Find Academic Supervisors

Ralph Müller-PfefferkornView profile

Researcher

• Data Management
• Research Data Infrastructure
• High Performance Computing
• +4 more

Ralph Müller-Pfefferkorn is Head of the Department of Distributed and Data Intensive Computing at the Center for Information Services and High Performance Computing, Technische Universität Dresden. His work focuses on data management, metadata systems, and high-performance computing infrastructure. He holds a PhD in Physics from TU Dresden (2001) and has extensive experience in research data infrastructure development, spanning roles from 1996 to present across physics and computational science domains. Education: 2001: Dr. rer. nat., Physics, TU Dresden (Thesis on BABAR detector calibration) 1996: Diploma in Physics, TU Dresden 1992–1996: Studies in Physics at TU Dresden 1990–1992: Studies at TU Chemnitz Research interests emphasize distributed data management, long-term archiving, and FAIR data principles. He contributes to initiatives like the Research Data Alliance (IG Co-Chair) and UNICORE Forum board. His publications address challenges in big data workflows, metadata standards, and interdisciplinary infrastructure integration. Labs/Teams: Leads the Distributed and Data Intensive Computing team within TU Dresden's HPC center, collaborating on projects like MoSGrid and MASi repository systems.

• Battery Materials Science
• Electrochemistry
• Energy Storage Technologies
• +3 more

Dr. Yug Joshi is a Researcher at the Max Planck Institute for Sustainable Materials , specifically within the Department of Microstructure Physics and Alloy Design . His research focuses on the microstructure and interfaces of battery materials, particularly investigating cathode and anode degradation mechanisms, solid-electrolyte interphase (SEI) formation, and lithium-ion transport dynamics. He employs advanced techniques such as operando optical microscopy, time-resolved microgravimetry, and atom probe tomography to study materials like lithium cobalt oxide, tin, silicon, and lithium titanate. His work emphasizes the development of high-capacity battery electrodes and understanding phase transitions, electrochemical cycling behavior, and material stability. Joshi collaborates on interdisciplinary projects, including modular atom probe concepts integrating APT-FIB/SEM systems for nanoscale analysis. His research contributes to improving energy storage technologies through fundamental insights into material microstructure and electrochemical processes. Awards and grants are not explicitly listed in the provided information. His affiliations include the Max Planck Institute, and he can be contacted at y.joshi@mpg.de .

• Federated Learning
• Privacy-Preserving Computing
• Cryptography
• +3 more

Marvin Xhemrishi is a Researcher and PhD Candidate at the Technical University of Munich (TUM), affiliated with the Institute for Communications Engineering. He holds a M.Sc. in Communications Engineering (high distinction, TUM 2019) and a B.Sc. in Telecommunications Engineering from the Polytechnik University of Tirana (2017). His research focuses on federated learning security, privacy-preserving computing, and distributed systems. He has conducted visiting research at Chalmers University of Technology and AI Sweden (2022-2023). His work addresses challenges in secure aggregation, malicious client detection, and sparsity-privacy trade-offs in distributed computing frameworks. He contributes to advancements in coded computing, cryptography applications in machine learning, and hardware security through Physical Unclonable Functions (PUFs). Key research directions include federated learning security (e.g., FedGT framework for malicious client identification), privacy-preserving distributed algorithms, and cryptographic techniques for communication systems. His publications span IEEE Transactions, international symposia, and workshops, with recent focus on group testing applications in federated learning and secure distributed matrix operations.

• Quantum Information Theory
• Quantum Many-Body Physics
• Mathematical Physics
• +7 more

Professor Michael Marc Wolf is a faculty member at the Technical University of Munich , affiliated with the School of Computation, Information and Technology and the Department of Mathematics . His research focuses on the mathematical and conceptual foundations of quantum theory and quantum information. Current research group: Quantum Information Theory Ongoing collaborations: Munich Center for Quantum Science and Technology, Munich Quantum Center, CRC TRR 352 Research Interests: Wolf works at the intersection of quantum information theory , quantum many-body physics , and mathematical physics . His work explores undecidability in physics, quantum tomography, spectral gaps, and quantum channel theory. Recent trends in his publications include entropy-constrained sets, quantum complexity, and differential forms applied to quantum systems. Teaching: Currently teaching Mathematics for Physicists 2 (Analysis 1) and advanced quantum information experiments in Winter 2024/25 and Summer 2025.

