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Gabriele BavotaView profile

Associate Professor

• Software Quality
• Empirical Software Engineering
• Mining Software Repositories
• +3 more

Gabriele Bavota is an Associate Professor at the Software Institute of Università della Svizzera Italiana (USI) in Lugano, Switzerland. He leads the SEART (Software Engineering Advanced Research Team) group and serves as Principal Investigator for the DEVINTA ERC starting grant focused on developer intelligence through mining software artifacts. Dr. Bavota's research spans Software Quality, Empirical Software Engineering, and Mining Software Repositories. His work has evolved from foundational studies on code smells and technical debt to cutting-edge research at the intersection of artificial intelligence and software development. He has made significant contributions to understanding API usage patterns, software quality metrics, and developer behavior through empirical studies of large software repositories. His recent publications reveal a strong focus on AI-assisted software development, with extensive research examining code generation, code summarization, and code review automation using large language models. He has also expanded his research to include quality assurance in game development (detecting game stuttering and low engagement events) and voice user interface testing. His work consistently bridges theoretical insights with practical applications for software developers. ACM SIGSOFT Distinguished Paper Award for API compatibility research (MSR 2019) ACM SIGSOFT Distinguished Paper Award for Hugging Face model documentation study (ICPC 2024) ACM SIGSOFT Distinguished Artifact Award for deep learning fault taxonomy (ICSE 2020) As an active member of the software engineering research community, Dr. Bavota serves on program committees for major conferences including ICSE, ASE, FSE, and MSR. He has held leadership roles such as Program Co-Chair for ICSME 2023 and Vision/Reflection Track Co-Chair for ICSE. His SEART research group develops practical tools like the SEART Data Hub that streamline large-scale source code mining and preprocessing for empirical software engineering research.

D. FrantzView profile

Assistant Professor

• Earth Observation
• Remote Sensing
• Geoinformatics
• +7 more

D. Frantz is an Assistant Professor at Trier University , leading the Geoinformatics - Spatial Data Science lab. Their research focuses on transforming Earth Observation (EO) data into actionable environmental insights through preprocessing, data cubes, and machine learning. Current Position : Assistant Professor for Geoinformatics - Spatial Data Science, Trier University (2021–today) Former Role : PostDoctoral Researcher at Earth Observation Lab, Humboldt-Universität zu Berlin (2017–2021) Education : PhD in Environmental Remote Sensing and Geoinformatics, Trier University (2013–2017) Frantz's research emphasizes open science , with all software and methods published as open source ( FORCE ). Key research areas include: EO data preprocessing to analysis-ready data Multi-sensor data integration (Sentinel, Landsat) Land cover fraction mapping and time series analysis Material stock quantification in buildings/infrastructure Climate change mitigation through urban-rural gradient analysis Recent publications highlight work on global material stock mapping , tree species classification in temperate forests, and temporally transferable crop mapping using deep learning. While no explicit awards are listed, their open-source contributions and leadership in the Geoinformatics lab underscore significant academic impact.

Sushil AwaleView profile

Researcher

• Multimodal Information Retrieval
• Question Answering
• Knowledge Graphs
• +3 more

Sushil Awale is a Research Associate at the Visual Analytics Research Group, TIB Hannover , Germany, and a PhD candidate at Leibniz Universität Hannover , supervised by Prof. Dr. Ralph Ewerth. His research focuses on Multimodal Information Retrieval , Question Answering , and Knowledge Graphs . Previously, he worked as a Student Assistant in the Language Technology Group at Universität Hamburg, developing NLP-driven scholarly systems. M.Sc. in Intelligent Adaptive Systems (2020–2023), Universität Hamburg B.Sc. in Computer Science (2015–2020), Tribhuwan University, Nepal His research interests span patent domain analysis , multimodal systems , and language resources for under-resourced languages . Recent work includes visual patent search interfaces and large vision-language models for classification tasks. Publications highlight contributions to scholarly knowledge graphs (e.g., DBLP-QuAD dataset) and AI-enhanced research advisory systems (ARDIAS). He also contributed to preprocessing Nepali language corpora and enriching Hindi WordNet via knowledge graph techniques. Currently affiliated with TIB’s Visual Analytics Group, he actively organizes events like the Scholarly Question Answering over Linked Data workshop at ISWC 2023.

