Find Academic Supervisors

• Gravitational Waves
• Mathematical Morphology
• Machine Learning
• +3 more

Dr. Raffi Enficiaud is a Researcher at the Max Planck Institute for Gravitational Physics (Albert Einstein Institute) in Potsdam, Germany. His work focuses on optimizing gravitational waveform generation tools for speed, robustness, and maintainability while enhancing computational efficiency for scientific users. Education: M.Sc. in Mathematical Morphology from Telecom SudParis Ph.D. in Mathematical Morphology from Paris School of Mines Raffi specializes in Gravitational Waves , Mathematical Morphology , and Software Engineering , with expertise in symbolic computation and translating complex mathematical models (e.g., Mathematica) into Python/C++ code. His research interests span Machine Learning , Computer Vision , and Symbolic Computation , informed by his academic and industry experience. At AEI, he develops and maintains the EOB gravitational waveforms generation package 'pySEOBNR' and tools for computational graph optimization. Prior roles include machine learning engineer at Reasonal (2020–2023), software architect at Pix4D (2018–2020), and leading the 'Software Workshop' at the Max Planck Institute for Intelligent Systems (2014–2018). Earlier roles involved computer vision research at DxO and INRIA. Contact: raffi.enficiaud@aei.mpg.de

• Particle Physics
• High Energy Physics
• Data Science in Physics
• +5 more

Lukas Heinrich is an Assistant Professor of Data Science in Physics at the Technical University of Munich (TUM), affiliated with the Department of Physics within the TUM School of Natural Sciences. His position specifically focuses on the intersection of data science methodologies and physics research, particularly in high-energy physics contexts. Based at the campus in Garching near Munich, he contributes to both research and teaching activities at one of Europe's leading technical universities. Professor Heinrich's research primarily centers on particle physics, with a strong emphasis on data analysis techniques for experiments conducted at the Large Hadron Collider (LHC), particularly using the ATLAS detector. His work spans Higgs boson physics, searches for new physics beyond the Standard Model, and the development of advanced machine learning methods for particle physics applications. The research fingerprint shows strong engagement with ATLAS Detector technologies, proton-proton collisions, Standard Model physics, lepton physics, transverse momentum analysis, Higgs boson studies, and Large Hadron Collider operations. His recent publications demonstrate a clear trend toward integrating sophisticated data science approaches with traditional particle physics analysis. The 2025 publications particularly highlight work on neural simulation-based inference for parameter estimation, searches for exotic Higgs boson decays, and combination of search channels for various physics phenomena. This reflects a growing emphasis on machine learning and advanced statistical methods to extract maximum information from complex particle collision data. Professor Heinrich is actively involved in teaching, with courses including Data Science Methods, Data Science Tools, Machine Learning and Deep Learning in Physics, and Experimental Physics. His teaching appointments for the 2024/25 and 2025 summer terms show a strong commitment to educating the next generation of physicists in modern data analysis techniques. He also participates in seminars on Physics of Strong Interaction, demonstrating breadth in his teaching responsibilities beyond pure data science topics. Through his Assistant Professorship of Data Science in Physics, Heinrich leads efforts to bridge traditional physics research with cutting-edge computational approaches, contributing significantly to both the academic curriculum and research output of the TUM physics department.

Marius LindauerView profile

Professor

• AutoML
• Machine Learning
• Artificial Intelligence
• +6 more

Marius Lindauer is a Professor of Machine Learning at the Department of Artificial Intelligence , Leibniz University Hannover , and Deputy Head of the Institute since 2025. Previously, he served as Spokesperson of Computer Science Professors (2023-2025) and Head of the Institute (2022-2024). PhD (Dr. rer. nat, 2010-2015), Master (2008-2010), and Bachelor (2005-2008) in Computer Science from University of Potsdam His research focuses on democratizing AI through AutoML innovations, including: Green AutoML for sustainable deep learning Human-Centered AutoML for user-centric optimization Dynamic Algorithm Configuration in reinforcement learning Generalization techniques for production and health applications Recent publications show strong multi-objective optimization trends across medical imaging , protein design , and time series forecasting , with 15+ papers in 2024-2025 at venues like NeurIPS, AAAI, and IEEE TPAMI. Key scientific awards : ERC Starting Grant (2022), NeurIPS BBO-Challenge winner (2020), multiple AutoML/ML competition victories Advisory role in 140+ publications and leadership of LUHAI Institute

