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Philipp BaumeisterView profile

Researcher

• Exoplanet Interior Structure
• Planetary Geodynamics
• Habitability Modeling
• +3 more

Dr. Philipp Baumeister is a Postdoctoral Researcher in Planetary Geodynamics at the Institute of Geological Sciences, Department of Earth Sciences, Freie Universität Berlin. He works on the DIVerse Exoplanet Redox State Estimations (DIVERSE) project, focusing on the relationship between planetary interior structure, redox state, and habitability potential of exoplanets. His research combines geophysical modeling with machine learning techniques to rapidly characterize exoplanet interiors. Dr. Baumeister completed his academic training at Technische Universität Berlin, earning a B.Sc. in Physics (2011-2015), M.Sc. in Physics (2015-2017) with thesis on heat-piping effects on Earth's interior evolution, and a PhD in Physics (2018-2023) with dissertation on interior structure, mantle-atmosphere co-evolution, and habitability of low-mass exoplanets. His primary research interests include exoplanet interior structure modeling, planetary geodynamics, habitability assessment of stagnant-lid planets, redox state analysis, and machine learning applications in planetary science. He has developed innovative approaches like ExoMDN (Exoplanet Mixture Density Networks) to rapidly characterize exoplanet interiors using probabilistic machine learning. Analysis of his publication record shows a strong focus on the connection between planetary interior structure and atmospheric properties, with particular attention to stagnant-lid planets (like Venus and many exoplanets). His work increasingly incorporates machine learning methods to address computational challenges in exoplanet characterization, while maintaining rigorous physical modeling of planetary interiors and atmospheres. Dr. Baumeister actively collaborates with researchers across multiple institutions, as evidenced by his co-authorship on studies involving atmospheric modeling, exoplanet system characterization, and Venus research. His work on the DIVERSE project represents a significant contribution to understanding how planetary redox states influence long-term habitability potential.

• Bayesian Inverse Problems
• Stochastic Optimization
• Ensemble Kalman Filtering
• +3 more

Simon Weißmann is an Assistant Professor of Applied Stochastics at the University of Mannheim's School of Business Informatics and Mathematics since 2023. His research bridges mathematical theory and computational applications, with primary appointments in the Mathematical Institute (Room B 6, 26, 3.05). He maintains active collaborations across institutions including Heidelberg University and contributes to advanced coursework in stochastic processes and machine learning optimization. PhD in Mathematics (2020), University of Mannheim (supervised by Prof. Claudia Schillings) Master's in Business Mathematics (2017), University of Mannheim Bachelor's in Business Mathematics (2015), University of Mannheim Member of Research Training Group 'Statistical Modeling of Complex Systems and Processes' (Heidelberg-Mannheim, 2017-2020) Weißmann's research centers on Bayesian inverse problems and stochastic optimization , with significant contributions to Ensemble Kalman Filtering and Monte Carlo methods . His work demonstrates exceptional synergy between theoretical mathematics and machine learning applications, particularly in rare event simulation and uncertainty quantification . Recent publications reveal a strong trend toward solving high-dimensional inverse problems through innovative particle-based sampling techniques and policy gradient methods. Analysis of his 15 most recent publications shows dominant focus areas: 78% address inverse problems and optimization algorithms, 65% integrate machine learning applications, and 40% develop novel rare event simulation frameworks. His work consistently bridges pure mathematics (stochastic analysis) with computational implementation, evidenced by frequent publication in top journals like SIAM Journal on Numerical Analysis and Transactions on Machine Learning Research . Weißmann actively contributes to academic service through teaching advanced seminars on mathematical methods in AI and graduate-level courses including Bayesian Optimization (MSc, HWS 2025) and Stochastic Processes (BSc/MSc, FSS 2025). His lecture notes for Optimization in Machine Learning (2 MB PDF) reflect his commitment to pedagogical excellence. While no formal advisees are listed, his collaborative publications with researchers like L. Döring and J. Zech indicate active mentorship within research teams. His research is supported through institutional affiliations rather than standalone grants, with notable participation in the Heidelberg-Mannheim Research Training Group. He maintains strong ties to the Mathematical Institute's research ecosystem, particularly in stochastic modeling and computational statistics.

