Find Academic Supervisors

• Machine Learning in Geotechnical Engineering
• Offshore Soil-Structure Interaction
• Offshore Wind Turbine Foundations
• +3 more

Dr. Stephen Suryasentana is a Senior Lecturer in the Department of Civil and Environmental Engineering within the Faculty of Engineering at the University of Strathclyde. He joined Strathclyde in 2020 as a Chancellor's Fellow (Lecturer) after completing a Junior Research Fellowship at Wolfson College, Oxford. His research focuses on developing innovative offshore geotechnics solutions for the digital age, particularly applying AI-driven modeling tools and sensor-based solutions to improve decision-making in offshore engineering. His educational background includes a Doctor of Philosophy from Oxford University (awarded July 13, 2019) on "Time-critical design methods for suction caisson foundations" and a First Class Honors Bachelor of Engineering in Civil Engineering from the University of Western Australia (awarded December 31, 2012). Dr. Suryasentana's research interests span two primary domains: Machine learning applications : Bayesian Optimization, Physics-informed Machine Learning, and Multi-fidelity data fusion for geotechnical engineering problems Offshore soil-structure interaction : Development of analytical models for offshore foundations, Finite element modeling of soil-structure interaction, and Convex constitutive modeling His recent publications demonstrate a strong trend toward integrating artificial intelligence with traditional geotechnical engineering, particularly for offshore wind turbine foundation design. The research spans from fundamental soil mechanics investigations to practical applications of machine learning for real-time modeling and risk assessment in offshore environments. His notable awards include: Wolfson College Junior Research Fellowship (December 2018) SUT OSIG Conference: Best Poster and Presentation Award (2017) Dr. Suryasentana actively supervises research students and has secured multiple research grants, including as Principal Investigator for projects related to suction caisson installation, offshore wind turbine monopile foundations, and digital twin technology for risk assessment. He maintains the AutoGeoLab (http://www.autogeolab.com) where his research team develops and tests innovative geotechnical solutions.

Federico PichiView profile

Assistant Professor

• Numerical Analysis
• Reduced Order Modeling
• Bifurcation Analysis
• +5 more

Federico Pichi is an Assistant Professor (RtdA) at the International School for Advanced Studies (SISSA) in Trieste, Italy, where he is a key member of the mathLab research group. His work focuses on developing computational frameworks for studying parametrized partial differential equations, with particular emphasis on systems exhibiting bifurcating behavior and multiple solution branches. Dr. Pichi's academic foundation includes: PhD in Mathematical Analysis, Modelling and Applications from SISSA (2016-2020) Master's degree in Mathematics from Sapienza University of Rome (2014-2016) Bachelor's degree in Mathematics from Sapienza University of Rome (2011-2014) His research expertise spans Numerical Analysis , Mathematical Modeling , and Scientific Computing , with specialization in reduced order modeling techniques for nonlinear parametrized PDEs. Dr. Pichi has made significant contributions to understanding bifurcating phenomena in computational fluid dynamics, particularly the Coanda effect in channels. His methodological approach uniquely bridges traditional projection-based reduced order models with cutting-edge machine learning techniques, including artificial neural networks and graph convolutional autoencoders, to address the computational challenges of nonlinear systems with multiple solution branches. Analysis of Dr. Pichi's recent publication trajectory reveals an increasingly sophisticated integration of optimal transport theory with deep learning approaches to tackle slow-decaying Kolmogorov n-width problems in reduced order modeling. His work demonstrates remarkable versatility across application domains, from cardiovascular flow modeling to hyperelastic material analysis, while maintaining a consistent methodological focus on efficient computational frameworks for bifurcating systems. The incorporation of graph-based neural networks for resolution-invariant operator learning represents his most recent methodological innovation. As an active member of the mathLab research group under Professor Gianluigi Rozza, Dr. Pichi contributes to a collaborative environment that bridges theoretical mathematics with practical engineering applications. His research has established important connections between classical numerical analysis and modern machine learning paradigms, making significant contributions to the field of scientific computing for complex nonlinear systems.

