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Mirel CosulschiView profile

Associate Professor

• Algorithms and Complexity
• Software Engineering
• Web Data Management
• +13 more

Mirel Cosulschi is an Associate Professor in the Department of Computer Science at the University of Craiova, Romania, where he has been a faculty member since 1997. He is affiliated with the Faculty of Automation, Computers and Electronics and teaches courses in Graph Algorithms, Software Engineering, Web Data Management, and Fundamentals of Programming at both undergraduate and master's levels. PhD in Computer Science, University of Craiova, 2006 MSc in Artificial Intelligence, University of Craiova, 1998 BSc in Computer Science, University of Craiova, 1997 His research interests include algorithms and complexity, software engineering, web data management, semantic web, distributed programming, and cloud computing. He has supervised numerous BSc and Master’s theses focusing on web applications, mobile development, data processing, and software modeling. His teaching emphasizes algorithmic thinking, structured programming, and modern software development practices. While no formal scientific publications are listed, his academic contributions include authored textbooks such as Algoritmica grafurilor si aplicatii (2014) and Practica programarii in C (2014), indicating active engagement in curriculum development and educational materials. He is involved with the MSDN Academic Alliance and utilizes tools like Windows Installer MSI and Advanced Web Ranking. Mirel Cosulschi has advised over 100 students in thesis projects related to software development, data management, and web technologies. Although no external grants or awards are mentioned, his long-standing academic career and consistent supervision reflect sustained scholarly activity. He maintains a professional web presence at http://inf.ucv.ro/~mirel and can be contacted via email at mirelc@central.ucv.ro . He is currently active in teaching and academic supervision, with no indication of retirement or part-time status.

Bernard HaasdonkView profile

Professor

• Model Reduction
• Parametrized Partial Differential Equations (PDEs)
• Kernel Methods
• +7 more

Bernard Haasdonk is a Professor at the University of Stuttgart, affiliated with the Institute of Applied Analysis and Numerical Simulation (IANS), part of the Faculty of Mathematics and Computer Science. His research focuses on model reduction techniques for parametrized partial differential equations (PDEs), kernel-based methods, numerical analysis, and machine learning applications in scientific computing. He leads a research group in numerical mathematics and has contributed to software tools like RBMatlab and KerMor. Affiliations: Institute of Applied Analysis and Numerical Simulation, University of Stuttgart Roles: Academic Researcher, Software Developer, Grant Principal Investigator His work bridges numerical simulation, machine learning, and reduced basis methods, addressing challenges in optimal control, fluid dynamics, and biomechanics. Haasdonk has held multiple funded projects, including those on kernel methods for model reduction and certified RB-ML-ROM surrogate models. Research Interests: Model reduction for PDEs, kernel methods, greedy algorithms, numerical analysis, optimal control, and applications in fluid dynamics and porous media. He emphasizes structure-preserving methods for Hamiltonian systems and data-driven approaches for surrogate modeling. Publications: Over 200 articles in journals like SIAM, BIT Numerical Mathematics, and Physica D, focusing on convergence analysis, kernel-based approximation, and reduced-order modeling. Recent trends include adaptive greedy algorithms, symplectic model reduction, and energy-conserving surrogates. Awards: IEEE PerCom 2017 Best Paper Award, Teaching Excellence Awards (2012-2017), and early-career research grants. Grants: DFG-funded projects on model reduction, SimTech Cluster contributions, and collaborations on fuel cells and biomechanics. Teams: Leads the Numerical Mathematics Research Group at IANS, collaborating with interdisciplinary teams on projects like MORCOS (Model Order Reduction of Coupled Systems) and KerMor (Kernel Methods for Model Reduction).

Carlos VázquezView profile

Professor

• Computer vision
• Medical imaging
• 3D reconstruction
• +4 more

Carlos Vázquez is a Professor at the Department of Software Engineering and IT, École de technologie supérieure (ÉTS). He holds a B.Eng. and M.Sc. from ISPJAE, Cuba, and a Ph.D. from INRS, Montreal. His research focuses on computer vision, medical imaging, 3D reconstruction, and immersive video technologies. He co-leads the Summit Tech Research Chair in Immersive and Interactive Video and is affiliated with the Multimedia Research Laboratory (LABMULTIMEDIA) and the Open Innovation Laboratory in Health Technologies (LIO-ÉTS). His expertise includes stereoscopic imaging, multi-view video coding, and GPGPU programming. Notable research axes are software systems, multimedia, cybersecurity, and health technologies. He has supervised numerous doctoral and master’s theses, including works on 3D spine reconstruction, personalized femur modeling, and immersive video compression. Key contributions include advancements in medical imaging analysis, 3D reconstruction from biplanar radiographs, and efficient video coding for virtual reality. His work bridges clinical applications and engineering, with implications in surgical planning, patient rehabilitation, and multimedia systems optimization.