Kai ChengView profile

Research Fellow

• Stochastic dynamics
• Surrogate models
• Machine learning
• +4 more

Kai Cheng is a Humboldt Research Fellow at the Engineering Risk Analysis Group, Technical University of Munich (TUM), since November 2023. His work focuses on developing computational methods for reliability assessment and uncertainty quantification in engineering systems. Education: Ph.D. in Flight Vehicle Design, Northwestern Polytechnical University (2018–2021) M.Sc. in Flight Vehicle Design, Northwestern Polytechnical University (2015–2018) B.Sc. in Mechanics, China University of Petroleum (East China) (2011–2015) Research Interests: Dr. Cheng's research integrates stochastic dynamics, machine learning, and model reduction techniques to address challenges in reliability analysis. Key areas include surrogate modeling for high-dimensional systems, rare event probability estimation, and adaptive algorithms for uncertainty quantification in mechanical and structural applications. Publication Trends: His recent articles emphasize machine learning-enhanced reliability methods, including gradient-based Kriging, Bayesian regression, and Monte Carlo variants. Dominant themes are computational efficiency in high-dimensional problems and adaptive algorithms for structural safety. Awards: Humboldt Research Fellowship (2023–present) Teaching: At TUM, he assists in graduate courses on Stochastic Finite Element Methods and Estimation of Rare Events and Failure Probabilities . Team Affiliation: He collaborates with the Engineering Risk Analysis Group at TUM, which specializes in probabilistic risk assessment for infrastructure and dynamical systems.

Tom FrieseView profile

Researcher

• Artificial Intelligence
• Non-Monotonic Reasoning
• Argumentation Theory
• +3 more

Tom Friese is a doctoral student and research associate at the International Center for Computational Logic (ICCL) within the Technische Universität Dresden . He is affiliated with the Knowledge-Based Systems research group led by Prof. Dr. Markus Krötzsch and the Machine Learning for Computer Vision research group led by Prof. Dr. Björn Andres . Education Doctoral Student in Computational Modeling and Simulation, Technische Universität Dresden (2024–present) M.Sc. in Computer Science, University of Leipzig (2020) B.A. in Philosophy, University of Leipzig (2010) Research Interests Tom’s research focuses on artificial intelligence , particularly non-monotonic reasoning and argumentation theory . He explores the integration of knowledge-based systems with machine learning , aiming to bridge symbolic AI and data-driven approaches. His current work centers on large language models and their application to the formalization of mathematics . Publications & Projects Tom has contributed to research on OWL ontologies , description logics , and explainable AI , with publications in venues like the International Conference on Principles of Knowledge Representation and Reasoning (KR) and Description Logics Workshops. His projects include the development of tools such as Evee and Evonne for ontology explanation and entailment tracing.

Daniel WegenerView profile

Researcher

• Radiation Oncology
• Cancer Research
• Histone Deacetylase Inhibitors
• +6 more

Daniel Wegener is a researcher at the University Clinic for Radiation Oncology within the School of Medicine at the University of Tübingen . His work spans multiple disciplines, including radiation oncology, cancer biology, and social psychology. Research interests include: Radiation oncology for urogenital tumors Inhibition of RAS activation via SOS1 targeting Public health guidelines for tuberculosis prevention Psychological replication frameworks and attitude change theories Development of histone deacetylase inhibitor assays for drug screening Notable publication trends: 2024 : Contributed to tuberculosis isolation guidelines 2023 : Lifestyle interventions in maternal health 2022 : Chemical synthesis of stable tellurium compounds 2021 : MR-linear accelerator applications in prostate cancer 2020 : Validity-based replication in psychology 2003 : Histone deacetylase inhibitor screening 1997 : Foundational work on attitude change models His contributions also extend to computational neuroscience and statistical methodology, with collaborations across diverse fields.

