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Hiba ArnaoutView profile

Academic

• Artificial Intelligence
• Natural Language Processing
• Knowledge Graphs
• +7 more

Hiba Arnaout is a postdoctoral researcher at the Ubiquitous Knowledge Processing (UKP) Lab, Department of Computer Science, TU Darmstadt, where she also served as a part-time lecturer from April to July 2024. She earned her PhD from the Max Planck Institute for Informatics under the supervision of Prof. Gerhard Weikum and Prof. Simon Razniewski, and her Master's and Bachelor's degrees from the American University of Beirut and Haigazian University, respectively. Education: PhD in Computer Science, Max Planck Institute for Informatics, Saarbrücken, Germany (Feb 2018 – Oct 2023) Master of Computer Science, American University of Beirut, Lebanon (Feb 2014 – Feb 2017) Bachelor of Computer Science, Haigazian University, Beirut, Lebanon (Feb 2010 – Jun 2013) Research Interests: Hiba's work lies at the intersection of Artificial Intelligence, Natural Language Processing, and Knowledge Graphs . She specializes in discovering and utilizing negative knowledge in open-world knowledge bases like Wikidata. Her current research extends into AI for mental health , research impact analysis , and culturally-aware NLP . She has pioneered methods for enriching knowledge graphs with informative negative statements, leading to interactive systems like UnCommonSense and Wikinegata . Publication Trends: Her recent publications (2023–2025) reflect a clear trajectory from foundational work on negation in knowledge bases toward broader applications in research evaluation and mental health. She increasingly employs large language models and temporal analysis, demonstrating a shift toward impact-driven and human-centric AI research. Her work spans journals, conferences, demos, and books, showcasing both technical depth and dissemination excellence. Scientific Awards: Semantic Web Science Association (SWSA) Distinguished Dissertation Award (2024) Bosch Reward for invention: Knowledge Graph Repair Using Ontologies and LMs (2022) Audience-choice Best Paper Award at AKBC (2020) Best Student Paper Award Nominee at IC3K'17 (2017) Full Graduate Assistantship at AUB (2014) Participation in DFG grant discussion (2021) Advising and Grants: Hiba actively supervises students, currently guiding multiple BSc and MSc theses on topics like mental health trend tracking, culturally-aware emotional support, and therapy session annotation. While no direct grant leadership is mentioned, she contributed to a DFG grant proposal on negative knowledge and has received recognition from Bosch for her inventive work, indicating strong industry and academic collaboration. Labs and Teams: She is a core member of the Ubiquitous Knowledge Processing (UKP) Lab at TU Darmstadt, led by Prof. Iryna Gurevych. Previously, she was affiliated with the Databases and Information Systems Group at Max Planck Institute for Informatics and collaborated with researchers at The University of Edinburgh and Bosch Center for Artificial Intelligence.

• Statistics
• Probability Theory
• Stochastic Modeling
• +3 more

María Del Rosario Rodríguez Griñolo is a Full Professor in the Department of Economics, Quantitative Methods and Economic History at Pablo de Olavide University in Spain. Her academic work focuses on Statistics and Operations Research within the School of Economics. Dr. Rodríguez Griñolo earned her PhD from the University of Seville in 2010 with a thesis titled "A new approach to multivariate lifetime distributions based on the excess-wealth concept: an application in tumor growth," supervised by Dr. Franco Pellerey and Dr. José María Fernández Ponce. Her research interests span multiple areas of statistical theory and applications, with particular expertise in stochastic ordering, multivariate analysis, reliability theory, and risk assessment. She has developed significant contributions to the understanding of multivariate aging concepts, excess-wealth functions, and orthant convex-type stochastic orders. Her work demonstrates both theoretical depth and practical applications across diverse fields including financial risk management, cultural heritage preservation, and biomedical research. Analysis of her recent publications reveals a strong focus on advancing theoretical statistical concepts while applying them to real-world problems. Her work shows consistent development in multivariate stochastic orders, with increasing applications in risk assessment, biomedical research, and educational methodologies. The interdisciplinary nature of her research is evident through collaborations across statistics, economics, biology, and cultural heritage fields. Dr. Rodríguez Griñolo has contributed to educational innovation in statistics teaching, particularly through developing web-based resources for secondary students and implementing new assessment methodologies for statistics courses in environmental sciences. Her academic activities include significant contributions to statistics education and public outreach, including the design of educational web resources for secondary students and the development of science communication initiatives related to cultural heritage preservation.

