Participants - Imageomics/Image-Datapalooza-2023 GitHub Wiki

Name GitHub ID Institution Interests Skills
John Bradley ORCID johnbradley Duke University Reproducibility, reusability, and scaling workflows python, R, workflows, containers, HPC Clusters, Relational Databases
Elizabeth Campolongo ORCID egrace479 Imageomics Institute, The Ohio State University Interdisciplinary Research, Imageomics, Mathematics, and Data Science; Reproducibility, reusability, and accessibility Python programming, data visualization and analysis, TDA and ML; FAIR software and data
Caleb Charpentier ORCID calcharp Virginia Tech Ontology-Informed Deep Learning, Phylogenetics and PCMs, Interpretable Deep Learning R, Python, PCMs, Interpretable Deep Learning
Kelly Diamond ORCID DiamondKMG Rhodes College Quantifying vertebrate morphology/locomotion/behavior Ecomorphology; behavioral ecology; R programing; DeepLabCut
Nathan Jacobs ORCID JacobsN Multimodal Vision Research Lab, Washington University in St. Louis Computer Vision System Development, Large Image Datasets, Ecology, Sustainable Development Computer Vision, Remote Sensing, Multimodal Deep Learning
Hilmar Lapp ORCID hlapp Duke University FAIR data and software, data and model infrastructure for ML in biology Collaborative research software engineering, technologies and best practices for automated and reproducible workflows; data and software licensing
Jenna Kline jennamk14 The Ohio State University Autonomous navigation for remote sensing, unmanned aerial vehicles (UAVs), edge computing Computer vision for autonomous navigation, Python, PyTorch, Data Analysis
Dom JebbiaORCID omnomdombomb Carnegie Mellon University Extensible, adaptable metadata pipelines; digital repositories; FAIR principles in workflows; AI ready data; serious play Python; metadata curation; data acquisition, cleaning, and pre-processing; repository management
Sriram Vijendran sriram98v The Iowa State University AI applications in Bioinformatics, Computer vision, String manipulation and process, Causal Inference and Discovery Computer vision, Python, PyTorch, Tensorflow, Rust, Javascript, Deep learning, Reinforcement Learning
Andrew Perrault aperrault Ohio State University AI/ML methods for decision-making and optimization Python, PyTorch, reinforcement learning, supervised learning, optimization
Amy Cogswell acogs-data Ohio State University Ethics, privacy, and governance in data; data literacy; metadata; ML in real-world applications; language and linguistics R, Python, metadata management, data catalogs
Rumali Perera ORCID RumaliPerera The Ohio State University Image feature analysis, Pattern recognition and analysis, Computer Vision Python, Computer Vision, Data analysis, Deep Learning, Tensorflow
Nicky Nicolson ORCID nickynicolson Science/digital, Royal Botanic Gardens, Kew Tooling for taxonomic researchers (echinopscis), digital extended specimen, FAIR data & software, provision of datasets from Kew herbarium digitisation project (KewBridge)
Workshop day 1 one minute intro slide 		Python, automation, continuous integration, open science
Sydney Decker ORCID skdecker Ohio State University 3D morphological analysis; Genotypes to phenotypes; Applying statistics and ML to phylogenetic questions Evolutionary biology; R; Data visualization, figure production, & graphic design
Hsun-Yi HsiehORCID hhsieh Michigan State University Ecology, Machine Learning, Deep Learning, Image processing, workflow development, and FAIR and CARE data R, SQL, supervised and unsupervised machine learning
Douglas Mbura ORCID douglasmbura Geo Indigenous Alliance Indigenous-led innovations in Earth Observation Data, Environmental Data Science, and Web-based Technologies. CARE with FAIR principles. R Programming, Remote Sensing, Web-Mapping.
