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Nicola TeuberView profile

Researcher

• Data Analytics
• Data Science
• Digital Transformation
• +9 more

Nicola Teuber is a Researcher at the Institute of Information Systems (IIS) at Leuphana University, located in Lüneburg. She leads the Team Nicola Teuber and is affiliated with the university's academic community. Her contact details include office C 4.308a and phone number +49 4131 677-1974. Her research interests focus on cutting-edge areas such as Data Analytics, Artificial Intelligence, Machine Learning, and Network Science, with an emphasis on Digital Transformation and Knowledge Management. She also contributes to interdisciplinary fields like E-Business and Computer Engineering. As part of her academic work, Teuber advises numerous students on theses, including projects like the ExRePo initiative for AI-based invoice item extraction. Her team collaborates on initiatives such as the USIN5G-Projekt and participates in events like the Wirtschaftsinformatik-Nachwuchstreffen (WINT 2024). Teuber’s professional activities include involvement with Leuphana’s academic infrastructure, such as the LÜFINS Computer Science Day Schools and the MKWI2018 initiative, reflecting her commitment to advancing educational and research programs in information systems.

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 .

Maximilian EißlerView profile

Researcher

• Explainable AI
• Robust Machine Learning
• Representation Learning
• +2 more

Maximilian Eißler is a Researcher and PhD candidate at the Machine Learning Group of Technische Universität Berlin (TU Berlin), affiliated with the Berlin Institute for the Foundations of Learning and Data (BIFOLD). He holds an M.Sc. in Computer Science (2024) from TU Berlin, alongside B.Sc. degrees in Computer Science (2021) and Industrial Engineering (2020), both from the same institution. His research focuses on counterfactual generative explanations for language models, encompassing Explainable AI , Robust Machine Learning , Representation Learning , and Generative Models . Prior to his PhD, he worked as a Machine Learning Engineer at Springbok AI, developing conversational agents for Würth IT. No scientific awards or grants are explicitly mentioned in the provided text. He currently contributes to the Machine Learning Group at TU Berlin.

Malte EsdersView profile

Researcher

• Machine Learning
• Force Fields
• Regularization of ML models
• +1 more

Malte Esders is a post-doctoral researcher in the Machine Learning group at Technical University of Berlin, affiliated with the Berlin Institute for the Foundations of Learning and Data (BIFOLD). His research focuses on applying machine learning to physical sciences, including regularization of neural networks for quantum systems, force fields, and graph neural networks. He holds a B.Sc. in Cognitive and Neurobiological Psychology from Utrecht University (2014), an M.Sc. in Computational Neuroscience from the Bernstein Center Berlin (2017), and is completing a Ph.D. in Machine Learning at TU Berlin (expected 2025). His work with BASF on chemical plant simulations addressed challenges in modeling cyclic processes using fixed-point iteration and ML surrogates. Key contributions include developing methods to stabilize ML-based simulations through fine-tuning and exploring industrial applications of machine learning. His interdisciplinary background spans computational neuroscience, quantum systems, and robotics. Beyond research, Esders engages in technical collaborations, such as optimizing machine learning models for chemical engineering processes, and maintains a personal blog exploring topics like meditation, Linux system administration, and scientific experiments (e.g., broccoli sprouting optimization).

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).

Johannes MaeßView profile

Researcher

• Explainable AI
• Graph Neural Networks
• Computational Neuroscience
• +2 more

Johannes Maeß is a PhD student in Machine Learning at Technische Universität Berlin and a full-time researcher at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) since 2023. He holds a Master’s in Computer Science from TU Berlin (2020-2022). His research focuses on the theoretical foundations of explainability in neural networks and applications of AI in language education, aiming to bridge theoretical concepts with practical implementations. Education: PhD in Machine Learning, Technische Universität Berlin (2023–present) MSc in Computer Science, Technische Universität Berlin (2020–2022) Industry Background: Amazon Web Services: Low-latency query processing for Redshift IBM: Machine learning-based cardinality estimation in databases Research interests include Explainable AI, Graph Neural Networks, Computational Neuroscience, Probabilistic ML, and Language Education. He contributes to projects like the SchNetPack toolbox for atomistic machine learning.

