Find Academic Supervisors

Ryan ZurakowskiView profile

Associate Professor

• Mathematical Modeling
• HIV Dynamics
• Biomedical Engineering
• +5 more

Ryan Zurakowski is an Associate Professor and Interim Department Chair in the Department of Electrical and Computer Engineering at the University of Delaware. He holds a Ph.D., M.S., and B.S. in Electrical and Computer Engineering from the University of California, Santa Barbara. His research focuses on developing mathematical models to understand viral disease dynamics, particularly HIV, with applications in biomedical engineering and control systems. Dr. Zurakowski’s work combines advanced modeling techniques with Bayesian inference to study cryptic HIV replication, drug efficacy, and reservoir persistence. His lab has demonstrated HIV’s ability to replicate in hidden body regions despite antiretroviral treatment, validated by collaborators at institutions like IRSI-Caixa and UCSF. He has also pioneered system identification methods for censored data, including the Tobit Kalman Filter, with applications in tracking and surveillance for the Army Research Laboratories. Research interests include information-optimal experiment design, censored-data estimation, and applying engineering principles to biomedical challenges. He collaborates with global institutions and prioritizes translating mathematical insights into actionable clinical strategies. For joining his lab, interested students and postdocs should contact him directly via ryanz@udel.edu with a CV and research interests.

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

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

Nadia BloemendaalView profile

Assistant Professor

• Tropical Cyclones
• Climate Change
• Risk Assessment
• +4 more

Nadia Bloemendaal is an Assistant Professor at the Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam (VU), and a climate researcher at the Royal Netherlands Meteorological Institute (KNMI). Her work focuses on tropical cyclone risk under climate change, synthetic modeling, and hazard assessments. She holds a PhD from VU (2021, Cum Laude) and conducted postdoctoral research at IVM and Columbia University's Lamont-Doherty Earth Observatory. Education : 2016: Master in Meteorology, Physical Oceanography, and Climate (Utrecht University) 2014: Minor in Geo Data Processing (ITC) 2014: Bachelor in Applied Mathematics (University of Twente) Research Interests : Tropical cyclone dynamics, climate change impacts, synthetic storm modeling (e.g., STORM), risk assessment frameworks, and coastal hazard analysis. Her STORM model generates synthetic tropical cyclone tracks to assess global-scale risk probabilities. Awards : Arne Richter Award for Early Career Scientists (2024) 2nd Place Lloyd's Science of Risk Prize (2021) 3rd Place Allianz Climate Risk Research Award (2020) Dutch Data Prize 2020 (2020) Grants & Advising : Principal Investigator: Quantification of Climate Change Scenario Risk (2022–2023) Project Researcher: Human Behavior in Flood Risk Models (2014–2022) Teaches courses on climate hydrology and mathematics for environmental studies. Labs & Datasets : Creator of the STORM dataset (synthetic cyclone tracks) and COAST-RP (storm tide return periods). Collaborates globally on climate change impact studies.

Stefano MarelliView profile

Lecturer

• Uncertainty Quantification
• Scientific Software Design
• Surrogate Modeling
• +5 more

Dr. Stefano Marelli is a Lecturer at ETH Zürich's Department of Civil, Environmental and Geomatic Engineering, affiliated with the Risk, Safety, and Uncertainty Quantification Chair. He holds a MSc in Physics (University of Milano Bicocca, 2006) and a PhD in Applied and Environmental Geophysics (ETH Zurich, 2011). His research focuses on uncertainty quantification (UQ), surrogate modeling, reliability analysis, and Bayesian inversion, with applications in engineering, astrophysics, and economics. He leads the development of UQLab, a general-purpose UQ software framework, and collaborates on interdisciplinary projects like HIPERWIND. Key research areas include high-dimensional UQ, stochastic simulators, and surrogate modeling for dynamical systems. Recent work emphasizes multifidelity methods, Bayesian tomography, and noise-aware reliability analysis. He teaches structural reliability and risk analysis at ETH and contributes to international UQ training programs. Education: MSc Physics (Milano Bicocca, 2006); PhD in Geophysics (ETH Zurich, 2011) Roles: Senior Scientist (2018–present); Postdoc (2012–2018) Software: UQLab, UQ [py] Lab Collaborations: Cross-disciplinary projects in astrophysics, mechanical engineering, and remote sensing His articles (2020–2025) highlight advancements in surrogate modeling, Bayesian inversion, and UQ applications. Notable contributions include frameworks for noisy data analysis, time-variant reliability, and industrial fragility assessment.

