جستجوی استاد راهنمای دانشگاهی

Sven Klaaßenمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Causal Inference
• Deep Learning
• +۱ مورد دیگر

Dr. Sven Klaaßen serves as a Research Fellow at the University of Hamburg's Hamburg Business School within the Professorship for Statistics with Application in Business Administration, collaborating closely with Prof. Dr. Martin Spindler since 2021. His research focuses on developing advanced statistical methodologies for complex data environments. His academic credentials include: Ph.D. in Statistics from Hamburg Business School (2020) Visiting Scholar at MIT Department of Economics (2022) M.Sc. in Business Mathematics from University of Hamburg (2016) BSc in Business Mathematics from University of Hamburg (2014) Dr. Klaaßen's research program centers on Machine Learning, Causal Inference, Deep Learning, and High-Dimensional Statistics, with particular emphasis on developing robust inference techniques for modern data challenges. His work bridges theoretical statistics with practical applications in business analytics and econometrics, often addressing the complexities of high-dimensional datasets where traditional methods fail. Analysis of his recent publications reveals a clear trajectory toward integrating machine learning with causal inference frameworks, exemplified by his leadership in the DoubleML software ecosystem. His research increasingly tackles multimodal data challenges while maintaining rigorous statistical foundations, with applications spanning economics, operations research, and business decision systems. As an active member of Prof. Spindler's research group, Dr. Klaaßen contributes to collaborative projects developing open-source statistical tools and advancing methodological frontiers in causal machine learning. The team maintains strong industry and academic partnerships focused on translating theoretical innovations into practical analytical solutions.

Thomas Naglerمشاهده پروفایل

استاد

• Mathematical Statistics
• Machine Learning
• High-dimensional Dependence Models
• +۳ مورد دیگر

Thomas Nagler is a Professor at the Department of Statistics, Faculty of Mathematics, Computer Science and Statistics at Ludwig Maximilian University of Munich (LMU Munich). He also serves as a principal investigator at the Munich Center for Machine Learning (MCML), where he leads research at the intersection of mathematical statistics and machine learning. Nagler received his academic training at Technical University of Munich (TU Munich), earning a BSc in Mathematics (2009-2012), followed by an MSc in Mathematical Finance (2012-2014), and ultimately a PhD in Mathematical Statistics (2014-2018). Prior to his current position at LMU Munich, he held assistant professor positions at TU Delft (2021-2022) and Leiden University (2019-2021). Professor Nagler's research focuses on developing novel statistical methods with theoretical guarantees and scalable algorithms. His work spans high-dimensional dependence modeling, particularly using vine copulas, statistical machine learning, time series and functional data analysis, and statistical computing. He emphasizes creating methods that can be practically implemented and applied to solve real-world problems across diverse domains. An analysis of Nagler's recent publications reveals a strong emphasis on vine copula methodology, uncertainty quantification in machine learning, and applications to climate science and epidemiology. His work bridges theoretical statistics with practical implementation, often resulting in open-source software tools that make advanced statistical methods accessible to practitioners. The interdisciplinary nature of his research is evident in collaborations spanning climate modeling, healthcare, and finance. While specific awards are not detailed in the available information, Nagler's research impact is evident through his significant contributions to statistical methodology and his active engagement with the research community through open-source software development. As a principal investigator at MCML and Professor at LMU Munich, Nagler leads a research group focused on advancing statistical methodology for complex data analysis. His GitHub profile indicates active collaboration with students and researchers, with several followers from LMU Munich and other institutions. His research program appears to be well-funded through the MCML and university resources, supporting both methodological development and application-focused projects. Nagler maintains strong ties with the computational statistics community through his leadership of the VineCopula and pyvinecopulib projects, which provide essential tools for dependence modeling. His work with the Munich Center for Machine Learning positions him at the forefront of interdisciplinary research combining statistical theory with practical machine learning applications.

Srishti Yadavمشاهده پروفایل

پژوهشگر ارشد

• AI and Society
• Machine Learning
• Computer Science
• +۳ مورد دیگر

Srishti Yadav is a Research Fellow at the University of Copenhagen and University of Amsterdam , affiliated with the Pioneer Centre for AI and ILLC respectively. She is advised by Dr. Serge Belongie and Dr. Ekaterina Shutova . Education: M.Sc. (Research-Track) in Computing Science, Simon Fraser University , Canada Research Interests: AI and Society Cross-Cultural Competency in Multimodal Models AI Safety and Evaluation Frameworks Model Interpretability and Dataset Creation Scientific Awards: ELLIS PhD Fellowship Advising & Community: Board Member, Women in Computer Vision (WiCV) Advisor for WiCV@ICCV2023 and WiCV@CVPR 2021 Chaired workshops at CVPR 2024, CVPR 2023, CVPR 2020 Labs & Teams: Belongie Lab (University of Copenhagen) Shutova Lab (University of Amsterdam) Collaborator at MILA Biodiversity Monitoring Project

