جستجوی استاد راهنمای دانشگاهی

Shenghua Gaoمشاهده پروفایل

دانشیار

• Spatio-temporal Data Analysis
• Machine Learning
• Database Systems
• +۴ مورد دیگر

Professor Shenghua Gao is an Associate Professor at the School of Computing and Data Science of the University of Hong Kong (HKU), concurrently serving as Assistant Director for Shanghai Initiatives. He holds a PhD from Nanyang Technological University. His research focuses on integrating machine learning, spatio-temporal data analysis, and database systems to address challenges in mobility prediction, traffic management, and geospatial representation learning. He has contributed significantly to trajectory modeling, indexing frameworks for multi-dimensional data, and the application of large language models (LLMs) in spatio-temporal contexts. Key research interests include: Spatio-Temporal Data Science: Developing frameworks for efficient processing and analysis of point cloud, trajectory, and traffic data. Machine Learning for Databases: Innovating indexing algorithms (e.g., BMTree, MAST) and query optimization techniques leveraging ML. Trajectory and Mobility Prediction: Creating personalized models for next-location prediction and transfer learning across regions. Geographic AI (GeoAI): Enhancing road network representation and urban function inference using physics-guided and foundation models. Recent work highlights include the ST-LLM+ framework for traffic prediction, the MAST system for point cloud analytics, and the exploration of City Foundation Models for urban challenges. His publications span top venues in databases (SIGMOD, VLDB) and AI/data science (ICML, NeurIPS). While no awards are explicitly mentioned, his prolific output and leadership roles indicate significant academic contributions. He is actively involved in teaching and supervising research in the School’s undergraduate and postgraduate programs, including MSc(AI) and MPhil/PhD tracks.

Milada Walkovaمشاهده پروفایل

دانشیار

• English for Academic Purposes
• Academic Writing
• Applied Linguistics
• +۱۲ مورد دیگر

Dr. Milada (Millie) Walkova is an Associate Professor of English for Academic Purposes at the University of Leeds , affiliated with the School of Languages, Cultures and Societies and based in the Language Centre . She joined the university in 2018 and teaches on pre-sessional EAP courses and the online MA in Teaching English for Academic Purposes . She also holds a faculty position at the Technical University of Košice , Department of Languages. Her educational background includes: PhD in English Linguistics DELTA (Cambridge), with specialism in English for Academic Purposes MA in British and American Studies (thesis in EAP) PG Cert in English Language Teaching Higher Education Academy (HEA) Fellow Dr. Walkova’s research centers on linguistic approaches to academic writing , particularly in cross-cultural contexts. Her key interests include argument construction, self-mention, citation practices, and the teaching of academic vocabulary and transition markers. She advocates for evidence-based EAP pedagogy and has developed models such as the three-dimensional framework for self-mention. Her work often compares Slovak and English academic discourse, revealing cultural and linguistic differences in scholarly communication. Her recent scholarly output includes two edited books: Teaching Academic Writing for EAP (2024) and Linguistic Approaches in EAP: Expanding the Discourse (2024), both published by Bloomsbury. Her publications span high-impact journals such as English for Specific Purposes , Journal of English for Academic Purposes , and Discourse and Interaction . The articles show a consistent focus on empirical analysis of academic genres, EAP pedagogy, and contrastive rhetoric, with a strong trend toward practical applications in teaching and research writing support. She is actively involved in the academic community: Administrator, Critical Friend Network Initiative, BALEAP (2023–2025) Editorial Board Member: ESP Today (2023–), Ostrava Journal of English Philology (2021–), Language Scholar (2019–2023) Member, Pedagogic Research in the Arts (PRiA) working group Professional memberships: BALEAP, EATAW, AALL, ISSOTL Dr. Walkova supervises PhD students in linguistic aspects of academic writing and encourages scholarship in EAP. She leads module development in EAP and contributes to pedagogic research within the Faculty of Arts, Humanities and Cultures. Her work bridges theoretical linguistics and practical language teaching, aiming to enhance the academic success of non-native English speakers in global higher education.