• Robotics
• Artificial Intelligence
• Machine Learning
• +8 more

Dr. Daniel Delgado Bellamy is a Lecturer and Graduate Tutor in Electronics at the Faculty of Environment and Technology , University of the West of England (UWE Bristol) , specializing in interdisciplinary research across robotics, extended reality, and human-computer interaction. Education: MSc in Robotics (UWE Bristol & University of Bristol) Current PhD: Extended reality systems for manufacturing His research focuses on Human-Machine Interaction , Sensing Integration , and Systems Safety , with applications in Industry 4.0 , Robotics , and Artificial Intelligence . He leads projects in Mixed Reality for industrial maintenance and Digital Transformation in manufacturing. Key collaborations include the Bristol Robotics Laboratory , DETI , and Synoptix Ltd. , with expertise in Electronics , Digital Manufacturing Technologies , and Industrial Data Analytics .

Luke HolbrookView profile

Professor

• Phylogeny
• Evolution
• Vertebrate Paleontology
• +5 more

Dr. Luke Holbrook is a Full Professor in the Department of Biological & Biomedical Sciences at Rowan University's College of Science & Mathematics. He specializes in the phylogeny and evolution of mammals, particularly perissodactyls (horses, rhinos, tapirs), using anatomical data from fossils and living species to reconstruct evolutionary relationships and analyze trait convergence. Post-Doctoral Associate, New York College of Osteopathic Medicine Ph.D. in Biology, University of Massachusetts M.S. in Biology, University of Massachusetts B.S. in Biology, Fordham University Dr. Holbrook's research focuses on mammalian evolutionary history, including the origin of perissodactyls in India, the role of the end-Cretaceous extinction in placental mammal diversification, and the convergent evolution of complex traits like large body size and tooth morphology. His work integrates fossil data with molecular clocks to determine lineage divergence timing. His 15 most recent publications span topics such as placental mammal brain evolution, early Eocene mammal biogeography, dental adaptations in perissodactyls, and vertebral specialization in hoofed mammals. These studies employ methodologies including cladistics, geometric morphometrics, and microCT scanning for virtual specimen analysis. Dr. Holbrook teaches courses in evolution, comparative anatomy, and interdisciplinary biology/history. He collaborates internationally with institutions like the American Museum of Natural History and uses advanced imaging technology in his lab. While no formal awards are listed here, his research has been widely shared across academic and mainstream platforms.

Susan LandView profile

Associate Professor

• Genomics
• Cancer biology
• Next-generation sequencing
• +4 more

Susan Land, PhD , is a full-time Associate Professor in Research at Wayne State University , where she also serves as Director of the Applied Genomics Technology Center (AGTC) . Spanning roles in research, teaching, and core-facility leadership, she supports investigators across the Department of Biology, the C. S. Mott Center for Human Growth and Development, and the Karmanos Cancer Institute. Education & Training: While specific degrees are not detailed in the provided text, Dr Land’s extensive instruction of graduate courses in genomics, cancer biology, and biomedical engineering indicates advanced graduate training in molecular biology or a closely related field. Research Interests: Dr Land’s work centers on genomic technologies , next-generation sequencing , single-cell genomics , and precision medicine . Her Applied Genomics Technology Center delivers DNA/RNA isolation, genotyping, expression profiling (qPCR, Illumina BeadArrays, Affymetrix GeneChips, digital gene expression), and whole-exome sequencing, supporting projects from cancer epidemiology to placental biology. Publication Trends: Across more than 60 peer-reviewed articles since 2002, her research demonstrates a clear trajectory from methodological tool development (e.g., SnpEff) to large-scale genetic epidemiology studies—particularly in lung and prostate cancer among African-American populations—and integrative systems-biology investigations of complex diseases such as preeclampsia and asthma pharmacogenomics. Scientific Awards & Honors: No specific awards are listed in the supplied text. Student Advising & Mentoring: Ahmed Ibrahim Ahmed, MD – PhD thesis on “Transcriptomic Analysis of Cell-Free RNA in the Amniotic Fluid of Patients with Preterm Labor using Next Generation Sequencing” Ongoing mentorship of MD/PhD students, Reproductive Endocrinology fellows, and junior faculty recruits via AGTC facility tours and collaborative projects. Laboratory & Core Facility: Dr Land directs the Applied Genomics Technology Center (AGTC) , a state-of-the-art Wayne State core facility equipped with low- and high-throughput genomic instrumentation. Services include DNA/RNA extraction, qPCR, Illumina BeadArrays, Affymetrix GeneChips, Illumina Genome Analyzer IIx, and comprehensive bioinformatics support.