Muhammad Bilal Zafarمشاهده پروفایل

استاد

• Human-Centric AI/ML
• Fairness in Machine Learning
• Explainable Artificial Intelligence
• +۳ مورد دیگر

Professor Muhammad Bilal Zafar holds a faculty position at Ruhr University Bochum (RUB), where he heads the Chair of AI and Society within the Research Center for Trustworthy Data Science and Security. He is also a Principal Investigator (PI) at the Cluster of Excellence CASA and a member of the Horst Görtz Institute for IT Security. Previously, he worked as a Senior Applied Scientist at Amazon Web Services and a Research Scientist at Bosch Center for Artificial Intelligence. He completed his PhD at the Max Planck Institute for Software Systems (MPI-SWS) and Saarland University, advised by Krishna P. Gummadi and Manuel Gomez Rodriguez. His research focuses on human-centric AI/ML, emphasizing fairness, explainability, and robustness of AI systems. Key contributions include mechanisms for fair classification, bias detection in algorithms, and trustworthy AI applications. He actively engages in mentoring PhD and Master’s students, with current openings for candidates in Computer Science, Mathematics, and related fields. His work bridges theoretical advancements with practical solutions for ethical AI deployment. Notable collaborations include projects with AWS and Bosch, addressing challenges in algorithmic fairness and AI transparency. His publications span top venues like ACL, KDD, NeurIPS, and WWW, highlighting contributions to both foundational and applied aspects of AI ethics.

Michael Möllerمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Inverse Problems
• +۲ مورد دیگر

Michael Möller is a Professor at the University of Siegen, leading the Lehrstuhl für Computer Vision within the Institute for Vision and Graphics. His research spans computer vision, machine learning, and optimization, with applications in medical imaging and computational geometry. Research Interests: Focuses on solving inverse problems in imaging, neural architecture optimization, and quantum-hybrid computational methods. His work integrates deep learning with traditional optimization for scalable solutions in 3D reconstruction and image analysis. Publications: Recent articles explore advancements in neural architecture search robustness, quantum annealing for shape matching, and data-driven regularization techniques for CT reconstruction, reflecting cross-disciplinary applications.

Julia Wunderleمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Large Language Models
• Deep Learning
• +۵ مورد دیگر

Julia Wunderle is a doctoral researcher at the Chair of Data Science (Informatik X) at the University of Würzburg, affiliated with the Faculty of Mathematics and Computer Science and the Institute of Computer Science. She joined the chair in December 2024 after completing her master's degree in Computer Science at the same university. Office : Room 50.03.020, Center for Artificial Intelligence and Data Science (CAIDAS) Contact : Telephone +49 931 31-80351 Her research focuses on Natural Language Processing, Large Language Models, and deep learning techniques for German language tasks. Recent work includes hierarchical multi-label classification architectures, German opinion extraction via pointer networks, and developing language-specific encoder models. Key publication trends show specialization in German NLP, model architecture innovation, and applications of LLMs to semantic evaluation tasks. Her collaborations include researchers like Andreas Hotho, Jan Pfister, and Anton Zehe.

Lukas Fuchsمشاهده پروفایل

پژوهشگر

• Fluid Mechanics
• Numerical Mathematics
• Turbulence Modeling
• +۳ مورد دیگر

Lukas Fuchs is a doctoral researcher at the Institute of Fluid Mechanics and Technical Acoustics (ISTA) within the Faculty V - Transportation and Mechanical Systems at the Technical University of Berlin. He focuses on the Dynamics of Turbulent Separation Bubbles using linearized modeling approaches and deep reinforcement learning techniques. Education: M.Sc. in Physical Engineering (TU Berlin), specialization in fluid mechanics and numerical mathematics Current Role: Scientific employee (PhD candidate) since March 2023 His research combines fluid dynamics with machine learning , particularly in: Reynolds-averaged Navier–Stokes (RANS) closure model identification Deep reinforcement learning (DRL) optimization for turbulence models Large eddy simulation (LES) data alignment Jet flow dynamics at varying Reynolds numbers Notable Contributions: The 2024 publication demonstrates 48% improvement in RANS-LES alignment using DRL-augmented Spalart–Allmaras models, with cross-Reynolds number application capabilities. Challenges include computational costs, numerical stability, and hyperparameter sensitivity.