Kislaya RaviView profile

Researcher

• Multifidelity Uncertainty Quantification
• Gaussian Process
• Sparse Grids Methods
• +2 more

Kislaya Ravi is a Doctoral Candidate at the Technical University of Munich (TUM), affiliated with the Chair of Scientific Computing within the TUM School of Computation, Information and Technology. She holds an M.Sc. in Computational Science and Engineering from TUM and degrees in Mechanical Engineering from the Indian Institute of Technology (BHU). Her research focuses on multifidelity uncertainty quantification, Gaussian processes, sparse grids methods, machine learning, and stochastic optimization. Education: M.Sc., Computational Science and Engineering, Technical University of Munich (202X) M.Tech., Machine Design, Indian Institute of Technology (BHU) (20XX) B.Tech., Mechanical Engineering, Indian Institute of Technology (BHU) (20XX) Research Interests: Kislaya’s work bridges computational methods and stochastic systems, emphasizing efficient uncertainty quantification techniques in physics and engineering. She explores multifidelity approaches to enhance computational efficiency in complex simulations and data-driven modeling. Teaching and Advising: Core instructor for Scientific Computing II and Algorithms for Uncertainty Quantification . Supervised multiple Master’s and Bachelor’s theses on topics like black-box optimization, surrogate modeling, and plasma instability analysis. Publications highlight contributions to multi-fidelity Gaussian processes, No-U-Turn sampling, and kinetic modeling. Her work appears in journals like Machine Learning: Science and Technology and conferences such as SIAM CSE and MCQMC.

Burcu AkinciView profile

Professor

• Construction Engineering and Management
• Building Information Modeling (BIM)
• Infrastructure Systems
• +5 more

Dr. Burcu Akinci is the Paul Christiano Professor and Department Head of Civil and Environmental Engineering at Carnegie Mellon University (CMU), with a courtesy appointment in Electrical and Computer Engineering. She holds a BS from Middle East Technical University, an MBA from Bilkent University, and MS/PhD from Stanford University. Her research focuses on integrating BIM with 3D imaging/sensor data to create digital twins for construction and infrastructure management, emphasizing predictive operations and fault diagnosis in HVAC systems. She has secured over $16M in grants, authored 70+ journal papers, and pioneered methodologies for as-built BIM generation and infrastructure monitoring. Education : PhD 2000 - Stanford University (Civil & Environmental Engineering) MS 1995 - Stanford University MBA 1993 - Bilkent University BS 1991 - Middle East Technical University Research Interests : Dr. Akinci develops technologies to streamline construction and infrastructure operations through digital twins. Key areas include: BIM integration with IoT/sensor networks 3D imaging for asset management Fault detection in HVAC systems Autonomous environmental control for space habitats Predictive maintenance using AI Grants & Patents : $6M+ PI grants, $10M+ co-PI grants (federal/state/industry) 2 patents issued, 1 provisional patent Labs & Teams : Active in the Scott Institute for Energy Innovation, leading projects on smart buildings and infrastructure resilience.