Olaf DimigenView profile

Assistant Professor

• Visual Cognition
• Neuroscience
• Cognitive Psychology
• +3 more

Olaf Dimigen is a tenured Assistant Professor of Experimental Psychology at the University of Groningen, Netherlands. Previously, he held a visiting professorship for Biological Psychology at Humboldt University Berlin (2018-2022) and conducted research at the Max Planck Institute for Human Development. His research focuses on understanding how the brain integrates eye movements with visual perception and cognition, leveraging EEG/eye-tracking co-registration and advanced signal analysis techniques. Key affiliations: Faculty of Behavioural and Social Sciences, Department of Experimental Psychology Education: PhD in Psychology from Humboldt University Berlin (2014) Research interests include: Neural mechanisms of active vision Fixation-related potentials (FRPs) and microsaccade-related activity EEG artifact correction and deconvolution modeling Cross-linguistic reading processes Development of open-source analysis tools (EYE-EEG, UNFOLD, OPTICAT) Selected contributions: Pioneered methods for analyzing temporally overlapping EEG signals using deconvolution (UNFOLD toolbox) Optimized ICA-based ocular artifact removal for free viewing EEG (OPTICAT method) Discovered cognitive modulation of microsaccade-related potentials as attention markers His work bridges cognitive psychology, neuroscience, and computational methods, with applications in language processing, scene perception, and cultural neuroscience.

Monika KorteView profile

Research Professor

• Geomagnetism
• Paleomagnetism
• Geophysical Modeling
• +7 more

Dr. Monika Korte is the Acting Head of the Geomagnetism Section (Section 2.3) at the GFZ German Research Centre for Geosciences. She leads the working group on the 'Evolution of the Earth's Magnetic Field' and has been instrumental in advancing research on geomagnetic field reversals, secular variation, and space weather effects. Her work integrates paleomagnetic data, satellite observations, and computational modeling to understand Earth's core dynamics and their climatic impacts. Education & Career: PhD in Geophysics, Free University of Berlin (1999) Diploma in Geophysics, Ludwig Maximilian University (1996) Acting Head of Section 2.3 since 2021 Leadership roles in international projects, including the ERC Synergy Grant GERACLE (2024–2030) Research Interests: Geomagnetic field reversals/excursions Global and regional magnetic field modeling Geomagnetic observatory networks Cosmogenic isotope analysis Machine learning applications in geophysics Awards & Grants: ERC Synergy Grant (2024): €10M for GERACLE project GFZ Research Award (2003) Feodor-Lynen Fellowship (2001) Labs & Collaborations: Leading the Niemegk Geomagnetic Observatory Coordinating the GERACLE international consortium Member of IAGA Executive Committee and AGU editorial boards

Adrian RebmannView profile

Researcher

• Process Mining
• Event Data Preprocessing
• Event Abstraction
• +3 more

Adrian Rebmann is a Researcher in the Process Analytics research group at the Data and Web Science Group, School of Business Informatics and Mathematics, University of Mannheim. He is supervised by Prof. Dr. Han van der Aa, focusing on advancing process mining methodologies through semantic event parsing and event abstraction. His work emphasizes preprocessing event data for meaningful process analysis, leveraging natural language processing and machine learning techniques. Research Interests: Process Mining, Event Data Preprocessing, Event Abstraction, Semantic Event Parsing, and Business Process Management. Teaching: Co-lecturer for Advanced Process Mining (since 2021) and Process Management and Analytics courses. Supervised Process Analysis Seminars and Team Projects. Service: PC Member of workshops like NLP4BPM and ZEUS (2021-2024), and reviewer for journals like Information Systems and conferences like ECIS and Modellierung. Labs/Teams: Active contributor to the Data and Web Science Group, focusing on Process Mining innovations and tool development (e.g., M-Viz for inductive miner visualization).