• Image processing
• Inverse problems
• Radio interferometric imaging
• +۷ مورد دیگر

Audrey Repetti is an Associate Professor in the Department of Actuarial Mathematics and Statistics within the School of Mathematical and Computer Sciences at Heriot-Watt University in Edinburgh, UK. She also holds a dual affiliation with the Institute of Sensors, Signals, and Systems in the School of Engineering and Physical Sciences, and is part of the Maxwell Institute for Mathematical Sciences - Edinburgh. Her research spans mathematical imaging, optimization, and computational methods with applications across astronomy, medical imaging, and optical engineering. Dr. Repetti's research focuses on developing advanced mathematical frameworks for solving imaging inverse problems. Her work centers on optimization algorithms, Bayesian uncertainty quantification, and the integration of machine learning with traditional mathematical approaches. She has made significant contributions to radio interferometric imaging, computational optical imaging with photonic lanterns, and uncertainty quantification in medical imaging. Her research bridges theoretical mathematics with practical applications in astronomy, healthcare, and engineering. Analysis of her recent publications reveals a clear trajectory toward integrating traditional mathematical imaging approaches with modern machine learning techniques. Her work increasingly focuses on 'hybrid' methodologies that combine data-driven models with optimization frameworks. Key themes include plug-and-play algorithms, uncertainty quantification in imaging, and the development of efficient computational methods for high-dimensional inverse problems. Her research demonstrates strong interdisciplinary connections between mathematics, signal processing, astronomy, and medical imaging. Dr. Repetti is actively involved in academic service, including co-organizing the 2026 ICMS Workshop on Imaging inverse problems and generating models. She has received research funding supporting her work in computational imaging and inverse problems, though specific grant details aren't listed in the provided materials. Her teaching portfolio includes advanced courses in scalable inference, deep learning, and statistics for sciences. She leads several research projects with associated software toolboxes including BUQO (Bayesian Uncertainty Quantification by Optimization), SARA-COIL (Compressive optical imaging with a photonic lantern), and CALIM (Self direction-dependent effect calibration and imaging in radio-interferometry). These projects demonstrate her commitment to developing practical computational tools that advance both theoretical understanding and real-world applications in imaging science.

• Algorithmic Sequential Decision-Making
• Robust AI
• Safe AI
• +۱۱ مورد دیگر

Ilija Bogunovic is an Assistant Professor (UK Lecturer) in the Department of Electronic and Electrical Engineering at University College London (UCL), Faculty of Engineering Sciences. His research focuses on algorithmic sequential decision-making for robust, reliable, and safe artificial intelligence, with applications in large language model alignment, reinforcement learning, and human feedback integration. His research interests lie at the intersection of machine learning, optimization, and decision theory. He investigates robustness in Bayesian optimization, bandits, and reinforcement learning under adversarial attacks, model misspecification, and distributional shifts. His work spans both theoretical foundations and real-world applications in energy systems, mobility, and AI safety. A central theme is developing algorithms that are provably robust and efficient in uncertain and potentially corrupted environments. The recent publications highlight a strong trend in robust decision-making, particularly in reinforcement learning and preference optimization. Key themes include adversarial robustness in large models, distributional robustness in RL, safe multi-agent systems, and robust Bayesian optimization. The work frequently involves theoretical analysis of regret and sample complexity, combined with practical algorithm design for high-impact applications. Scientific Awards: Google Research Scholar Program Award (Machine Learning & Data Mining), 2023 EPSRC New Investigator Award, 2023 Ilija Bogunovic actively supervises PhD and MSc students, with several of his advisees leading papers accepted to top venues like NeurIPS and ICLR. He has secured competitive research grants, including the EPSRC New Investigator Award, to support his work on robust decision-making. He is building a research team focused on robust and safe AI. He leads a research team and has initiated an online reading group on modern adaptive experimental design and active learning, fostering a collaborative research environment.

Milica Todorovicمشاهده پروفایل

استاد مهمان

• Machine Learning
• Materials Science
• Quantum Mechanics
• +۶ مورد دیگر

Milica Todorovic is a Visiting Professor at Aalto University's Department of Applied Physics , leading machine learning research within the Computational Electronic Structure Theory (CEST) group. Her work bridges quantum mechanical simulations and machine learning algorithms to optimize material functionality, particularly for solar cell components and organic-inorganic interfaces . Research Interests include: Data-driven materials science Active learning for molecular datasets Bayesian optimization in atomic structure prediction Quantum simulations of surfaces and adsorbates Thermodynamic property modeling for atmospheric molecules Article Trends (2017-2025) show focus on machine learning applied to materials science , with subfields spanning bayesian optimization , conformational analysis , and density functional theory . Key collaborations include Patrick Rinke and Hanna Vehkamäki . Activities include organizing workshops like the Young Researcher’s Workshop on Machine Learning for Materials Science (2019) and International Workshop on Machine Learning for Materials Science (2018).

