Find Academic Supervisors

Alexander VerlView profile

Professor

• Industrial Robotics
• Control Engineering
• Mechatronics
• +5 more

Prof. Dr.-Ing. Alexander Verl is a leading academic at the University of Stuttgart , serving as Principal Investigator at the Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) . His work bridges control engineering , industrial robotics , and digital twin technologies to enhance precision in manufacturing systems. Research Interests : Improving positioning accuracy of industrial robots through transmission error modeling and compliance compensation. Developing adaptive preload control mechanisms for cable-driven parallel robots and rack-and-pinion systems. Advancing IT/OT convergence via Time-Sensitive Networking (TSN) and cloud-edge integration. Creating digital twin platforms for real-time simulation and quality monitoring in CNC machining. Exploring deep learning applications for perception of deformable linear objects in automation. Recent Work Trends show expertise in: smart manufacturing , Industry 4.0 , and data-driven control systems . Publications emphasize practical validation through industrial testbeds (e.g., KUKA KR210–2 robotics, CNC machine simulations) and theoretical contributions to elastokinematic models and nonlinear dynamics . Advising & Grants : Collaborates extensively with researchers like Armin Lechler and Michael Neubauer. Projects funded through academic-industry partnerships in automotive production, precision engineering, and Gaia-X-based data ecosystems.

Kevin BönischView profile

Researcher

• Artificial Intelligence
• Natural Language Processing
• 3D Visualization
• +7 more

Kevin Bönisch is a researcher at the Text Technology Lab, Goethe University Frankfurt. With a background in software development (5 years as C# .NET full-stack developer) and concurrent Master's studies in Computer Science, he bridges practical software engineering with academic research through projects like the Unified Corpus Explorer and Viki LibraRy virtual reality systems. His work spans NLP, 3D visualization, and machine learning applications. DFG New Data Spaces program contributor Kaggle competition participant GitHub demonstrator Research focuses include annotation-based corpus exploration , causal inference in LLMs , and collaborative hypertext systems . His 2025 NAACL Best Demo Paper showcased UCE system innovations, while 2024 LIRAI workshop work demonstrated legal document retrieval via SVR ensembles. Notable awards include IAV-Coding competition first place , DESIGNRUSH website design recognition , and Goethe-University Innovation Prize finalist . Key projects: ROBERT dialogue system , BIOfid biodiversity service , and Bundestags-Mine legislative analysis .

• Scientific Machine Learning
• Simulation-Based Inference
• Hierarchical Bayesian Inference
• +3 more

Jonas Arruda is a Ph.D. student and Researcher at the Life and Medical Sciences Institute (LIMES) of the University of Bonn, affiliated with the Hasenauer Lab. His work bridges Scientific Machine Learning , Simulation-Based Inference , and Computational Biology , with a focus on Hierarchical Bayesian Inference and Neural Posterior Estimation . Education: BSc and MSc in Mathematics at the University of Bonn (with a year at the University of Barcelona), MSc in Economics (2024) Experience: Research visitor at Rensselaer Polytechnic Institute (2025) His research explores amortized inference for large-scale models and neural posterior estimation in non-linear mixed-effects modeling, contributing to both computational statistics and secure distributed learning frameworks. He has presented at the BioInference Conference 2023, where he received the best talk award for early career researchers. Contact: jonas.arruda@uni-bonn.de . Affiliated with the University of Bonn since 2022.

• Land-atmosphere interactions
• Climate-carbon cycle system
• Machine learning in Earth system research
• +5 more