Klaus-Robert MüllerView profile

Professor

• Machine Learning
• Computational Neuroscience
• Brain-Computer Interfaces
• +8 more

Prof. Dr. Klaus-Robert Müller is a Full Professor and Chair of Machine Learning at Technical University of Berlin, leading the Berlin Institute for the Foundations of Learning and Data (BIFOLD). He holds joint appointments at Korea University (Department of Artificial Intelligence) and collaborates with institutions like Google Research and the Max Planck Society. With a PhD in theoretical computer science from the University of Karlsruhe, Müller has pioneered work in statistical learning theory, brain-computer interfaces, and explainable AI. His research spans computational neuroscience, molecular dynamics, and medical imaging applications such as digital pathology and neuroimaging analysis. He has directed major initiatives like the Berlin Center for Machine Learning (BZML) and the Berlin Big Data Center (BBDC), now integrated into BIFOLD. Müller is a member of prestigious academies, including the Leopoldina and Berlin-Brandenburg Academy of Sciences, and has received awards like the Vodafone Innovations Award (2017) and Berlin Science Prize (2014). Education: PhD in Theoretical Computer Science (1992), University of Karlsruhe Diploma in Mathematical Physics (1989), University of Karlsruhe Research Interests: Müller’s work integrates machine learning with interdisciplinary challenges, emphasizing explainability and ethical AI. Key areas include neural network interpretability, non-stationary data analysis, and applications in healthcare (e.g., tumor classification, genomic data analysis) and materials science (e.g., quantum chemistry simulations). His contributions to brain-computer interfaces and neuroimaging have advanced understanding of neural oscillations and brain connectivity. Awards & Recognition: Member, German National Academy of Sciences Leopoldina (2012) Member, Berlin-Brandenburg Academy of Sciences (2017) Science Prize of Berlin (2014) Vodafone Innovations Award (2017) Labs & Leadership: Directs BIFOLD and co-leads initiatives like the TEA Challenge in molecular dynamics. Supervises interdisciplinary teams focused on AI in pathology, computational chemistry, and neurotechnology. His work bridges theory and practice, with impactful contributions to open datasets (e.g., QCML) and scalable machine learning frameworks.

Robert VandermeulenView profile

Researcher

• Anomaly Detection
• Nonparametric Statistics
• Deep Learning
• +3 more

Dr. Robert Vandermeulen is a Postdoctoral Researcher at the Berlin Institute for the Foundations of Learning and Data (BIFOLD), Technical University of Berlin. His research focuses on anomaly detection, nonparametric statistics, and aligning neural networks with human cognitive processes. He holds a PhD and two Master's degrees from the University of Michigan in Electrical Engineering and Mathematics. Education: PhD in Electrical Engineering, University of Michigan (2016) MS in Mathematics, University of Michigan (2015) MS in Electrical Engineering, University of Michigan (2012) Research Interests: Deep anomaly detection in high-dimensional data Nonparametric density estimation and tensor methods Human vs. neural network alignment for interpretable AI Applications in medical imaging and explainable AI Key Research Trends: His recent work addresses challenges in anomaly detection across domains like healthcare imaging and text analysis. He explores nonparametric methods to overcome the curse of dimensionality and integrates human cognitive insights to improve neural network robustness. Contributions include novel frameworks like input Hessian regularization and VICE (Variational Inference for Concept Embeddings). Affiliations & Prior Work: Previously a postdoctoral researcher at Technische Universität Kaiserslautern, he collaborates with BIFOLD on foundational machine learning research. His work bridges theoretical statistics and applied deep learning, with a focus on interpretable and reliable AI systems. Labs & Teams: Affiliated with BIFOLD, a cross-disciplinary institute advancing learning theory and data science fundamentals at TU Berlin.

