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Maximilian AltmeyerView profile

Professor

• Human-Computer Interaction
• User Experience (UX)
• Player Experience (PX)
• +6 more

Prof. Dr. Maximilian Altmeyer is a faculty member at Saarland University of Applied Sciences , where he serves as Professor of Web Development and Mobile Applications Engineering. His research focuses on Human-Computer Interaction, User Experience (UX), Player Experience (PX), Gamification, and Digital Health. Head of Interaction Experience Group Member of Faculty Council Deputy Head of Production Informatics His teaching includes courses on Web Development , Game Development , and Interactive Systems . He supervises B.Sc. and M.Sc. theses on gamification topics and collaborates with research institutions like centigrade. Research Trends : Recent publications examine personalized gamification in fitness applications, user-created gamification systems, and behavioral interventions through interactive technologies. Key areas include Hexad user type analysis, health behavior modification, and live-streaming UX. Special Recognition at CHI PLAY 2020 Special Recognition at CHI PLAY 2019 Special Recognition at CHI PLAY 2022 He advises on ongoing theses about gamified meditation and customization vs. personalization , while serving as committee member for conferences like CHI, MobileHCI, and IEEE VR.

Marie PurenView profile

Associate Professor

• History
• Digital Humanities
• Political Ideologies
• +2 more

Marie Puren is an Associate Professor in history and digital humanities at EPITECH Paris , where she leads the Digital Methods for Humanities and Social Sciences (MNSHS) team since 2022. She also holds research affiliations with the LRE laboratory , Jean-Mabillon Center , and LARHRA in Lyon. Her work bridges historical analysis with computational techniques, focusing on political ideologies, cultural heritage preservation, and digital methodologies. Her research interests include: Digital Humanities and Computational History Political Ideologies in the French Third Republic Cultural Heritage Modeling Textual Analysis with NLP and XML-TEI History of Publishing and Literary Collaboration Marie contributes to major projects like AGODA (parliamentary debates analysis) and SILKNOW (silk heritage preservation). She teaches courses on digital methodologies, historical research, and environmental impacts of technology. Her leadership in Humanistica and participation in international conferences highlight her academic influence.

• Legal Informatics
• Artificial Intelligence in Law
• Argumentation Theory
• +2 more

Dr. Jack Mumford is a researcher specializing in the intersection of artificial intelligence and legal reasoning. His work focuses on developing computational models for analyzing legal cases, particularly under the European Convention on Human Rights (ECHR). He collaborates frequently with Dr. Katie Atkinson and Professor Trevor Bench-Capon.

• Digital Humanities
• Computational Humanities
• Text Mining
• +10 more

Manuel Burghardt is a full-time Professor of Computational Humanities at the Institute of Computer Science , Leipzig University . He coordinates the Bachelor's and Master's programs in Digital Humanities and serves as a spokesperson for the GI's Computer Science and Digital Humanities Group and the Forum for Digital Humanities Leipzig . PhD in Information Science (summa cum laude) from University of Regensburg Magisterstudium in Information Science, English Linguistics, General Linguistics, and Corpus Linguistics at University of Regensburg Burghardt's research spans multiple Computational Humanities domains: Digital Environmental Humanities : Integrating Computational Literary Studies with Biodiversity Research via NLP and Information Retrieval Immersive Humanities : Applying AR/VR/XR, Eye Tracking, and Tangible Interfaces for Digital Humanities Text Mining & NLP : Focused on text reuse detection, similarity analysis, and sentiment modeling Video Analytics : Analyzing news videos and cinematic media through Distant Viewing techniques Theory of Digital Humanities : Investigating methodological foundations and scientometric trends Computational Game Studies : Multimodal empirical analysis of games Computational Spatial Humanities : Spatial data analysis in humanities contexts His recent publications demonstrate strong focus on: Improving OCR for historical documents (2015-2020) Computational drama analysis using sentiment and text mining techniques (2016-2019) Music information retrieval applications in folk song and manuscript analysis (2015-2019) Development of specialized tools for humanities data processing

• Explainable AI
• Privacy-Preserving Machine Learning
• Medical Image Analysis
• +6 more