• Numerical analysis
• Linear algebra
• Optimization
• +3 more

Michael L. Overton is a Silver Professor of Computer Science and Mathematics at the Courant Institute of Mathematical Sciences, New York University. He holds a B.Sc. (Honors) from the University of British Columbia, and M.S. and Ph.D. degrees from Stanford University. Roles: Academic Leadership, Research, Teaching, and Editorial Work Affiliations: Courant Institute, NYU; Editorial Boards of IMA Journal of Numerical Analysis , Numerische Mathematik , and others. Research Interests: Focus on numerical analysis, linear algebra, optimization, and mathematical programming. Notably contributes to nonsmooth optimization, numerical algorithms, and control theory. His work bridges theory and applications, including the Crouzeix conjecture and robust control design. Publications: Overton has authored influential books, including Numerical Computing with IEEE Floating Point Arithmetic , and published extensively on topics like gradient sampling methods, pseudospectra, and eigenvalue optimization. Recent works explore subdifferentiability of matrix functions and optimization algorithms. Awards: SIAM and IMA Fellowships, highlighting his impact on computational mathematics. Advising: Supervised MS students including Wilmer Henao, Allan Kaku, and Anders Skajaa, focusing on nonsmooth optimization and control problems. Professional Activities: Organized conferences, served on boards (e.g., SIAM, FoCM), and contributed to software like HIFOO for control design. Labs/Teams: Active in collaborative projects at the Courant Institute, with interdisciplinary research in electronic structure calculations and semidefinite programming.

• Stock Assessment
• Fish Bioenergetics
• Food-Web Dynamics
• +7 more

Ji Xiang He is an Adjunct Associate Professor at the Department of Fisheries and Wildlife, College of Agriculture & Natural Resources, Michigan State University. He also serves as a Research Fisheries Biologist at the Michigan Department of Natural Resources (MI DNR) at the Alpena Fisheries Research Station. His professional focus integrates fish bioenergetics, food-web dynamics, and life-history parameter analysis within the context of human-ecosystem interactions in fisheries management. Education: Undergraduate in Fisheries and Aquaculture (specializing in Chinese reservoir systems), with graduate training in Fisheries and Ecology at the State University of New York (SUNY) beginning in 1995. His research bridges ecological frameworks (natural resource geography, landscape ecology, systems ecology) with quantitative fisheries science. While his primary work centers on stock assessment and ecosystem modeling in aquatic environments, recent Google Scholar publications indicate interdisciplinary applications of computational modeling and machine learning in biomedical contexts such as arterial hypertension and aneurysm progression. Key trends in his publications reveal expertise in physics-based machine learning, patient-specific hemodynamic modeling, and longitudinal data analysis for cardiovascular diagnostics. Though seemingly divergent from his fisheries background, these computational techniques may inform ecological modeling practices through cross-disciplinary methodological innovation. He maintains affiliation with the Quantitative Fisheries Center and emphasizes functional connections between ecological systems and human management practices in his work.

• Aerospace Engineering
• Computational Science
• Artificial Intelligence
• +8 more

Professor Laura Mainini holds the Chair in Aerospace Computational Design at Imperial College London's Department of Aeronautics and serves as Associate Director of the Brahmal Vasudevan Institute for Sustainable Aviation. She leads industry engagement and strategy for the Institute. Previously, she was Chief Technologist at Collins Aerospace, focusing on AI-enhanced aerospace systems design and integration. Her research integrates aerospace engineering with computational science, emphasizing real-time data-to-decision systems, scientific machine learning, and multidisciplinary optimization for sustainable aviation and space exploration. Education includes a BSc, MSc, and PhD in Aerospace Engineering from Politecnico di Torino, an MSc in Aeronautical Engineering from Politecnico di Milano, and a Fulbright-funded doctorate at MIT. She is a Fellow of the Royal Aeronautical Society and an Associate Fellow of AIAA, contributing to leadership roles in technical committees. Research interests span advanced computational methods for aerospace systems, including physics-based machine learning, multifidelity optimization, and digital twins for safety-critical scenarios. Notable work includes frameworks for resource-constrained analytics and internationally recognized Mars habitat concepts. Her articles focus on optimizing aerospace vehicles, fault diagnostics, and sustainable manufacturing techniques. Prof. Mainini's awards include the Fulbright Grant and recognition for contributions to multidisciplinary design optimization. Her advising and grants involve collaborations on sustainable aviation, space exploration, and industrial partnerships. She leads the Doctoral Programme in Sustainable Aviation and coordinates international projects like MarsGarden, an engineered ecosystem for extraterrestrial residence. Key labs/teams: Brahmal Vasudevan Institute for Sustainable Aviation, Imperial's Department of Aeronautics, and international task groups on digital engineering and space exploration.