• Cultural Heritage Conservation
• Archaeometry
• Mineralogy
• +3 more

Maria Cristina Caggiani is an Assistant Professor at the Department of Biological, Geological, and Environmental Sciences, University of Catania. She specializes in archaeometry, cultural heritage conservation, and mineralogy using advanced spectroscopic techniques (Raman, FTIR-ATR, FORS) combined with microscopy and thermal analysis. Education: Master’s in Science and Technology for Cultural Heritage (University of Bari); PhD in Chemical and Molecular Sciences (University of Bari). Research: Focus on Roman/medieval glass, Islamic enamels, Apulian pottery, and gemology. Developed geopolymer materials for restoration and portable diagnostic protocols for cultural heritage. Key Collaborations: Sorbonne University, Sapienza University of Rome, Ca' Foscari University of Venice. Teaching: Courses in Mineralogy, Applied Petrography, and Precious Minerals at University of Catania. Member of examination committees for multiple programs. Scientific Output: 24 peer-reviewed publications and 36 conference communications (2011–2021).

Rasmus AagaardView profile

Associate Clinical Professor

• Emergency Medicine
• Critical Care Medicine
• Point-of-Care Ultrasound
• +6 more

Rasmus Aagaard is a Clinical Associate Professor at Aarhus University's Faculty of Health Sciences, affiliated with the Department of Cardiothoracic and Vascular Surgery. His research focuses on emergency medicine applications of point-of-care ultrasound, resuscitation science, and critical care diagnostics. Primary Affiliation: Department of Clinical Medicine, Aarhus University Department: Cardiothoracic and Vascular Surgery Research Pillars: Emergency Ultrasound, Cardiac Arrest, Pulmonary Embolism Key research areas include: Teleultrasound implementation in critical care Prospective cohort studies for respiratory failure prediction Randomized controlled trials evaluating ultrasound-guided diagnostic pathways Development of scoring systems for critical care His publications span method validation studies, systematic reviews, and clinical trials with significant applications in emergency departments and critical care settings.

Francois HussonView profile

Professor

• Multivariate Data Analysis
• Principal Component Analysis (PCA)
• Missing Data Imputation
• +5 more

Francois Husson is a Full Professor at the French Higher Education Institution in Agriculture, Food, Horticultural and Landscape Sciences, affiliated with the Department of Statistics and Computer Science in Rennes, France. His research focuses on multivariate data analysis, missing data imputation, and sensory analysis. Key methodologies: Principal Component Analysis (PCA), Multiple Factor Analysis (MFA), Multiple Correspondence Analysis (MCA) Software contributions: FactoMineR, SensoMineR packages in R Recent work includes: Dementia detection using longitudinal data from SHARE surveys Missing value imputation via multilevel singular value decomposition and data depth Applications in sensory analysis of food products and clinical studies His publications span statistical theory, biomedical research, and food science, with a strong emphasis on computational methods and open-source tools.