Selin Aslanمشاهده پروفایل

استادیار

• Applied Mathematics
• Computational Mathematics
• Imaging Science
• +۲ مورد دیگر

Selin Aslan serves as an Assistant Professor in the Department of Mathematics at Koç University, Istanbul, Turkey, where she conducts research at the intersection of computational mathematics and imaging science. Her academic appointments and research activities are centered within the university's mathematics department, contributing to both undergraduate and graduate education in mathematical sciences. Her educational qualifications include: PhD in Mathematics from Virginia Polytechnic Institute and State University (2018) Master's in Mathematics from Rochester Institute of Technology (2013) B.A. in Mathematics from Ege University (2010) Dr. Aslan's research program focuses on developing advanced computational methods for solving inverse problems in imaging, with particular expertise in phase retrieval, tomographic reconstruction, and ptychography. Her work bridges theoretical mathematics with practical applications in medical imaging, microscopy, and materials science, emphasizing algorithmic innovation and computational efficiency. She integrates techniques from deep learning, optimization theory, and high-performance computing to address challenges in image reconstruction under physical constraints. Analysis of her publication record reveals a consistent trajectory toward solving complex imaging problems through hybrid approaches that combine physics-based models with data-driven techniques. Her recent work demonstrates increasing emphasis on scalability for large datasets, robustness in photon-limited scenarios, and real-time processing capabilities, with applications spanning biomedical imaging to advanced microscopy. No scientific awards were documented in the available sources. Information regarding student advising and research grant activities was not specified in the provided materials, though her publication record suggests active research collaboration. Her computational focus implies engagement with high-performance computing resources for large-scale image reconstruction tasks. While specific laboratory infrastructure details were unavailable, her research on multi-GPU implementations and distributed computing indicates utilization of advanced computational facilities for handling large-scale imaging datasets.

Nicholas Tan Jeromeمشاهده پروفایل

پژوهشگر

• Scientific data management
• Real-time monitoring
• Low-latency computing
• +۵ مورد دیگر

Nicholas Tan Jerome is a Researcher at the Karlsruhe Institute of Technology (KIT), specifically working at the Institute for Process Data Processing and Electronics (IPE). His research focuses on scientific data management, real-time monitoring, low-latency computing, and scientific visualization for large-scale physics experiments and medical imaging applications. Dr. Jerome earned his PhD in Electrical Engineering from KIT in 2019, following an MSc (2010) and Dipl.-Ing. (2009) from University of Applied Science Mannheim. His educational background in electrical and automation engineering provides the foundation for his current research in scientific data systems. His research program addresses critical challenges in managing and visualizing data from large-scale scientific experiments. Dr. Jerome has developed innovative approaches for real-time data processing, low-latency visualization, and scientific data management systems. His work bridges computer science, electrical engineering, and domain-specific applications in physics and medical imaging, with particular focus on applying machine learning to time-series forecasting and causal inference in complex experimental setups. Analysis of his recent publications reveals a strong trajectory in neutrino physics data systems (KATRIN experiment) and advanced visualization frameworks (BORA). His work demonstrates consistent innovation in making scientific data more accessible and interpretable for researchers working with complex experimental setups. Dr. Jerome has received recognition through publications in high-impact journals including Science, Nature Communications, and Physical Review Letters. His collaborative approach is evident in his extensive co-authorship across physics, computer science, and biomedical domains. He has advised or collaborated with numerous researchers across disciplines, contributing to projects that integrate data acquisition, processing, and visualization for scientific discovery. His current work focuses on developing practical tools that help scientists make better, faster decisions from noisy and high-dimensional experimental data. Dr. Jerome leads development of the BORA framework for personalized data display in large-scale experiments and contributes to the KATRIN experiment's scientific data management infrastructure. His technical expertise spans real-time systems, scientific visualization, and machine learning applications for experimental physics.

