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Jovita LukasikView profile

Researcher

• Computer Vision
• Machine Learning
• Neural Architecture Search
• +4 more

Jovita Lukasik is a Ph.D. candidate in Computer Science at the University of Mannheim , affiliated with the Data and Web Science Group and the Computer Vision and Machine Learning Group at the Max Planck Institute for Informatics . Supervised by Prof. Dr.-Ing. Margret Keuper , her research focuses on Neural Architecture Search (NAS) and its applications in computer vision and graph-based learning systems. Education Ph.D. (2019–present), Computer Science, University of Mannheim M.Sc. (2016–2019), Mathematics in Business and Economics, University of Mannheim B.Sc. (2012–2015), Mathematical Finance, University of Konstanz Lukasik's research bridges generative NAS , graph neural networks , and inverse problem solutions . She has developed novel approaches for variational graph embeddings and stability analysis in NAS frameworks, contributing to the democratization of efficient deep learning architecture discovery. Her publication trends reveal expertise in automated machine learning with specific attention to search space optimization , graph-based NAS , and stability in differentiable architecture search . She has presented at premier venues including European Conference on Computer Vision and International Conference on Learning Representations . Lukasik actively contributes to academic communities through workshop organization (NAS @ ICLR 2021) and virtual seminar leadership on AutoML advancements.

Marius LindauerView profile

Professor

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

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

• Automated Machine Learning (AutoML)
• Machine Learning Systems
• Human-in-the-Loop Machine Learning
• +3 more

Kevin Armbruster is a Researcher and PhD candidate at the Technical University of Munich (TUM) , affiliated with the Chair of Information Infrastructures. His work bridges academic research with industry experience, focusing on Automated Machine Learning (AutoML) , machine learning systems , and human-in-the-loop optimization . Prior to academia, he spent six years in the software industry at Markant Gruppe as a full-stack developer. Education : Master's in Information Systems Engineering (Wirtschaftsinformatik) from TU Darmstadt; semester abroad at KTH Royal Institute of Technology Research Focus : Human-AI collaboration in AutoML, ethical impacts of ML automation Teaching : Assistant for Bachelorpraktikum courses in Human-Centered Artifact Design Current projects include KASTEL - Collusion in Information Systems . No formal awards or publications are listed in the provided text.

• Database Systems
• Heterogeneous Computing
• GPU Acceleration
• +4 more

David Broneske is a Researcher at the Otto von Guericke University of Magdeburg , Germany. His work spans Database Systems , Heterogeneous Computing , and Machine Learning Applications , with a focus on GPU/FPGA Acceleration and Non-volatile Memory (NVM) Optimization . He has contributed to projects like ADAMANT (co-processor integration) and GridTables (H2TAP data stores). Key Research Areas : Database acceleration via specialized hardware, Graph database applications in clinical/biological domains, and AutoML for domain-aware model selection. Collaborations : Frequent co-author with Gunter Saake, Bala Gurumurthy, and Sajad Karim on topics like NVM Storage and GPU-based Query Execution . Publications : Over 105 papers (2012–2025) covering Protein Identification Systems , Entity Resolution , and Software Evolution Datasets . Workshops : Co-organized the Workshop on Novel Data Management Ideas on Heterogeneous (Co-)Processors (NoDMC) and contributed to standards like Backlogs/Interval Timestamps for temporal graph queries.

• 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.

Konstantin LübeckView profile

Researcher

• Machine Learning
• Deep Learning
• Computer Architecture
• +3 more

Konstantin Lübeck is a Researcher at the Chair of Embedded Systems, Department of Computer Science, University of Tübingen. He holds a B.Sc. and M.Sc. in Computer Science from the same institution (2015, 2018), with academic focus on computer engineering and embedded systems. He studied at Uppsala University in 2016 as an Erasmus student and received a Stiftung Industrieforschung scholarship for his Master's thesis in 2017. B.Sc. and M.Sc. from University of Tübingen Erasmus exchange student at Uppsala University (2016) Stiftung Industrieforschung scholarship recipient (2017) His research centers on machine learning accelerator performance modeling , combining computer architecture descriptions (from register-transfer to abstract diagrams) with DNN parameters for rapid design space exploration in neural network-hardware co-design. Key methodologies include analytical models for latency, throughput, and roofline analysis of AI accelerators. The 8 listed publications (2016-2025) reveal trends in: Neural network-hardware co-design Abstract architecture modeling Ultra-low power AI accelerators Performance representatives for benchmarking Formal hardware description languages AutoML for accelerator optimization Scientific contributions include Stiftung Industrieforschung scholarship (2017) He has lectured on computer architecture since 2018 for the Bosch Learning Company initiative at Tübingen's technology transfer center and supervised 12+ theses projects (4 completed) involving Pico-CNN frameworks, cache modeling, RISC-V implementations, and systolic array architectures.