Alexander HeimerlView profile

Researcher

• Human-Centered Artificial Intelligence
• Social Robotics
• Emotion Recognition
• +3 more

Alexander Heimerl is a Researcher at the Chair for Human-Centered Artificial Intelligence, part of the Institute of Computer Science at the University of Augsburg. His work focuses on interdisciplinary research at the intersection of artificial intelligence, social robotics, and human-computer interaction. He is involved in projects such as FORSocialRobots, TherapAI, and the ForDigitStress dataset initiative, which aim to advance emotion recognition, stress detection, and explainable AI systems. His research interests include multimodal behavior analysis, personalized training systems, and computational models of social emotions. Notably, he contributed to the development of NOVA, a tool for explainable cooperative machine learning, and earned a Best Paper Award at ACII 2019 for his work on affective computing. He is affiliated with the Faculty of Applied Computer Science and collaborates with the Human-Centered AI team, including Prof. Dr. Elisabeth André and other researchers. His publications span topics like engagement estimation in social interactions, adversarial learning for feedback systems, and stress recognition via pupillometry.

• Robot Vision
• Machine Learning
• Autonomous Systems
• +9 more

Prof. Dr. Sebastian Houben is a Professor of Robot Vision and Machine Learning at Bonn-Rhein-Sieg University of Applied Sciences (H-BRS). He holds roles as International Affairs Representative and Head of the Master's Examination Board for Autonomous Systems. His research focuses on AI-driven solutions for autonomous systems, environmental monitoring, and energy efficiency. Key projects include: INNERVATE : Enhancing driver maneuver evaluation via AI for automotive safety. DKZ.2R : Promoting data literacy and high-performance computing across disciplines. DigitalTwin-4-Multiphysics Lab : Urban and industrial digital twins for efficiency optimization. GARRULUS : AI-driven reforestation using drones and precision seeding. He leads the Institute for AI and Autonomous Systems (A2S) and collaborates with industry partners like GKN Driveline. His research spans robotics, computer vision, and sustainable energy systems. Prof. Houben is a member of the Promotionskolleg Nordrhein-Westfalen (Doctoral College North Rhine-Westphalia). His work emphasizes explainable AI (XAI), safety-critical systems, and interdisciplinary applications.

Jörn HeesView profile

Professor

• Data Science
• Deep Learning
• Anomaly Detection
• +3 more

Prof. Jörn Hees is a Professor for Data Science at the Department of Computer Science, Hochschule Bonn-Rhein-Sieg (H-BRS), and serves as Chairman of the Computer Science Faculty Council. He is affiliated with the Institute for Artificial Intelligence and Autonomous Systems (A2S) and the Institute of Technology, Resource and Energy-efficient Engineering (TREE). His research focuses on Data & Graph Mining, Deep Learning, Anomaly Detection, Explainable AI, and AI-assisted education (e.g., automated grading systems). He teaches courses such as Stochastics, Data Analysis and Visualization, Artificial Intelligence (Bachelor level), and Deep Learning Foundations, Natural Language Processing (Master level). His work bridges theoretical advancements with practical applications in finance, remote sensing, and education. Notable projects include federated outlier detection systems (Fin-Fed-OD), the TreeSatAI benchmark dataset for tree species classification, and AI-driven grading solutions using LLMs. His publications emphasize anomaly detection techniques, multimodal fusion, and interpretable machine learning. Despite no explicit awards listed, his contributions to AI education and computational methods are evident through his extensive publication record. He coordinates research teams and advises on infrastructure for AI education and infrastructure monitoring via Earth observation data.

Heiner StuckenschmidtView profile

Professor

• Artificial Intelligence
• Machine Learning
• Knowledge Representation
• +5 more

