects including "Modellering av komplekse systemer" (Complex Systems Modeling) and "Intermittent fluctuations in physical systems." She also contributes to "Transforming ocean surveying by the power of DL and statistical methods." Contact: Tromsø, Norway. sigrunn.sorbye@uit.no | +47 77 64 55 04

• Machine Learning
• Probabilistic Machine Learning
• Artificial Intelligence
• +5 more

Tim G. J. Rudner is a Faculty Fellow at New York University , an AI Fellow at the Georgetown Center for Security & Emerging Technology (CSET) , and an incoming Junior Research Fellow at Trinity College, University of Cambridge . He holds a PhD in computer science from the University of Oxford (advised by Yee Whye Teh and Yarin Gal), a master’s in statistics from Oxford, and a bachelor’s in mathematics and economics from Yale University . Rudner’s research focuses on robust and transparent machine learning models for safety-critical and high-stakes applications. His work spans probabilistic machine learning , uncertainty quantification , generative LLMs , Bayesian neural networks , AI safety , and biomedical discovery . He has developed methods like group-aware priors for subpopulation robustness, function-space regularization techniques, and uncertainty-aware fine-tuning approaches. His recent publications include work on 2025 papers about AI governance frameworks, 2024 studies on robust generative models and uncertainty quantification, and influential studies on vision-language control and diabetic retinopathy detection . His research has been recognized with awards such as the AISTATS Notable Paper Award and the GenAI4DM Outstanding Paper Award . Scientific Awards: 2024 Rising Star in Generative AI Qualcomm Innovation Fellow Rhodes Scholar AISTATS Notable Paper Award (2024) GenAI4DM Outstanding Paper Award (2024) Grants: $30,000 Apple Seed Grant (2024) $700,000 Foundational Research Grant to improve LLM trustworthiness (2024) Rudner actively mentors first-generation low-income students and collaborates with institutions like University of Oxford , Yale University , and New York University . His work bridges theoretical advancements with real-world deployment challenges, emphasizing reliable uncertainty estimation and robust generalization in high-stakes domains.

• Statistics
• Computer Science
• Economics
• +6 more

Pedro Galeano San Miguel is an Associate Professor at the Department of Statistics, Universidad Carlos III de Madrid. He is affiliated with the Nonparametric Inference for Complex Data and its Applications (NICDA) research group and contributes to the Flores de Lemus Institute and UC3M-Santander Big Data Institute. His work bridges statistics, computer science, and economics with a focus on financial and high-dimensional data. Research Interests: Functional data analysis and outlier detection Bayesian nonparametric methods and stochastic volatility models Copula models for systemic risk and portfolio selection High-dimensional statistical inference and dynamic correlation Big data applications in economics and finance Publication Trends: His recent work (2024–2016) emphasizes copula models for financial risk, functional regression techniques with missing data, and Bayesian inference for high-dimensional time series. He explores systemic banking risks, volatility prediction, and correlation structure changes across economic and financial domains. Grants & Projects: He leads or contributes to projects on computational statistics for complex dependencies, big data customer network analysis, and multivariate asymmetric GARCH modeling, funded by institutions like the State Research Agency (AEI) and Banco Santander.

• Machine Learning
• Probabilistic Modeling
• Neural Networks
• +4 more

Carl Allen is a Laplace Junior Chair in Machine Learning at École Normale Supérieure, Paris, working in the research group of Stéphane Mallat, Giulio Biroli and Garbiele Peyré. Previously, he was a postdoctoral fellow at ETH Zurich and completed his PhD in Machine Learning in 2021 at the University of Edinburgh under the supervision of Professors Tim Hospedales and Iain Murray. His educational background includes a BSc in Mathematics & Chemistry from the University of Southampton, an MSc in Mathematics and the Foundations of Computer Science (MFoCS) from the University of Oxford, and MScs in Artificial Intelligence and Data Science from the University of Edinburgh. Before transitioning to AI/ML research, he spent several years in Project Finance. Allen's research focuses on mathematically understanding mechanisms behind successful machine learning methods, particularly neural networks. He investigates how machine learning models exploit aspects of data distribution from a probabilistic perspective. His current topics include explaining how VAEs disentangle independent factors of data, identifying mathematical models behind self-supervised learning, and deriving probabilistic interpretations of softmax classification. His PhD work investigated neural representations of discrete objects and their relationships, with a main result explaining how word embeddings can seemingly be added and subtracted (e.g., queen ≈ king - man + woman), which received Best Paper (honorable mention) at ICML 2019. His research spans theoretical foundations of machine learning, with particular emphasis on representation learning, disentanglement, and probabilistic modeling of neural networks. His work connects mathematical principles with practical machine learning applications, aiming to develop more interpretable and reliable algorithms. Allen has received notable recognition including a Best Paper honorable mention at ICML 2019 and a research grant from the Hasler Foundation. He has delivered invited talks at prestigious institutions including Harvard Center of Mathematical Sciences & Applications and Astra-Zeneca. His collaborative work spans multiple institutions, including a notable internship at Samsung AI Centre, Cambridge, where he worked at the intersection of representation learning and logical reasoning. His research has significant implications for developing more interpretable, reliable, and theoretically grounded machine learning systems.