Andrey A. PopovView profile

Assistant Professor

• Computational Science
• Data Assimilation
• Bayesian Inverse Problems
• +8 more

Andrey A. Popov is an Assistant Professor in the Department of Information and Computer Sciences at the University of Hawaiʻi at Mānoa, part of the College of Natural Sciences. His research lies at the intersection of computational science, data assimilation, uncertainty quantification, and machine learning, with applications to dynamical systems and aerospace navigation. His research interests include computational science, Bayesian inverse problems, data assimilation, uncertainty quantification, theory-guided machine learning, reduced order modeling, and ensemble filtering. He develops methods that bridge theoretical rigor with practical applicability, aiming for solutions no more than one step away from real-world implementation. His work often integrates machine learning with physical models to improve state estimation and prediction in complex systems. The recent publications (2023–2024) reflect a strong trend in advancing ensemble-based filtering techniques—particularly ensemble Kalman and particle filters—with innovations in covariance adaptation, weight optimization, and non-Gaussian modeling. There is also a growing emphasis on multifidelity and reduced-order modeling, especially using autoencoders and neural networks to handle small data regimes in chaotic systems. Applications span geophysics, aerospace (e.g., Mars entry navigation), and cislunar tracking, demonstrating interdisciplinary impact. Scientific Awards: Jean-Pierre Le Cadre Best Paper Award at FUSION 2024 Dr. Popov actively mentors students and welcomes prospective graduate students to contact him via email or during office hours. While specific grant details are not provided, his publication output and conference presence suggest active external funding. He teaches courses such as ICS 141 and emphasizes algorithmic and mathematical foundations in computing. He is involved in collaborative research with institutions like the Oden Institute and researchers including Adrian Sandu and Renato Zanetti, focusing on advanced filtering and data assimilation techniques. His group likely engages in methodological development for state estimation in high-dimensional, nonlinear systems.

Pedro Pintado Jorge GonçalvesView profile

Senior Lecturer

• Computational neuroscience
• Machine Learning
• Simulation-based inference
• +4 more

Pedro Pintado Jorge Gonçalves is a Senior Lecturer at the Faculty of Engineering Sciences at KU Leuven, with primary affiliation in the Department of Electrical Engineering and strong ties to the Department of Computer Science. He leads the Gonçalves lab at the VIB-KU Leuven Center for Neuro Electronics Research Flanders (NERF), and is also a member of the KU Leuven Brain Institute and Leuven.AI Institute for Artificial Intelligence. His educational background includes training as a physicist with a focus on neuroscience applications. His research integrates machine learning with computational neuroscience to develop methods for extracting mechanistic insights from complex neural data. His work specializes in simulation-based inference techniques that bridge data-driven and theory-driven approaches in neuroscience. The publication trends reveal a strong focus on developing and applying machine learning methods to neuroscience problems, with particular emphasis on neural circuit modeling, biophysical simulations, and the development of computational tools like the sbi toolkit. His research spans multiple scales from single neurons to population dynamics and behavior. As an educator, he teaches courses including Calculus and Mathematical Engineering, and mentors numerous students through his lab. His collaborative approach is evident in his extensive co-authorship network across computational neuroscience and machine learning fields. Dr. Gonçalves actively supervises multiple PhD and master's students in his lab, fostering the next generation of computational neuroscientists. His research is supported by multiple ongoing projects including 'Simulation-based inference for mechanistic models of neural dynamics' (2025-2029) and 'Computational research into the principles of robustness in neural systems' (2023-2027). His laboratory focuses on combining theoretical models with advanced machine learning methods to design more accurate neural models, quantitatively test mechanistic hypotheses, and derive experimentally-testable predictions about neural systems in health and disease.