Dr. Alexander Winkler serves as a Group Leader in the Department of Biogeochemical Integration at the Max Planck Institute for Biogeochemistry in Jena, Germany. His research focuses on the complex interactions between the atmosphere and biosphere, particularly examining energy, water, and carbon exchanges within the Earth system. He leads the research group on Atmosphere-Biosphere Coupling, Climate and Causality, and contributes significantly to the USMILE ERC Project that explores statistical and machine learning applications in Earth system research. Winkler's research interests span land-atmosphere interactions, climate-carbon cycle feedbacks, and the application of modern statistical and machine learning methods to Earth system science. His work particularly emphasizes hybrid modeling approaches that combine process-based and data-driven models to better understand land-atmosphere interactions. He investigates how rising atmospheric CO 2 concentrations affect Earth observations and model simulations through causal inference techniques, and examines constraints on key entities in the climate-biosphere continuum by linking multi-model ensemble simulations with observational data. His recent publications reveal a strong trend toward integrating machine learning with traditional Earth system modeling, with significant contributions in carbon cycle dynamics, climate-carbon feedbacks, and vegetation responses to climate change. His research demonstrates how hybrid modeling approaches can enhance our understanding of complex Earth system processes, particularly in disentangling the effects of rising CO 2 on land-atmosphere exchanges of carbon and water. Winkler actively contributes to academic education, teaching courses such as 'Klimatologie und Klimawandel' at Friedrich-Schiller-Universität Jena's Chemisch-Geowissenschaftliche Fakultät, and has previously taught 'Terrestrial Ecosystem Processes & Carbon Feedbacks within Earth System Models' at Universität Hamburg. His teaching reflects his research expertise, focusing on the intersection of climate science, carbon cycle dynamics, and advanced data analysis techniques.

Assaf TalView profile

Professor

• Neuroimaging
• Magnetic Resonance Spectroscopy
• Brain Disease Monitoring
• +3 more

Prof. Assaf Tal is a faculty member in the Department of Bio-Medical Engineering at The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University. His research focuses on developing advanced neuroimaging methodologies using magnetic resonance spectroscopy (MRS) and imaging (MRI) to investigate brain function and disease mechanisms. His primary research interests center on neuroimaging physics and brain disease monitoring , with specific emphasis on: Developing novel MRS/MRI techniques combining spin physics and signal processing Tracking neurochemical changes during cognitive processes Detecting and monitoring neurodegenerative diseases including multiple sclerosis, traumatic brain injury, and Alzheimer's Disease Understanding brain encoding mechanisms across neurochemical, electrophysiological, and structural levels His work bridges biomedical engineering with clinical neuroscience to create improved diagnostic and monitoring tools. Prof. Tal's recent publications (2022-2025) demonstrate strong focus on functional MRS and advanced spectral-temporal analysis , with significant contributions to motion correction, uncertainty estimation, and microstructural modeling in neuroimaging. His research shows increasing integration of computational methods like Bayesian inference and machine learning for precision neuroimaging. His laboratory develops specialized software tools including the Visual Display Interface (VDI) for MRS data processing and simulation, supporting both preclinical and clinical neuroimaging research.

Christian A. NaessethView profile

Assistant Professor

• Generative Modeling
• Uncertainty Quantification
• Probabilistic Machine Learning
• +5 more

Christian A. Naesseth is an Assistant Professor of Machine Learning at the University of Amsterdam, where he is a member of the Amsterdam Machine Learning Lab and serves as lab manager of the UvA-Bosch Delta Lab 2. He is also an ELLIS member, actively contributing to the European AI research community. His work bridges theoretical machine learning with practical applications across scientific domains. University of Amsterdam - Faculty of Science Amsterdam Machine Learning Lab (AMLab) UvA-Bosch Delta Lab 2 (Lab Manager) ELLIS Institute member Naesseth's research focuses on generative modeling, uncertainty quantification, and probabilistic machine learning. His work spans diffusion models, flow matching techniques, stochastic differential equations, and their applications in scientific domains. He has made significant contributions to simulation-free training frameworks like SDE Matching, which eliminates the need for discretization and simulation when fitting latent SDE models to data. His research also addresses critical challenges in uncertainty quantification, including conformal prediction, risk monitoring in test-time adaptation, and multiple hypothesis testing. His recent publications demonstrate a consistent focus on improving efficiency and reliability in generative modeling while maintaining theoretical rigor. The work on SDE Matching represents a major advancement in training efficiency for latent stochastic differential equations, achieving speed improvements of several orders of magnitude. His research on risk monitoring and conformal prediction addresses practical deployment challenges for AI systems operating under distribution shift. Best Workshop Paper Award at AABI 2025 for SDE Matching 100% acceptance rate across major ML conferences in the 2024-2025 cycle (5/5 NeurIPS, 2/2 AISTATS, 2/2 ICML, 1/1 UAI) Naesseth actively mentors PhD students and postdocs, including Grigory Bartosh, Hany Abdulsamad, and several visiting researchers from institutions worldwide. He serves as program chair for AABI 2024 and has been involved in organizing multiple workshops at major conferences including ICML and NeurIPS. His lab has secured funding through collaborations with Bosch and likely other industry partners, supporting postdoctoral researchers and PhD students working at the intersection of theory and applications. He leads the UvA-Bosch Delta Lab 2, which focuses on advancing the theoretical foundations of machine learning while developing practical applications. The lab maintains strong connections with the broader Amsterdam Machine Learning ecosystem, including collaborations with ELLIS units in Amsterdam, Delft, and Nijmegen.