Ana CukarskaView profile

Researcher

• Machine learning for dynamical systems
• Numerical aspects of machine learning algorithms
• Koopman operator theory
• +3 more

Ana Cukarska is a PhD candidate at the Technical University of Munich (TUM) within the School of Computational Engineering and Science, Department of Computer Science. She joined TUM in November 2023 under the supervision of Prof. Felix Dietrich. Her academic background includes an M.Sc. in Computational Science and Engineering (2021-2023) and a B.Sc. in Mechanical Engineering (2017-2023). Research Interests: Ana focuses on machine learning applications in dynamical systems, with particular interest in numerical aspects of ML algorithms, Koopman operator theory, and its applications in crowd dynamics modeling. Her work bridges computational science and machine learning methodologies. Teaching Involvement: She has contributed to various courses at TUM, including Scientific Computing and Machine Learning , Machine Learning in Crowd Modeling & Simulation , and multiple iterations of Physics-Informed Machine Learning exercises.

Daniel BuschekView profile

Professor

• Human-Computer Interaction (HCI)
• Artificial Intelligence
• Machine Learning
• +4 more

Professor Daniel Buschek leads the research group in Mobile Intelligent User Interfaces at the University of Bayreuth's Faculty of Mathematics, Physics and Computer Science. His work focuses on integrating Human-Computer Interaction (HCI) with Machine Learning/AI to design novel user interfaces that enhance the effectiveness, efficiency, and security of digital technology use. He explores both 'AI for better UIs' and 'better UIs for AI,' addressing challenges in interactive systems, generative models, and ethical considerations in human-AI collaboration. His research spans topics such as AI writing assistants, user perception of AI-generated content, and adaptive touch interfaces. Buschek's team includes prominent researchers like Florian Lehmann and Hai Dang, contributing to conferences like CHI and ACM DIS. He has published extensively on topics ranging from generative AI interaction design to behavioral biometrics and continuous authentication systems. Key contributions include frameworks for understanding intelligent systems, tools like SummaryLens and GestureMap, and studies on user behavior in AI-driven environments. Buschek's work emphasizes user-centric design, ethical AI integration, and the societal implications of emerging technologies.

• Quantum Computing
• Quantum Walks
• Quantum Simulation
• +4 more

Jingbo Wang is Professor of Physics at the University of Western Australia, where she heads the Physics department and directs the QUISA Research Hub for Quantum Information, Simulation, and Algorithms. Her research pioneers quantum walk applications in complex systems analysis and quantum compiler development. After completing her PhD at the University of Adelaide, she established a leading quantum computation group at UWA. Her work bridges quantum dynamics, algorithm design, and complex network theory. Recent publications demonstrate strong focus on quantum machine learning, optimization algorithms, and experimental quantum computing implementations. Her team consistently develops efficient quantum circuits for practical problem-solving in finance, biology, and material science. Fellow of Australian Institute of Physics (2021) Vice-Chancellor’s Award in Research Mentorship (2022) School Research Mentorship Award (2022)

Beatriz TucciView profile

Researcher

• Cosmology
• Astrophysics
• Theoretical Physics
• +3 more

Beatriz Tucci is an academic affiliated with the Institute of Theoretical Physics (IFT) at UNESP in São Paulo, Brazil, and the University of São Paulo (USP). Her research focuses on cosmology, astrophysics, and theoretical physics, with a particular emphasis on applying machine learning to cosmological problems and studying the large-scale structure of the universe. She has collaborated with prominent researchers such as Raul Abramo (USP) and has contributed to topics like Baryon Acoustic Oscillations (BAO), galaxy clustering, and halo-galaxy connections. Her academic interests are reflected in her work on improving BAO constraints via field-level inference, exploring the kinetic Sunyaev-Zel’dovich effect, and analyzing galaxy formation through EFTofLSS. She is also engaged with educational initiatives, citing resources from institutions like ICTP-SAIFR and lecturers such as David Tong and Daniel Baumann. Beatriz’s publications span theoretical and applied cosmology, with a recent focus on combining machine learning with astrophysical data analysis. While no specific awards or grants are listed, her active research output indicates ongoing contributions to the field.