Kaveh Haghighi MoodView profile

Researcher

• High Performance Computing
• GPU Computing
• Quantum Monte Carlo
• +3 more

Dr. Kaveh Haghighi Mood is a computational researcher at Forschungszentrum Jülich's Jülich Supercomputing Centre (JSC), specializing in high-performance computing with emphasis on GPU acceleration and scientific application enablement. His work bridges atmospheric science, materials simulation, and exascale computing through the Helmholtz Association research infrastructure. His research focuses on optimizing computational methods for next-generation supercomputers, particularly in three domains: GPU-accelerated atmospheric modeling through the MPTRAC framework for Lagrangian transport simulations Exascale benchmarking via the JUPITER suite for evaluating future supercomputing architectures Quantum Monte Carlo methods applied to electronic structure theory and materials science Recent publications demonstrate expertise in CUDA, OpenACC, and performance portability across diverse hardware platforms. Haghighi Mood's publication trends reveal an evolving focus from foundational quantum chemistry (2010-2019) toward GPU optimization and exascale readiness (2020-2025). His work increasingly addresses atmospheric science applications while maintaining strong connections to materials simulation, reflecting JSC's strategic emphasis on climate modeling and computational materials design. The interdisciplinary nature spans computer architecture, numerical methods, and domain-specific scientific computing.

Samuel MaloneyView profile

Researcher

• High Performance Computing
• Computer Architecture
• Computational Methods
• +1 more

Dr. Samuel Maloney is a Researcher at Forschungszentrum Jülich's Jülich Supercomputing Centre (JSC), where he leads the Research Group "Next Generation Architectures and Prototypes". His work focuses on advancing supercomputing infrastructure through innovative hardware and software solutions, contributing to JSC's role as a leading European high-performance computing facility. Maloney's research spans High Performance Computing (HPC), computer architecture, and computational numerical methods. He specializes in optimizing resource utilization via advanced monitoring systems, developing job scheduling algorithms for disaggregated memory architectures, and creating meshfree schemes for anisotropic field simulations. His interdisciplinary approach bridges computer science, computational physics, and materials engineering. Analysis of his 2023-2024 publications reveals a strategic focus on exascale computing challenges, particularly efficient resource management and novel computational techniques for next-generation supercomputers. Key themes include disaggregated memory systems, anisotropic field modeling, and sustainable HPC operations, reflecting current industry priorities in scalability and energy efficiency. The "Next Generation Architectures and Prototypes" group under Maloney's leadership actively prototypes future supercomputing technologies through international collaborations, positioning JSC at the forefront of architectural innovation for upcoming exascale and post-exascale systems.

Rainer NiekampView profile

Researcher

• Computational Mechanics
• Uncertainty Quantification
• Multiphysics Systems
• +6 more

Dr.-Ing. Rainer Niekamp is a Researcher at the Department of Civil Engineering, Faculty of Engineering, University of Duisburg-Essen, where he has worked since 2016. He holds a diploma and doctorate in mathematics with a focus on computational mechanics and stochastic modeling. Studied mathematics and computer science at the University of Hannover (1986–1993) Doctoral degree in Civil Engineering from the University of Hannover (2000) Rainer Niekamp’s research spans computational mechanics, multiscale modeling, and stochastic methods. His work includes polynomial chaos expansion for data-driven modeling, partitioned systems for coupled simulations, and parallel software frameworks for large-scale problems. He specializes in model reduction , nonlinear dynamics , and component-based software engineering . His publications highlight multiphysics problems (e.g., thermo-mechanical coupling in steel processing, offshore wind energy converters) and software architecture for scientific computing. He has supervised numerous theses on topics like PeriDynamics simulations , multi-objective optimization , and heterogeneous network modeling .