• Statistics
• Meteorology
• Econometrics
• +4 more

Dr. Annette Christine Möller is a Professor at Bielefeld University's Faculty of Economics, where she leads the Chair of Data Science and is affiliated with the Department of Empirical Methods. She serves as a key researcher at the Bielefeld Center for Data Science (BiCDaS). Her contact information includes email (annette.moeller@uni-bielefeld.de) and phone (+49 521 106-4877). Her research focuses on: Developing statistical methods for weather forecast post-processing Copula-based modeling of multivariate dependencies Ensemble forecasting for climate risk mitigation Applications in renewable energy prediction Statistical computing and open-source tool development Her publication record (2019-2025) demonstrates strong emphasis on: Probabilistic weather forecasting techniques Vine copula applications in meteorology Statistical calibration of ensemble models Interdisciplinary work spanning climatology, epidemiology and education She leads significant research grants including: DFG project: 'Statistical post-processing of ensemble forecasts' (2018-2024) DFG project: 'Mitigating climate risks via copula-based forecast improvement' (2024-2026) She collaborates with the Technical University of Munich and maintains active research teams focused on statistical meteorology and climate informatics.

Miriam SchulteView profile

Professor

• High-Performance Computing (HPC)
• Parallel and Distributed Systems
• Scientific Computing
• +5 more

Miriam Schulte is a Professor at the Institute for Parallel and Distributed Systems (IPVS) at the University of Stuttgart . As Dean of Studies SimTech , she leads academic programs in simulation technology. Her research focuses on high-performance computing , multi-physics simulations , and scientific software development , with significant contributions to coupling libraries like preCICE and biophysical frameworks like OpenDiHu . Key Research Areas: High-Performance Computing (HPC) Multi-physics and Fluid-Structure Interaction (FSI) Sparse Grids and Hierarchical Numerical Methods Machine Learning in Simulation Software Parallel and GPU-Accelerated Algorithms Advising: Guided student projects on quantum neural networks , GPU-optimized sparse grids , and SYCL-based HPC frameworks . Coordinated SimTech Research Modules and IPVS/SGS team initiatives. Software Leadership: Maintains preCICE (coupling library for multi-physics) Develops OpenDiHu (neuromuscular simulations) Advances PLSSVM (parallel SVM library) and SG++ (sparse grids) Her recent publications (2022–2025) emphasize machine learning integration with multi-physics simulations , including groundwater heat pumps , brain tumor modeling , and neuromuscular EMG prediction . She actively promotes open-source software sustainability and collaborative research infrastructure at the University of Stuttgart.

Conor Mayo-WilsonView profile

Associate Professor

• Formal Epistemology
• Logic
• Philosophy of Mathematics
• +5 more

Conor Mayo-Wilson is an Associate Professor in the Department of Philosophy at the University of Washington , specializing in formal epistemology, logic, and philosophy of science. His research spans interdisciplinary methodologies bridging philosophy with statistics, game theory, and causal inference. Education: Ph.D., Philosophy, Carnegie Mellon University (2012) M.S., Logic and Computation, Carnegie Mellon University (2009) M.S., Mathematics, Carnegie Mellon University (2009) B.S., Mathematics, Stanford University (2006) B.A., Philosophy, Stanford University (2006) His research focuses on the intersection of epistemology , statistics , and causal inference , with notable contributions to robust Bayesianism, causal discovery algorithms, and the philosophical foundations of statistical methods. Recent articles address computational philosophy, measurement theory, and severe testing frameworks. Mayo-Wilson has taught courses including Introduction to Logic, Seminar in Epistemology, and Statistics and Philosophy of Voting. He is affiliated with the Center for Statistics in the Social Sciences at the University of Washington. Scientific Awards: Best Paper Award, 2014

