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Brooke N. Wolford is a Marie Skłodowska-Curie Postdoctoral Fellow at the Norwegian University of Science and Technology (NTNU), affiliated with the Department of Public Health and Nursing within the Faculty of Medicine and Health Sciences. She leads the ProtectHearts and HUNT AI for Heart Health projects at the HUNT Center for Molecular and Clinical Epidemiology, while also contributing to the EU-funded INTERVENE project through the Finnish Institute for Molecular Medicine. Her work bridges statistical genetics, computational biology, and clinical applications to advance precision public health initiatives. Dr. Wolford earned her PhD in Bioinformatics and Master's in Statistics from the University of Michigan, where she completed her dissertation on 'Genetic Discovery and Precision Medicine in Cardiovascular Diseases Using Electronic Health Record-linked Biobanks' under Dr. Cristen Willer and Dr. Michael Boehnke. Her undergraduate training includes a Bachelor of Science in Quantitative Biology with highest honors from the University of North Carolina at Chapel Hill, where she was a Phi Beta Kappa honors graduate. Her research program focuses on artificial intelligence-driven precision public health, particularly in cardiovascular disease prediction and prevention. By integrating genomics, proteomics, and clinical data from large biobanks like HUNT, UK Biobank, and FinnGen, she develops novel risk prediction models that address critical gaps in women's heart health and young adult disease prevention. Her work on polygenic risk scores and proteomic biomarkers aims to transform population-scale screening programs through explainable AI methodologies. Dr. Wolford's recent publications demonstrate significant contributions to genetic epidemiology, with high-impact work in Nature Genetics, Nature Communications, and Circulation: Genomic and Precision Medicine. Her research outputs reveal consistent themes in cross-population polygenic score development, sex-specific disease mechanisms, and innovative biobank data integration approaches that enhance cardiovascular risk prediction across diverse populations. 2022/2023 Best Dissemination Award (K.G. Jebsen Centers) 2022 ASHG Trainee Research Excellence Award Finalist 2021 ASHG Trainee Research Excellence Award Semi-Finalist National Science Foundation Graduate Research Fellowship Genome Sciences Predoctoral Traineeship University of Michigan Program in Biomedical Sciences 20th Anniversary Award As a dedicated mentor, Dr. Wolford supervises multiple graduate students across Master's and PhD programs at NTNU, focusing on proteomic risk prediction for diabetes, cardiovascular-Alzheimer's disease connections, and polygenic scoring for women's heart health. She actively promotes open science through R-ladies Trondheim leadership and develops educational resources like the Health AI in R workshop. Her ongoing projects with INTERVENE and ProtectHearts secure substantial European research funding for translational genomics initiatives. Dr. Wolford co-leads the HUNT AI for Heart Health initiative and ProtectHearts project within the HUNT Center for Molecular and Clinical Epidemiology. These teams integrate computational biologists, clinicians, and epidemiologists to develop explainable AI models that translate genomic discoveries into clinical prevention strategies, with particular emphasis on addressing health disparities in cardiovascular outcomes for women and younger populations.

• Statistical Genetics
• Precision Public Health
• Cardiovascular Disease Epidemiology
• +5 more