• Multi-agent Systems
• Educational Technology
• Intelligent Tutoring Systems
• +5 more

Demetrio Arturo Ovalle Carranza is a Professor in the Department of Computer Science at the National University of Colombia's School of Engineering. With an academic career spanning over three decades since earning his PhD from Joseph Fourier University in Grenoble, France in 1991, he has established himself as a prominent researcher in multi-agent systems and educational technology. His research focuses on developing intelligent systems for education, particularly in the areas of multi-agent architectures, adaptive learning environments, context-aware educational applications, and learning object recommendation systems. His work bridges artificial intelligence with practical educational applications, creating systems that adapt to individual learner needs and contexts. Analysis of his recent publications (2015-2020) reveals a strong trend toward developing sophisticated multi-agent systems for educational applications, with increasing emphasis on context-awareness, personalization, and adaptive learning techniques. His research demonstrates consistent evolution from foundational multi-agent work to sophisticated educational applications integrating fuzzy logic, ontologies, and collaborative filtering. Ovalle Carranza has mentored numerous researchers who have become active contributors in the field, including Oscar M. Salazar, Néstor D. Duque, Valentina Tabares Morales, and others who frequently appear as co-authors on his publications. His collaborative network extends across Colombian institutions and international research groups. His laboratory work centers around developing and testing multi-agent educational systems, with particular emphasis on creating adaptive learning environments that can respond to individual student needs and group dynamics. Current projects appear to focus on serious games, health-care applications of intelligent agents, and advanced recommendation systems for educational resources.

Felix SchindlerView profile

Researcher

• Numerical Analysis
• Machine Learning
• Scientific Computing
• +3 more

Felix Schindler is a Researcher at the Institute for Analysis and Numerical Analysis , part of the Department of Mathematics and Computer Science at the University of Münster . His work bridges numerical analysis, machine learning, and scientific computing, with a focus on model reduction for partial differential equations (PDEs), adaptive algorithms, and computational efficiency. Research Interests include: Numerical analysis of parametric and multiscale PDEs Localized reduced basis methods (LRBM) and adaptive enrichment Integration of model order reduction (MOR) with machine learning (ML) Conservative flux reconstruction techniques Development of software libraries like dune-xt and pyMOR Recent Publications highlight trends in applying deep kernel models for surrogate modeling, localized training strategies for PDE-constrained optimization, and hybrid full/reduced-order pipelines for reactive flow prediction. His work emphasizes certified error control, hierarchical adaptivity, and cross-disciplinary computational frameworks. Collaborations span institutions such as AIMS Senegal, Springer Nature, and DUNE project teams. He actively contributes to conferences like GAMM, ENUMATH, and Algoritmy.

Margarete A. JadamecView profile

Associate Professor

• Geodynamics
• Tectonics
• Numerical Modeling
• +5 more

Margarete A. Jadamec serves as Associate Professor at the University at Buffalo, SUNY, holding dual appointments in the Department of Earth Sciences within the College of Arts and Sciences and the Institute for Artificial Intelligence and Data Science. She directs the Computational and Data-Enabled Science and Engineering PhD Program, leading interdisciplinary research at the intersection of geodynamics and advanced computing. Her research focuses on solid Earth deformation processes, particularly plate and mantle dynamics at subduction zones. Using high-resolution numerical models and high-performance computing, she investigates how 3D slab geometries influence mantle rheology and subduction mechanics. Key interests include slab-driven mantle flow, multi-plate interactions, and dynamically evolving plate-asthenosphere decoupling, moving beyond traditional 2D paradigms toward comprehensive 3D modeling. Her publication record (2014-2018) reveals a strong emphasis on computational geodynamics, with recurring themes in mantle flow dynamics, subduction zone mechanics, and virtual reality applications for geoscientific visualization. Notable contributions include developing the ShowEarthModel framework for global slab geometry analysis and advancing 3D modeling of the Alaska tectonic system. Dr. Jadamec actively mentors students through the Department of Earth Sciences and Computational and Data-Enabled Science and Engineering PhD Program, collaborating with UB's Research Engineering Laboratory for Advanced Computational Science. Her Geodynamics Research and Visualization Group leverages modern technologies to pioneer convergent research in plate tectonics. Laboratory work centers on the Geodynamics Research and Visualization Lab, where her team develops data-driven models of natural subduction systems using high-performance computing and 3D virtual reality frameworks like TECT_Mod3D Software and geosolver Cluster Partition.