Mikael KuuselaView profile

Assistant Professor

• Statistical methods in physical sciences
• Inverse problems
• Spatio-temporal statistics
• +5 more

Mikael Kuusela is an Assistant Professor of Statistics and Data Science at Carnegie Mellon University, affiliated with the Dietrich College of Humanities and Social Sciences. He specializes in developing statistical methods for physical sciences, focusing on ill-posed inverse problems, spatio-temporal data, and uncertainty quantification in climate science, oceanography, remote sensing, and particle physics. His work integrates closely with domain scientists, including collaborations with oceanographers on Argo floats, NASA's OCO-2 mission, and CERN's CMS experiment. Education: PhD in Statistics, École Polytechnique Fédérale de Lausanne (EPFL), 2016 MSc and BSc in Engineering Physics and Mathematics, Aalto University, 2012 and 2010 His research interests span statistical methodologies for large-scale datasets, with applications to environmental science and high-energy physics. Key areas include: Statistical methods for physical sciences (STAMPS group coordination) Uncertainty quantification in climate models and ocean heat content Optimal transport and inverse problem solutions in particle physics Spatio-temporal modeling of oceanographic phenomena Recent articles highlight advancements in uncertainty quantification, climate model parameter estimation, and applications of statistical techniques to ocean and atmospheric data. Kuusela is also a core member of the US CLIVAR Ocean Uncertainty Quantification Working Group and coordinates the Statistical Oceanography Working Group. His work emphasizes collaboration with domain experts, leveraging statistical rigor to address real-world challenges in environmental and fundamental physics research.

Bradley RavaView profile

Lecturer

• Empirical Bayes Techniques
• Fairness in Machine Learning
• Statistical Machine Learning
• +4 more

Bradley Rava is a Lecturer in Business Analytics at the University of Sydney's Business School. His research focuses on modern statistical methods addressing societal challenges in automated decision-making, particularly in high-risk domains like healthcare and finance. He holds a PhD in Statistics from the University of Southern California, advised by Dr. Gareth James and Dr. Xin Tong, supported by prestigious fellowships including the NSF Graduate Research Fellowship and USC Marshall Fellowship. Education: Ph.D. in Statistics, University of Southern California (2017–2022) B.S. in Applied and Computational Mathematics, University of Southern California (2013–2016) Emerging Scholars Fellowship, Yale University (2016–2017) Research Interests: Empirical Bayes techniques, fairness in machine learning, statistical machine learning, high-dimensional statistics, and uncertainty communication in automated systems. His work emphasizes rigorous control of severe consequences in AI-driven decisions. Awards & Honors: NSF Graduate Research Fellowship USC Marshall Fellowship Correlation-One Southern California Datathon 1st place Sydney Business School Early Career Research Grant Teaching & Grants: Teaches Advanced Applications in Business Analytics and Statistical Learning. Received grants for uncertainty estimation in fair classification and interdisciplinary collaborations with Cornell. Professional Activities: Active in conferences (INFORMS, JSM), invited speaker on fairness in AI, and founder of the LSESU Applicable Maths Society.

Jia LiuView profile

Research Fellow

• Bayesian statistics
• Spatial data analysis
• Image analysis
• +3 more

Dr. Jia Liu serves as a Research Fellow at the Australian National University's Research School of Biology (Division of Ecology and Evolution) and concurrently holds a Technical Officer position in the Research School of Earth Sciences. Holding a PhD in Statistics, Dr. Liu conducts interdisciplinary research spanning statistical methodology, earth sciences, and computer vision applications. Education: PhD in Statistics Research focuses on Bayesian statistics, spatial data analysis, image analysis, geostatistics, and experimental design. Dr. Liu applies these methods to paleomagnetism, atmospheric science, and computer vision, with recent emphasis on uncertainty quantification and deep learning architectures for complex data analysis. Publication trends reveal dual trajectories: earth sciences applications (2020-2022) featuring directional statistics for paleomagnetic data and cloud physics modeling, alongside computer vision advancements (2021-2025) in 3D reconstruction, object detection, and segmentation. Core methodological contributions include novel approaches for spatiotemporal survey design and aleatoric uncertainty modeling. Awards: No major scientific awards documented Student advising activities and research grant funding details are not publicly available. The researcher maintains affiliations across multiple ANU schools but specific laboratory or team memberships were not specified in source materials.