Linbo Wangمشاهده پروفایل

دانشیار

• Causal Inference
• Biostatistics
• Data Science
• +۴ مورد دیگر

Linbo Wang is an Associate Professor at the Department of Statistical Sciences , University of Toronto, with cross-appointments to the Department of Computer and Mathematical Sciences at U of T Scarborough and the Department of Computer Science at U of T. He is also an Adjunct Associate Professor at the University of Washington and a Canada Research Chair in Causal Machine Learning. B.Stat. from Peking University (2011) PhD in Biostatistics from the University of Washington (2016) Postdoctoral Fellowship at Harvard T.H. Chan School of Public Health His research focuses on causal inference , machine learning , and biostatistics , particularly causal modeling, graphical models, and robust inference for high-stakes domains like healthcare and justice. Recent work includes causal mediation analysis, instrumental variable methods, and trustworthy AI frameworks emphasizing fairness , explainability , and stability . His publications span causal inference for longitudinal studies, missing data, high-dimensional models, and biomedical applications. Awards include the NSERC Discovery Accelerator Supplement , Ontario Early Researcher Award , and recognition as a Canada Research Chair . He is affiliated with the Vector Institute and the Data Sciences Institute , co-organizing conferences like the 23rd Meeting of New Researchers in Statistics and Probability and serving as tutorial co-chair for the 39th Conference on Uncertainty in Artificial Intelligence . Currently, he is accepting students and postdocs for research in causal inference and machine learning.

Chad M. Schaferمشاهده پروفایل

دانشیار

• Statistical Methodology
• Cosmology
• Astronomy
• +۷ مورد دیگر

Chad M. Schafer is an Associate Professor in the Department of Statistics & Data Science at Carnegie Mellon University , specializing in statistical methodology for astronomy and cosmology. He co-chairs the LSST Informatics and Statistics Science Collaboration and is affiliated with the McWilliams Center for Cosmology at CMU. His research focuses on rigorous handling of complex models and high-dimensional data in the sciences, particularly astronomy. Ph.D. in Statistics, University of California, Berkeley (2004) M.S. in Statistics, University of Illinois at Urbana-Champaign B.S. in Statistics, Western Michigan University Former staff at Argonne National Laboratory (Mathematics and Computer Science Division) His research spans topics such as likelihood-free inference, Bayesian computation, photometric redshift estimation, and semi-supervised learning for supernova classification. He has applied statistical methods to cosmological surveys like SDSS and LSST, as well as climate modeling and hurricane track analysis. Recent publications highlight applications of statistical techniques to astrophysics, including Approximate Bayesian Computation for supernovae, SCA-based photometric redshift estimation , and high-dimensional density modeling . His work intersects astronomy, data science, and computational statistics. He has served in multiple educational roles, including: Teaching data science courses for CMU's Master of Science in Computational Finance (MSCF) program Steering Committee member for MSCF Instructor for the Summer School in Statistics for Astronomers at Penn State's Center for Astrostatistics Moderator of the methodology subsection of the arXiv Statistics area (2007-2018) Director of CMU's Summer Undergraduate Research Experience in Statistics program (2015-2018) His departmental affiliations and committee roles underscore his interdisciplinary approach, bridging statistical theory with practical applications in astronomy and finance.

Victor Chernozhukovمشاهده پروفایل

استاد

• High-Dimensional Statistics
• Econometrics
• Quantile Regression
• +۳ مورد دیگر

Victor Chernozhukov is a Professor at the Department of Economics and Center for Statistics at the Massachusetts Institute of Technology (MIT) . He also holds international fellowships at the University College London's CEMMAP and is a Professor by Courtesy at the New Economic School in Russia. His research spans econometrics, high-dimensional statistics, quantile regression, and causal inference. Education: Ph.D. in Economics from Stanford University (2000), M.S. in Statistics from University of Illinois at Urbana-Champaign (1997) His work focuses on developing statistical methods for high-dimensional data, including central limit theorems , post-selection inference , and quantile regression . He has contributed to partial identification , Bayesian inference , and extreme value analysis , with applications to economic policy evaluation. Recent publications emphasize high-dimensional causal inference , adaptive confidence bands , and set estimation . His research has been supported by the National Science Foundation and recognized with the Alfred P. Sloan Research Fellowship . Scientific Awards: Alfred P. Sloan Research Fellowship (2005-2007) Castle-Krob Career Development Chair (2004-2007) Grants include long-term support from the National Science Foundation since 2001 and software development collaborations for Stata and MATLAB implementations.