Jakub NowosadView profile

Researcher

• Spatial Association
• Information Theory
• Landscape Metrics
• +7 more

Jakub Nowosad is a researcher affiliated with Adam Mickiewicz University in Poznań and currently holding a prestigious Marie Skłodowska-Curie Actions Postdoctoral Fellowship at the University of Münster's Remote Sensing and Spatial Modeling group (August 2024-August 2026). His work bridges geography, computer science, and environmental science with a focus on spatial data analysis and machine learning applications. Nowosad's research interests center on spatial association methods, landscape metrics, information theory applications in geography, and spatial machine learning techniques. He has made significant contributions to developing and implementing computational methods for analyzing spatial patterns, particularly through R programming packages that address spatial autocorrelation challenges in machine learning. His work spans environmental monitoring, landscape ecology, and geospatial analysis with practical applications in understanding climate change impacts, forest fragmentation, and permafrost degradation. His publication record demonstrates a strong focus on methodological development in spatial data science, with numerous recent publications on spatial machine learning frameworks, computational landscape ecology, and specialized software tools. Nowosad's work shows a clear trajectory toward integrating advanced machine learning techniques with traditional spatial analysis methods to overcome limitations in spatial prediction and pattern recognition. Marie Skłodowska-Curie Actions Postdoctoral Fellowship (MSCA-PF) for the PRISM project (PReservation and RecognItion of Spatial patterns using Machine learning) Nowosad actively contributes to the open-source geospatial community through software development, including packages like spatialRF and contributions to spatial machine learning frameworks. His collaborative work spans multiple institutions including the University of Cincinnati, International Institute for Applied Systems Analysis, and various European research groups. He appears to be developing methodologies that will significantly impact how spatial patterns are recognized and preserved in environmental datasets, with applications ranging from Arctic landscape monitoring to forest conservation planning.

Panagiotis HadjidoukasView profile

Associate Professor

• Parallel Computing
• Distributed Systems
• High Performance Computing
• +3 more

Panagiotis Hadjidoukas is an Associate Professor and Head of the Laboratory for Computing at the Computer Engineering and Informatics Department, University of Patras, within the School of Engineering. His work focuses on high-performance computing systems and parallel programming models. His research spans parallel and distributed computing , runtime support for parallel programming models , and automation of AI/ML workloads . Key contributions include developing the torc runtime system for task parallelism and pioneering work in extreme-scale scientific simulations. His interests bridge theoretical computer science with practical applications in scientific computing and AI acceleration. Notable achievements include the ACM Gordon Bell Prize Winner (2013) for 11 PFLOP/s cloud cavitation simulations and Finalist (2015) for in-silico lab-on-a-chip microfluidics. His software tools ( torc_lite , torcpy ) enable efficient parallelism across diverse architectures. Doctor of Philosophy (2003), University of Patras Master of Science (2001), University of Patras Diploma in Computer Engineering (1998), University of Patras As Head of the Laboratory for Computing, he leads infrastructure development while maintaining active research collaborations with IBM Research and ETH Zurich. His teaching portfolio includes graduate courses on high-performance computing for data sciences and parallel processing principles.