• Computer Science
• Electrical Engineering
• Machine Learning
• +3 more

Xue Jiang is a faculty member at Shanghai Jiao Tong University's School of Electronic Information and Electrical Engineering, China. Their research focuses on interdisciplinary areas including machine learning, signal processing, biomedical engineering, and cybersecurity. With a prolific publication record, they specialize in neural networks, fault diagnosis systems, federated learning, and brain-computer interfaces. Key research interests include developing intelligent algorithms for fault detection in mechanical systems, advancing medical imaging techniques through deep learning, and enhancing privacy in distributed data frameworks. Their work spans applications in robotics, cybersecurity, and bioinformatics. Notable contributions include adversarial robustness studies for EEG-based systems and PML-based computational methods for wave propagation analysis. Publications highlight innovations across engineering and computer science, with a focus on practical implementations in real-world systems. Collaborations with institutions like the Hong Kong University of Science and Technology and Harbin Institute of Technology reflect their extensive academic network.

Mihail BogojeskiView profile

Researcher

• Geometric Deep Learning
• Quantum Chemistry
• Computational Biology
• +3 more

Dr. Mihail Bogojeski is a postdoctoral researcher at the Technical University of Berlin , affiliated with the Machine Learning Group and the Berlin Institute for the Foundations of Learning and Data (BIFOLD) . His research focuses on advancing machine learning techniques for applications in the physical sciences and medical sciences , particularly in quantum chemistry , computational biology , brain-computer interfaces , and sequential data analysis . B.Sc. in Software and Information Engineering (2014), Vienna University of Technology M.Sc. in Computer Science (2017), TU Berlin Ph.D. in Computer Science (2023), TU Berlin His research integrates geometric deep learning with quantum chemistry to develop models for predicting electronic structure of molecular systems and electron densities . Recent work includes equivariant neural networks for molecular wavefunction prediction, generative time-series models for industrial aging processes, and explainable AI frameworks for catalyst discovery. Publications highlight his expertise in machine learning , quantum chemical accuracy , and multimodal data augmentation . His 15 most recent articles focus on machine learning applications in chemistry , neuroscience , and industrial process modeling , with subfields such as generative models , equivariant networks , density functional theory , and neural signal processing .

Ali HashemiView profile

Researcher

• Machine Learning
• Statistics
• Optimization
• +3 more

Ali Hashemi is a Researcher at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and a Postdoctoral Researcher at Technical University of Berlin. He holds a PhD (Dr. rer. nat.) with distinction from TU Berlin (2022) and an MSc in Electrical Engineering from Sharif University of Technology (2013). His research focuses on machine learning, Bayesian methods, and neuroimaging applications, particularly in inverse problems and explainable AI. He has contributed to advancements in structured noise modeling, robust source localization, and 3D neural networks for brain imaging. His work bridges signal processing and AI to improve biomedical data analysis. Education: PhD in Machine Learning, Technical University of Berlin (2022) MSc in Electrical Engineering, Sharif University of Technology (2013) Research Interests: Machine Learning & Optimization Bayesian Inference Neuroimaging (M/EEG, fMRI) Explainable AI & Diffusion Models Signal Processing for Biomedical Applications Key Achievements: Developed the 'Champagne Algorithm' for noise estimation in brain imaging Published widely in top venues like NeurIPS and NeuroImage Recipient of PhD distinction award for outstanding dissertation Labs & Affiliations: Active in TU Berlin's Uncertainty & Inverse Modeling group and Charité’s Brain and Data Science lab. Collaborates on open-source tools for M/EEG analysis (e.g., contributions to MNE-Python).