Nadine von Frankenberg und LudwigsdorffView profile

Researcher

• Ubiquitous computing
• Smart environments
• Building control systems
• +4 more

Dr Nadine von Frankenberg und Ludwigsdorff is a Postdoctoral Researcher at the Technical University of Munich (TUM) , affiliated with the Institute for Informatics I1 . She conducts research and teaches in the areas of ubiquitous computing, smart environments, and occupant-centric building control systems. Her office is located in Garching b. München, and she can be reached at nadine.frankenberg@tum.de . Education & Dissertation: In 2022 she defended her doctoral dissertation “Rationale Management for Event-Based Arbitration of Thermal Quality in Shared Spaces” at TUM, laying the groundwork for her subsequent work in human-in-the-loop building controls. Research Interests: Ubiquitous and pervasive computing Smart environments and IoT Building control systems with human-in-the-loop Occupant-centric & personalized indoor environmental quality Digital twins, machine learning, and occupant health Her work unites computer science, HCI, and building technology to create adaptive systems that balance energy efficiency with individual comfort and well-being. Publication Profile: Across 2016-2022 she authored or co-authored more than a dozen peer-reviewed papers in top venues such as BuildSys , CHI , Ubicomp , ICSE , and SIGCSE . A clear trend emerges: early work (2016-2017) focused on visualization and assessment tools for smart buildings, followed by 2018-2020 contributions on sensor-based thermal comfort inference, machine-learning control algorithms, and cloud architectures for multi-device studies. Most recently, her 2021-2022 publications center on conflict resolution and rationale management for shared thermal spaces, culminating in her 2022 dissertation. Scientific Service & Awards: Program Committee member: ICSE 2021-2023, SIGCSE 2017-2023, HICSS-56 2023, CSEE&T 2020-2022, SEUH 2020 Publicity Chair: CSEE&T 2020, SEUH 2020 Publication Co-Chair: BuildSys 2020 Teaching & Student Supervision: She has been a recurring instructor and lab supervisor for Introduction to Software Engineering (EIST) , iPraktikum , and Interactive Learning seminars since 2018. Additionally, she has advised or co-advised more than 25 Bachelor’s, Master’s, and Guided Research theses on topics spanning thermal comfort systems, digital twins, mHealth applications, and IoT network simulation. Labs & Teams: She is a core member of the Research Group for Applied Software Engineering (ASE) at TUM, collaborating closely with Prof. Bernd Brügge’s chair and interdisciplinary partners in architecture, psychology, and health informatics.

Sebastian BrandstäterView profile

Lecturer

• Scientific Machine Learning
• Inverse analysis & Bayesian Inference
• Uncertainty Quantification
• +5 more

Dr. Sebastian Brandstäter serves as Lecturer at the Institute for Mathematics and Computer-Based Simulation, Bundeswehr University Munich since 2022. His academic trajectory includes research associate positions at Hamburg University of Technology (2021) and Technical University of Munich (2016-2021). His research focuses on: Scientific Machine Learning for biomechanical systems Uncertainty Quantification and Bayesian Inference Global Sensitivity Analysis of complex models Multi-Physics & Multi-Scale Modeling of biological tissues Open-source scientific software development Dr. Brandstäter's work centers on gastrointestinal biomechanics, particularly computational modeling of gastric electromechanics and motility. He has pioneered applications of Gaussian-process metamodelling for sensitivity analysis in vascular and gastric systems, and develops open-source frameworks (QUEENS, 4C) that enable efficient multi-query analysis of large-scale models. He actively supervises student theses on patient-specific modeling and computational biomechanics, teaches advanced numerical methods courses, and contributes to the scientific community through conference organization, peer review, and international collaborations. His recent work demonstrates increasing emphasis on data-driven surrogate modeling and solver-independent computational frameworks for biomedical applications.

