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Alexander von RohrView profile

Researcher

• Robotics
• Machine Learning
• Bayesian Optimization
• +4 more

Alexander von Rohr is a postdoctoral researcher at the Technical University of Munich , affiliated with the Learning Systems and Robotics Lab . Previously, he was a doctoral researcher at the Max Planck Institute for Intelligent Systems and RWTH Aachen University , supported by IAV . His educational background spans Computer Science (RWTH Aachen), Electrical Engineering (BHT Berlin), and University of Lübeck . Research Focus : Embodied AI, Bayesian optimization, risk-aware reinforcement learning, robust control, probabilistic models, data-driven controller synthesis. His recent publications (2024-2025) emphasize event-triggered learning for time-varying systems, diffusion models with constraints , and robust safety via entropy regularization in RL. These works demonstrate applications in robot manipulators , digital twins , and underactuated systems . Awards include the Best Reviewer Award at AISTATS 2025 . Collaborations include Prof. Sebastian Trimpe and Angela P. Schoellig , with co-authorships on 15 recent articles. His lab focuses on probabilistic control for dynamical systems with formal guarantees.

• Trustworthy Artificial Intelligence
• Automated Machine Learning
• Metalearning
• +5 more

Jan N. van Rijn serves as an Assistant Professor at Leiden University's Leiden Institute of Advanced Computer Science (LIACS), working within the Computer Science department and the Automated Design of Algorithms cluster (ADA). His research focuses on: Trustworthy Artificial Intelligence Automated Machine Learning (AutoML) Metalearning Machine Learning robustness Data Science methodologies Dr. van Rijn's work aims to democratize access to machine learning across societal institutions by developing tools that support domain experts. His research particularly emphasizes exploring and enhancing the robustness of AI systems, which is critical for building trustworthy machines. During his PhD at Leiden University (completed in 2016), he developed OpenML.org, an open science platform for machine learning that enables sharing of machine learning results. His recent publications demonstrate a clear progression toward making AI systems more reliable, transparent, and accessible. Key research projects include Low-Resource Chat-based Conversational Intelligence (LESSEN), TAILOR (Trustworthy AI through integration of learning), and HealthBox. Low-Resource Chat-based Conversational Intelligence (LESSEN) TAILOR - Trustworthy AI through the integration of learning HealthBox Dr. van Rijn has supervised multiple PhD candidates including Annelot Bosman, Inês Isabel Correia Gomes, Nansheline Daal, Matthias König, Andreas Paraskeva, and Sietse Schröder. He previously held postdoctoral positions at the University of Freiburg (Germany) and Columbia University (USA), and is co-author of the book 'Metalearning: Applications to Automated Machine Learning and Data Mining' published by Springer.

• Artificial Intelligence
• Machine Learning
• Automated Algorithm Configuration
• +3 more

Holger Hoos is a Professor in the Department of Methodology of Artificial Intelligence at RWTH Aachen University. His research spans artificial intelligence, automated algorithm configuration, and machine learning robustness, with applications in optimization, earth observation, and quantum computing challenges. Research Focus: His work emphasizes: Robustness verification and efficiency improvements in neural networks Automated Machine Learning (AutoML) frameworks and benchmarking Multi-objective optimization and algorithm configuration AI applications in remote sensing, time-series analysis, and recommender systems Recent publications (2024-2025) show a dominant trend toward enhancing AI reliability through rigorous verification methods, scalability solutions for large-scale problems, and adaptable frameworks for dynamic data environments. Quantum computing applications and energy-efficient AI also feature prominently. He leads research initiatives at RWTH Aachen focusing on methodological advances in AI, though specific labs/teams are not detailed.