Michael PalkView profile

Researcher

• Graph Theory
• Data Science
• Public Transport
• +4 more

Michael Palk is an external PhD student (since 2024) and former research associate (2019-2023) at the Institute of Information Systems, University of Hamburg under Prof. Stefan Voß. He holds an MSc (2019, Distinction) and BSc (2017) in Business Mathematics from the University of Hamburg. His research focuses on applying graph theory, data science, and optimization techniques to public transport and energy systems, alongside social media analytics. Key contributions include advancements in graph neural networks for combinatorial optimization and bilingual sentiment analysis frameworks. He has published 3 peer-reviewed articles in IEEE Access, Information Systems Frontiers, and international workshops. Education 2017-2019: MSc Business Mathematics (Distinction), University of Hamburg 2012-2017: BSc Business Mathematics, University of Hamburg Research Graph neural networks for NP-hard problems Social media analytics for product events Optimization models for power distribution networks Affiliations Institute of Information Systems Faculty of Business Administration

• Prescriptive Process Monitoring
• Generative AI
• Autonomous Enterprise
• +3 more

Hans Weytjens is a postdoctoral researcher at the Technical University of Munich (TUM) and a guest professor at KU Leuven in Belgium. He holds a Ph.D. in Machine Learning for Predictive and Prescriptive Process Monitoring, an M.Sc. in Business and Information Science, and an MBA in Finance. His current academic role at TUM involves research in the Department of Information System Development and Operation. Ph.D., KU Leuven (2023) M.Sc., KU Leuven (1990-1991) MBA, University of Chicago (1990-1991) Hans Weytjens specializes in Machine Learning applications for Business Processes , with a focus on Prescriptive Process Monitoring , Generative AI , and Autonomous Enterprise systems. His work bridges theoretical advancements with practical implementations in process optimization and AI-driven decision-making frameworks. Hans Weytjens' publications span topics like Event Log Dynamics , Reinforcement Learning , and Time Series Forecasting , reflecting his expertise in integrating Uncertainty Quantification and Visual Analytics into process mining methodologies. At TUM, Hans contributes to the Chair of Information System Development and Operation , advancing research on AI-driven enterprise automation and predictive process analytics.

• Robotics
• Navigation Systems
• Sensor Fusion
• +5 more

Chan Gook Park is a prominent researcher in robotics, navigation systems, and sensor fusion with an extensive publication record spanning over two decades. His work primarily focuses on visual-inertial odometry, LiDAR-based navigation, indoor positioning systems, and applications in both terrestrial and space environments. Dr. Park's research interests encompass robotics, sensor fusion, computer vision, and navigation systems. His work demonstrates a consistent focus on developing robust algorithms for visual-inertial odometry under challenging conditions, including low-light environments and indoor settings. He has made significant contributions to pedestrian navigation using wearable devices, lunar landing navigation systems, and infrared target detection. Analysis of his recent publications (2023-2025) reveals a strong trend toward deep learning integration in traditional navigation systems, with increasing focus on event-based vision sensors, multi-sensor fusion techniques, and applications in wearable technology. His work spans both theoretical algorithm development and practical implementations for real-world navigation challenges. Dr. Park has mentored numerous researchers who frequently appear as first authors on his publications, including Jae Hong Lee, Hanyeol Lee, and In Ho Lee. His collaborative network extends across various Korean institutions, with frequent co-authorship patterns suggesting a well-established research laboratory focused on navigation and sensor systems. His research group appears to maintain strong connections with the aerospace and robotics communities, regularly publishing in top-tier IEEE conferences and journals including IEEE Transactions on Robotics, IEEE Access, and proceedings from ICRA and PLANS conferences. The laboratory seems particularly focused on practical implementations of navigation systems for both terrestrial and space applications.

