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Rezaul KarimView profile

Researcher

• Machine Learning
• Explainable AI
• Natural Language Processing
• +6 more

Rezaul Karim is a Sr. Data Scientist at ALDI SÜD - International Data & Analytics and Guest Researcher at RWTH Aachen University's Faculty of Computer Science, Department of Computer Science 5. He completed his PhD in Explainable AI at RWTH Aachen University with distinction (Summa Cum Laude) under the supervision of Prof. Dr. Stefan Decker. His research focuses on machine learning, explainable AI, and their applications in healthcare and bioinformatics. Karim has developed methods for cancer susceptibility prediction from multiomics data, explainable knee osteoarthritis diagnosis, and privacy-preserving analytics for sensitive medical data. His work bridges the gap between theoretical machine learning and practical applications in medical diagnostics and biological data analysis. Karim's publication record shows consistent output in top venues, with recent work emphasizing interpretable models for complex biomedical problems. His research integrates machine learning with semantic web technologies to create data and knowledge-driven analytical solutions that maintain model transparency. Best paper (NLP) award at a top data science conference ICT Young Researcher Award 2020 for significant contributions to ICT-related research As an educator, Karim teaches Data Science in Medicine Seminar (WS 2025) and has previously taught courses on building Large Language Model Applications and Semantic Web technologies. His industry experience at Fraunhofer FIT and ALDI SÜD provides practical context to his academic work, ensuring his research addresses real-world challenges in data science and machine learning deployment.

• Epigenetics
• Plant Reproduction
• Transposable Elements
• +3 more

Dr. Haoran Peng is a Researcher at the Max Planck Institute of Molecular Plant Physiology (Germany), focusing on epigenetic mechanisms in plant reproduction. His work integrates transposable elements (TEs) and their roles in siRNA generation and transcription factor binding sites within the Brassicaceae genome. Previously, he served as a Postdoctoral scientist (2024–present) and Scientific collaborator at Agroscope (Switzerland, 2023–2024). Educational Background: Ph.D. in Life Science, University of Geneva (Switzerland, 2023) Master's Degree of Agronomy, Southwest University (China, 2018) Bachelor's Degree of Agronomy, Southwest University (China, 2015) Research Focus: Dr. Peng explores TEs' evolutionary dynamics and functional roles in plants, leveraging computational tools like ecc_finder to analyze extrachromosomal circular DNA. His studies bridge molecular mechanisms of plant defense (e.g., jasmonic acid signaling) with epigenetic regulation in reproductive processes. Publications: His work spans plant pathology, genomics, and epigenetics, with contributions to tools for genomic data analysis and insights into viral resistance mechanisms. Recent research emphasizes circular DNA's underappreciated role in plant biology. Awards/Grants: No specific awards or grants mentioned in the text. Labs/Teams: Active within Prof. Köhler's department, contributing to studies on plant reproductive biology and epigenetic regulation.

• Cheminformatics
• Bioinformatics
• Perturbation-Theory Machine Learning
• +10 more

Humbert González-Díaz is an Ikerbasque Research Professor at the University of the Basque Country (UPV/EHU), affiliated with the Faculty of Science and Technology, Department of Organic and Inorganic Chemistry, where he leads the CHEMIF.PTML LAB. His research integrates computational and theoretical methods into chemistry and biology, focusing on predictive modeling for drug discovery and complex systems. Research Interests: His primary research areas include Cheminformatics, Bioinformatics, Perturbation-Theory Machine Learning (PTML), Predictive Algorithms, Organic and Medicinal Chemistry, Pharmaceutical Sciences, Neurosciences, Nanotechnology, Biophysics, Biotechnology, Systems Biology, and Complex Networks. He develops machine learning models to predict molecular behavior and biological activity, contributing significantly to computational drug design and systems-level analysis of biological networks. Scientific Awards: Award for PhD in Organic Chemistry (2005) Multiple awards for published papers in scientific journals Advising and Grants: Humbert has supervised over 10 PhD theses and co-authored two patents and more than 10 scientific software tools. His research is funded by the Ministry of Science, Innovation and Universities, the Ministry of Economy, the Basque Government, the European Commission, and industrial partners, reflecting a strong record of competitive grant acquisition and impactful research translation. Labs and Teams: He leads the CHEMIF.PTML LAB at UPV/EHU, a research group dedicated to developing and applying PTML methods in cheminformatics and bioinformatics. The lab fosters interdisciplinary collaboration across chemistry, biology, and computer science, contributing to both theoretical advances and practical applications in pharmaceutical and biotechnological domains.