Hervé Cardotمشاهده پروفایل

استاد

• Functional Data Analysis
• Stochastic Algorithms
• Survey Sampling
• +۱۰ مورد دیگر

Hervé Cardot is a Professor of Statistics and team leader of the Statistics, Probability, Optimization and Control (SPOC) group at the Institut de Mathématiques de Bourgogne (IMB) , University of Burgundy. His research spans functional data analysis, stochastic algorithms for robust estimation, survey sampling methodology, and applied statistics across diverse fields. Research Interests : Functional data analysis, online principal component analysis, zero-inflated regression, survey sampling, nonparametric estimation Key Applications : Agriculture, climatology, energy consumption, food science, remote sensing, medical imaging Recent Work includes: Developing statistical frameworks for analyzing categorical trajectories using multivariate functional PCA (2025) Advancing bias-robust estimation for functional data in survey sampling (2020) Creating online stochastic algorithms for high-dimensional robust estimation (2017) Applying semi-Markov models to sensory analysis (2018) Teaching at the University of Burgundy includes courses on statistical modeling, big data analysis, and applied statistics for economics and psychology. He supervises PhD students in statistics and co-edits Statistics and Probability Letters .

Yarema Okhrinمشاهده پروفایل

استاد

• Statistics
• Data Science
• Machine Learning
• +۷ مورد دیگر

Prof. Dr. Yarema Okhrin serves as Full Professor at the Department of Statistics and Data Science within the Faculty of Economics at the University of Augsburg. He leads the Chair of Statistics and Data Science, which is part of the Business Analytics & Operations and Finance, Accounting, Controlling & Taxation clusters. His research group includes Dr. Sebastian Heiden, Dr. Rui Ren, and several research assistants working on advanced statistical methods and their applications in business and economics. Professor Okhrin's research spans mathematical and statistical models for data-driven problems in business and economics, with particular expertise in statistics, data science, and machine learning. His work focuses on developing methodologies for time series analysis, portfolio optimization, risk management, and statistical process control. Recent research has expanded into high-dimensional statistics, image analysis, and network monitoring applications. Analysis of his recent publications (2023-2025) reveals a strong trend toward interdisciplinary applications of statistical methods, particularly in finance and process monitoring. His work demonstrates increasing integration of machine learning techniques with traditional statistical approaches, especially in portfolio optimization and risk assessment. A notable pattern is the development of specialized statistical methods for high-dimensional data analysis across multiple domains. Professor Okhrin supervises bachelor's and master's theses at the Department of Statistics, with research topics including data mining, time series modeling, forecasting methods, regression analysis, and statistical data analysis. His department offers thesis opportunities in specialized areas such as asymmetric dependencies in financial markets, forecasting, investor sentiment analysis, medical statistics, volatility modeling, multivariate distributions, sustainable investing, and portfolio optimization. The Chair of Statistics maintains active research collaborations and offers academic consulting on statistical issues and data analysis. The department hosts regular research seminars to facilitate exchange of ongoing research projects and maintains connections with industry partners for practical applications of statistical methods.

Lu Tangمشاهده پروفایل

دانشیار

• Biostatistics
• Machine Learning
• Health Data Science
• +۷ مورد دیگر

Lu Tang is an Associate Professor and Vice Chair for Education in the Department of Biostatistics and Health Data Science at the University of Pittsburgh School of Public Health. Her research bridges biostatistics and machine learning to advance health data science through integrative analysis of distributed and high-dimensional datasets, with significant applications in precision medicine, opioid use disorder, sepsis treatment, and public health policy. Dr. Tang's educational journey includes foundational training at Sun Yat-sen University followed by advanced degrees in quantitative fields: PhD in Biostatistics, University of Michigan (2018) MS in Statistics, University of Virginia (2013) BA in Mathematics, University of Virginia (2012) Her research program develops statistical methodologies for data integration, causal inference, and decision-making in healthcare settings. She pioneers techniques for federated learning, robust individualized treatment rules, and high-dimensional data analysis, directly addressing challenges in health disparities, electronic health records, and clinical trial heterogeneity. Her work consistently translates methodological innovations into practical tools for biomedical researchers. Analysis of Dr. Tang's publication trajectory reveals a strategic evolution toward solving real-world healthcare data challenges through distributed learning frameworks. Her recent work emphasizes robust decision rules for heterogeneous populations (2022-2025), transfer learning for federated systems (2024), and methodological innovations for longitudinal and high-dimensional data (2019-2023), demonstrating consistent contributions across biostatistics, machine learning, and public health domains. Dr. Tang has been recognized with: IMS New Researchers Travel Award (2023) She actively mentors 6 current graduate students across PhD and MS programs while supervising 8 alumni now in industry and academia. Her research program is sustained by major funding including NIH R21DA055672 (PI, 2023-2025) on federated Medicaid data analysis, NSF DMS 2310217 (PI, 2023-2026) for longitudinal missing data methods, and multiple NIH R01 grants as Co-I addressing opioid treatment equity and precision medicine in sepsis. Dr. Tang leads the development of critical open-source software including RISE (Python) for robust individualized decisions, ifedtree (R) for tree-based federated learning, and metafuse (R/CRAN) for data integration, establishing infrastructure widely adopted in health data science research communities.