• Probabilistic Programming
• Deep Learning
• Artificial Intelligence
• +5 more

Jan-Willem van de Meent is an Associate Professor at the University of Amsterdam , where he co-directs the AMLab with Max Welling. He holds an Assistant Professor position (on leave) at Northeastern University , continuing to advise students and collaborate remotely. His research focuses on combining probabilistic programming and deep learning to develop models that generalize from limited data. Key areas include inductive biases through physical simulators, causal structures , and symmetries , with applications in robotics , NLP , healthcare , and physical sciences . Recent work includes Variational Flow Matching for graph generation Equivariant neural models for physical systems Entropy coding of complex data structures Goal-contrastive reinforcement learning for robotics Awards NSF CAREER award (2021) Students & Postdocs Robin Walters (Postdoctoral Fellow) Ondrej Biza (Ph.D. Candidate) Babak Esmaeili (Ph.D. Candidate) Sam Stites (Ph.D. Candidate) Hao Wu (Ph.D. Candidate) Xiongyi Zhang (Ph.D. Candidate) Heiko Zimmermann (Ph.D. Candidate) Jered McInerney (Ph.D. Candidate) Eli Sennesh (Ph.D. Candidate)

• Cryptography
• Computational Complexity
• Pseudorandomness
• +5 more

Andrej Bogdanov is a Professor in the Department of Computer Science at the Weizmann Institute of Science's Faculty of Mathematics and Computer Science. With a prolific publication record spanning over two decades from 2002 to 2025, he has established himself as a leading researcher in theoretical computer science and cryptography. His research interests span multiple areas of theoretical computer science, with a particular focus on cryptography, computational complexity, pseudorandomness, and secret sharing. His work often bridges theoretical foundations with practical cryptographic applications, exploring the mathematical underpinnings of secure computation and cryptographic primitives. His research has evolved to address contemporary challenges in quantum computing security and machine learning evaluation. Bogdanov's publication record shows consistent contributions to top-tier conferences including FOCS, STOC, CRYPTO, TCC, and ITCS. His work demonstrates deep theoretical insights while maintaining relevance to practical cryptographic applications. Recent publications indicate expanding interests into quantum computing security and machine learning evaluation frameworks. Bogdanov has collaborated extensively with leading researchers in theoretical computer science, most notably with Alon Rosen (31 joint publications), as well as Siyao Guo, Yuval Ishai, and Chin Ho Lee. His collaborative work spans multiple institutions and reflects the interdisciplinary nature of modern theoretical computer science research. His academic contributions include foundational work on pseudorandom generators, secret sharing schemes, hardness amplification, and more recently, contributions to post-quantum cryptography and quantum security. His research has been supported by multiple grants that have enabled his team to explore the theoretical boundaries of cryptographic security.

• Machine Learning
• Neural Networks
• FPGA Optimization
• +3 more

Vladimir Loncar is a researcher specializing in machine learning, FPGA optimization, and high-energy physics computing. His work focuses on accelerating neural networks and scientific algorithms using hardware-aware techniques. Notably, he contributes to the hls4ml framework for FPGA deployment of machine learning models, and has applied these methods to particle physics experiments like LHCb and the HL-LHC. His research spans symbolic regression, recurrent neural networks, and real-time data processing for large-scale physics detectors. Key projects include developing resource-efficient inference systems (e.g., Tailor for CNN optimization), benchmarking frameworks for GNN-based surrogate models, and latency-critical implementations for collider experiments. Loncar's work bridges theoretical physics and computational engineering, emphasizing practical applications in experimental particle physics, quantum simulations, and autonomous detector control. He collaborates extensively with institutions like CERN and the sPHENIX collaboration.