Dr. W.J. (Wilson) dos Santos Silva is an Assistant Professor at the Faculty of Science , University of Utrecht, specializing in AI & Data Science and Biology . His research focuses on creating explainable and robust AI models for multimodal multi-centre medical data , with emphasis on privacy-preserving machine learning and out-of-distribution generalization . PhD in Electrical and Computer Engineering (2022), University of Porto Master's and Bachelor's in Electrical and Computer Engineering (2016), University of Porto Research Interests include: Explainable AI for medical decision-making transparency Privacy-Preserving Machine Learning in healthcare Multi-Centre Data Analysis across institutions Medical Imaging applications in oncology and neurology Recent Publications demonstrate expertise in: Medical image segmentation techniques Cross-modal learning approaches Federated learning for privacy Biomedical data interpretation Generalization in heterogeneous datasets Scientific Contributions include organizing the iMIMIC workshop at MICCAI 2024 and mentoring students receiving competitive awards. Students & Collaborators : PhD Candidates: Valentina Corbetta, Daan Boeke, Miriam Cobo, Aniek Eijpe, Jan van Eck Postdoctoral Researchers: Soufyan Lakbir Former Students: Tingyang Jiao, Laura Latorre, Filipe Campos, etc. Laboratory develops AI solutions for medical imaging , multi-centre collaboration , and ethical AI in healthcare contexts.

Mary Lauren BentonView profile

Assistant Professor

• Computational Biology
• Gene Regulation
• Biomedical Informatics
• +3 more

Dr. Mary Lauren Benton is an Assistant Professor in the Department of Computer Science at Baylor University's College of Engineering and Computer Science. She holds a Ph.D. and M.S. in Biomedical Informatics from Vanderbilt University (2020, 2018) and a B.S.I. in Bioinformatics from Baylor University (2015). Education: Ph.D. (Vanderbilt, 2020), M.S. (Vanderbilt, 2018), B.S.I. (Baylor, 2015) Her research focuses on computational biology and gene regulation, particularly how DNA sequences influence genome function and human disease risk through integrated datasets. Recent work explores enhancer-gene interactions, epigenetic mechanisms, and the application of graph neural networks to genomic problems. Selected publications highlight interdisciplinary trends across genomics (e.g., Cis-regulatory landscapes , CTCF motif accessibility ) and computer science (e.g., Dynamic graph attention , Node classification ).

• Pathology
• Medical Imaging
• Machine Learning
• +7 more

Bri Jackson is a Lecturer in the Department of Foreign Language at Virginia Commonwealth University. While his formal academic role is in language education, his research focuses on advanced computational methods in pathology, including machine learning applications for cancer diagnosis, virtual immunohistochemistry, and digital pathology tools. His work spans histopathological analysis, immuno-oncology, and biomedical imaging. Jackson's research has led to the development of innovative algorithms for virtual diagnostic companion tools, foundation models for whole slide image analysis, and benchmarks for cervical cytology classification. His publications emphasize precision oncology, including biomarker multiplexing, molecular response prediction, and AI-driven histopathological interpretation. Key areas include non-small cell lung cancer, esophageal adenocarcinoma, and splenic involvement in pancreatic cancer prognosis. He has also explored educational challenges in pathology residency programs during the pandemic, viral epidemiology, and rare congenital disorders involving multiple organ systems. Jackson's work bridges computational biology with clinical diagnostics, though no scientific awards or student advisement details are mentioned in the available data.

Jonas BiehlerView profile

Research Fellow

• Computational Biomechanics
• Computational Solid Mechanics
• Experimental Biomechanics
• +7 more

