جست‌وجوی استاد و پژوهشگر

Pedro Pintado Jorge Gonçalvesمشاهده پروفایل

مدرس ارشد

• Computational neuroscience
• Machine Learning
• Simulation-based inference
• +۴ مورد دیگر

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. Sapsisمشاهده پروفایل

استاد

• Nonlinear Dynamical Systems
• Probabilistic Modeling
• Data-Driven Methods
• +۸ مورد دیگر

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 Qianمشاهده پروفایل

استادیار

• Scientific machine learning
• Model reduction
• Multifidelity methods
• +۵ مورد دیگر

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.

• Evolutionary Algorithms
• Multi-objective Optimization
• Engineering Design Optimization
• +۵ مورد دیگر

Professor Tapabrata Ray is a distinguished faculty member at the School of Engineering and Technology, University of New South Wales (UNSW) Canberra. He serves as the founder and leader of the Multidisciplinary Design Optimization Research Group at UNSW, with his research profile available at www.mdolab.net. His office is located in Building 17 Room 202 at the University of New South Wales, ACT 2610, Australia, and he can be reached at +61 2 5114 5201 or t.ray@unsw.edu.au. Professor Ray's research interests span a wide range of optimization-related fields, with particular expertise in evolutionary algorithms, multi-objective optimization, and engineering design optimization. His work bridges computational intelligence with practical engineering applications across multiple domains including aerospace engineering, structural optimization, biomedical device design (particularly stents), and energy systems. His research approach often focuses on developing computationally efficient methods for solving complex optimization problems, with special attention to multi-fidelity approaches, surrogate modeling, and handling computationally expensive evaluations. Professor Ray's publication record demonstrates consistent high productivity across decades, with his most recent work (2023-2025) showing continued strong activity in evolutionary computation, multi-objective optimization, and biomedical applications. His research trends indicate growing interest in multi-concept optimization frameworks, stent design optimization, and applications in transportation and energy systems. His work often appears in top-tier journals including IEEE Transactions on Evolutionary Computation, Journal of Mechanical Design, and Swarm and Evolutionary Computation. Professor Ray has made significant contributions to the field of evolutionary algorithms and optimization, with applications spanning from aerospace engineering to biomedical device design. His work on multi-fidelity optimization, surrogate-assisted evolutionary algorithms, and multi-concept design frameworks represents cutting-edge research in computationally efficient optimization methods.

Daniel Weigertمشاهده پروفایل

پژوهشگر

• Computational Mechanics
• Crash Simulation
• Topology Optimization for Nonlinear Impact Processes
• +۴ مورد دیگر

Daniel Weigert is a Researcher at Audi AG with expertise in computational mechanics and crash simulation. His work focuses on: Topology optimization for nonlinear impact processes Multifidelity shape optimization and robustness analysis for crash simulations Finite element modeling to minimize injuries Multi-level material modeling for fiber composites Research Projects: He contributed to "Automatic Model Reduction in Crash Simulation" during 2013–2015. His publications include: Identification of sub-models for crash simulation (2014) Automatic model reduction using pre-existing simulations (2014) Robustness assessment via stochastic and geometric analyses (2012)

• Computational design of aerospace systems
• Multidisciplinary design optimization
• Surrogate-based modeling and optimization
• +۶ مورد دیگر

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 Millerمشاهده پروفایل

استادیار

• Experimental Fluid Mechanics
• Wind Turbine Aerodynamics
• MEMs Sensor Development
• +۳ مورد دیگر

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 Konomiمشاهده پروفایل

دانشیار

• Bayesian statistics
• Spatial statistics
• Uncertainty quantification
• +۲ مورد دیگر

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
• +۳ مورد دیگر

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 Chenمشاهده پروفایل

استادیار

• Scientific Machine Learning
• Uncertainty Quantification
• Bayesian Inference
• +۵ مورد دیگر

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 Qianمشاهده پروفایل

استادیار

• Model Reduction
• Scientific Machine Learning
• Uncertainty Quantification
• +۱ مورد دیگر

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 Vuducمشاهده پروفایل

دانشیار

• High-Performance Computing
• Algorithms
• Machine Learning
• +۵ مورد دیگر

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 Warneمشاهده پروفایل

مدرس

• Statistical Data Science
• Bayesian Inference
• Computational Biology
• +۳ مورد دیگر

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 Hillsمشاهده پروفایل

استاد

• Computational Fluid Dynamics (CFD)
• Gas Turbine Engineering
• Parallel Computing
• +۳ مورد دیگر

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.

Daniele Schiavazziمشاهده پروفایل

دانشیار

• Uncertainty Quantification
• Computational Mathematics
• Medical Image Processing
• +۴ مورد دیگر

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.