Sebastian SchmelzleView profile

Researcher

• Functional Morphology
• Animal Morphology
• Arthropod Structure
• +5 more

Dr. Sebastian Schmelzle is a researcher at the Department of Biology, Technische Universität Darmstadt , where he works on functional morphology and biomechanics of arthropods. Since 2022, he has also served as CEO of Small World Vision GmbH . His academic career includes a magna cum laude PhD in Biology (2015) and research roles in DFG- and BMBF-funded projects like LIS, Waldklimafonds, NOVA, and ASTOR. He specializes in 3D imaging techniques, particularly synchrotron microtomography , and has contributed to open-source platforms like Biomedisa . Education : PhD in Biology (2015, TU Darmstadt), Diploma in Biology (2008, Eberhard Karls University Tübingen) Research interests focus on arthropod defensive mechanisms , notably the ptychoid defense in oribatid mites. His work explores how evolutionary lineages independently develop exoskeletal and muscular adaptations for survival. He bridges entomology , materials science , and computational imaging to study biomechanics, surface chemistry, and structural efficiency. Publications highlight his expertise in 3D microtomography (e.g., analyzing ant mandibles, insect cuticular hydrocarbons, and primate retinal structures). Collaborative projects span biomedical image segmentation , insect biomechanics , and synchrotron radiation applications . His recent work integrates machine learning and real-time data filtering for enhanced visualization. Additional contributions include public engagement initiatives like the AR Museum Station and Insect Excursions , aiming to democratize scientific knowledge through interactive exhibits and field studies.

• Machine Learning
• Sentiment Analysis
• Hybrid Filtering Algorithms
• +5 more

Hayder Murad is a researcher with expertise in machine learning, sentiment analysis, and hybrid filtering algorithms, particularly applied to intelligent tutoring systems and medical diagnostics. His work bridges computational methods with practical applications in education and healthcare. He earned his PhD from the University of Portsmouth in 2019 with a thesis titled An integrated approach to recommending online video materials through sentiment analysis and hybrid filtering algorithms . His recent publications focus on knowledge graph construction, large language model applications, and open science initiatives. Notable contributions include enhancing systematic literature reviews, improving dataset interoperability, and advancing FAIR data principles across disciplines. Research Interests Machine learning for healthcare diagnostics Hybrid recommendation systems Knowledge graph development Open science infrastructure Intelligent tutoring systems LLM-based research synthesis

• Educational Data Mining
• Learning Analytics
• Cognitive Psychology
• +5 more

Caitlin Mills is an Assistant Professor in the Department of Educational Psychology at the University of Minnesota's College of Education and Human Development. She also maintains affiliations with the University of Southern California's Keck School of Medicine (Division of Bioinformatics) and Harvard Medical School. Her research bridges educational psychology, cognitive science, and learning analytics. Her primary research interests focus on mind wandering detection during learning , educational data mining , learning analytics , and eye tracking technologies for educational purposes. She investigates how cognitive processes like attention, task-unrelated thoughts, and comprehension can be measured and enhanced through technological interventions. Her work increasingly addresses neurodivergent learners and inclusive educational technologies. Analysis of her recent publications shows a strong trend toward developing non-invasive methods for monitoring cognitive states during learning, particularly through webcam-based eye tracking and keystroke analysis. Her research spans multiple domains including virtual reality learning environments, automotive safety interfaces, and language-based cognitive assessment. She has made significant contributions to understanding how mind wandering impacts learning outcomes across diverse populations. Mills has served as co-editor for the Proceedings of the International Conference on Educational Data Mining (EDM 2025) and regularly publishes in top venues including the Journal of Learning Analytics (LAK), Educational Data Mining (EDM), and Cognitive Science. Her collaborative work includes major projects with researchers from USC, Harvard, and other institutions, focusing on developing technologies that can detect and respond to learners' cognitive states in real-time to improve educational outcomes. She has developed innovative approaches to measuring attentional states during learning tasks using various physiological and behavioral metrics.