Winfried RipkenView profile

Researcher

• Machine Learning
• Deep Learning
• Graph Neural Networks
• +4 more

Winfried Ripken is a PhD candidate and research associate at Technical University of Berlin, affiliated with BIFOLD (Berlin Institute for the Foundations of Learning and Data). He pursues doctoral studies in Computer Science under Stefan Chmiela's supervision and participates in the BIFOLD graduate school. His academic background includes: M.Sc. in Computer Science from Hasso Plattner Institute (2021) B.Sc. in Computer Science from TU Chemnitz (2017) Ripken specializes in applying machine learning to natural sciences, with core expertise in Graph Neural Networks, Geometric Deep Learning, and Quantum Chemistry. His research bridges scientific computing and deep learning to solve complex problems like boundary value equations and molecular simulations, emphasizing interpretability and operator learning. Recent publications reveal a strong trend toward scientific machine learning applications, particularly in physics-informed neural networks and image processing. His work demonstrates consistent innovation in adapting graph-based architectures for scientific domains while advancing whitebox methodologies in computer vision. As a doctoral researcher, Ripken has no supervisees but contributes significantly through active GitHub development (26 repositories, 141 stars) and prior industry experience. He previously worked two years as a full-time machine learning researcher at Merantix Momentum, focusing on Graph Neural Networks and interpretability. He operates within Stefan Chmiela's research group at BIFOLD, collaborating on interdisciplinary projects that integrate machine learning with chemistry, physics, and engineering challenges through Berlin's prominent AI research ecosystem.

Ankur SinghaView profile

Researcher

• Lattice Field Theory
• Generative Models
• Renormalization Group Theory
• +3 more

Ankur Singha is a Postdoctoral Researcher at Technical University of Berlin, where he is part of the Machine Learning group. His research focuses on advancing Generative AI models to optimize sampling processes in Lattice Field Theory, particularly for lattice QCD simulations. He holds a Ph.D. in Physics from the Indian Institute of Technology Kanpur (2023), an M.Sc. in Physics from Tezpur University (2017), and a B.Sc. in Physics from Gauhati University (2015). His research interests include Lattice Field Theory, Generative Models, Renormalization Group Theory, and Equations Learner (EQL). During his doctoral studies, he applied conditional GANs and flow-based models to simulate lattice models such as the Gross-Neveu model, phi4 theory, and U(1) gauge theory in two dimensions. While no specific articles or awards are listed in the provided information, his work bridges machine learning techniques with theoretical physics challenges, contributing to computational efficiency in complex simulations. He has not listed any grants or advising roles in the provided information. He is part of the Machine Learning group at Technical University of Berlin.

Pengzhou (Abel) WuView profile

Research Fellow

• Causality
• Machine Learning
• Generative Models
• +3 more

Pengzhou (Abel) Wu serves as a Postdoctoral Researcher at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and the Technical University of Berlin since July 2023, focusing on the intersection of causality and machine learning methodologies. His academic foundation includes: Ph.D. in Statistical Science (2022) from The Graduate University of Advanced Studies M.E. in Information and Communication Engineering (2013) from Zhejiang University B.E. in Electronic and Informational Engineering (2010) from Xiamen University Dr. Wu's research spans causal inference and discovery, generative deep learning models, and nonlinear independent component analysis (ICA) with emphasis on identifiability. His work bridges machine learning with physical sciences and cognitive science, developing science-inspired ML frameworks and advancing ML applications for scientific discovery. Key methodologies include probabilistic modeling and causal structure learning. He operates within BIFOLD's research ecosystem, a premier Berlin-based institute dedicated to data science foundations and machine learning innovation.

• Theoretical Physics
• Quantum Many-Body Systems
• Quantum Phase Transitions
• +5 more

Prof. Markus Heyl is a Professor of Theoretical Physics III at the University of Augsburg, affiliated with the Faculty of Mathematics, Natural Sciences, and Materials Engineering. Prior to this role, he held positions as a Leibniz Group Leader at the Max-Planck Institute for the Physics of Complex Systems and a postdoctoral fellow at institutions such as TU Munich and IQOQI Innsbruck. His research focuses on quantum many-body systems, dynamical quantum phase transitions, and the application of machine learning techniques to quantum physics. Heyl's academic journey includes a PhD from Ludwig-Maximilians-Universität München under Prof. Stefan Kehrein, followed by postdoctoral work exploring quantum dynamics, localization phenomena, and quantum simulation. He currently leads a vibrant research group comprising postdoctoral researchers and PhD students investigating topics like neural quantum states, adaptive quantum algorithms, and non-equilibrium quantum systems. Teaching responsibilities include courses on 'Machine Learning in Physics' and advanced seminars on quantum many-body theory. His recent workshops, such as 'Connecting Physics and Computer Science' (2024) and 'Bridging the Divide' (2023), reflect his interdisciplinary approach to advancing quantum research. Key research themes include dynamical criticality, Trotterization optimization for NISQ devices, and the interplay between quantum chaos and integrability. His work bridges theoretical insights with experimental validations, leveraging tools like neural networks and classical networks to model complex quantum systems.