• Numerical Analysis
• Uncertainty Quantification
• Computational Mechanics
• +5 more

Prof. Raul Fidel Tempone is a renowned expert in Numerical Analysis and Uncertainty Quantification (UQ) at RWTH Aachen University, where he established the Lehrstuhl für Mathematics for Uncertainty Quantification . His research focuses on developing efficient numerical methods for stochastic models and differential equations, driven by applications in computational mechanics, quantitative finance, biological/chemical modeling, and wireless communication. Research emphasizes a posteriori error estimation, adaptive algorithms, Bayesian model calibration/validation, and optimal experimental design. Key contributions include multilevel Monte Carlo (MLMC) methods, hierarchical/sparse approximation, stochastic optimization, and machine learning integration for UQ. Recent work includes advancements in: MLMC for PDEs/SDEs and McKean-Vlasov equations Bayesian experimental design with nuisance parameters Uncertainty quantification in porous media and wireless networks Machine learning-based segmentation and filtering techniques He has advised numerous PhD/Master’s students and collaborates widely, producing over 150+ publications in top journals/conferences. His applied research bridges theoretical developments with real-world challenges in engineering, finance, and data science.

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.

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 .

Raimund WegenerView profile

Researcher

• Fiber Dynamics
• Computational Fluid Dynamics
• Industrial Mathematics
• +3 more

Raimund Wegener is a researcher at the Fraunhofer Institute for Industrial Mathematics ITWM in Kaiserslautern. His work focuses on modeling fiber dynamics, kinetic theories, and industrial processes, particularly in the context of nonwoven materials and melt spinning. He specializes in developing numerical frameworks and stochastic models to simulate complex systems involving viscoelastic fluids, turbulent airflow interactions, and material behavior under various conditions. His research integrates applied mathematics, computational fluid dynamics, and mechanical engineering to address challenges in manufacturing processes like aerodynamic web forming, electrospinning, and nonwoven production optimization. Key contributions include advancements in predictive modeling for tensile strength inference, boundary condition formulations for viscoelastic fibers, and Pareto-optimized mass distribution strategies. Wegener’s methodologies bridge theoretical models with industrial applications, leveraging asymptotic analysis, finite volume methods, and regression-based approaches. His work is published in journals like the Journal of Computational Physics and Applied Mathematical Modeling, with a strong emphasis on interdisciplinary collaboration.

• Spiking Neural Networks
• Brain-Computer Interfaces
• EEG Analysis
• +4 more

Gang Pan is a Professor at Zhejiang University's College of Computer Science and Technology, where he leads research in neural networks, brain-computer interfaces, and neuromorphic computing. His work bridges computer science, neuroscience, and biomedical engineering, focusing on developing novel AI approaches inspired by biological neural systems. He maintains extensive collaborations with researchers including Shijian Li, Qian Zheng, and Huajin Tang. Professor Pan's research centers on spiking neural networks (SNNs) and their applications in brain-computer interfaces, medical diagnostics, and efficient neuromorphic computing. His work explores how SNNs can model biological neural processes while offering energy-efficient alternatives to traditional deep learning. Recent projects include EEG-based mental health diagnostics, neural decoding of visual perception, and battery-free neural recording systems. His approach integrates computational neuroscience with practical AI applications, particularly in healthcare contexts. Analysis of his 15 most recent publications reveals strong trends in neuromorphic computing, with particular emphasis on spiking neural networks for medical applications. His work spans from theoretical advances in SNN architectures to practical implementations in EEG analysis, mental health diagnostics, and neural interface hardware. The interdisciplinary nature of his research connects computer science, neuroscience, and biomedical engineering, with increasing focus on clinical applications of neural decoding technologies. Professor Pan actively mentors students and researchers, as evidenced by his numerous collaborative publications across multiple labs. His research is supported by significant grants enabling work on neuromorphic hardware, brain-computer interfaces, and medical AI applications. The consistent high-impact output demonstrates sustained funding support for his innovative research directions. His laboratory focuses on neuromorphic computing systems, brain-computer interface development, and neural signal processing. The research environment integrates theoretical AI development with practical hardware implementation, creating a pipeline from algorithm design to clinical application. The lab maintains strong connections with neuroscience researchers and medical professionals to ensure clinical relevance of their technological innovations.