• Sustainable Machine Learning
• Human Activity Recognition
• Wearable Sensor Systems
• +3 more

Bo Zhou is a researcher at the German Research Center for Artificial Intelligence (DFKI) within the Embedded Intelligence unit. His work focuses on sustainable machine learning systems, human activity recognition (HAR), and energy-efficient computing, particularly through interdisciplinary projects involving wearable sensor systems and textile electronics. Organizational Unit: Embedded Intelligence Projects: STELEC, SustainML_EI, VidGenSense, SocialWear Key Conferences: IEEE PerCom, DATE, ARCS His research emphasizes hardware-software co-design to reduce the carbon footprint of ML applications while advancing real-world deployment of HAR systems. Contact: Bo.Zhou@dfki.de

Philipp BachView profile

Assistant Professor

• Econometrics
• Causal Machine Learning
• Data Science
• +2 more

Prof. Philipp Bach holds the Junior Professorship for Econometrics at the Department of Economics, Free University of Berlin . His research bridges causal machine learning, econometrics, and statistical methods, with a focus on software implementation and economic applications. He teaches courses in econometrics, causal machine learning, and data science using Python and R.

Zhi-liang ZhuView profile

Professor

• information integration
• complexity software system
• network coding
• +4 more

Professor Zhi-liang Zhu is a faculty member at Northeastern University, China, specializing in software engineering and communication systems. His work bridges theoretical research in complex networks with practical software ecosystem analysis. His research focuses on information integration , complex software systems , and chaos-based communications , with recent emphasis on cross-language dependencies, vulnerability propagation, and dependency conflict resolution in modern software ecosystems. Key contributions include novel approaches for binary analysis, ecosystem health monitoring, and software modularization. His publication trends reveal a consistent trajectory from foundational work in network coding and cryptography toward contemporary software engineering challenges, particularly dependency management across programming language ecosystems. Recent papers demonstrate expertise in Python, JavaScript/TypeScript, and C/C++ contexts. 9 academic awards at national, ministerial, and provincial levels Fellow of China Institute of Communications Senior member of Chinese Institute of Electronics Prof. Zhu actively contributes to academic service as Co-Chair of the International Workshop on Chaos-Fractals Theories and Applications and serves on the Ministry of Education's Software Engineering Teaching Guiding Committee. His work includes authoring 5 books and over 230 publications.

Florian HaselbeckView profile

Professor

• Machine Learning in Agriculture
• Time Series Forecasting
• Protein Engineering
• +5 more

Prof. Dr. Florian Haselbeck is a Professor in the Department Sustainable Agriculture and Energy Systems at the University of applied sciences Weihenstephan-Triesdorf (HSWT). His research bridges machine learning, agricultural science, and biological applications with a strong focus on practical implementations. His primary research interests include: Machine Learning applications for time series forecasting in agriculture and energy systems Protein engineering and thermostability prediction using deep learning Computer vision for precision agriculture and weed detection Development of open-source frameworks for scientific computing Smart farming applications with practical industry relevance Dr. Haselbeck's recent publications reveal a strong interdisciplinary approach, combining cutting-edge AI techniques with domain-specific challenges. His work on protein thermostability prediction using graph neural networks (ProtGCN) and protein language models (ProLaTherm) demonstrates innovative applications of AI in biochemistry. He has also developed influential open-source frameworks including ForeTiS for time series forecasting and easyPheno for phenotype prediction, which have gained significant traction in their respective fields. As project lead for the smartBattery initiative, Dr. Haselbeck is applying AI to optimize large-scale battery storage systems for renewable energy integration. His research consistently emphasizes practical applicability, with numerous publications focusing on real-world implementation challenges and solutions in agricultural contexts.

• Explainable Machine Learning
• Computer Vision
• Natural Language Processing
• +3 more