• Causal Machine Learning
• Mechanistic Machine Learning
• Dynamical Systems
• +6 more

Niki Kilbertus is a Professor in the Department of Informatics at the Technical University of Munich and a group leader at Helmholtz AI (Helmholtz Munich). They are also affiliated with MCML, the Konrad Zuse School relAI, and the Munich Unit of ELLIS. Since 2024, they have been a member of the Junge Akademie and received the Leopoldina Prize for Young Scientists. In 2025, they were awarded an ERC Starting Grant and achieved tenure at TUM. Professor Kilbertus's research focuses on causal machine learning, mechanistic ML, dynamical systems, and AI for science. Their work spans theoretical foundations of causal inference and practical applications across scientific domains. They have made significant contributions to causal effect estimation, causal discovery in stochastic processes, learning differential equations, and fair machine learning. Their research often bridges computer science with physics, biology, and climate science, demonstrating the interdisciplinary nature of their work. Professor Kilbertus has published extensively in top machine learning venues including NeurIPS, ICML, and ICLR, with numerous publications in 2024-2025. Their recent work shows a strong trend toward causal discovery in continuous-time systems, intervention modeling, and physics-informed machine learning applications. Scientific Awards: Leopoldina Prize for Young Scientists (2024) ERC Starting Grant (2025) Professor Kilbertus actively supervises multiple PhD students and collaborates with researchers across institutions including Max Planck Institutes and Helmholtz centers. They serve as an Action Editor for TMLR and regularly review for major ML conferences. The research group is well-funded through the ERC grant and institutional support from TUM and Helmholtz AI, enabling active recruitment of new PhD students and postdocs. Based at Technical University of Munich and Helmholtz AI, Professor Kilbertus's team works at the intersection of theoretical machine learning and scientific applications, with particular strengths in causal reasoning for complex dynamical systems.

• Quantitative Linguistics
• Speech Production
• Articulatory Speech Synthesis
• +4 more

Konstantin Sering is a researcher at the University of Tübingen within the Faculty of Philosophy, specifically working at the Seminar for Linguistics as part of the Quantitative Linguistics group. His academic journey bridges physics and psychology with computational linguistics, culminating in his 2023 PhD thesis on the PAULE (Predictive Articulatory speech synthesis Utilizing Lexical Embeddings) model. He maintains active collaborations with the Cognitive Modeling Group led by Martin V. Butz and the Quantitative Linguistics Group led by Harald Baayen. Sering's research interests center on the computational modeling of human speech production, with particular focus on the articulatory aspects of language. He approaches spoken language from the perspective of computer-aided modeling and simulation, extending the VocalTractLab model to produce spontaneous human speech. His work emphasizes the information-bearing nature of language over its meaning-bearing aspects, investigating how language signals contextual states and conditions. He combines artificial neural networks with predictive forward approaches to model vocal tract control parameters across multiple target spaces, including meaning and acoustic representations. Analysis of Sering's publication record reveals a consistent trajectory in articulatory speech synthesis and quantitative linguistics. His work demonstrates increasing sophistication in modeling the human speech apparatus, with recent publications focusing on improving the PAULE model through speech/non-speech classification, somatosensory feedback integration, and benchmarking methodologies. His research spans both theoretical computational linguistics and practical applications, with strong connections to cognitive science and psycholinguistics. The publications show interdisciplinary collaboration across linguistics, computer science, and cognitive psychology. Sering actively contributes to the academic community through teaching and software development. He has taught courses including Introduction to Psycholinguistics, Physics of Human Speech Production, and seminars on Predictive Articulatory Speech Synthesis. As software author and maintainer, he has developed several important tools for the field: Articubench (an articulatory speech synthesis benchmark), PAULE (his namesake speech synthesis model), create_vtl_corpus, and contributions to pyndl and other packages. His technical expertise spans Python, R, and specialized instrumentation for speech analysis. Based in room 188 at Keplerstr. 2 in Tübingen, Sering maintains an active research program with regular publications and software releases since 2013. His work environment benefits from the resources of the University of Tübingen's linguistics infrastructure and collaborative opportunities across disciplines.