• Machine Learning
• Operations Research
• Computer Science
• +3 more

Bosung Kim is affiliated with the Ulsan National Institute of Science and Technology (UNIST), Republic of Korea . His research spans Machine Learning , Operations Research , Natural Language Processing , and Autonomous Systems , with a focus on practical applications in technology and data science. Key Research Areas: Machine Learning, Autonomous UAV Systems, Knowledge Graph Completion, Class Imbalance Solutions, Cognitive Radio Networks His work includes 15 recent publications (2021–2025) covering topics like on-device AI generation , zero-shot triplet extraction , and autonomous insect tracking . These contributions highlight interdisciplinary methodologies in robotics , language models , and signal processing .

Isabel ValeraView profile

Professor

• Machine Learning
• Robust Machine Learning
• Interpretable AI
• +10 more

Isabel Valera is a full Professor in the Department of Computer Science at Saarland University in Saarbrücken, Germany, and an Adjunct Faculty member at the Max Planck Institute for Software Systems (MPI-SWS). She is also a fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS), contributing to the Robust Machine Learning Program and the Saarbrücken AI & ML (Sam) Unit. Department of Computer Science, Saarland University Adjunct Faculty, MPI for Software Systems ELLIS Fellow, Robust ML Program Sam Unit, Saarbrücken AI & ML She obtained her PhD and MSc from Universidad Carlos III de Madrid, followed by postdoctoral research at the University of Cambridge and MPI for Software Systems. She previously led an independent research group at MPI for Intelligent Systems in Tübingen and held the Humboldt Post-Doctoral Fellowship and Minerva Fast Track Fellowship. PhD in Machine Learning, Universidad Carlos III de Madrid, 2014 MSc in Multimedia and Communications, Universidad Carlos III de Madrid, 2012 Telecommunications Engineering, Technical University of Cartagena, 2009 Her research centers on developing machine learning methods that are flexible, robust, interpretable, and fair, particularly for heterogeneous, temporal, and high-stakes decision-making systems. She emphasizes applications in medicine, psychiatry, and social domains such as hiring, bail, and lending. Her methodological contributions include Bayesian nonparametric models, latent feature modeling, and temporal point processes. Her recent publications reflect a strong focus on fairness, robustness, and interpretability in machine learning. Key themes include latent feature modeling for mixed data types, clustering temporal event streams, source separation, and fair classification. Her work bridges theoretical innovation with practical applications across healthcare, social networks, and policy-relevant domains. Scientific awards and recognitions include: Humboldt Post-Doctoral Fellowship Minerva Fast Track Fellowship (Max Planck Society) ELLIS Fellow She has been actively involved in teaching and dissemination, delivering tutorials at NIPS and MLSS on temporal point processes and social network analysis. She has also supervised research assistants and mentored junior researchers. Her research has been supported through prestigious fellowships and institutional affiliations. She leads the development of open-source tools such as GLFM, HDHP, and iFDM, promoting reproducibility and accessibility in machine learning research. She is affiliated with the following labs and research groups: Max Planck Institute for Intelligent Systems (former group leader) Max Planck Institute for Software Systems (adjunct, postdoctoral) ELLIS Sam Unit (Saarbrücken AI & ML) Robust Machine Learning Program (ELLIS)

Oliver MüllerView profile

Professor

• Human-AI Interaction
• Machine Learning
• Artificial Intelligence
• +4 more

Prof. Dr. Oliver Müller serves as Chair of Business Information Systems, especially Data Analytics, at the University of Paderborn within the Faculty of Economics . His research focuses on sociotechnical approaches combining machine learning with human decision-making, sustainable energy systems, and sports analytics. Human-AI Interaction AI for Sustainable Energy Sports Analytics Process Mining & Digital Transformation His team operates the soda.lab – a high-performance computing lab with GPUs for deep learning and real-time visualization. Current projects include DC2HEAT (Data Center Heat Recovery), HeatTransPlan (Industrial Heat Supply Transformation), and KIAM (AI Competence Center for Industrial SMEs). His group has successfully supervised doctoral candidates like Matthew Caron and Dirk Leffrang. Their recent publications analyze causal machine learning for energy systems, uncertainty visualization in forecasting, and algorithmic advice utilization. They received three international awards in 2021 for AI and social media research, plus multiple teaching recognitions (2021, 2020). Prof. Müller's team collaborates with industry partners including FORVIA HELLA, hellblau Software GmbH, and Böllhoff.

