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Masha FolkView profile

Assistant Professor

• Gas Turbine Aerodynamics
• Sustainable Propulsion
• Technology Transfer
• +3 more

Masha Folk is the Charles Stark Draper Career Development Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology (MIT). She holds a Ph.D. from the University of Cambridge and has extensive industry experience at Rolls-Royce as an Aerothermal Specialist and Turbine Aerodynamicist. Her research focuses on sustainable aerospace technologies, particularly gas turbine aerodynamics and sustainable propulsion systems, with a goal to accelerate carbon-neutral aviation. Education: Ph.D. in Energy, Fluids and Turbomachinery, University of Cambridge (2020) M.Res. in Energy, Fluids and Turbomachinery, University of Cambridge (2015) M.S. in Aerospace Engineering, Purdue University (2014) B.S. in Aerospace Engineering, Ohio State University (2010) Research Interests: Her work emphasizes gas turbine aerodynamics, sustainable propulsion systems, and technology transfer to real-world applications. Key topics include combustor turbulence effects on turbine performance, aerothermal design optimization, and climate change mitigation strategies for aviation. Publications: Her recent work explores multifidelity optimization of combustor turbulence, boundary layer dissipation, and turbine loss mechanisms. These studies highlight advancements in computational fluid dynamics and experimental validation of turbine systems. Awards: 2021 ASME Gas Turbine Award for Most Outstanding Technical Paper 2019 ASME/IGTI Turbomachinery Committee Best Paper (GT2019-90307) 2015 Zonta International Amelia Earhart Fellowship Lab Affiliation: Gas Turbine Lab at MIT, focusing on experimental and computational studies of turbine aerodynamics and sustainable propulsion systems.

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.

Elizabeth QianView profile

Assistant Professor

• Scientific machine learning
• Model reduction
• Multifidelity methods
• +5 more

Elizabeth Qian is a tenure-track Assistant Professor at Georgia Tech with joint appointments in the Schools of Aerospace Engineering and Computational Science and Engineering. She holds a visiting Hans Fischer Fellowship at the Technical University of Munich Institute for Advanced Study (TUM-IAS) since 2023. Her research develops computational methods for scientific machine learning, model reduction, and multifidelity approaches to accelerate engineering decision-making. Education : PhD (2021), SM (2017), and SB (2014) in Aerospace Engineering from MIT Her work focuses on enabling efficient simulations for complex systems through model reduction , scientific machine learning , and multifidelity methods . Recent publications address Bayesian inverse problems, neural operator learning trade-offs, and ensemble Kalman methods. Scientific awards include the 2025 NSF CAREER Award, 2024 AFOSR YIP Award, 2023 TUM-IAS Fellowship, 2022 SIAM Student Paper Prize, and 2014 Hertz/NSF/Fulbright fellowships. She mentors GT PhD researchers and contributes to diversity initiatives in engineering communities.

Hamid DalirView profile

Professor

• Composites materials design and manufacturing
• Sustainable and recyclable polymers
• Polymer processing and characterization
• +9 more

Hamid Dalir is a full professor in the School of Mechanical Engineering at Purdue University, Indianapolis, and directs the Advanced Composite Structures Engineering Lab (ACSEL). He holds a dual appointment in Motorsports Engineering and is affiliated with the Purdue University campus in Indianapolis. His career includes roles as Technical Director at the Syracuse Center of Excellence, Associate Professor at Syracuse University, and Senior Airframe Structures Engineer at Bombardier Aerospace. He earned a Ph.D. from Tokyo Institute of Technology and a postdoc at Polytechnique Montreal. Education: Ph.D., Tokyo Institute of Technology, 2009 M.S., Sharif University of Technology, 2005 B.S., Ferdowsi University of Mashhad, 2003 Postdoctoral Fellow, Polytechnique Montreal, 2011 Research Interests: Focuses on sustainable composites, polymer processing, recycling, hybrid manufacturing, and multiscale modeling. His work applies to aerospace, automotive, and transportation industries, emphasizing lightweight materials and structural optimization. Recent trends in publications include additive manufacturing (e.g., laser-based processes) and nanocomposite sensors. Awards: Trustees Teaching Award (2022) XTECH Army’s Prize (2022) Elevate Ventures Indiana Statewide Winner (2020) IEEE Best Paper Awards (2018, 2016) Advising & Grants: Supervised over 50 graduate students/postdocs, focusing on composites, sensors, and energy storage. Research funding from NSF, DOE, DOD, NASA, and industry partners (e.g., racing OEMs). Labs/Teams: Leads ACSEL, which innovates in composites for aerospace and motorsports. Current projects include multifunctional batteries, nanoscale sensors, and crash attenuation systems.

• 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.