Professor Heiner Stuckenschmidt holds the Chair of Artificial Intelligence at the University of Mannheim within the School of Business Informatics and Mathematics. He has been a W3 Professor for Practical Computer Science specializing in Artificial Intelligence since 2011 and previously held a W2 professorship from 2009-2011. He serves as a member of the directorate of the Mannheim Center for Data Science since 2020 and was the CIO of the University of Mannheim from 2011-2022. His research spans multiple areas of Artificial Intelligence including knowledge representation, machine learning, business process management, and semantic web technologies. He leads the Artificial Intelligence research group within the Data and Web Science Group at Mannheim, conducting both fundamental and applied research addressing specific challenges in industry and society. His recent work focuses on applications of AI in business processes, education, transportation, healthcare, and supply chain management. Professor Stuckenschmidt has published extensively in top-tier conferences and journals, with over 100 publications spanning from 2010 to 2025. His research output shows a strong focus on practical applications of AI methods across various domains, with recent emphasis on business process management, educational data mining, transportation systems, and supply chain optimization. His group has received recognition including a Best Paper Award at ICPM for their work on AgentSimulator. His scientific contributions include: Nomination for Best Paper Award at CAiSE 2025 Best Paper Award at ICPM for 'AgentSimulator: An Agent-based Approach for Data-driven Business Process Simulation' Development of tools like AnyBURL (rule learner for Knowledge-Base Completion), ALCOMO (tool for repairing ontology alignments), and Rockit (query engine for Markov Logic) Professor Stuckenschmidt teaches several courses including Industrial Applications of Artificial Intelligence, Decision Support, Data Science in Action, and Business Informatics II: Fundamentals of Modeling. He supervises PhD students and offers thesis topics in his research areas. His research is supported by multiple projects including NEST-bw, Meeting KI, TransforMA, sMArt roots, CAIUS, and KISync.

• Human-Computer Interaction
• Social Robotics
• Artificial Intelligence Ethics
• +3 more

Kaisa Väänänen is a Professor at Tampere University, specializing in Human-Computer Interaction (HCI), Social Robotics, and Artificial Intelligence Ethics. Her research focuses on societal participation, user experience (UX) in technology design, and ethical integration of AI systems. She has contributed extensively to understanding how social robots can foster civic engagement and how AI can collaborate effectively with humans in knowledge work and smart environments. Her work bridges theoretical frameworks with practical applications, including studies on robot-mediated youth engagement, sustainable HCI practices, and the design of ethical AI systems. She has led projects exploring AI collaboration in manufacturing, healthcare, and education, emphasizing human-centric approaches. Väänänen has also edited major conference proceedings, such as the 30th International Conference on Intelligent User Interfaces (IUI 2025), showcasing her leadership in academic discourse. Key research interests include: Social robotics for youth empowerment AI ethics and human-AI collaboration UX design for smart cities Tangible interfaces for explainable AI Her recent articles highlight trends in AI-driven societal participation, sustainability in technology design, and the role of large language models in transforming research and collaboration methods. She advocates for frameworks that ensure technology aligns with human values and societal needs.

• Artificial Intelligence
• Business Process Management
• Machine Learning
• +3 more

Lior Limonad is a Researcher at IBM Research, Haifa, Israel, specializing in the intersection of artificial intelligence (AI), business process management (BPM), and ontology engineering. His work focuses on leveraging large language models (LLMs) for explainable business processes, causal reasoning in AI systems, and enhancing decision-support mechanisms through advanced analytics. Research Interests: AI-driven process optimization Explainability in machine learning Cybersecurity for IoT systems Ontology development for enterprise systems Human-AI collaboration in education and motor skill acquisition Publications highlight trends in applying LLMs to business processes, causal inference frameworks, and hybrid systems integration. Collaborations include institutions like the University of New Brunswick and industry partners like Siemens.

• Artificial Intelligence
• Semantic Web
• Knowledge Graphs
• +7 more

James A. Hendler is a Professor at Rensselaer Polytechnic Institute, renowned for pioneering work in Artificial Intelligence, Semantic Web, and Web Science. His research spans knowledge graphs, explainable AI, and biomedical informatics Rensselaer Polytechnic Institute, Troy, NY, USA Research Interests : Hendler's work bridges AI and Semantic Web technologies, focusing on knowledge integration, quantum machine learning applications, and mental health prediction through NLP. He develops explainable systems for medical imaging and contributes to open government data frameworks Publications & Collaborations : Recent articles highlight collaborations in quantum ML for climate modeling, clinical decision support semantics, and chest X-ray pathology explainability. He explores active learning architectures and human-in-the-loop AI systems Scientific Recognition : AAAS Fellow ACM Fellow AAAI Fellow Advising & Grants : Co-authored over 100 publications with researchers in biomedical NLP, quantum computing, and social network analysis. Key grants include NIH and NSF funding for semantic health data projects