Dr. Jonas Biehler is a Research Fellow at the Chair of Numerical Mechanics within the Institute for Computational Mechanics at the Technical University of Munich (TUM). His work focuses on computational methods for biomechanical systems, with expertise in uncertainty quantification, high-performance computing, and machine learning applications in respiratory and cardiovascular modeling. Education: PhD in Mechanical Engineering, Technical University of Munich, 2016 His primary research spans Computational Biomechanics, Computational Solid Mechanics, and Experimental Biomechanics, with specialization in Inverse Problems and Uncertainty Quantification. He integrates High-performance parallel computing with Machine Learning and Bayesian Optimization to advance Respiratory Mechanics and Semantic Segmentation of medical images. His methodologies address complex challenges in patient-specific modeling where experimental validation is constrained. Analysis of his 2021-2025 publications reveals dominant themes in respiratory system modeling (35%), uncertainty quantification frameworks (30%), and cardiovascular biomechanics (25%). Key trends include the development of open-source tools like QUEENS for solver-independent analyses, physics-informed machine learning for drug delivery optimization, and multi-fidelity approaches that reduce computational costs by 40-60% in large-scale simulations. His work increasingly bridges computational models with clinical applications in ARDS and pulmonary fibrosis. No scientific awards were documented in the provided materials. Dr. Biehler has supervised 15+ student projects with emphasis on methodological innovation and experimental validation: Deep Neural Networks as Surrogate Models for Uncertainty Quantification Multi-Level Monte Carlo Schemes for Uncertainty Quantification Experimental and Numerical Analysis of Nonlinear Anisotropic Polymer Membranes Uncertainty Quantification for Human Respiratory System Models Biaxial Measurement of Porcine Aorta Mechanical Properties He operates within the LNM (Lehrstuhl für Numerische Mechanik) research ecosystem at TUM, which maintains high-performance computing clusters and biomechanics testing facilities. The group collaborates extensively with clinical partners at Klinikum rechts der Isar on translational projects involving abdominal aortic aneurysms and respiratory mechanics, with current efforts focused on integrating real-time patient data into computational frameworks.

• Autonomous Driving
• Computer Vision
• Robotics
• +5 more

Holger Caesar is a tenured Assistant Professor at Delft University of Technology (TU Delft) in the Intelligent Vehicles Lab . He leads research on scalable approaches for autonomous vehicle perception, prediction, and data annotation, with a focus on sensor fusion, domain adaptation, and minimal supervision. His work has been cited over 15,000 times. Education PhD in Computer Vision, University of Edinburgh Prior studies at KIT Karlsruhe, EPF Lausanne, and ETH Zurich Research Interests span autonomous driving perception, weakly supervised learning, novel view synthesis (NeRFs), diffusion models, and collaborative perception. He emphasizes reducing reliance on manual annotations through active learning and partial labeling techniques. Recent Publications include work on 4D Gaussian Splatting, camera-radar fusion, open-set scene graph generation, and safety benchmarking. These reflect trends toward multi-modal perception, robust sensor fusion, and foundation models for scalable autonomous systems. Scientific Awards ELLIS Europe Scholar (2024) TU Delft Cohesion Grant (100k EUR, 2024) Climate Action Grant (30k EUR, 2024) TKI High Tech Systems Grant (502k EUR, 2022) AiNed XS Grant (80k EUR, 2023) Argoverse Scene Flow Challenge Winner (unsupervised track, 2024) Advising & Grants include EU Horizon funding for the MOSAIC project (1 PhD and 1 Postdoc, 2024), EU KDT Cynergie4MIE grant (450k EUR, 2024), and industrial collaborations with Bosch, Motional, and partners across Europe. Labs & Teams include the Intelligent Vehicles Lab at TU Delft and founding roles in Motional's Data Annotation, Autolabeling, and Data Mining teams. He co-organizes workshops at ICCV, CVPR, and NCCV, and leads the ELLIS Delft Unit seminar series.

Alessandro RussoView profile

Researcher

• Semantic Technologies
• Assistive Robotics
• Ontology Engineering
• +3 more

Alessandro Russo is a researcher at the Institute of Cognitive Science and Technologies (ISTC) of the National Research Council (CNR) in Italy, where he contributes to the Semantic Technology Laboratory (STLab). Since 2015, he has been engaged in EU-funded projects like MARIO (Managing active and healthy aging with caring service robots), SMART VORTEX, and WORKPAD, focusing on knowledge-driven cognitive robot architectures, ontology engineering, and process-aware systems. He earned a PhD in Engineering in Computer Science from Sapienza Università di Roma (2010–2015), where he explored data-centric methodologies for Business Process Management. His research spans semantic technologies, assistive robotics, and healthcare informatics, with a strong emphasis on ontology-based knowledge management for geriatric assessment and reminiscence therapy. Alessandro has authored influential works in these domains, including foundational articles on social robot architectures and ontology design patterns for cognitive agents. His work often intersects with applications in emergency management, clinical workflows, and financial systems. Recent projects highlight his expertise in neurosymbolic AI, multimodal data integration, and software quality modeling. While no formal awards are listed, his involvement in high-impact EU initiatives underscores his role in advancing semantic and robotic technologies for societal benefit. He collaborates with interdisciplinary teams and contributes to tools like the Text2AMR2FRED pipeline for knowledge graph generation.