Leanna House llhouse2 Virginia Tech Bayesian Statistics, Probabilistic Modeling, Visual Analytics, Education and Outreach (e.g., creating accessible, public, analytic tools and relatable datasets) Bayesian Statistics, Data Visualization, R (including R markdown and bookdown)
Anna LewkowiczORCID AnnaLew KU Leuven Bioinformatics, Phylogeny, Astrobiology, SynBio, DeSCi R, Java, bash, Python, BEAST
Matt Thompson ORCID thompsonmj Imageomics Institute, The Ohio State University Making data ML-ready, FAIR research products, Interdisciplinary biology/computational research Python, whatever helps get the job done
Amber D. York ORCID adyork BCO-DMO (Woods Hole Oceanographic Institution) Enabling FAIR data, bioinformatics and taxonomic name resolution, imaging informatics, data curation, education and outreach, Carpentries Instructor. Nowadays usually python and SPARQL, others occasionally as needed.
Barbara Marebwa bmarebwa The Michael J Fox Foundation for Parkinson's Research Biomarkers, Neuroimaging, MRI, PET, Reproducibility, Neurodegenerative Diseases SPM, FSL, FreeSurfer, Matlab, R
Chuck Stewart ORCID cvstewart Rensselaer Polytechnic Institute (RPI) Individual animal identification (re-ID), species detection and counting, Imageomeics, helping to facilitating research Computer vision, machine learning, Python
Otto Brookes ORCID obrookes University of Bristol Great Ape Behaviour Recognition, Welfare and Conservation Computer Vision, Machine Learning, Deep Learning, Python, Pytorch
Preetika Kaur ORCID preetika11 Auburn University Image processing, Remote sensing, Machine learning, Deep learning Data analysis with Python, R, SAS, Image Processing, Hydrologic Modeling
Namrata Banerji namratabanerji The Ohio State University Network Inference, SpatioTemporal Analysis, Computational Social Science R, Python, Keras, Tensorflow, Time Series Models, Statistical Inference
Ikenna Onyekwelu Ikenna10 Kansas State University Remote Sensing, Machine Learning, Image Processing and Segmentation, Mathematical Modeling, Process-based Crop Modeling, Hydrological Modeling Python, Pandas, Numpy, Crop Modeling, Hydrological Modeling, Statistical Inference and Linear Models
Joanna Malukiewicz ORCID Callithrix-omics University of Hamburg Primatology (Brazilian primates), Flaviruses, Population Genomics, Phylogenomics, Immunogenetics, Animal Hybridization, Conservation, Large Image Datasets, Using Computer Vision to Distinguish between Visual Phenotypes Python, R, Evolutionary Biology, HPC, Bayesian Statistics, GLMs, Bioinformatics, Bayesian Statitics
Luis Gonzalez ORCID (remote) luis-gonzalez-m Universidad de los Llanos Developing and applying anatomy ontologies for Arthropoda, phenotype annotation, Imageomics R programming, ontology term development, Python, Relational Databases
Nathaniel F. Shoobs ORCID Nathaniel F. Shoobs The Ohio State University Museum of Biological Diversity Image Processing and Segmentation, imaging workflow development, phenotype annotation, Imageomics museum data generation and curation, historical ecology, relational databases
David Carlyn ORCID David Carlyn The Ohio State University Machine Learning, Explainable AI, Computer Vision, Generative Models Python, C++, JavaScript, PyTorch, Classification, Object Detection, Generative Modeling, Software Engineering
Jim Balhoff ORCID (remote) balhoff RENCI, University of North Carolina at Chapel Hill Knowledge representation and data integration with ontologies, semantic web, biological data OWL & RDF, bio-ontologies, Scala, Java
T. Mason Linscott ORCID mason-linscott Virginia Tech Object detection, PCMs, Phylogenetics, Genomics, Biological Data R programming, python, Darknet/YOLO
Maksim Kholiavchenko (remote) dirtmaxim Rensselaer Polytechnic Institute Animal Behavior Recognition, Animal Detection and Tracking Computer Vision, Deep Learning, Python
Lisa Wu work4cs The Ohio State University Deep Learning, Modality Python, Pandas, PyTorch, Huggingface
Dipanjyoti PaulORCID dipanjyoti The Ohio State University Explainable AI, Machine Learning, Computer Vision Python, PyTorch, Classification, Object Detection, Unsupervised Data, Streaming Data

This event is sponsored by the Imageomics Institute and supported by the National Science Foundation under Awards No. OAC-2118240 and AWD-111317. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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