Fabian MautheView profile

Researcher

• Prognostics and Health Management (PHM)
• Condition monitoring and diagnostics
• Technical systems degradation analysis
• +4 more

Fabian Mauthe serves as a Research Associate at the Faculty of Mechanical and Systems Engineering at Esslingen University of Applied Sciences since April 2020 (previously in Mechatronics and Electrical Engineering until February 2021). He works at the Institute for Technical Reliability and Prognostics (IZP) under Prof. Dr.-Ing. Peter Zeiler, focusing on cutting-edge research in Prognostics and Health Management (PHM). His research interests center on Prognostics and Health Management (PHM) , condition monitoring and diagnostics of technical systems , condition prediction , and degradation data set development . Mauthe's work bridges theoretical research with industrial applications, particularly in developing simulation models for rolling bearing degradation and creating publicly available datasets that address real-world industrial scenarios. Mauthe's publication record shows a consistent focus on advancing PHM methodologies, with particular emphasis on remaining useful life prediction , neural network architectures for condition data analysis , and public dataset creation to support research validation. His work demonstrates strong collaboration with industry and academic partners to address practical challenges in predictive maintenance. His educational background includes a Master of Science in Advanced Precision Engineering - Mechanical Engineering from Furtwangen University (2019) and a Bachelor of Science in Mechanical Engineering and Mechatronics from the same institution (2017). Mauthe actively contributes to significant research projects including the Prognostics project (MWK) and TheoMation project (DFG), developing practical solutions for technical reliability assessment and prognostics applications in industrial settings.

Thomas BroxView profile

Professor

• Computer Vision
• Deep Learning
• Visual Representation Learning
• +4 more

Thomas Brox is a Professor for Pattern Recognition and Image Processing at the University of Freiburg , where he has headed the Computer Vision Group since 2010. He holds a PhD in computer science from Saarland University (2005) and has held postdoctoral positions at UC Berkeley, TU Dresden, and University of Bonn. Research Interests Education Publications & Awards Teaching & Leadership Roles His research focuses on computer vision and deep learning , particularly visual representation learning , video analysis , and training deep networks without manual supervision . He investigates how deep networks learn capabilities and applies these insights to action-perception cycles in robotics . Recent publications highlight trends in vision-language models , diffusion-based 3D generation , and anomaly detection . His work includes U-Net (seminal biomedical image segmentation) and FlowNet (optical flow estimation). Koenderink Prize (2014) ERC Starting Grant (2011) Longuet-Higgins Best Paper Award (2004) Multiple GCPR/ICCV/CVPR Best Paper Honors He teaches courses in Optimization , Statistical Pattern Recognition , and Computer Vision . He has served as Dean of Studies , Director of Computer Science Department , and will become Dean in 2026. His lab mentors 25+ PhD/MSc students and collaborates with institutions like Amazon (2020-2024).

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.

Ke Thia YaoView profile

Research Professor

• Artificial Intelligence
• Machine Learning
• Quantum Computing
• +3 more

Ke-Thia Yao is a Research Professor at the University of Southern California's Information Sciences Institute (ISI), where he leads initiatives in the Artificial Intelligence Division. With a Ph.D. in Computer Science from Rutgers University and a B.S. in Electrical Engineering and Computer Science from UC Berkeley, his career spans 25+ years in research and development across artificial intelligence, machine learning, and multidisciplinary engineering design. B.S., Electrical Engineering and Computer Science, UC Berkeley (with honors) M.S. and Ph.D., Computer Science, Rutgers University His research focuses on leveraging AI and machine learning for predictive modeling in engineering systems, quantum computing applications for neural networks, and data-driven approaches in environmental and medical contexts. He has authored influential work on knowledge graph augmentation, simulation data analysis, and automated machine learning pipelines. Recent trends in his publications highlight interdisciplinary applications, including quantum-assisted Boltzmann machines for materials science, environmental toxicology studies linking PM2.5 exposure to corneal diseases, and scalable cyber warfare test environments. His work bridges high-performance computing, neuromorphic systems, and quantum architectures for advanced AI solutions. Ke-Thia Yao is affiliated with the University of Southern California's Information Sciences Institute, collaborating with experts in computer science, quantum computing, and environmental health. His projects emphasize scalable simulation frameworks, failure prediction systems, and semantic interoperability in data management.

• Recommender Systems
• Context-Aware Recommendations
• Machine Learning
• +3 more

Alexander Tuzhilin is a distinguished academic affiliated with New York University, specializing in data science and information systems. He has been actively contributing to the field of recommender systems, context-aware recommendations, and machine learning since the mid-1980s. His work focuses on enhancing recommendation algorithms through contextual analysis, deep learning, and multi-criteria evaluation, with applications ranging from e-commerce to healthcare and transportation. His research interests include improving recommendation accuracy by incorporating user context (e.g., location, time, and preferences), developing novel techniques for unexpected recommendations to boost user satisfaction, and advancing cross-domain recommendation systems. He has also explored the intersection of AI with real-world applications such as emergency detection via social sensors and medical diagnosis using deep learning. Recent publications highlight advancements in hierarchical contextual embeddings, adversarial learning for cross-domain recommendations, and applying deep learning to CT scan analysis for disease detection. His work often emphasizes practical impact, such as optimizing travel routes or enhancing user trust in recommendation systems through transparent design. Prof. Tuzhilin has collaborated extensively with industry and academic partners, contributing to workshops like CARS (Context-Aware Recommender Systems) and ComplexRec. He has advised numerous researchers and remains a key figure in advancing the theoretical and applied aspects of AI-driven recommendation technologies.