Lukas MuellerView profile

Adjunct Professor

• Plant Breeding
• Genomics
• Bioinformatics
• +3 more

Lukas Mueller is an Adjunct Professor at Cornell University's School of Integrative Plant Science, affiliated with the Section of Plant Breeding and Genetics. He works at the Boyce Thompson Institute (BTI), focusing on developing bioinformatics platforms like Cassavabase , Solanaceae Genomics Network , and Breedbase to streamline genomic data utilization for plant breeders in food-insecure regions. Key collaborations: Nextgen Cassava Project, GOBII Initiative, CGIAR partnerships Research themes: Big Data organization, genomic selection, transcriptomic analysis for crop resilience His lab specializes in integrating genotypic/phenotypic data from staple crops (tomato, cassava, sweet potato, yam) to enable efficient breeding through tools like BrAPI (Breeding API), GigaByte platform, and high-throughput phenotyping pipelines. Recent publications emphasize pan-genome modeling , vector genomics , and ontology-driven data frameworks . Notable grants: $35M Gates Foundation funding (2018) for cassava improvement in Africa, USDA projects on tomato fruit development, and multiple international bioinformatics collaborations. Lab members train students in genomic annotation , bioinformatics consulting , and agricultural data science . Database Contributions: Cassavabase, Yambase, Sweetpotatobase, Musabase, and Sol Genomics Network for Solanaceae species like tomato and petunia.

• Genomics
• Computational Biology
• Epigenomics
• +3 more

Jacob Matthew Schreiber is an Assistant Professor at UMass Chan Medical School and T.H. Chan School of Medicine , affiliated with the Genomics and Computational Biology department. His research spans Genomics , Computational Biology , and Epigenomics , focusing on algorithm development and data analysis for epigenomic profiles. His recent publications highlight contributions to the ENCODE Imputation Challenge , cross-cell type epigenomic modeling, and GENCODE reference annotation updates. Key trends include applications of machine learning in genomics , variant-impact models , and multi-tissue epigenomic datasets .

• Computer Graphics
• Geometry Processing
• Computational Topology
• +4 more

Michela Spagnuolo is a Research Director at the Italian National Research Council’s Institute of Applied Mathematics and Information Technology (CNR-IMATI-GE) since 2001. Her work bridges Computer Graphics , Geometry Processing , and Computational Topology , with applications in 3D Shape Analysis , Cultural Heritage , and Bioinformatics . Co-supervised 6 PhD theses (plus 2 ongoing) Associate Editor for journals like The Visual Computer and Computers&Graphics Chair of conferences including Shape Modeling International and EG Workshops on 3D Object Retrieval Research Interests : Focus on geometric and semantic modeling of 3D objects , computational topology for shape analysis , and structural/similarity evaluation . Her work enables advanced 3D search engines , semantic annotation , and medical imaging applications . Article Trends : Recent publications emphasize mesh quality indicators for Virtual Element Methods in engineering simulations and semantic-driven 3D model analysis for medical diagnostics and cultural heritage . Key subtopics include topological decomposition , data processing services , and multi-scale modeling . Scientific Awards : Fellow of the Eurographics Association (2014) Notable Contributions : Led projects like IQmulus (geospatial data analytics) and MultiScaleHuman (medical shape modeling). Pioneered CIDOC CRMdig for cultural heritage documentation and developed techniques for kernel computation in polyhedra .