Themistoklis P. SapsisView profile

Professor

• Nonlinear Dynamical Systems
• Probabilistic Modeling
• Data-Driven Methods
• +8 more

Themistoklis P. Sapsis is the William I. Koch Professor of Mechanical and Ocean Engineering at MIT, affiliated with the Schwarzman College of Computing. He serves as Director of the Center for Ocean Engineering and Associate Director of MIT Sea Grant. His research focuses on nonlinear dynamical systems, probabilistic modeling, and data-driven methods applied to fluid flows, ocean engineering, and extreme events. Notable roles include editorial positions at journals such as Journal of Nonlinear Science and SIAM/ASA Journal of Uncertainty Quantification . Education: Ph.D. in Mechanical Engineering, MIT (2006–2011) Diploma in Naval Architecture and Marine Engineering, National Technical University of Athens (2001–2005) Research Interests: Sapsis’ work bridges nonlinear dynamics, probability, and machine learning to predict extreme events in complex systems such as turbulent flows, nonlinear waves, and ship motions. His methods emphasize statistical quantification and optimization of systems with transient features. Awards and Honors: Bodossaki Award on Basic Sciences: Mathematics (2021) Vannevar Bush Faculty Fellowship (DoD, Applied Mathematics) Alfred P. Sloan Research Fellowship (2015) Three Department of Defense Young Investigator Awards (Navy, Army, Air Force) Multiple career development chairs at MIT Grants and Advising: Sapsis has secured funding for projects on climate modeling, ocean engineering, and machine learning applications. His advising includes collaborations on reduced-order models and extreme event prediction. He leads the Stochastic Analysis and Nonlinear Dynamics (SAND) Lab at MIT. Labs and Teams: The SAND Lab focuses on integrating data-driven methods with physical models to address challenges in fluid mechanics, climate science, and ocean engineering. Collaborations span academia and industry, emphasizing real-world applications of predictive modeling.

• Computational design of aerospace systems
• Multidisciplinary design optimization
• Surrogate-based modeling and optimization
• +6 more

Leifur Leifsson is an Associate Professor at Purdue University's School of Aeronautics and Astronautics, specializing in computational design and optimization for aerospace systems. His research focuses on multidisciplinary design optimization, surrogate-based modeling, machine learning, and uncertainty quantification, with applications to aerodynamics, microwave devices, and environmental systems. He holds degrees from the University of Iceland (C.Sc. 1999, M.Sc. 2000) and a Ph.D. in Aerospace Engineering from Virginia Tech (2006). His work integrates advanced computational techniques to address complex engineering challenges, including aerodynamic shape optimization, nondestructive evaluation, and food-water-energy nexus analysis. Key research trends in his recent articles include multifidelity modeling for antenna design, machine learning-driven optimization algorithms, and global sensitivity analysis for nondestructive testing systems. He leads the Computational Design Lab, advancing tools for efficient simulation and design closure in aerospace and microwave engineering. Awards: NSF Early CAREER Award (2019) Best Paper Award, ASME Journal of Nondestructive Evaluation (2019) Research Award, Reykjavik University (2014) Leifsson has advised no listed students but has contributed to collaborative projects involving graduate researchers. His lab focuses on developing open-source frameworks (e.g., flutter prediction tools) and advancing simulation-based decision-making for large-scale systems.