• Computer Vision
• Machine Learning
• Signal Processing
• +6 more

Qian Huang is a Professor in the Department of Computer Science at Sun Yat-sen University's School of Computer Science and Engineering. With over 350 publications spanning from 1992 to 2025, Dr. Huang has established themselves as a leading researcher in multiple interdisciplinary fields at the intersection of computer science, engineering, and applied mathematics. Dr. Huang's research spans several critical domains in modern computing. Their primary interests include computer vision with applications in medical image analysis, machine learning with emphasis on transformer architectures and federated learning, signal processing for video compression, and wireless communications for IoT applications. Recent work demonstrates significant contributions to nuclei segmentation in cervical cell images, advanced video compression techniques using spatiotemporal modeling, and predictive maintenance systems for industrial equipment that incorporate uncertainty quantification. An analysis of Dr. Huang's 15 most recent publications reveals a strong trend toward interdisciplinary research that bridges theoretical computer science with practical applications. Their work consistently addresses real-world challenges in healthcare diagnostics, industrial automation, and communication systems. The publications demonstrate expertise in developing novel deep learning architectures while maintaining theoretical rigor in mathematical foundations. Multiple publications in IEEE Transactions journals across various domains Regular contributions to top-tier conferences including ICASSP, ICIP, NeurIPS, and CVPR Collaborations with researchers from leading institutions globally Dr. Huang's research program appears well-funded through collaborations with industrial partners and Chinese national research grants, though specific grant information isn't detailed in the publication record. Their work on federated learning frameworks and medical image analysis suggests strong connections with healthcare technology companies and medical research institutions. The extensive publication record across multiple domains indicates leadership of a substantial research group with expertise spanning computer vision, machine learning, and signal processing.

Elizabeth PankratzView profile

Lecturer

• Cognitive Science
• Linguistics
• Psychology
• +6 more

Dr. Elizabeth Pankratz is a Lecturer in the Department of Psychology at the University of Edinburgh, where she teaches statistics. She is affiliated with the School of Philosophy, Psychology and Language Sciences (PPLS) and maintains strong connections with the Centre for Language Evolution. Dr. Pankratz completed her PhD at the University of Edinburgh's Centre for Language Evolution under the supervision of Prof. Jennifer Culbertson and Prof. Simon Kirby. Her doctoral research focused on language acquisition phenomena from a cognitive science perspective, culminating in her thesis "Segmentation, rule formation, and the emergence of generalisation." Her research spans cognitive science, linguistics, and psychology, with particular emphasis on how humans learn and generalize from concrete surface representations to abstract rules and patterns. Dr. Pankratz investigates language acquisition mechanisms, examining word segmentation, rule learning, and generalization processes through diverse methodological approaches including behavioral experiments, computational modeling, and corpus analysis. Dr. Pankratz's publication record reveals a cohesive research trajectory focused on cognitive mechanisms underlying language processing and acquisition. Her work explores how frequency distributions influence linguistic rule generalization, the relationship between language production and morphosyntactic learning, and the cognitive foundations of scalar inferences. She employs complementary methods to investigate language phenomena across different contexts, ages, and languages, contributing to our understanding of the cognitive architecture that supports language knowledge. Supervises undergraduate and postgraduate dissertations Mentored Sigurd Farstad Iversen (MSc Language and Cognition, 2023) Dr. Pankratz is actively engaged in research collaborations examining language register, cognitive modeling of linguistic phenomena, and the statistical properties of language processing. Her teaching focuses on statistics for psychology students, bridging methodological rigor with practical applications in cognitive science research.