Eric Fosler-LussierView profile

Professor

• Speech Processing
• Natural Language Processing
• Machine Learning
• +4 more

Eric Fosler-Lussier is the John I. Makhoul Professor and Acting Department Chair of Computer Science and Engineering at The Ohio State University. He holds courtesy appointments in Linguistics and Biomedical Informatics, and serves as Assistant Dean for Faculty Lifecycle in the College of Engineering. His research focuses on speech processing, natural language understanding, and clinical NLP applications. PhD in Computer Science, University of California, Berkeley (1999) B.A.S. in Computer and Cognitive Science, University of Pennsylvania (1993) B.A. in Linguistics, University of Pennsylvania (1993) His research spans core technologies for speech recognition and NLP, with applications in medical training systems and clinical record analysis. Recent work includes neural diarization techniques, conversational AI for healthcare, and cross-domain text-to-SQL frameworks. Recent publications highlight innovations in end-to-end speech systems, knowledge transfer between textual and speech models, and robust error prediction frameworks. His lab has produced 15+ publications since 2022, with a focus on educational and medical applications. NSF CAREER award recipient IEEE Signal Processing Best Paper Award IMIA Yearbook Best Paper (twice) IBM Faculty Award IEEE and ISCA Fellow He advises numerous PhD students and has mentored graduates now working at Amazon, Google, IBM, and universities worldwide. His Speech and Language Technologies Laboratory develops systems for speech enhancement, virtual patients, and knowledge transfer.

• Bioengineering
• Genetic Diversity Analysis
• Plant Stress Response
• +3 more

Dr. Jasmin Šutković is an Associate Professor in the Department of Genetics and Bioengineering at the International University of Sarajevo (IUS) , where he has served since 2010. He completed his BSc, MSc, and PhD at IUS, and currently directs the Innovation and Entrepreneurship Center . His career includes administrative roles at the Research and Development Center (RDC) from 2013-2023. BSc: Biological Science and Bioengineering, IUS (2009) MSc: Genetics and Bioengineering, IUS (2014) PhD: Genetics and Bioengineering, IUS (2020) Dr. Šutković's research spans plant genetics and human molecular studies , focusing on: Cadmium stress response in Brassica varieties STR marker analysis for kinship verification COVID-19 pathogenesis and long-term effects Phytoremediation mechanisms Transgenic plant methodologies Metabolic syndrome biomarkers Protein-nanoparticle interactions His recent publications highlight trends in Computational biology applications for engineering Plant-metal transporter gene expression Viral contributions to lymphoma Phytoremediation strategies Multi-omics analysis of Long-COVID Dr. Šutković actively leads educational initiatives, including the DMED Program Coordination , and has contributed to over 30 peer-reviewed publications. His work bridges academic research with practical applications in biotechnology and environmental science.

Anikó VágnerView profile

Assistant Professor

• Database Systems Education
• Medical Signal Processing
• Clustering of Datasets
• +3 more

Dr. Anikó Vágner is an Assistant Professor at the University of Debrecen , affiliated with the Faculty of Informatics and the Department of Information Technology . Her research focuses on Educational issues of database systems , Medical signal processing , and Clustering of datasets , with a strong emphasis on NoSQL databases, smart city applications, and health informatics. Academic Rank: Assistant Professor Email: vagner.aniko@inf.unideb.hu Research Trends: Analysis of her 15 most recent publications reveals sustained work in NoSQL database performance optimization (MongoDB, Redis), GTFS-based transportation algorithms, medical signal processing using microcontrollers, and clustering techniques like OPTICS. Applications span smart cities, health monitoring, and educational technologies. Labs/Teams: She is part of the Department of Information Technology, which focuses on databases, data mining, and smart city solutions. The department collaborates on projects like participatory sensing frameworks and time series analysis.

Robert F. CooperView profile

Assistant Professor

• Biomedical Imaging
• Retinal Disease
• Adaptive Optics
• +4 more

Dr. Robert F. Cooper is an Assistant Professor in the Joint Department of Biomedical Engineering at Marquette University and the Medical College of Wisconsin, with a dual appointment in MCW's Department of Ophthalmology and Visual Sciences. He directs the Ocular & Computer Vision Laboratory (OCVL) and collaborates with the Advanced Ocular Imaging Program (AOIP). His research focuses on non-invasive retinal imaging techniques , particularly adaptive optics ophthalmoscopy for assessing photoreceptor structure/function. Key interests include: Development of image processing pipelines for automated retinal analysis Cellular-scale functional assessment in degenerative diseases Clinical translation of optical imaging technologies Establishing normative databases for retinal morphology Recent publications (2024-2025) demonstrate methodological innovations in adaptive optics resolution enhancement, optoretinography for subclinical disease detection, and reproducibility studies for clinical applications. Research consistently targets quantitative biomarkers for retinal pathologies. Significant grants include: NIH R01: 'Non-Invasive Functional Assessments for Translational Retinal Therapeutics' (2023-2026) NIH U01: 'Retinal Contributions to Vision Loss in Albinism' (2022-2026) Foundation Fighting Blindness Award: 'Assessing Function of Individual Cells in Inherited Retinal Degenerations' (2020-2023) Holds a patent for 'Non-invasive Assessment of Human Cone Photoreceptor Function' (US Patent, 2023). Leads the OCVL lab developing novel ophthalmic imaging solutions.