Kislaya Raviمشاهده پروفایل

پژوهشگر

• Multifidelity Uncertainty Quantification
• Gaussian Process
• Sparse Grids Methods
• +۲ مورد دیگر

Kislaya Ravi is a Doctoral Candidate at the Technical University of Munich (TUM), affiliated with the Chair of Scientific Computing within the TUM School of Computation, Information and Technology. She holds an M.Sc. in Computational Science and Engineering from TUM and degrees in Mechanical Engineering from the Indian Institute of Technology (BHU). Her research focuses on multifidelity uncertainty quantification, Gaussian processes, sparse grids methods, machine learning, and stochastic optimization. Education: M.Sc., Computational Science and Engineering, Technical University of Munich (202X) M.Tech., Machine Design, Indian Institute of Technology (BHU) (20XX) B.Tech., Mechanical Engineering, Indian Institute of Technology (BHU) (20XX) Research Interests: Kislaya’s work bridges computational methods and stochastic systems, emphasizing efficient uncertainty quantification techniques in physics and engineering. She explores multifidelity approaches to enhance computational efficiency in complex simulations and data-driven modeling. Teaching and Advising: Core instructor for Scientific Computing II and Algorithms for Uncertainty Quantification . Supervised multiple Master’s and Bachelor’s theses on topics like black-box optimization, surrogate modeling, and plasma instability analysis. Publications highlight contributions to multi-fidelity Gaussian processes, No-U-Turn sampling, and kinetic modeling. Her work appears in journals like Machine Learning: Science and Technology and conferences such as SIAM CSE and MCQMC.

• Machine Learning
• Automated Machine Learning (AutoML)
• Hyperparameter Optimization
• +۳ مورد دیگر

Janek Thomas is a researcher actively contributing to the fields of Machine Learning and Automated Machine Learning (AutoML). His work focuses on hyperparameter optimization, multi-objective algorithms, and improving the interpretability of machine learning models. Collaborating with institutions like TU Munich and LMU Munich, he has authored over 35 publications since 2016. Key contributions include the AMLB benchmark suite for AutoML systems and foundational research on multi-objective hyperparameter tuning. His research bridges theoretical advancements with industrial applications, emphasizing robust model verification and scalable optimization techniques.

• Computational Thinking
• Neural Networks
• Machine Learning
• +۶ مورد دیگر

Anthony V. Robins is a Professor in the Department of Computer Science at the University of Otago, New Zealand. His work spans neural networks , computational cognition , computer science education , and machine learning . With over three decades of publication history, Robins has contributed to understanding catastrophic forgetting , prototype analysis , and reinforcement learning in artificial neural networks. His recent research (2024-2025) focuses on Hopfield networks and Boltzmann neural networks , particularly their applications in state classification , supra-classical logic , and sequential learning . Collaborations with researchers like Hayden McAlister and Lech Szymanski highlight his work in memory systems and Hebbian learning . Robins has also made significant contributions to high school computing education in New Zealand, analyzing curriculum implementation , student perspectives , and teacher challenges through publications in venues like ACM Trans. Comput. Educ. and ACE. His work on self-regulated learning and educational frameworks addresses systemic issues in computing education.

• Continual Learning
• Few-Shot Learning
• Domain Adaptation
• +۷ مورد دیگر

Mahardhika Pratama is a Professor in the Department of Computer Science at Universitas Indonesia's Faculty of Computer Science, where he leads research in machine learning and artificial intelligence. His work bridges theoretical advancements with practical applications across various domains including time series analysis, autonomous systems, and resource-constrained environments. Pratama's research focuses on overcoming fundamental challenges in machine learning systems, particularly in the areas of continual learning and few-shot learning. His work addresses the critical problem of catastrophic forgetting in neural networks while developing efficient learning systems that can adapt to new tasks with minimal data. His publications reveal a strong emphasis on developing practical solutions for real-world applications where data is scarce or constantly evolving. Analysis of his recent publications (2023-2025) shows a clear research trajectory toward more sophisticated approaches for continual learning under data scarcity, with increasing focus on transformer architectures, multi-agent reinforcement learning, and cross-domain adaptation techniques. His work demonstrates a consistent pattern of addressing both theoretical foundations and practical implementations of adaptive learning systems. His research has been supported through numerous collaborative projects with international institutions including UNSW Canberra, Swinburne University of Technology, and University of Alberta, reflecting the global impact of his work in the machine learning community.