Daniela BeisserView profile

Professor

• Bioinformatics
• Computational Biology
• Microbial Ecology
• +6 more

Prof. Dr. Daniela Beisser is a Professor at the Department of Engineering and Natural Sciences (FB 8) of the Westphalian University of Applied Sciences in Recklinghausen, Germany. Her research focuses on bioinformatics and biostatistical methods for high-throughput 'omics data, applied to biomedicine and freshwater ecology. She previously held academic roles at the University of Duisburg-Essen (2017–2023) and University Hospital Essen. 2004–2008: B.Sc. in Molecular Biology with Bioinformatics focus, FH Gelsenkirchen 2006–2008: M.Sc. in Molecular Biology with Bioinformatics focus, FH Gelsenkirchen 2008–2011: Ph.D. in Bioinformatics, University of Würzburg Her research integrates computational approaches with experimental data to study molecular responses to environmental stressors in freshwater organisms, genome analyses in human and protists, and proteomic studies in plants. She also investigates eco-evolutionary theories in microorganisms and links biodiversity to ecosystem functions. Recent publications highlight her work on amplicon sequencing (Natrix2 pipeline), metatranscriptomic analysis of microbial communities, and machine learning frameworks for environmental data. She contributes to software tools like TaxMapper and BioNet for reproducible workflows. Best Poster Award, German Conference on Bioinformatics (2013) Travel scholarships: DAAD, DAAD PROMOS, German Symposium on Systems Biology E-fellows.net scholarship (2006–2008) She has supervised numerous PhD, Master’s, and Bachelor’s students on topics such as protist community dynamics , fungal degradation processes , and stressor recovery mechanisms . Her lab collaborates on the CRC 1439 'RESIST' project and develops tools for environmental DNA analysis.

• Marine Geophysics
• Seismotectonics
• Oceanic Crust Formation
• +3 more

Yongshun Chen is a Chair Professor at Southern University of Science and Technology (SUSTech), where he serves as the founding director of the Department of Marine Science and Engineering. Previously, he was a Professor at Peking University's School of Earth and Space Sciences and an Associate Professor at Oregon State University's Institute of Oceanography. He is currently the President of InterRidge, the international association for mid-ocean ridge studies. Chair Professor, Southern University of Science and Technology (2013-present) Professor, Peking University, School of Earth and Space Sciences (2001-2013) Associate Professor, Oregon State University, Institute of Oceanography (1990s-2001) PhD in Geophysics, Princeton University (1989) Bachelor's degree in Geophysics, University of Science and Technology of China (1982) Professor Chen's research focuses on marine geophysics and seismotectonics, with particular emphasis on the dynamics of oceanic spreading plate boundaries and the formation mechanisms of the oceanic crust. His pioneering work has provided explanations for seismic observations of mid-ocean ridges and continental ophiolite suites. After returning to China in 2001, he pioneered the field of "seismic geotectonics" in mainland China, conducting mobile seismic array observations and developing seismological methods to study the three-dimensional structure of the crust and upper mantle in key tectonic regions. His research has led to several significant contributions, including the "corner flow" model for the asthenosphere in southern Tibet, a new model of Indian lithosphere subduction beneath Tibet, and a "local upper mantle convection model" to explain intraplate volcanism in Northeast China. His work has had substantial impact on the international geoscience community, with his oceanic crust formation model being widely adopted by marine geologists. Professor Chen has published over 130 academic papers with more than 2,038 citations, including a single paper with 270 citations. His research spans marine geophysics, seismic tomography, crustal and mantle structure, plate tectonics, and volcanic processes, with a particular focus on the Tibetan Plateau and Northeast China. National Science Fund for Distinguished Young Scholars (2002) Editor-in-Chief of Marine Geophysical Researches (2004-2008) Editor of Journal of Geophysical Research-Solid Earth Reviewer for the Earth Science Department of the National Natural Science Foundation of China Professor Chen has supervised numerous graduate students and postdoctoral researchers. He has led more than ten major research projects, including those funded by the National Natural Science Foundation of China, 973 and 863 programs of the Ministry of Science and Technology, and special projects for earthquake research. His research group utilizes advanced seismic techniques and collaborates with international institutions to study Earth's structure and dynamics. As the founding director of the Department of Marine Science and Engineering at SUSTech, Professor Chen has established research teams focusing on marine geophysics, ocean dynamics, and seafloor exploration. His department conducts field observations using mobile seismic arrays and collaborates on international ocean drilling programs to advance understanding of Earth's structure and evolution.