• Machine Learning
• Bioinformatics
• Biomedical Applications
• +6 more

Marc Vaisband is a researcher at the Life and Medical Sciences Institute (LIMES) of the University of Bonn, where he pursues his PhD under Prof. Hasenauer. His research focuses on machine learning applications in biomedical imaging and oncology, supported by funding from the Salzburg Cancer Research Institute. Education B.Sc. and M.Sc. in Mathematics at the University of Bonn Specialized in stochastics and numerical analysis, with theses on KPZ theory and Totally Asymmetric Simple Exclusion Process Research Interests His work bridges machine learning and biomedical challenges, particularly in age-related macular degeneration diagnostics, cancer therapy optimization, and stochastic modeling. He develops neural networks for medical imaging and survival analysis in hematologic malignancies. Scientific Contributions Marc's 15 most recent publications include machine learning-driven AMD detection via fluorescence microscopy, precision dosing in anticancer therapy, and deep learning validation for NGS genetic variants. His articles emphasize statistical modeling, image analysis, and clinical outcome prediction. Professional Context As part of the Hasenauer Lab, Marc contributes to computational medicine projects. His interdisciplinary approach integrates mathematical rigor with clinical applications.

Lukas StelzlView profile

Researcher

• Liquid-Liquid Phase Separation
• Computational Biology
• Biophysics
• +5 more

Lukas Stelzl is a ReALity Junior Group Leader and IMB Associate Group Leader at Johannes Gutenberg University Mainz (JGU) and the Institute of Molecular Biology (IMB). He is affiliated with the Faculty of Biology, Institute of Physics, and the Mainz Institute of Multiscale Modeling (M3ODEL). His research focuses on liquid-liquid phase separation (LLPS) and its role in gene regulation, aging, and disease. He employs multi-scale simulations to study biomolecular condensates formed by disordered proteins, investigating how mutations and post-translational modifications disrupt LLPS specificity. Recent work includes modeling FUS protein condensates, analyzing conformational dynamics in DNA and oxidoreductases, and developing integrative methods combining simulations with experimental data. His publications highlight collaborations with institutions like the Max Planck Institute of Biophysics. He contributes to initiatives such as the Collaborative Research Center SFB/TRR 146, ReALity (Resilience, Adaptation, Longevity), and the International Max Planck Research School on Cellular Biophysics.

• Computer Science
• Bioinformatics
• Algorithmics
• +3 more

Prof. Dr. Markus Nebel is a faculty member at the Faculty of Engineering , University of Bielefeld , specializing in Algorithmics and Bioinformatics . He serves as Chair of the Computer Science (Master) examination board and holds administrative roles, including Dean of the Faculty of Engineering. His research interests span mathematical modeling, algorithms, and computational approaches in bioinformatics. Email: nebel@tf.unibi.de, markus.nebel@uni-bielefeld.de Office: CITEC 0-107 Key Roles: Dean (Faculty of Engineering), Chair of examination boards, Module responsibility for algorithms, programming, and network security courses.

Peter NejjarView profile

Assistant Professor

• Probability Theory
• Stochastic Processes
• Mathematical Physics
• +3 more

Peter Nejjar is a Tenure-Track Juniorprofessor for Probability Theory at the University of Potsdam since January 2023. His research addresses fundamental questions of universality in stochastic systems , investigating why identical probability distributions emerge across disparate contexts like random matrices and growth models, with connections to combinatorics, mathematical physics, and numerical analysis. His primary research domains include stochastic particle systems (TASEP/ASEP), KPZ universality class phenomena, and Markov chain mixing behavior —particularly the cutoff phenomenon. Nejjar's work reveals deep structural parallels between shock fluctuations in interacting particle systems and random matrix eigenvalue distributions, leveraging connections to algebraic combinatorics through Schur processes. Recent publications demonstrate evolving focus from shock fluctuation theory (2015-2018) toward dynamical phase transitions in KPZ systems (2020-2022) and emerging interdisciplinary applications like DNA-based molecular tagging (2025). His collaborative network prominently features Patrik Ferrari across 7 publications, reflecting sustained focus on universality in exclusion processes.

Verena WolfView profile

Professor

• Artificial Intelligence
• Machine Learning
• Autonomous Systems
• +3 more

Verena Wolf is a Professor at the German Research Center for Artificial Intelligence (DFKI) in Saarbrücken, Germany, leading the Neuro-mechanical Modeling research group. Her work bridges artificial intelligence with real-world applications in autonomous systems, computational biology, and ethical AI development. Her research focuses on neuro-mechanical modeling to simulate neural and mechanical processes, with significant contributions to autonomous driving algorithms, ethical frameworks for artificial moral agents, and computational drug discovery. She integrates machine learning with domain-specific challenges, emphasizing interpretable AI systems and interdisciplinary solutions. Recent publications demonstrate expertise across diverse domains: developing reason-sensitive moral agents for ethical AI decision-making, optimizing motion primitives in deep reinforcement learning for autonomous vehicles, and advancing kinase drug discovery through guided docking techniques. These works highlight her ability to translate theoretical AI concepts into practical applications. No scientific awards were explicitly mentioned in the source material. Prof. Wolf supervises research students within her group at DFKI, though specific advisee names were not provided. Her work is supported by research grants in autonomous systems and computational biology, though grant details remain unspecified in the available text. She directs the Neuro-mechanical Modeling research unit at DFKI Saarbrücken, which investigates the interplay between neural computation and mechanical systems to develop next-generation AI solutions for complex real-world problems.