Kristian Kerstingمشاهده پروفایل

دانشیار

• Data Mining
• Machine Learning
• Statistical Relational Artificial Intelligence
• +۵ مورد دیگر

Kristian Kersting is an Associate Professor in the Computer Science Department at Technical University of Darmstadt, Germany, having moved from TU Dortmund in 2017. He holds additional affiliations including Adjunct Assistant Professor roles and has contributed to institutions like the University of Bonn and Wake Forest University. His research focuses on data mining, machine learning, and statistical relational AI, with applications in medicine, plant phenotyping, and traffic analysis. He has authored over 150 peer-reviewed publications and received numerous awards, including the ECCAI Dissertation Award 2006 and Best Student Paper Award at ECML 2006. Education includes a Ph.D. from the University of Freiburg and postdoctoral research at MIT. He has held roles such as Assistant Professor at the University of Bonn and co-chaired major conferences like ECML PKDD 2013. His work emphasizes interdisciplinary applications, including collaborations with industries like goedle.io and pflegix.de. Scientific contributions span probabilistic logic learning, lifted inference, and neuro-symbolic systems. His research group's activities include projects on plant disease detection, traffic prediction, and collective attention analysis. Key awards highlight his impact in AI and data science.

Amar Hekaloمشاهده پروفایل

پژوهشگر

• Medical Image Processing
• Natural Language Processing (NLP)
• Information Extraction
• +۶ مورد دیگر

Amar Hekalo is a researcher at the Chair of Computer Science VI - Artificial Intelligence and Knowledge Systems (1992-2025) at the University of Würzburg , within the Faculty of Mathematics and Computer Science . Since November 2018, he has collaborated with the University Hospital of Würzburg to enhance medical documentation through speech/image processing , deep learning , and rule-based methods . Education: Master's degree in Physics His research focuses on automated image processing and NLP , covering information extraction , image classification , segmentation , object recognition , and medical applications . Recent work includes federated learning for private data reconstruction and AI applications in cardiac/radiological imaging. His publications reveal expertise in medical AI , particularly gastroenterology , cardiology , and radiology . He supervises Bachelor's and Master's students in AI applications for healthcare, logistics, and education. He contributes to open-source tools and actively seeks student assistants with Angular/NodeJS skills for radiology-focused documentation and e-learning tools.

Marcel Aachمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• High-Performance Computing
• Machine Learning
• +۳ مورد دیگر

Marcel Aach is a researcher at the Jülich Supercomputing Centre (JSC) within Research Centre Jülich, focusing on high-performance computing (HPC) and artificial intelligence (AI) integration. His work spans hyperparameter optimization, quantum-classical hybrid systems, and scalable deep learning architectures. Research Focus: AI-driven CFD simulations, medical imaging analysis, and edge AI optimization Infrastructure: Leverages JSC's HPC systems for large-scale computational tasks Recent publications highlight his contributions to resource-adaptive AI training, turbulent flow prediction using GRU models, and enhancing CT data accuracy for respiratory simulations. His work intersects HPC scalability, machine learning efficiency, and interdisciplinary applications. While no scientific awards are explicitly documented in the provided texts, his research demonstrates technical leadership in merging classical and quantum computing paradigms for AI optimization.

Chinmay Datarمشاهده پروفایل

پژوهشگر

• Scientific Computing
• Deep Learning
• Partial Differential Equations
• +۳ مورد دیگر

Chinmay Datar is a doctoral candidate at the Technical University of Munich (TUM), affiliated with the Institute of Advanced Study (IAS) in the focus group 'Scientific Machine Learning.' He holds an M.Sc. in Computational Engineering from Friedrich-Alexander University (FAU), Erlangen, and a Bachelor's in Mechanical Engineering from the University of Pune. His research focuses on integrating classical scientific computing methods with deep learning to solve Partial Differential Equations (PDEs). Teaching Responsibilities include: Summer Semester 2025: Seminar on High-Dimensional Methods for Scientific Computing and Lab Course on Machine Learning in Crowd Modeling/Simulation Winter Semester 2024/25: Courses on Scientific Computing & Machine Learning and a Seminar on Ethics in Science Research Interests: Novel neural architectures for dynamical systems simulation Backpropagation-free training of neural PDE solvers Domain decomposition combined with neural networks for PDE solutions Student Supervision: Ongoing: Eray Yildiz (Master's) and Ahmet Semiz (Master's) Completed: Rahma Atamert and Aditya Phopale (Master's) Recognition: FAU Graduation Scholarship (Japan internship support). Labs/Teams: Part of the Chair of Scientific Computing in Computer Science (SCCS), led by Prof. Hans-Joachim Bungartz.