Zeynep Akata is a Professor of Computer Science at Technical University of Munich and Director of the Institute of Explainable Machine Learning (EML) at Helmholtz Munich. She holds a PhD from INRIA Rhone Alpes and has held academic positions including Senior Researcher at Max Planck Institutes, Professor at University of Tübingen, and Tenure Track Assistant Professor at University of Amsterdam where she co-directed the UvA-Bosch Delta Lab. Her research focuses on explainable AI, multimodal learning, zero-shot learning, and ethical implications of AI systems. Key areas include improving transparency of AI models while maintaining GDPR compliance, and applying machine learning to societal challenges through computer vision and natural language processing. Notable awards include the Alfried Krupp Award (2023), ECVA Young Researcher Award (2022), and ERC Starting Grant (2019). Her work emphasizes both theoretical advancements and practical applications such as gender bias mitigation in generative models and cross-modal understanding through vision-language integration. Current research trends in her publications emphasize model merging, lifelong learning, and ethical AI practices. She explores techniques like concept-guided interpretability, sparse autoencoders, and low-rank adaptation for efficient model updates. Her work bridges theoretical machine learning foundations with real-world deployments in healthcare and environmental applications. Zeynep has contributed to major conferences such as NeurIPS, ICLR, and CVPR. Her group actively develops open-source tools for explainable AI and maintains partnerships with industrial collaborators including Bosch and leading research institutes.

Thorsten PapenbrockView profile

Professor

• Data Management
• Distributed Systems
• Data Profiling
• +4 more

Prof. Dr. Thorsten Papenbrock is a Professor in the Department of Mathematics and Computer Science at Philipps-Universität Marburg, leading the Big Data Analytics research group. He also holds an affiliation with the Hasso Plattner Institute (HPI) as a Senior Researcher. His research focuses on data management, scalable systems, distributed computing, and environmentally friendly applications. He earned his IT-Systems Engineering degrees from HPI, including a Ph.D. in 2016 on Data Profiling. His work includes projects like Metanome for big data profiling and TimeEval for anomaly detection benchmarking. Research interests span data profiling, distributed systems, and parallel computing, with contributions to functional dependency discovery, time series analysis, and actor-based database systems. He has advised numerous students on master’s theses, including topics in distributed duplicate detection and graph-based nearest neighbor search. Collaborations include the HPI and industry partners like SAP and BMW. Publications highlight advancements in holistic data profiling, anomaly detection, and distributed systems optimization. He teaches courses on distributed data management, database systems, and data profiling. His lab’s work emphasizes practical applications of theoretical insights to real-world challenges in big data analytics and system efficiency.

Yoshiyasu RaiView profile

Assistant Professor

• Theoretical Econometrics
• Statistical Modeling
• Econometric Theory
• +3 more

Prof. Yoshiyasu Rai is an Assistant Professor of Theoretical Econometrics and Statistics at the University of Mannheim's Department of Economics. His research focuses on econometrics and statistical modeling, with a strong emphasis on machine learning and knowledge graph embeddings. He holds a Ph.D. in Economics from the University of Wisconsin-Madison and degrees from Keio University, Tokyo. His work bridges theoretical econometrics with computational methods, addressing challenges in large-scale data analysis and distributed systems. Key contributions include advancements in knowledge graph embeddings, parameter server optimization, and scalable machine learning frameworks. Education: BA and MA in Economics (Keio University), Ph.D. in Economics (University of Wisconsin-Madison). Research Interests: Theoretical Econometrics Machine Learning Applications Knowledge Graphs and Embeddings Distributed Systems for Large-Scale Data Optimization Algorithms Articles trends focus on knowledge graph embeddings, parameter server design, and scalable machine learning techniques. Recent work emphasizes adaptive systems, dynamic resource allocation, and reproducible research through libraries like LibKGE. Labs/Teams: Involved in the Junior Professorship for Theoretical Econometrics and Statistics at the University of Mannheim, focusing on interdisciplinary data analysis projects.