Yüksel YurtayView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Data Science
• +8 more

Assoc. Prof. Dr. Yüksel Yurtay is an active faculty member at Sakarya University's Faculty of Computer and Information Sciences, Department of Data Science and Analytics. With a career spanning over three decades at the university since 1993, he has established himself as a prominent researcher and educator in the fields of data science, artificial intelligence, and business analytics. Education: Doctorate (2011-2020): Sakarya University, Institute of Business Administration/Production Management and Marketing, Thesis: "An empirical study on the impact of the dashboard on decision processes in the context of supply chain management" Master's (1996-1998): Sakarya University, Institute of Social Sciences/Production Management and Marketing, Thesis: "Activity based costing system and an application in electro-mechanical industry" Bachelor's (1987-1991): Anadolu University, Faculty of Arts and Sciences/Mathematics Dr. Yurtay's research focuses on the intersection of data science and business applications, with particular emphasis on dashboard systems, key performance indicators, and supply chain management. His work bridges theoretical computer science with practical business applications, developing innovative solutions for performance monitoring, decision support, and process optimization. His research portfolio demonstrates consistent productivity with publications spanning from 2018 through 2025, showing no signs of slowing down. Analysis of Dr. Yurtay's recent publications (2018-2025) reveals several key research trajectories: a strong focus on dashboard and KPI systems for business decision-making, growing interest in AI applications for emotion recognition (particularly in call center contexts), development of novel metaheuristic algorithms for optimization problems, and application of computer vision techniques for specialized detection tasks. His work consistently combines theoretical algorithm development with practical business applications. Dr. Yurtay has successfully secured research funding from prestigious sources including TÜBİTAK (project 1505) and industry partnerships with companies like Turkcell Global Bilgi. His current projects focus on real-time emotion recognition using deep learning and business process optimization. As an educator, Dr. Yurtay teaches core courses in numerical analysis, software engineering, computer engineering design, and data science. He has supervised graduate theses, including Akın YAŞAR's work on real estate photo quality evaluation using deep learning. His teaching portfolio reflects his expertise across both theoretical computer science and applied data analytics.

Hannah ElfnerView profile

Professor

• Theoretical Nuclear Physics
• Heavy-Ion Collisions
• Quark-Gluon Plasma
• +5 more

Hannah Elfner is a Professor for Theoretical Physics at the Goethe University Frankfurt and Head of Department 'Hot and Dense QCD Matter' at GSI Helmholtzzentrum. She coordinates the Theory Pillar at GSI and leads the SMASH hadronic transport code development. Her work bridges FAIR experiments and CERN's LHC with Neutron Star mergers through QCD phase diagram exploration. Education: PhD (2009) from Goethe University in collaboration with Helmholtz Research School on Quark Matter 2010 Feodor Lynen Fellowship at Duke University 2012-2018 Helmholtz Young Investigator Group at GSI/FIAS 2013- W2 Professorship at Goethe University Research Focus on dynamical heavy-ion collision modeling through SMASH, covering non-equilibrium stages , ideal hydrodynamics , and hadronic rescattering . Key areas include QCD critical point detection , viscosity constraints , and machine learning applications in Au+Au collisions at FAIR (up to 12 GeV/nucleon) and neutron-rich systems for nuclear symmetry energy studies. Scientific Contributions: 2016 Heinz Maier-Leibnitz Prize 2018 Zimanyi Medal (Quark Matter conference) 2021 Scientist of the Year (Goethe University) 2024 Outstanding Referee (APS) Her advising spans BSc, MSc, and PhD theses on topics like resonance lifetimes , neutron skin effects , and spin-magnetohydrodynamics . Grants include JETSCAPE (2020-2025) , CRC-TR-211 (2017-) , and PUNCH4NFDI (2021-) for national research data infrastructure . Laboratory leads the international SMASH developer team with members across Germany, Italy, and China. Collaborations with Duke University's QCD group , Central China Normal University , and McGill University on transport coefficients and photon production mechanisms.