Professor Mick Urbaniak is a Professor in the Biomedical and Life Sciences Department at Lancaster University, affiliated with the Data Science Institute. His research focuses on tropical infectious diseases, particularly trypanosomatid parasites including Trypanosoma brucei and Leishmania species. His research interests include: Molecular mechanisms of parasite-host interactions Cell cycle regulation in trypanosomatids Iron sensing and response mechanisms in parasites Heat shock signaling in Trypanosoma species Translating basic research into early-stage drug discovery for neglected tropical diseases Professor Urbaniak's laboratory employs a combination of genetics, cell biology, and proteomics approaches. They have pioneered stable isotopic labelling (SILAC) in T. brucei for global quantitative proteomic analysis and developed methods for quantifying phosphorylation changes in cells. His recent publications reveal a strong focus on molecular detection methods for parasites, genome analysis of Leishmania, and understanding cell cycle regulation in trypanosomatids. The research spans from basic molecular mechanisms to potential therapeutic applications. Professor Urbaniak has received several professional recognitions: Senior Fellow of the Higher Education Academy Fellow of the Royal Society of Biology Senior Fellow of Advance HE Member of the Royal Society of Biology Royal Society of Biology Ambassador He actively supervises PhD and MRes students, offering projects on parasite sensing mechanisms, cell division cycle regulation, and behavioral changes related to infection. Professor Urbaniak serves as an external examiner for PhD and master's programs and reviews grants for organizations including Wellcome Trust, BBSRC, and MRC. He also engages in outreach activities as a registered STEM ambassador.

• Tropical Infectious Diseases
• Parasitology
• Trypanosomatid Biology
• +7 more

Kevin L Childs is a Fixed Term Associate Professor in the Department of Plant Biology at Michigan State University. His research focuses on plant genomics, bioinformatics, and stress tolerance mechanisms through computational approaches. Education: Ph.D. in Plant Physiology from Texas A&M University M.S. in Computer Science from Texas A&M University B.S. in Botany from the University of Michigan Dr. Childs specializes in integrating computational biology with plant stress responses and pathogen interactions. His work includes genome assembly, effector protein identification, and regulatory network analysis across diverse species such as sweetpotato, sour cherry, and Arabidopsis. Recent research highlights include: whole genome duplication in Teucrium chamaedrys, NLR gene characterization in hexaploid sweetpotato, ZCT transcription factor analysis in Catharanthus roseus, and molecular diagnostics development for Ceratocystis fimbriata. His publications demonstrate expertise in long-read sequencing, transcriptomics, and comparative genomics. Dr. Childs maintains affiliations with plant biology laboratories at MSU and collaborates on projects involving biofuel crops, food security, and environmental stress adaptation.

• Plant Genomics
• Bioinformatics
• Stress Tolerance Mechanisms
• +3 more

Pierrette Bouillon is affiliated with the University of Geneva as a researcher, focusing on machine translation, accessible communication, and language technology applications in healthcare. Her work bridges computational linguistics, speech processing, and inclusive design. Institution: University of Geneva Research Focus: Medical translation, sign language processing, text/pictograph translation Research Interests : Bouillon specializes in machine translation and computational linguistics , with particular emphasis on: Accessible communication for patients with language barriers Sign language and pictograph translation systems (e.g., BabelDr) Speech-to-speech translation in emergency settings Text simplification for intellectual disabilities Historical French normalization for NLP Multilingual speech recognition and post-editing Article Trends : Her recent publications highlight the integration of large language models with accessibility tools in healthcare, focusing on Swiss French sign language corpora , pictograph sequences , and spontaneous speech simplification . Work spans both NLP and human-computer interaction in medical contexts. Education and Teaching : While specific educational details are omitted, Bouillon contributes to translation pedagogy through tools like COPECO and MT3 , integrating speech technologies into post-editing workflows.

• Machine Translation
• Natural Language Processing
• Computational Linguistics
• +7 more

Hermann Stolte is a scientific collaborator at the Institute of Computer Science , Humboldt University of Berlin . His work focuses on data science, database systems, and information systems, with applications in astrophysics, educational technologies, and mobile app analysis. Institution: Humboldt University of Berlin Department: Databases and Information Systems His research includes developing validation frameworks for data analysis workflows, plausibility checks in exploratory data analysis, and predictive modeling for user feedback. Recent work explores astrophysical data analysis and educational robotics. Hermann's publications span data validation, star rating prediction from reviews, and transformative educational technologies. His work frequently addresses data quality, interpretability in machine learning, and interdisciplinary applications.