Ingo SteinwartView profile

Professor

• Statistical Learning Theory
• Kernel-based learning algorithms
• Cluster Analysis
• +6 more

Ingo Steinwart is a Full Professor and Head of the Institute for Stochastics and Applications at the University of Stuttgart, within the Faculty of Mathematics and Physics. He holds a Chair for Stochastics and has held academic positions since 2010, including roles at Los Alamos National Laboratory and the University of California, Santa Cruz. His research focuses on statistical learning theory, kernel-based methods, cluster analysis, and neural networks, with significant contributions to the theoretical foundations of machine learning. Education: He earned a Doctorate (Dr. rer. nat.) in Mathematics from Friedrich-Schiller University, Jena (2000) and a Diploma in Mathematics from Carl-von-Ossietzky University, Oldenburg (1997). Research Interests: Steinwart's work emphasizes rigorous mathematical analysis of learning algorithms, including kernel methods, support vector machines (SVMs), and density estimation. His research explores topics like the capacity of function classes, generalization bounds, and applications to large-scale data. Notable contributions include advancements in SVM theory, reproducing kernel Hilbert spaces, and cluster analysis techniques. Publications: His work spans foundational papers on SVMs, kernel methods, and learning theory, with a focus on theoretical guarantees and algorithmic efficiency. Recent contributions address topics like adaptive learning rates, neural network initialization, and the theoretical limits of kernel-based methods. Grants & Editorial Roles: Steinwart serves as Associate Editor for journals such as the Journal of Complexity and the Annals of Statistics. He has organized conferences like COLT and contributed to software tools like liquidSVM for SVM implementation. Labs & Software: He leads development of software packages like liquidSVM (for SVMs) and liquidCluster (for cluster analysis), emphasizing computational efficiency and automated hyperparameter selection.

Ali SunyaevView profile

Professor

• Health Information Technology
• Cloud Computing
• Distributed Ledger Technologies
• +3 more

Prof. Dr. Ali Sunyaev is a Professor of Computer Science at the Technical University of Munich (TUM), serving as Vice President of TUM Campus Heilbronn. He holds dual affiliations with the TUM School of Computation, Information and Technology and the TUM School of Management. His academic career includes roles as Director of the Institute for Applied Informatics and Formal Description Methods at the Karlsruhe Institute of Technology (KIT) and professorships at the Universities of Cologne and Kassel. Education: Studied Computer Science at TUM (2000–2005), earned a PhD in Computer Science and Information Systems focused on healthcare telematics security analysis. Research Interests: Interdisciplinary work on digital systems' societal impacts, health IT, cloud computing, blockchain, trustworthy AI, and information security. Notable Projects: Funded by DFG, Helmholtz Association, and Russian Science Foundation, exploring topics like federated learning, AI accountability, and decentralized systems. Awards: AIS Distinguished Member (Cum Laude), KIT Faculty Teaching Award (2020) Labs/Teams: Leader of the Karlsruhe Decision & Design Lab (KD²Lab), part of DFG Review Board and German Informatics Society (GI) Board. Grants: Multiple projects on health AI, blockchain in mobility, and cloud security certifications. Future Focus: Developing AI accountability frameworks, advancing federated learning applications in healthcare, and addressing ethical challenges in decentralized technologies.

Mike SchwarzView profile

Professor

• Semiconductor Device Physics
• Schottky Barrier Transistors
• Compact Modeling
• +5 more