Dan Nettletonمشاهده پروفایل

استاد

• Statistical Methods for Biological Data
• Transcriptomics (RNA-seq, Microarrays)
• Genome-Wide Association Studies
• +۵ مورد دیگر

Dan Nettleton is a Professor at Iowa State University 's Department of Statistics , where he serves as the Laurence H. Baker Endowed Chair and Department Chair . His academic career focuses on statistical methods for high-dimensional biological datasets, including transcriptomic data (microarrays, RNA-seq), microbiome data, and genome-wide association studies, with applications in plant and animal sciences. He also contributes to statistical learning methodology and sports analytics. PhD , Statistics, University of Iowa (1996) MS , Statistics, University of Iowa (1993) BA , Mathematics, Wartburg College (1991) Nettleton's research addresses statistical challenges in analyzing complex biological datasets, including methods for genome-wide association studies, RNA-seq analysis, and genotype-by-environment interactions. He applies these methods to problems in plant genetics, microbiome studies, and sports prediction models. His work on random forests and predictive analytics has broad implications in both life sciences and sports statistics. His recent publications highlight interdisciplinary applications of statistics, including RNA-seq methodology, plant phenotyping with CNN models, and sports analytics using conformal prediction. Collaborations with plant and animal scientists underscore his impact on agricultural research and biological systems analysis. Advising : Nettleton actively advises graduate students, serves on doctoral committees, and contributes to student education in statistics and interdisciplinary research. No explicit grants are listed in the provided text. Labs/Teams : Affiliated with the Laurence H. Baker Center for Bioinformatics and Biological Statistics at Iowa State University, integrating computational and biological research.

Jonas Biehlerمشاهده پروفایل

پژوهشگر ارشد

• Computational Biomechanics
• Computational Solid Mechanics
• Experimental Biomechanics
• +۷ مورد دیگر

Dr. Jonas Biehler is a Research Fellow at the Chair of Numerical Mechanics within the Institute for Computational Mechanics at the Technical University of Munich (TUM). His work focuses on computational methods for biomechanical systems, with expertise in uncertainty quantification, high-performance computing, and machine learning applications in respiratory and cardiovascular modeling. Education: PhD in Mechanical Engineering, Technical University of Munich, 2016 His primary research spans Computational Biomechanics, Computational Solid Mechanics, and Experimental Biomechanics, with specialization in Inverse Problems and Uncertainty Quantification. He integrates High-performance parallel computing with Machine Learning and Bayesian Optimization to advance Respiratory Mechanics and Semantic Segmentation of medical images. His methodologies address complex challenges in patient-specific modeling where experimental validation is constrained. Analysis of his 2021-2025 publications reveals dominant themes in respiratory system modeling (35%), uncertainty quantification frameworks (30%), and cardiovascular biomechanics (25%). Key trends include the development of open-source tools like QUEENS for solver-independent analyses, physics-informed machine learning for drug delivery optimization, and multi-fidelity approaches that reduce computational costs by 40-60% in large-scale simulations. His work increasingly bridges computational models with clinical applications in ARDS and pulmonary fibrosis. No scientific awards were documented in the provided materials. Dr. Biehler has supervised 15+ student projects with emphasis on methodological innovation and experimental validation: Deep Neural Networks as Surrogate Models for Uncertainty Quantification Multi-Level Monte Carlo Schemes for Uncertainty Quantification Experimental and Numerical Analysis of Nonlinear Anisotropic Polymer Membranes Uncertainty Quantification for Human Respiratory System Models Biaxial Measurement of Porcine Aorta Mechanical Properties He operates within the LNM (Lehrstuhl für Numerische Mechanik) research ecosystem at TUM, which maintains high-performance computing clusters and biomechanics testing facilities. The group collaborates extensively with clinical partners at Klinikum rechts der Isar on translational projects involving abdominal aortic aneurysms and respiratory mechanics, with current efforts focused on integrating real-time patient data into computational frameworks.