• Database Systems
• Data Science
• Information Systems
• +2 more

Pedro Henrique Luz de Araujo is a member of the Faculty of Computer Science affiliated with the Research Group Data Mining and Machine Learning . His work focuses on Natural Language Processing , Language Modeling , and Behavioral Learning , particularly in Brazilian legal text domains. He has contributed to advancements in named entity recognition , hate speech detection , and specification-driven AI systems . Active in 2022-2025 with publications in PLoS ONE and top NLP conferences Organized the KONVENS 2024 conference Research emphasizes LLM calibration , multimodal classification , and domain-specific datasets His recent publications show expertise in large language models (2025: persona effects; 2024: specification overfitting, text-guided image clustering), NER techniques (2024), and legal document processing (2023: multimodal classification; 2020: VICTOR dataset, LeNER-Br).

• Natural Language Processing
• Machine Learning
• Data Mining
• +7 more

Katelin Dannen, PhD, is an Assistant Professor of Neuroscience and Pharmacology and Associate Chair for Education at the University of Iowa's Department of Neuroscience and Pharmacology. Her research focuses on improving pharmacology education through active learning strategies and understanding the impact of pharmacology training on drug consumer knowledge. She also investigates the molecular mechanisms of RGS6 proteins in neurodegenerative disorders and cancer. Education: B.A. in Biology, Augustana College (Sioux Falls, SD) Ph.D. in Biology (Neurobiology emphasis), University of Iowa Postdoctoral Fellow, Department of Pharmacology, University of Iowa Research Interests: Dr. Dannen combines educational innovation with molecular biology to address gaps in pharmacology pedagogy. Her work includes developing application-based small-group exercises and diagramming activities to enhance student engagement. Concurrently, she explores RGS6’s role in Parkinson’s disease, intellectual disability, and cancer progression, focusing on protein isoforms and signaling pathways. Publications: Her research spans pharmacology education and molecular studies, with recent contributions to RGS6’s role in neurodegeneration, cardiovascular development, and tumor suppression. Notable work includes identifying novel RGS6 isoforms linked to Parkinson’s and demonstrating RGS6’s tumor suppressor function in bladder carcinogenesis. Awards: Favorable peer review from MERLOT (2023) for her Critical Thinking Pharmacology Worksheets Advising & Grants: No student advisees are listed in available records. Grant information is not specified in the provided data.

• Pharmacology Education
• Neurobiology
• Molecular Pharmacology
• +3 more

Jalila Filali is a Researcher at Université Laval, affiliated with the Computer Vision and Systems Laboratory. Her work focuses on advancing computer vision techniques through ontology integration and deep learning models, with applications in environmental monitoring and wildlife tracking. She holds a post-doctoral position and specializes in image classification, object detection, and semantic annotation systems. Her research interests include developing ontology-driven systems for image annotation, comparing machine learning models like HMAX and Bag-of-Visual-Words for large-scale classification, and applying YOLOv5 frameworks to analyze wildlife behavior via camera-collar videos. She has contributed to projects involving environmental information extraction in northern Quebec ecosystems. Key technical trends in her publications include hybrid ontology-machine learning architectures, classifier fusion techniques, and the integration of visual features with semantic knowledge bases. She has explored both theoretical advancements in computer vision algorithms and practical implementations in ecological monitoring contexts. Jalila Filali's work is supported by the Computer Vision and Systems Laboratory at Université Laval, where she contributes to interdisciplinary projects merging artificial intelligence with ecological research.