• Numerical Analysis
• Ordinary Differential Equations (ODEs)
• Differential-Algebraic Equations (DAEs)
• +5 more

Dr. Helmut Podhaisky is a researcher at the Institute of Mathematics , Martin Luther University Halle-Wittenberg , within the Faculty of Natural Sciences II. His work focuses on numerical methods for differential equations, particularly stiff ODEs, DAEs, and Volterra equations. Email: podhaisky@mathematik.uni-halle.de Research Interests: Numerical methods for stiff and oscillatory differential equations Runge-Kutta and peer methods Parallel computing algorithms Mathematical modeling of calcium waves in biological systems Recent Publications highlight his work on barycentric rational methods, adaptive integrators, and bifurcation analysis in oscillating systems. His research spans both theoretical developments and practical applications in computational mathematics.

Fatih GedikliView profile

Professor

• Artificial Intelligence
• Big Data Analytics
• Software Engineering
• +5 more

Prof. Dr. Fatih Gedikli is a full-time Professor of Artificial Intelligence and Big Data at the Institute of Computer Science, Ruhr West University of Applied Sciences . His academic work spans software engineering, web engineering, and applied artificial intelligence, with a focus on recommendation systems . Key research areas: Recommender Systems , Big Data Analytics , Natural Language Processing , Deep Learning Contribution: Development of AI-based data pipelines for unstructured data analysis from news, social media, and scientific publications Entrepreneurial Activities : Co-founder and Co-CEO of graphworks.ai , a German AI startup offering student internships. Regular speaker at workshops and keynotes, including events on entrepreneurship and sustainable supply chains.

Marcellus SiegburgView profile

Researcher

• Formal Methods
• Automated Exercise Generation
• Programming Languages
• +3 more

Marcellus Siegburg is a Research Associate in the Department of Computer Science / AI at the University of Duisburg-Essen, working under Prof. Dr. Janis Voigtländer in the Formal Methods in Computer Science group. His responsibilities include teaching courses on programming paradigms and modeling, with ongoing instructional activities scheduled through 2025. His research focuses on applying formal methods to educational contexts, particularly in: Automated generation of exercises for modeling tools (Petri nets, UML diagrams) Development of assessment systems for programming languages (Haskell) Integration of formal verification tools like Alloy into pedagogical workflows Model-driven development for educational technologies His publications demonstrate a consistent focus on automating computer science education components, combining formal methods with practical teaching tools. The work emphasizes scalability and diversity in exercise generation while maintaining academic rigor. As part of the Formal Methods research group, he collaborates on projects involving domain-specific languages and automated assessment systems. No awards, grants, or student advising relationships are documented in available materials.

• Distributed Systems
• Blockchain Technology
• Middleware Integration
• +4 more

Julian Kalinowski is a researcher at the University of Hamburg's Department of Computer Science within the Faculty of Mathematics, Informatics, and Natural Sciences (MIN). His work focuses on distributed systems, blockchain technology, and middleware integration for mobile environments. Key projects include Jadex BDI Agent System and the cadeia initiative for distributed code execution in information markets. PhD candidate in Computer Science Email: kalinowski@informatik.uni-hamburg.de Room F510, Office hours by appointment Research interests span decentralized information markets, blockchain compliance (e.g., GDPR), and mobile middleware optimization. His 2015 best paper award at MATES conference highlights contributions to agent platform customization. Publications from 2012-2020 reveal trends in blockchain applications, smart grid optimization, and IoT security. Supervised 9 student theses covering decentralized billing, smart home systems, and localization technologies. Key awards: Best Paper Award (MATES-2015).