• EEG analysis
• Eye-tracking
• Computational neuroscience
• +4 more

Benedikt Ehinger is a computational neuroscientist at the University of Stuttgart, leading an Emmy Noether research group on "EEG in motion" funded by DFG. He specializes in integrating EEG with eye-tracking, developing statistical methods for neuroimaging and creating open-source tools. DDFG Emmy Noether grant recipient (2019) Director of Computational Cognitive Science lab Co-developer of open-source tools: Unfold toolbox, ClusterDepth algorithm His research focuses on three main areas: 1. Methodological foundations of EEG/MEG analysis, particularly cluster-based statistics and deconvolution methods; 2. Eye-tracking methodology and visualization techniques; 3. Statistical modeling of human perception using linear mixed models and Bayesian approaches. Key trends in his recent publications include: Advancing EEG methodology with linear deconvolution and cluster permutation tests Developing open-source neuroscience tools in Julia/MATLAB Investigating perceptual inference and reliability estimation Creating art-science interfaces through "thesis art" projects Scientific contributions: Emmy Noether research group leadership Over 10 thesis supervisions (Master's/Bachelor's) Co-development of multiple open-source toolboxes Methodological innovations in ERP analysis and statistical testing As an educator, he creates interactive tutorials on statistical concepts and provides thesis art for each supervised student. His lab maintains strong software engineering practices with GitHub-hosted code repositories.

Jean-Noël GradView profile

Researcher

• Computational Physics
• Soft Matter Simulations
• Chemical Physics
• +2 more

Jean-Noël Grad is a postdoctoral researcher at the Institute for Computational Physics , University of Stuttgart. His work focuses on computational physics and software engineering for soft matter simulations. Institute for Computational Physics, University of Stuttgart Research Interests: Grad specializes in computational physics and soft matter simulations, with a particular focus on molecular dynamics and simulation frameworks. He contributes to open-source software development and sustainability in research computing. Workshop Contributions: He regularly organizes and participates in workshops on soft matter simulation tools like ESPResSo, PyStencils, and LbmPy. His roles include co-organizer of events such as "Simulating soft matter across scales" and "Simulating energy materials with ESPResSo". Teaching Activities: Grad teaches the ESPResSo Block course annually, focusing on practical applications of soft matter simulation software. Software Contributions: Active in multiple software projects including ESPResSo (core contributor), pyMBE (co-developer), and tools like EESSI and waLBerla for cross-platform simulation environments.

• Theoretical Computer Science
• Database Theory
• Formal Logic
• +5 more

Nicole Schweikardt is a Professor at the Institute of Computer Science within Humboldt University of Berlin . Her research focuses on Theoretical Computer Science , particularly in Database Theory , Formal Logic , and Algorithmic Meta-Theorems . Academic Rank: Professor Contact: schweikn@informatik.hu-berlin.de Research Interests : Nicole investigates logical characterizations of database query languages, algorithmic meta-theorems for sparse graphs, and efficient enumeration techniques. Her work bridges formal logic, computational complexity, and practical database systems. Scientific Awards : 2018 ACM PODS Alberto O. Mendelzon Test-of-Time Award Recent Article Trends : Nicole's recent publications emphasize schema matching , spanner evaluation , first-order logic extensions , and query enumeration . Her work spans theoretical foundations (e.g., counting quantifiers, Hanf normal forms) and practical applications (e.g., event stream analysis, document compression).