• Computer Vision
• Machine Learning
• Image Classification
• +3 more

Dr. Dena Bazazian is a Lecturer in Robotics and Machine Vision at the University of Plymouth's School of Engineering, Computing and Mathematics. Previously, she held positions as a Senior Research Associate at the University of Bristol's Visual Information Laboratory, Research Scientist at CTTC (Barcelona), and postdoctoral researcher at Universitat Autònoma de Barcelona, where she earned her PhD in 2018. Her international research includes visiting positions at Barcelona Supercomputing Center (2024), NAVER LABS Europe (2019), and University of Florence (2017). Her research focuses on robotic vision, underwater technologies, and AI-driven applications. Key interests include: 3D reconstruction and neural rendering techniques Anomaly detection using graph neural networks Underwater perception systems for marine environments Intergenerational co-design of immersive technologies Point cloud processing and geometric deep learning Publication analysis reveals strong emphasis on computer vision applications across robotics, industrial inspection, heritage preservation, and marine technology. Recent works increasingly integrate human-centered approaches with XR technologies and underwater systems. She currently supervises 8 PhD students across robotics, healthcare, and environmental monitoring domains: Offshore wind turbine maintenance drones (EPSRC-UDLA funded) Autonomous sea vessel swarms for climate forecasting (ARIA funded) AI for marine biodiversity monitoring (Cefas funded) Elderly fall detection systems (URS General funded) Healthcare anxiety detection through visual analysis (EPSRC DTP HMT funded) Dr. Bazazian leads modules including Artificial Vision and Deep Learning and Computer Vision . She co-organizes workshops including the Deep Learning for Geometric Computing series (CVPR/ICCV) and Women in Computer Vision workshops (CVPR/ECCV).

• Robotics
• Machine Vision
• Artificial Intelligence
• +3 more

James Martin is a Professor of Computer Science at the University of Colorado at Boulder and a Fellow in the Institute of Cognitive Science. He holds a B.S. in Computer Science from Columbia University and a Ph.D. in Computer Science from the University of California at Berkeley. His research focuses on computational semantics, particularly how languages convey meaning to humans and computers, with a specific emphasis on metaphor processing and non-literal language analysis. He co-authored the widely used textbook Speech and Language Processing (3rd edition in progress), and his work extends to applications in healthcare and education through projects like the TalkMoves dataset analyzing classroom discourse. Current research includes cross-document event coreference resolution, AMR parsing tools (e.g., X-AMR), and AI-driven educational systems via the Institute for Student-AI Teaming (iSAT). Education History: Bachelor of Science in Computer Science, Columbia University Doctor of Philosophy (Ph.D.) in Computer Science, University of California, Berkeley Research Interests: Natural Language Processing (NLP) and Computational Linguistics Metaphor Analysis in Language and AI Semantic Parsing and Knowledge Representation AI Applications in Healthcare and Education Dialogue Systems and Classroom Discourse Analysis Recent Work Trends: His 2023-2024 publications emphasize multimodal NLP, cross-document semantic analysis (e.g., event coreference resolution), and AI tools for education (e.g., TalkMoves application for teacher feedback). NSF-funded National AI Institutes collaborations highlight strategic technology development. No scientific awards explicitly listed, though his textbook and research contributions are widely recognized in the field. Currently not actively recruiting PhD students. Labs/Teams: Core contributor to NLP initiatives at University of Colorado, particularly in semantic parsing and educational AI through iSAT. Involved with projects like the TalkMoves dataset and AMR annotation tools (CAMRA, X-AMR).

• Artificial Intelligence
• Cognitive Science
• Natural Language Processing
• +5 more

Professor Zhihua Jiang leads research in Animal Phenomics at Washington State University's Department of Animal Sciences within the College of Agricultural, Human, and Natural Resource Sciences. His work focuses on bridging genomics and phenomics to optimize livestock systems, emphasizing RNA variants' roles in phenotype-genome interactions. Key areas include transcriptome profiling, epigenomics, and alternative polyadenylation studies across species like sheep, pigs, and rats. Research highlights include the FarmGTEx project analyzing livestock transcriptomes and studies on hybridization in peafowl. He actively recruits students for the Genome-to-Phenome PhD Program. Advising and grants details are pending specific mentions in provided texts. His lab's contributions span domestication genomics, microbiome interactions, and disease-related transcriptomic mechanisms.

• Animal Phenomics
• RNA Variants Analysis
• Genome-to-Phenome Research
• +5 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).