Florian MatthesView profile

Professor

• Software Engineering
• Business Information Systems
• Natural Language Processing
• +6 more

Florian Matthes is a Professor of Software Engineering for Business Information Systems at the Technical University of Munich (TUM), specifically within the TUM School of Computation, Information and Technology. Born in 1963, he has established himself as a leading researcher in software engineering, business information systems, and their applications across various domains. Prof. Matthes' research focuses on business information systems and software engineering, with particular emphasis on natural language processing applications in medical, legal, and enterprise contexts, privacy-enhancing technologies for digital platforms, and blockchain-based systems engineering. His work bridges theoretical computer science with practical industry applications, as evidenced by his co-founding of several successful IT startups including CoreMedia AG (1996), infoAsset AG (1999), Tr8cy UK Ltd (2018), and silver AI GmbH (2024). His publication record demonstrates consistent research productivity with over 400 research outputs spanning from 1990 to the present, with significant activity in recent years (2023-2025). His work spans multiple subfields including privacy-enhancing technologies, natural language processing, blockchain systems, and agile software development methodologies. The research shows a clear trend toward addressing contemporary challenges in AI ethics, data privacy, and the integration of emerging technologies into business information systems. Ernst Denert Award for Software Engineering 2022 Prof. Matthes has held significant leadership roles including Head of the Software Architecture working group of Gesellschaft für Informatik, membership on the advisory board of the Ernst Denert foundation for software engineering, and organization of several international conferences. He previously served as Dean of Studies at the Faculty of Computer Science at TUM until 2010 and has been a venture ambassador of the TUM Venture Labs since 2020. His academic journey began at J.W. Goethe University in Frankfurt (graduated 1988), followed by a doctorate at the University of Hamburg in 1992, and research at Digital Equipment Systems' research center in Palo Alto (1992-93). He was a professor at TU Hamburg-Harburg from 1997 to 2002 before joining TUM. His work demonstrates strong industry-academia collaboration through his startup activities and advisory roles, reflecting his commitment to translating research into practical applications while maintaining academic rigor.

Carsten TrinitisView profile

Professor

• High Performance Computer Architectures
• Microprocessor Architectures
• Co-design
• +5 more

Prof. Dr. Carsten Trinitis is a full professor at the Chair of Computer Architecture & Parallel Systems within the TUM School of Computation, Information and Technology. Specializing in high-performance computer architecture with a unique focus on spaceflight applications and informatics ethics, he leads the 'Gesellschaft für Informatik und Ethik' (Society for Informatics and Ethics). Ph.D. in Electrical Engineering from TUM (1998) Industry experience before returning to academia Former Assistant Professor in History of Science (2002-2010) at Universität der Bundeswehr München Full Professor of Distributed Computing at University of Bedfordshire (2010-2014) His research spans three major domains: High-Performance Computing: Focused on microprocessor architectures, hardware-oriented optimizations, and co-design approaches. Recent work includes GPU power capping strategies and Data Distribution Service (DDS) middleware enhancements. Space Systems: Pioneering computer architectures for nanosatellites with projects like MOVE-II mission and MicroPython-based satellite control systems. Digital Ethics: Through his 'Gewissensbits' initiative, he explores ethical decision-making frameworks for technology and contributes to the German Informatics Society's ethical guidelines. Key publication trends show interdisciplinary work connecting: HPC systems and edge AI applications FPGA-based verification and RISC-V processors Time series analysis at petascale performance Hardware-software co-design for extreme environments Continued emphasis on ethical computing frameworks Scientific Recognition: TeachInf Award for Outstanding Teaching (2023) Hans Meuer Award for Best Paper (2020) ZARM Master Thesis Awards (2016, 2017) Prof. Trinitis leads multiple research projects including: SEANERGYS (EuroHPC) - Energy-efficient computing systems PlasmaPEPS - Plasma physics simulation environments OpenCUBE - Open computing frameworks for space MUNIQC-ATOMS - Quantum computing integration