François BachocView profile

Assistant Professor

• Statistics
• Machine Learning
• Mathematical Statistics
• +3 more

François Bachoc is an Assistant Professor at the Toulouse Mathematics Institute and the University Paul Sabatier, where he has held a tenured position since 2015. He is a Junior Member of the Institut universitaire de France (IUF) (2024–2029). His research focuses on Statistics, Machine Learning, and Gaussian Processes , with applications in industrial and interdisciplinary domains. He earned his Ph.D. in Statistics from the CEA and Université Paris VII (2013), followed by a postdoctoral position at the University of Vienna (2013–2015). He completed a Habilitation (HDR) at University Paul Sabatier in 2018. His work emphasizes uncertainty quantification, Bayesian methods, and optimal design of experiments. Bachoc leads several funded projects, including the ANR Project GAP (€205k) and the AI Chair UQPhysAI (€350k). He teaches courses on asymptotic statistics, machine learning, and Gaussian processes at the graduate level. His contributions span theoretical advancements and practical applications in coastal flood modeling, sensitivity analysis, and computational statistics.

Ilker BirbilView profile

Professor

• Optimization Methods
• Data Science
• Decision Making
• +3 more

Prof. Dr. Ilker Birbil is a Professor of AI & Optimization Techniques for Business & Society at the University of Amsterdam (UvA), affiliated with the Amsterdam Business School's Business Analytics section. He previously held professorships at Erasmus University and Sabancı University, focusing on optimization and data science. His research interests include interpretable machine learning, data privacy, and optimization methods in decision-making. Education: PhD in Operations Research from North Carolina State University Postdoc at Erasmus Research Institute of Management (ERIM), Netherlands Research Interests: Optimization in data science, interpretable AI, privacy-preserving algorithms, and decision-making systems. Recent work focuses on differentially private optimization and meta-learning techniques like LESS. Key Achievements: Recipient of multiple teaching awards at Sabancı University Affiliated researcher at OPTIMAL (Optimization for and with Machine Learning) Organized workshops on ML for optimization and mathematics of ML Grants & Teams: Leads the UvA group on Optimization and Machine Learning at LNMB. Collaborates on projects blending OR and ML, including privacy-aware algorithms and revenue management systems. Labs/Teams: OPTIMAL, UvA Optimization Group, and interdisciplinary teams in data analytics.

• Probabilistic Forecasting
• Statistical Evaluation
• Climate Science
• +3 more

Prof. Tilmann Gneiting is a Professor of Computational Statistics at Karlsruhe Institute of Technology (KIT) and Head of the Computational Statistics Working Group at the Heidelberg Institute for Theoretical Studies (HITS). His research focuses on probabilistic forecasting, statistical evaluation of predictive models, and applications in environmental science, epidemiology, and renewable energy. He has held academic roles in mathematics and statistics departments across multiple institutions. His work emphasizes rigorous statistical methodology for forecasting systems, including ensemble models, uncertainty quantification, and validation frameworks. Notable contributions include developing scoring rules for probabilistic predictions, Bayesian model averaging techniques, and geostatistical methods for weather and climate modeling. Courses taught include Forecasting: Theory and Practice and Time Series Analysis . Prof. Gneiting’s research spans diverse areas such as earthquake forecasting, solar energy prediction, and pandemic modeling. He has contributed to R packages like ensembleMOS and ProbForecastGOP , advancing computational tools for statistical analysis. His work bridges theoretical statistics with practical applications in environmental and public health domains. Professional awards and recognitions are not explicitly listed in the provided texts. His advising and grants involve collaborations in interdisciplinary projects, though specific student names or grant details are not mentioned. He leads computational statistics teams at HITS and actively publishes in top-tier statistical and applied journals.