Verena LaupertView profile

Researcher

• Transcriptional Regulation
• Computational Biology
• Genomics
• +3 more

Verena Laupert is a Researcher at the Department of Computational Molecular Biology within the Max Planck Institute for Molecular Genetics (Max Planck Society). Her work bridges computational biology , epigenetics , and genomics , focusing on chromatin organization and gene regulation. Her research explores transcriptional dynamics in evolutionarily rearranged genomes, mechanisms of promoter repression , and enhancer-target prediction using machine learning. She has contributed to studies on germline chromatin structure , regulatory conflicts , and the interplay between DNA methylation and chromatin domains . Her publications highlight interdisciplinary approaches, combining Hi-C , long-read sequencing , and computational modeling . Collaborative projects include developmental gene regulation , retrotransposon activity , and computational tools for epigenetic analysis.

Dieter BeuleView profile

Professor

• Translational Bioinformatics
• Computational Biology
• Biomedical Data Integration
• +2 more

Dieter Beule is a University Professor (Univ.-Prof.) and Dr. rer. nat. (Doctor of Natural Sciences) specializing in Translational Bioinformatics . He serves as Head of the Core Unit Bioinformatics (CUBI) and the High-Performance Compute (HPC) Unit at the Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin. With a PhD in Theoretical Physics and a career transition to bioinformatics in the late 1990s, he has built CUBI from scratch since 2015 and focuses on developing systems to transform biomolecular data and algorithms into healthcare innovations. Professor for Translational Bioinformatics Head of Core Unit Bioinformatics (CUBI) Head of HPC Unit Room 03 003, Phone: +49 30 450 543 601 Email: dieter.beule@bih-charite.de, dieter.beule@charite.de, dieter.beule@mdc-berlin.de Research Focus : Integration of multimodal clinical data, mass spectrometry analysis for treatment resistance, German Human Genome Archive (NFDI-GHGA), and computational workflows for precision medicine. He leads BMBF-funded MSTARS project and DFG-funded NFDI-GHGA initiatives. Affiliations : Member of the scientific board of the DKTK Master Programme, Co-founder of the Annual European Bioinformatics Core Community (AEBC²), and manager of BIH's HPC infrastructure.

• Automated Theorem Proving
• Machine Learning
• Formal Verification
• +6 more

Konstantin Korovin is a Reader at the Department of Computer Science, The University of Manchester. He has held various academic roles including Senior Lecturer (2015-2023), Royal Society University Research Fellow (2007-2015), and Research Associate (2004-2007). Current research focuses on automated theorem proving , machine learning integration , and verification of hardware/software . His work includes developing systems like iProver , iProver-ML , and SMLP , which combine formal methods with ML techniques. Key contributions span non-linear constraint solving , quantified Boolean logic , and DNA computing . He has won over 20 international awards, including SMT-COMP and CASC categories. Scientific Awards : Ackermann Award, Best Thesis Prize, Best Paper at FroCoS'19, CASC and SMT-COMP prizes. He supervises PhD and postdoc researchers, with alumni working at Intel, Google, and MathWorks. His tools are applied in industry, notably by Intel for hardware optimization.

• Bioinformatics
• Applied Computer Science
• Machine Learning
• +3 more

Prof. Dr.-Ing. Piotr Wojciech Dabrowski is a faculty member at HTW Berlin - University of Applied Sciences , affiliated with the Department 4: Computer Science, Communication and Business . His academic rank is Professor, and he serves as a University Lecturer and Deputy Chair of the Examination Board for multiple programs. His research focuses on: Development of reproducible genome sequencing data pipelines using Nextflow Comparative algorithm analysis for evidence-based bioinformatics Machine learning applications in genome sequence identification Container/microservice architectures for federal budget systems Documentation systems for penetration tests in IT security Contact: Piotr.Dabrowski@HTW-Berlin.de . Office hours available in-person or online.