Alexandre MercierView profile

Researcher

• Natural Language Processing
• Dialogue Systems
• Voice Assistants
• +3 more

Alexandre Mercier is a Research Associate at the Software Engineering for Business Information Systems (sebis) chair within the Faculty of Informatics at the Technical University of Munich (TUM) since July 2024. He holds a Master's degree in Computer Science from TUM, specializing in Natural Language Processing with research focus on medical applications, dialogue systems, and text generation evaluation. Mercier completed his Master's thesis titled 'Investigating data to text approaches to achieve diversity of generated marketing text in the music industry' in January 2024 through a collaboration with startup Connactz. His academic journey began with data science work at Würth Elektronik during Bachelor studies before specializing in NLP at TUM. His research centers on enhancing text diversity in generated content while maintaining fluency and relevance, with specific interests in medical NLP applications, voice assistant interpretability (using SHAP values), and evaluation methodologies. He investigates techniques to overcome repetitive outputs in large language models, particularly for marketing contexts in the music industry. Mercier's publication 'JaccDiv' (2024) establishes a metric and benchmark for quantifying inter-sample diversity in generated marketing text. This work explores input data representations and model configurations to produce non-repetitive content for digital platforms, contributing significantly to natural language generation evaluation frameworks. He advises Master's level courses including 'Natural Language Processing - Methods and Applications' and 'Software Engineering for Business Applications', and organizes the Advanced Seminar. His research is funded through projects AssistD (a digital health assistant initiative launching March 2025) and MedTech (medical information extraction from clinical trials with MRI). Mercier operates within the sebis research group led by Prof. Dr. Florian Matthes, contributing to NLP and digital health initiatives. The group maintains active collaborations with industry partners and medical institutions on enterprise architecture management, legal AI applications, and healthcare technology development.

• Connected Mobility
• Vehicular Networks
• Mobility Modeling
• +3 more

Ljubica Kärkkäinen is a post-doctoral researcher at the Chair of Connected Mobility within the Department of Informatics at the Technical University of Munich (TUM), where she focuses on vehicular mobility systems and connected infrastructure. Her academic background includes: PhD in Electrical Engineering from KTH Royal Institute of Technology, Network and Systems Engineering Department Her research specializes in mobility analysis, modeling and simulation of vehicular networks, with emphasis on developing mathematical frameworks and software tools for connected mobility applications. Current projects include the Virtual Mobility World initiative exploring next-generation transportation systems. She actively contributes to TUM's teaching curriculum through courses such as Connected Mobility Basics, Internet for All, and Applications of Machine and Deep Learning in Mobile Networking, demonstrating expertise at the intersection of networking, mobility systems, and AI. As a core member of Prof. Jörg Ott's research group, she collaborates on edge computing, IoT, and networking projects while maintaining focus on vehicular communication systems and mobility pattern analysis.

Álvaro LópezView profile

Researcher

• Machine Learning
• 5G Networks
• Wi-Fi
• +3 more

Álvaro López is a Research Associate at the Fraunhofer Heinrich Hertz Institute (HHI) in Berlin, Germany, where he joined the Multimedia Communications Group in 2023. His work focuses on next-generation mobile communications and 5G standardization within the 3GPP RAN2 activities. His educational background includes: B.Sc. in Telecommunications Systems Engineering from Universitat Politècnica de Catalunya (UPC), 2015 M.Sc. in Wireless Communications Systems from Universitat Pompeu Fabra (UPF), 2017 Ph.D. in Information and Communication Technologies from UPF, 2022 Dr. López's research interests center on the intersection of machine learning and wireless communications . He develops autonomous learning techniques for 5G and beyond, with emphasis on Wi-Fi multi-link operations , traffic allocation , and network optimization to enhance next-generation communication systems. His publications (2016-2022) reveal a consistent focus on wireless networking evolution, particularly IEEE 802.11 standards (Wi-Fi 6/7) and 5G. Key trends include machine learning integration for self-organizing networks, multi-link resource management, and performance optimization in heterogeneous wireless environments. No information is available regarding students advised or specific research grants. His 3GPP standardization participation indicates industry-collaborative project involvement. At Fraunhofer HHI, Dr. López contributes to the Multimedia Communications Group's work on video coding, 5GXR, volumetric video, and Versatile Video Coding (VVC), advancing multimedia transmission for emerging applications like virtual reality.