Mark MillerView profile

Assistant Professor

• Experimental Fluid Mechanics
• Wind Turbine Aerodynamics
• MEMs Sensor Development
• +3 more

Mark Miller is an Assistant Professor of Aerospace Engineering at Pennsylvania State University, affiliated with the Institute for Energy and the Environment (IEE). His research focuses on experimental investigations of full-scale wind turbine aerodynamics, particularly Reynolds number effects on power production, thrust loading, and wake structure scaling. He leads the Miller Fluids Lab, specializing in high Reynolds number flows and MEMs sensor development. Key research interests include wind turbine modeling/design, experimental fluid mechanics, and compressed air wind tunnel experimentation. Recent work involves analyzing wake dynamics, rotor performance at high Reynolds numbers, and turbine array interactions. The lab's Compressed Air Wind Tunnel (CAWT) enables full-scale similarity testing for rotorcraft and wind energy systems. Recipient of Department of Defense fellowships (2024) Featured in Growing Impact podcast series discussing floating offshore wind energy Collaborations with Penn State's IEE on sustainable energy infrastructure projects Major contributions include publications on vortex dynamics in wind turbine wakes, hexagonal roughness characterization, and aeroelastically scaled model design. Advised Zarif Rahman's M.S. thesis on CAWT design/characterization.

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.

• Environmental Statistics
• Applied Statistics
• Machine Learning
• +3 more

Pietro Colombo is a Lecturer in Statistics at the University of Glasgow, specializing in advanced statistical methodologies with applications in environmental science and spatial analysis. His current academic roles include joint lecturing for courses such as 'Introduction to Statistical programming in R and Python' and 'Flexible Regression.' His research focuses on Gaussian processes, environmental data fusion, and predictive modeling for climate-related phenomena such as hurricane forecasting and space environment analysis. He has contributed to interdisciplinary projects involving sparse data challenges in femtosatellite networks and multifidelity frameworks for wind speed prediction. Colombo has been recognized with the Maclaurin Scholarship in Statistics. His recent work emphasizes methodological innovations in handling skewed environmental datasets and comparative statistical modeling for real-world applications.

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.

Richard VuducView profile

Associate Professor

• High-Performance Computing
• Algorithms
• Machine Learning
• +5 more

Richard Vuduc is an Associate Professor in the School of Computational Science and Engineering at the Georgia Institute of Technology (Georgia Tech), part of the College of Computing. His research focuses on high-performance computing (HPC), algorithms, performance engineering, and their applications in numerical simulation and data analysis. He leads the HPC Garage research lab and has held roles such as Associate Chair and Director of graduate programs in his department. He has received notable awards including the NSF CAREER Award, Gordon Bell Prize (2010), and teaching excellence recognition. Education and Background: PhD in Computer Science from UC Berkeley (2003) Postdoctoral scholar at Lawrence Livermore National Laboratory Research Interests: High-Performance Computing (HPC) systems and algorithms Parallel algorithms for graph processing and machine learning Performance analysis and optimization Tensor decomposition and multiway data analysis Key Contributions: Pioneering work in distributed-memory parallel algorithms Development of tools like Jack (autotuner) and SPARTan (PARAFAC2) Advances in HPC applications for bioinformatics and knowledge graphs Awards and Recognition: Gordon Bell Prize (2010) NSF CAREER Award Lockheed-Martin Aeronautics Teaching Excellence Award (2013) Best Paper Awards at SDM (2012) and IPDPS (2015) Professional Service: Chair of SIAM Activity Group on Supercomputing (2018-2020) Co-chair of SC16 Technical Papers Program Associate Editor for International Journal of High-Performance Computing Applications and IEEE TPDS Labs and Teams: The HPC Garage lab, focused on scalable HPC systems and applications, and contributions to interdisciplinary projects like the C21U Center.

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.

Nicholas HillsView profile

Professor

• Computational Fluid Dynamics (CFD)
• Gas Turbine Engineering
• Parallel Computing
• +3 more

Professor Nicholas Hills is a Professor of Computational Engineering and Faculty Associate Dean of the Doctoral College at the University of Surrey. He leads the Rolls-Royce University Technology Centre (UTC) in Thermo-Fluid Systems, focusing on advanced computational methods for gas turbine engineering. His research emphasizes massively parallel CFD simulations, multi-physics modeling, and thermal-fluid systems optimization. Previously, he held the Royal Academy of Engineering/Rolls-Royce Chair and served as Head of the Department of Mechanical Engineering Sciences until 2021. His work has been funded by EPSRC, InnovateUK, the EU, and industry partners. Research interests include turbine aerodynamics, high-pressure systems, and multi-physics frameworks for industrial applications. He leads the UK Applied Aerodynamics Consortium, leveraging national supercomputing resources for aerospace innovation.