Professor Mike Schwarz is a faculty member at the Technical University of Mittelhessen (THM), where he holds a position in the Department of Electrical Engineering and Information Technology. He has been serving as Professor for Embedded Systems and Simulation since October 2020. Prior to his academic appointment, he worked at Robert Bosch GmbH as Head of Diode Design and Head of Process and Device Simulations MEMS from 2016-2020, and as a MEMS Sensor Design & Simulation Engineer from 2013-2020. His educational background includes a Doctor of Philosophy from Universitat Rovira i Virgili, Tarragona, Spain (2009-2012), where he completed his dissertation on "Two-Dimensional Analytical Predictive Modeling of Schottky Barrier SOI and Multi-Gate MOSFETs." He also holds a Master of Engineering from the same institution (2008-2009) and a Diploma in Engineering from Gießen-Friedberg University of Applied Sciences (2003-2008). Professor Schwarz's research spans semiconductor device physics, particularly Schottky barrier transistors and compact modeling, MEMS sensor design, neuromorphic computing using memristive devices, and machine learning applications in complex plasma physics. His recent work shows a clear trend toward applying AI techniques to plasma particle tracking and advancing cryogenic electronics through detailed physics-based modeling of Schottky barrier devices. He has made significant contributions to understanding device behavior at deep cryogenic temperatures, which has implications for quantum computing applications. His publication record demonstrates consistent productivity across multiple venues, with recent articles appearing in Solid-State Electronics, Machine Learning & Knowledge Extraction, New Journal of Physics, and Nano Futures. His work bridges theoretical device physics with practical applications in sensor technology and neuromorphic computing. Best paper honorable mention award LAEDC conference, Guatemala 2024 IEEE Transactions on Electron Devices List of Golden Reviewers 2015, 2012 IEEE Electron Devices Letters List of Golden Reviewers 2017, 2015 Awarded for the second best dissertation of the year (DEEEA, URV) 2013 Doctorate with distinction 2012 Awarded the Friedrich Dessauer Prize (best diploma thesis of the year) 2008 Professor Schwarz serves on multiple IEEE committees, including as Editor for IEEE Transactions on Electronic Devices since 2025, and as Chair of the IEEE EDS Germany Chapter from 2020-2025. He has been actively involved in organizing the Symposium Schottky Barrier MOS Devices since 2016 and has served on technical program committees for MIXDES and other conferences. His professional activities demonstrate strong engagement with both academic and industrial communities in semiconductor device research.

Philip KennewegView profile

Researcher

• Machine Learning
• Deep Learning
• Computer Vision
• +11 more

Philip Kenneweg is a Researcher at the Faculty of Engineering , University of Bielefeld , working within the Machine Learning Group at Center for Cognitive Interaction Technology (CITEC) . Since 2023, he serves as a Research assistant with teaching duties , supporting courses like Deep Learning and Introduction to Machine Learning. Education : PhD in Computer Science (2020–2025), M.Sc. in Natural and Computer Sciences (2016–2019), B.Sc. in Natural and Computer Sciences (2013–2016), Abitur (2005–2013) His research focuses on advancing optimization techniques for neural networks and transformer models , with significant contributions to line search adaptation , debiasing embeddings , and generative modeling . Philip has pioneered the SaLSa optimizer for automated learning rate determination and explored diffusion models for synthetic genotype generation in bioinformatics. Recent publications span transformer fine-tuning , retrieval-augmented generation systems , and reinforcement learning architectures , reflecting his expertise in bridging theoretical innovation with practical AI solutions. His work emphasizes ethical considerations in NLP and computational efficiency in large-scale model training. Scientific Awards : 1st place, it's OWL Makeathon 2022 1st place, RoboCup@Home 2016 As part of CITEC, Philip contributes to cognitive interaction technology research, combining machine learning with real-world applications in robotics and data analysis.

Maik ThieleView profile

Professor

• Database Systems
• Data Engineering
• Big Data Analytics
• +4 more

Prof. Dr.-Ing. Maik Thiele is a Professor at the Faculty of Computer Science and Mathematics , Dresden University of Applied Sciences. He leads the Professorship for Database Systems , focusing on advanced database technologies, big data platforms, and data engineering. Academic Leadership: Dean of Master's program in Applied Computer Science (since 2021), elected member of Faculty Council (since 2024) Research: Data analysis along the data value chain from acquisition to AI-driven processing, with emphasis on Active Learning , Transformer Models , and Mathematical Information Retrieval Applied Projects: Director of Dresden Database Forum , member of Digital Transformation Development Workshop (EDiT) His recent work analyzes Active Learning strategies for biomedical image classification and synthetic data, develops time series matching techniques , and explores ethical AI evaluation for e-commerce chatbots. He supervises student theses on topics ranging from PDF data extraction to Smart Grid monitoring and Speech-to-Text optimization . Professor Thiele serves on program committees for leading conferences including ADBIS , DAWAK , and DOLAP , and contributes to journals like Transactions on Knowledge and Data Engineering and International Journal on Very Large Data Bases .