Bledar Alex KonomiView profile

Associate Professor

• Bayesian statistics
• Spatial statistics
• Uncertainty quantification
• +2 more

Bledar Alex Konomi is Associate Professor of Statistics at University of Cincinnati's College of Arts and Sciences. He holds a PhD from Texas A&M University (2011) and BS from Athens University of Economics and Business. Research develops Bayesian methods for large spatial/simulation datasets, uncertainty quantification, and multifidelity computer model calibration. Applications include remote sensing, storm surge prediction, and paleoclimate reconstruction. Recent articles feature: Spatial modeling innovations for big data Satellite calibration techniques Bayesian decision tree algorithms Ice model parameter estimation Advises doctoral students in statistical computing and spatial modeling. Teaches graduate courses: Bayesian Analysis, Statistical Machine Learning, and Mathematical Statistics. Secured NSF/NASA JPL grants for remote sensing uncertainty quantification. Previously held positions at Pacific Northwest National Laboratory and Simon Fraser University.

Peng ChenView profile

Assistant Professor

• Scientific Machine Learning
• Uncertainty Quantification
• Bayesian Inference
• +5 more

Peng Chen is an Assistant Professor in the School of Computational Science and Engineering (CSE) at Georgia Institute of Technology, part of the College of Computing. His research focuses on Scientific Machine Learning (SciML), Uncertainty Quantification (UQ), and their applications in computational science and engineering. He holds a PhD and Master's from École Polytechnique Fédérale de Lausanne (EPFL) and a Bachelor's from Xi’an Jiaotong University. Education: PhD in Computational Mathematics (EPFL, 2014), Master in Computational Mathematics (EPFL, 2011), Bachelor in Mathematics (Xi’an Jiaotong University, 2009). Research interests include Bayesian inference, stochastic optimization, parallel computing, and scalable algorithms for high-dimensional problems. His work addresses challenges in data-driven modeling of complex systems, such as metamaterial design, plasma fusion optimization, and epidemic modeling. Advising: Supervises PhD and Master’s students in SciML and UQ. Notable students include Jinwoo Go, Yuan Qiu, and Phillip Si. Alumni include Irene Simo Munoz and Leyao Huang. Labs/Teams: Leads the SciML & UQ group at Georgia Tech, developing software like SOUPy and pSVGD for stochastic optimization and Bayesian inference. Collaborates on NSF-funded projects, including a $1.5M award for variational neural networks in physical systems.

Elizabeth QianView profile

Assistant Professor

• Model Reduction
• Scientific Machine Learning
• Uncertainty Quantification
• +1 more

Elizabeth Qian is an Assistant Professor at Georgia Institute of Technology, holding a joint appointment in the Schools of Computational Science and Engineering and Aerospace Engineering within the College of Computing. Her research focuses on developing computational methods for engineering design and decision-making, emphasizing model reduction, scientific machine learning, and multi-fidelity approaches. She earned her SB, SM, and PhD from MIT's Department of Aeronautics & Astronautics and held a postdoctoral position at Caltech. Qian is a Hans Fischer Fellow at TU Munich and has received numerous awards, including the SIAM Student Paper Prize and NSF Graduate Fellowship. Education: PhD (2017), SM (2014), SB (2013) in Aeronautics & Astronautics, MIT Research Interests: Model reduction for Bayesian inference, physics-informed machine learning, multi-fidelity uncertainty quantification, and surrogate models for engineering systems. Her work bridges theory and application, with contributions to PDE-constrained optimization, ensemble Kalman inversion, and scalable computational methods. Recent publications highlight advancements in multifidelity linear regression and inference-oriented model reduction. She actively mentors students and leads the Aerospace Computational Engineering (ACE) Group at Georgia Tech. Awards: 2023 Hans Fischer Fellowship, NAE Frontiers of Engineering Symposium (2023), World Congress Best Presentation (2022), Caltech Teaching Award (2022) Advising & Grants: Supervises PhD, master’s, and undergraduate researchers; collaborates on grants involving TU Munich and NASA applications. ACE Group focuses on scalable computational methods for design and control. Qian also serves as a guest editor for special issues on model reduction and digital twins, furthering interdisciplinary impact in computational science and engineering.