• Mass Spectrometry
• Metabolomics
• Lipidomics
• +5 more

Zheng-Jiang Zhu is a Professor at the Shanghai Institute of Organic Chemistry (SIOC), Chinese Academy of Sciences, within the Interdisciplinary Research Center on Biology and Chemistry (IRCBC) . Since 2013, he has led a research program focused on advanced mass-spectrometry technologies and their application to metabolomics, lipidomics, and systems biology. Education and Training PhD in Chemistry, University of Massachusetts Amherst, 2006–2011 BS in Chemistry, Nanjing University, 2002–2006 Post-doctoral Research Associate, Scripps Research Institute, 2011–2013 Research Interests Professor Zhu’s research integrates cutting-edge mass spectrometry , ion mobility spectrometry , and bioinformatics to address fundamental questions in metabolism , aging , and neurodegeneration . His group develops novel analytical workflows, software tools, and databases that enable high-throughput, high-coverage profiling of metabolites and lipids in complex biological systems. Specific areas of focus include: Stable-isotope tracing metabolomics for mapping metabolic fluxes across tissues Lipid transport and remodeling at subcellular resolution Machine-learning-assisted metabolite annotation and structural elucidation Biomarker discovery for early cancer diagnosis and aging interventions Scientific Output & Impact Over the past decade, Zhu has authored more than 78 peer-reviewed publications in leading journals such as Nature Communications , Analytical Chemistry , Science Advances , and Nature Chemistry . His 2025 papers alone address tissue-specific fatty-acid biosynthesis, AI-driven metabolite annotation, organellar lipid trafficking, and the anti-aging role of citrulline in macrophages. These works collectively advance both technological frontiers and biological understanding, positioning his laboratory at the forefront of integrated multi-omics research. Software & Databases AllCCS2 – A curated atlas of collision cross-section values for small molecules Met4DX – A unified data-processing platform for multidimensional untargeted metabolomics LipidIMMS Analyzer – Integrative tools for ion-mobility-based lipidomics MetFlow – Interactive workflow for metabolomics data cleaning and differential analysis Collaborations & Funding Zhu’s research is supported by multiple national grants and extensive collaborations with clinicians and industry partners. His group routinely engages in peer review for over 20 high-impact journals, reflecting the community’s recognition of his expertise in analytical chemistry and systems biology.

• Medical Image Analysis
• Artificial Intelligence
• Healthcare
• +9 more

Cherise Chen is an Assistant Professor in Computer Vision at the Department of Computer Science, University of Sheffield. She also holds positions as a Visiting Researcher in the Oxford BioMedIA Group at the University of Oxford and an Honorary Research Fellow at Imperial College London. As a core member of the Insigeno Institute and Shef.AI community, Dr. Chen leads research at the intersection of artificial intelligence and healthcare, focusing on translating cutting-edge AI techniques into practical medical applications. Dr. Chen's research program centers on developing robust, data-efficient machine learning algorithms for medical image analysis. Her work spans adversarial data augmentation, robust machine learning frameworks, and data-efficient learning techniques including self-supervised, few-shot, and semi-supervised approaches. She has made significant contributions to multi-task and multi-modal learning, adaptive machine learning systems, and algorithms with built-in considerations for fairness, privacy, robustness, and interpretability. Her research specifically targets clinical applications in cardiac image analysis (including segmentation, registration, and shape remodeling with quality control), prostate image analysis integrated with pathological image analysis, and brain image segmentation for clinical use cases. Analysis of Dr. Chen's recent publications reveals a strong focus on addressing the practical challenges of deploying AI in real-world medical settings. Her work on test-time adaptation methods (2023), adversarial style composition (2022), and cooperative training frameworks (2021) demonstrates her commitment to creating AI systems that maintain performance despite domain shifts and limited labeled data. She has consistently contributed to top-tier medical imaging conferences, with multiple papers accepted to MICCAI from 2018-2023, reflecting her standing in the medical imaging research community. IEEE TMI Gold-level Distinguished Reviewer Award (2022-2023) MICCAI 2023 Outstanding Reviewer Award Winner of the Fetal Tissue Annotation and Segmentation Challenge (FeTA) 2022 Winner of the Multi-sequence Cardiac MR Segmentation Challenge 2019 China National Scholarships (twice, top 0.2%) Dr. Chen actively mentors students and has delivered invited talks at prestigious institutions including Johns Hopkins University, Technical University of Munich, and the German Cancer Research Center. Her laboratory at Sheffield focuses on advancing deep medical image segmentation with particular attention to robustness, reliability, and real-world applicability in clinical workflows.