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

James Matthew Rehg is a Professor at the University of Illinois at Urbana-Champaign, holding concurrent roles in multiple departments and research centers. He is the Founder Professor at the Siebel School of Computing and Data Science, and also serves as a Professor in Industrial and Enterprise Systems Engineering. Additionally, he directs the Health Care Engineering Systems Center within the Grainger College of Engineering and is affiliated with the Coordinated Science Lab. His research focuses on advanced computer vision, egocentric video analysis, action recognition, and medical imaging applications. Key areas include developing algorithms for 3D object segmentation, egocentric action prediction, and integrating audio-visual data for gaze anticipation. Recent work also explores mixed reality systems in clinical and training environments. Dr. Rehg collaborates internationally on large-scale projects like the Ego4D initiative, involving over 3,000 hours of egocentric video data to advance understanding of human activities and social interactions. His contributions span theoretical advancements and applied solutions in healthcare engineering and robotics. In advising and grants, while specific student names are not listed, his extensive research output indicates active mentorship in interdisciplinary teams. His work bridges computer science with healthcare, emphasizing practical applications through collaborations across academia and industry.

• Computer Vision
• Egocentric Video Analysis
• Action Recognition
• +3 more

Gene E. Robinson is the Maybelle Leland Swanlund Endowed Chair and Professor of Entomology at the University of Illinois at Urbana-Champaign. He leads the Carl R. Woese Institute for Genomic Biology (IGB) and holds affiliate appointments in multiple departments, including the Center for Advanced Study, Neuroscience Program, and Beckman Institute. His work integrates genomics, neuroscience, and behavioral ecology to study social insect behavior, primarily using honey bees as a model. Robinson earned his Ph.D. in Entomology from Cornell University (1986) and joined UIUC in 1989. His research focuses on the genetic and neuroendocrine mechanisms underlying social behavior, division of labor, aggression, and communication in honey bees. Key projects include studying nutritional regulation of brain gene expression, gene regulatory networks in social evolution, and automated behavioral monitoring using RFID and AI. He has authored over 350 publications, secured $70M+ in research funding, and trained ~60 early-career scientists. His honors include the Wolf Prize in Agriculture, Guggenheim Fellowship, and memberships in the National Academy of Sciences and National Academy of Medicine. Robinson’s interdisciplinary approach bridges molecular biology, ecology, and computational methods to address societal challenges in agriculture and health. Affiliations: Carl R. Woese Institute for Genomic Biology, Department of Entomology, Center for Advanced Study Leadership: Director of IGB (2001–present), former Neuroscience Program co-director Research Themes: Social evolution, genomics, neurogenomics, and bee health

• Entomology
• Genomics
• Social Behavior
• +6 more

Michael A Norman is an Associate Professor in the University Library at the University of Illinois Urbana-Champaign, serving as Discovery Services Librarian and Integrated Library System (ILS) Coordinator. His work focuses on advancing discovery systems, transaction log analysis, and web-scale library technologies. He has contributed to innovations like the Bento discovery framework and conducted extensive research on library resource management, user behavior, and digital preservation. Norman has collaborated on projects involving Primo, SFX, and schema.org integration, emphasizing usability and interoperability in library systems. Education: Details not explicitly stated in provided texts. Research Interests: Discovery systems architecture, user-centered design in library technology, web-scale resource management, metadata standards implementation, and historical collection analysis. His work bridges technical innovation with practical library services, addressing challenges in digital resource access and preservation. Publications: Over 13 peer-reviewed articles and datasets, focusing on discovery systems evaluation, copyright renewal studies, and collection management strategies. Recent work emphasizes Bento architecture and user interaction analysis within digital libraries. Awards: No specific awards listed in provided materials. Advising/Grants: No formal advisee list provided; contributions include collaborative research grants related to library technology projects.

• Discovery Systems
• Transaction Log Analysis
• Web-scale Discovery Services
• +3 more