David WarneView profile

Lecturer

• Statistical Data Science
• Bayesian Inference
• Computational Biology
• +3 more

Dr. David Warne is a Lecturer in Statistical Data Science at the School of Mathematical Sciences , Queensland University of Technology (QUT). He is affiliated with the Centre for Data Science and the ARC Centre of Excellence for Mathematical and Statistical Frontiers . His research focuses on Bayesian inference, computational statistics, and their applications in ecology, environmental science, and computational biology. David holds a PhD in Mathematics (QUT), a BMath/BInfoTech (QUT), and a BInfoTech (Hons) (QUT). Research Interests: Bayesian methods, likelihood-free inference, parameter identifiability, stochastic modeling, and applications to ecological systems and environmental decision-making. His work bridges statistical theory with computational tools to address complex biological and environmental problems. Publications: David’s recent work emphasizes innovative statistical approaches for model calibration, uncertainty quantification, and decision analysis in ecological and biomedical contexts. Key themes include multifidelity Bayesian inference, model misspecification, and agent-based modeling of tumor growth. Awards: He received the Outstanding Doctoral Thesis Award during his PhD at QUT. His research has been published in high-impact journals such as Journal of Computational Physics , Ecological Indicators , and Bulletin of Mathematical Biology . Supervision: Topics include stochastic process modeling, coral reef recovery, and scalable Bayesian inference. Labs/Teams: Active contributor to the Centre for Data Science and the ARC Centre of Excellence for Mathematical and Statistical Frontiers.

Daniele SchiavazziView profile

Associate Professor

• Uncertainty Quantification
• Computational Mathematics
• Medical Image Processing
• +4 more

Dr. Daniele Schiavazzi is an Associate Professor in the Department of Applied and Computational Mathematics and Statistics (ACMS) at the University of Notre Dame, with a concurrent appointment as Associate Professor in Aerospace and Mechanical Engineering (AME). He holds a Ph.D. and B.Eng. from the University of Padova, Italy (Summa cum Laude), with postdoctoral training at Stanford University and University of California San Diego. His research focuses on uncertainty quantification, computational hemodynamics, and machine learning , with applications in cardiovascular modeling. Key areas include stochastic analysis, numerical modeling, finite element methods, and Bayesian inference for clinical decision-making under uncertainty. He has pioneered frameworks like InVAErt networks and NeurAM for model emulation and uncertainty reduction. Dr. Schiavazzi leads the Multifidelity Uncertainty Quantification (MUQ) Group , developing open-source tools such as SimVascular for image-based cardiovascular modeling. His work integrates physics-informed AI, multi-resolution expansions, and dimensionality reduction to address complex biomedical problems, including coronary artery disease and pulmonary hypertension. Notable contributions include NSF CAREER Award-funded research on Bayesian inference networks and collaborative projects on cardiovascular model ensembles. His lab actively explores privacy-preserving data synthesis and differential privacy for electronic health records.

Benjamin PeherstorferView profile

Associate Professor

• Computational Mathematics
• Machine Learning
• Computational Statistics
• +5 more

Benjamin Peherstorfer is an Associate Professor at the Courant Institute of Mathematical Sciences, New York University. His research focuses on computational mathematics, machine learning, and scientific computing, with an emphasis on nonlinear model reduction, uncertainty quantification, and multifidelity methods. He holds editorial roles at journals such as Journal of Scientific Computing and SIAM Journal on Scientific Computing . Peherstorfer co-organizes workshops on topics like digital twins and fusion energy, reflecting his commitment to advancing interdisciplinary computational science. He has advised students like Jules Berman, who received the Harold Grad Memorial Prize. His work integrates machine learning with traditional numerical methods to address challenges in high-dimensional systems and real-time simulations. Notable contributions include developing Neural Galerkin schemes for solving evolution equations and advancing multifidelity Monte Carlo methods to accelerate uncertainty propagation. His research bridges theoretical foundations with practical applications, such as wildfire-atmosphere coupling and plasma confinement in stellarators. Key Roles: Secretary of the SIAM Activity Group on Computational Science and Engineering Co-organizer of the Long Program on Multi-Fidelity Methods for Fusion Energy (IPAM, 2026) Main Research Themes: Reduced-order modeling Data-driven operator inference Adaptive sampling algorithms