Katarina GrolingerView profile

Associate Professor

• Sensor Data Analytics
• Machine Learning
• Deep Learning
• +5 more

Katarina Grolinger is an Associate Professor at the Department of Electrical and Computer Engineering at Western University and holds the Canada Research Chair (Tier 2) in Engineering Applications of Machine Learning . She is also a Vector Institute Faculty Affiliate . Ph.D. and M.Eng. in Software Engineering from Western University M.Sc. and B.Sc. in Mechanical Engineering from University of Zagreb, Croatia Her research focuses on machine learning applications in energy systems , deep learning for structural and medical imaging , federated learning , and big data management . Recent publications emphasize energy forecasting , IoT data analytics , and reinforcement learning for autonomous systems . 2022 Engineering Junior Faculty Award for Excellence in Research She teaches courses in data analytics , operating systems , and database management . Her work integrates industry collaboration and has contributed to IEEE and Energy Reports publications.

Carine Pierrette MukamakuzaView profile

Lecturer

• Digital Healthcare Solutions
• Machine Learning
• Recommender Systems
• +4 more

Dr. Carine Pierrette Mukamakuza is a Lecturer at Carnegie Mellon University Africa's College of Engineering, specializing in Digital Healthcare Solutions , Machine Learning , and Data Science . Her work bridges Computer Science , Public Health , and Social Network Analysis , with a focus on malaria detection systems and cardiac diagnostics. Ph.D. in Informatics, Technical University of Vienna MS & BS in Computer Science, Central South University, China Research Themes : AI applications in malaria diagnostics (U-Net, YOLOv5, SVM) Deep Learning for ECG/PCG signal analysis Social network impacts on recommender systems Digital health infrastructure for Africa Article Trends : Combines Machine Learning and Public Health to develop digital diagnostic tools for malaria and heart disease, with recent emphasis on system reliability and scalability across Africa. Projects : Developing automated malaria screening tools for Rwanda, integrating AI into UN SDG-aligned health systems.

Mario FloresView profile

Assistant Professor

• Computational Biology
• Machine Learning
• Artificial Intelligence
• +3 more

Mario Flores is an Assistant Professor at the University of Texas at San Antonio (UTSA), with a joint appointment in the Department of Electrical and Computer Engineering and the Department of Biomedical Engineering. He holds his Ph.D., M.S., and B.S. from UTSA and the Autonomous Metropolitan University, Mexico City. His work integrates Artificial Intelligence , Machine Learning , and Spatial Transcriptomics to address challenges in oncology, immunology, and medical diagnostics. Ph.D., University of Texas at San Antonio M.S., University of Texas at San Antonio B.S., Autonomous Metropolitan University, Mexico City His research interests include computational biology , gene regulation , and AI applications in medicine , focusing on diseases like cancer, diabetic foot ulcers, and severe COVID-19. He has published extensively on topics such as spatial transcriptomics for tumor and disease analysis, predictive modeling for diabetic ulcers, and deep learning for immune cell diversity. Key trends in his work involve leveraging multimodal data (e.g., electronic health records, transcriptomics) for risk prediction, developing deep learning models for gene expression and language fluency analysis, and creating databases for competing endogenous RNA (ceRNA) networks and methylation patterns. His recent publications (2023–2025) emphasize transformer-based models , spatial biology tools , and immuno-oncology applications. His contact information includes the email mario.flores@utsa.edu and office in the Biotechnology Science and Engineering Building (BSE 1.534).