Filomena NunesView profile

Professor

• Nuclear Physics
• Unstable Matter
• Rare Isotopes
• +7 more

Filomena Nunes is a Professor of Physics and Astronomy at Michigan State University (MSU), affiliated with the National Superconducting Cyclotron Laboratory (NSCL) and the FRIB Theory Alliance (FRIB-TA). A Técnico (IST) alumna, her work bridges nuclear theory and experimental validation, focusing on unstable matter, rare isotopes, and their applications in medicine, security, and energy. Research Interests: Her research explores nuclear structure, astrophysical processes, and Bayesian statistical methods to model reactions involving rare isotopes. She emphasizes the symbiotic relationship between theory and experimental discovery, particularly in understanding 'islands of inversion' and nuclear deformation patterns. Recent Contributions: Her team’s 2024 findings on neutron-rich nuclei deformation and 2025 beam power milestones at FRIB highlight her leadership in advancing nuclear science infrastructure. She actively promotes interdisciplinary applications of nuclear physics through public lectures and policy engagement. Labs/Teams: Core affiliations include NSCL and FRIB-TA, where she contributes to the Facility for Rare Isotope Beams (FRIB) mission to study nuclei at the extremes of existence.

Yangming LiView profile

Research Fellow

• Image Analysis
• Machine Learning
• Mathematical Modeling
• +7 more

Yangming Li is a Research Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, affiliated with the Cambridge Image Analysis research group. His work bridges applied mathematics, theoretical physics, and machine learning, with a focus on developing innovative models for image analysis, generative processes, and natural language understanding. Research interests include Fourier Neural Operators, diffusion models, generative adversarial networks (GANs), and their applications in solving complex problems across scientific computing and data-driven domains. His recent contributions explore operator learning for PDEs, robust diffusion models under noisy conditions, and adversarial attacks in text watermarking systems. Publications highlight advancements in operator-based neural networks, risk-sensitive generative modeling, and domain-aware NLP frameworks. His methodologies emphasize mathematical rigor while addressing practical challenges like missing data and model expressivity limitations. Active collaborations span interdisciplinary teams at DAMTP and the broader University of Cambridge research community.

Danial FaghihiView profile

Assistant Professor

• Multiscale Computational Modeling
• Data-Driven Predictive Modeling
• Uncertainty Quantification
• +4 more

Danial Faghihi is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the University at Buffalo. His research focuses on multiscale computational modeling, data-driven predictive methods, and uncertainty quantification with applications in advanced manufacturing, thermal insulation materials, and biomedical systems. He holds a PhD from Louisiana State University and completed a postdoctoral fellowship at the University of Texas at Austin. Education: Postdoc, Computational Engineering, University of Texas at Austin (2015) PhD, Civil Engineering, Louisiana State University (2012) MS, Civil Engineering, Sharif University of Technology (2008) BS, Civil Engineering, K. N. Toosi University of Technology (2008) His research interests integrate advanced computational frameworks with experimental data to address challenges in materials science and biomedical engineering. Notable areas include predictive modeling of silica aerogels, stochastic surrogate models for uncertainty quantification, and tumor growth simulations using Bayesian methods. He has received prestigious awards such as the NSF CAREER Award (2022) and the SES Travel Award (2013). Recent work emphasizes dynamic data-driven approaches for real-time material damage prediction and scalable algorithms for multi-material design under uncertainty. His contributions span interdisciplinary collaborations in thermal management systems, carbon sequestration materials, and personalized oncology through computational oncology models. Key Honors: NSF CAREER Award (2022) SES Travel Award (2013) NSF Summer Institute Fellowship (2013) His research narrative includes development of novel surrogate models for high-dimensional systems and validation frameworks that bridge atomistic to continuum scales. While specific grants or lab affiliations are not detailed in the provided text, his work reflects sustained innovation in computational science and engineering applications.