• Database Systems
• Integration of heterogeneous databases
• Data quality and data integrity
• +6 more

Thomas Kudraß serves as Professor for Database Systems at the Faculty of Computer Science, Mathematics and Natural Sciences (IMN) at Leipzig University of Applied Sciences (HTWK Leipzig). With over 25 years of academic and industry experience, he teaches courses including Database Basics, Database Application Programming, Data Warehousing, and Big Data Technologies. His leadership roles include Internship Coordinator for Computer Science since 2001, Member of the Academic Senate since 2011, and Contact person for the IMN faculty alumni association since 2005. Dr. Kudraß earned his Dipl.-Ing. in Computer Science from Technical University of Dresden (1985-1990) and completed his doctorate at Darmstadt University of Technology (1992-1997), where his dissertation received the prestigious Jos Schepens Memorial Award. Prior to academia, he worked as an Information Systems Architect at UBS AG Zurich and Database Specialist at Swiss Bank Corporation Basel. His research spans database technologies with focus on heterogeneous database integration, data quality, data privacy, and modern database concepts. Recent work explores cloud-native billing applications for 5G, NoSQL databases, and privacy-preserving record linkage techniques. His publication record shows continuous evolution from traditional database systems to contemporary challenges in big data and distributed systems. Jos Schepens Memorial Award for doctoral dissertation (1997) Active participation in German Informatics Society (GI) specialist groups Organizer of multiple database-related workshops and conferences Professor Kudraß has significantly contributed to academic governance as E-Learning Officer (2000-02), Faculty Council member (2009-12), and liaison lecturer for the German Society for Computer Science (2001-14). He has coordinated numerous student projects and served on program committees for major conferences including INFORMATIK 2017 and BTW series.

Jochen MerkerView profile

Professor

• Mathematical Analysis
• Numerical Methods
• Partial Differential Equations
• +5 more

Jochen Merker serves as Professor for Analysis and Optimization at the Faculty of Computer Science and Media, Leipzig University of Applied Sciences (HTWK Leipzig). His academic profile demonstrates deep expertise in mathematical analysis, numerical methods, and computational mathematics with applications across various scientific domains. Institution: Leipzig University of Applied Sciences (HTWK Leipzig) Faculty: Computer Science and Media Position: Professor for Analysis and Optimization Contact: Available by appointment via email Professor Merker's research spans multiple mathematical disciplines with particular emphasis on partial differential equations, numerical analysis, and mathematical modeling. His work bridges theoretical mathematics with practical applications in fluid mechanics, epidemiology, and machine learning. He has made significant contributions to the understanding of doubly nonlinear evolution equations, positivity preservation in numerical methods, and rate-induced tipping phenomena. His research demonstrates how advanced mathematical techniques can solve complex problems in physical systems and data science. Analysis of his publication trends reveals a consistent focus on mathematical rigor combined with practical applicability. His recent work shows increasing integration of mathematical theory with computational approaches, particularly in digital learning environments and e-assessment systems for STEM education. The interdisciplinary nature of his publications demonstrates how mathematical analysis serves as a foundation for solving problems across physics, engineering, epidemiology, and computer science. Primary research areas: Mathematical Analysis, Numerical Methods, Partial Differential Equations Application domains: Fluid Mechanics, Epidemiology, Machine Learning Methodological focus: Positivity preservation, Maximum principles, Numerical stability Educational contributions: Digital teaching in STEM fields, E-assessment systems Professor Merker actively contributes to the academic community through his research publications and educational initiatives. His work on digital teaching methods for STEM disciplines reflects his commitment to modernizing mathematical education. While specific grant information isn't available in the provided materials, his extensive publication record suggests sustained research activity across multiple projects. His laboratory or research team likely focuses on computational mathematics and numerical analysis, though specific details aren't provided in the source material.

Michael WilliamsView profile

Research Fellow

• Gravitational Wave Physics
• Neutron Stars
• Black Holes
• +3 more

Dr. Michael Williams is a Research Fellow at the Institute of Cosmology & Gravitation under the Faculty of Technology , University of Portsmouth. His work focuses on gravitational wave physics, neutron star dynamics, and black hole binaries through collaborations with LIGO, Virgo, and KAGRA detector networks. Active in gravitational wave detection and multi-messenger astronomy Developing AI algorithms for telescope targeting and signal processing Recipient of the IOP Trusted Reviewer status (2025) His recent research includes premerger characterization of black hole binaries , continuous wave searches from pulsars , and gravitational wave-FRB counterpart detection . He contributes to open-source tools like nessai and bayesbeat , with 25 datasets released. Williams participates in conferences like the European AI for Fundamental Physics Conference and workshops on milli-hertz gravitational wave astrophysics. Key collaborations: LIGO, Virgo, KAGRA, GEO600 Technical expertise: matched filtering , Bayesian inference , normalizing flows