Joaquin Correa works on the development and deployment of integrated tools for high-throughput bio-image processing & analysis (LDRD-DOE). He is a former member and active affiliate of Auer's Lab at LBNL’s Life Sciences Division where he has prototyped algorithms and applications for image segmentation and 3D structural analysis in projects related to Hearing Loss and Deafness, Breast Cancer, Microbial Communities and Biofuels (plants and lignocellulosic degradation).
Prior to his appointment at LBNL, Joaquin worked as a Chem.E at a leading international manufacturing company on, but not limited to R&D, algorithm development, energy management and optimization, environmental legal compliance, strategic planning, product and process design and process simulation. He also has participated in projects for different energy-related Colombian government agencies. Correa holds a B.Sc. degree in Chemical Process Engineering from the National University of Colombia.
His interests include coupled-phenomena modeling & simulation; high-performance computing; signal processing; image processing, machine learning, computer vision and effective technology transfer for science and industry.
Wen-Ting Tsai, Ahmed Hassan, Purbasha Sarkar, Joaquin Correa, Zoltan Metlagel, Danielle M. Jorgens, Manfred Auer, "From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data", August 13, 2014, doi: 10.3791/51673
The bottleneck for cellular 3D electron microscopy is feature extraction (segmentation) in highly complex 3D density maps. We have developed a set of criteria, which provides guidance regarding which segmentation approach (manual, semi-automated, or automated) is best suited for different data types, thus providing a starting point for effective segmentation.
Correa, J., Skinner D., Auer M., "Integrated tools for next generation bioimaging", Conference in Medical Image Understanding and Analysis (MIUA) | Royal Holloway, Egham, July 9, 2014,
Correa, J., "Enabling Science from Big Image Data using Cross Cutting Infrastructure", International Conference on Big Data Science and Computing | Stanford University, May 27, 2014,
Correa, J., OME Workshop (ybd), OME Users Meeting | Institut Pasteur, Paris, June 5, 2014,
Joaquin Correa, Scalable web-based computational bioimaging solutions at NERSC, CryoEM meeting - SLAC National Accelerator Laboratory | Stanford University, April 17, 2014,
- Download File: CorreaCryoEM.pdf (pdf: 30 MB)
Joaquin Correa, Integrated Tools for NGBI--Lessons Learned and Successful Cases, LBNL Integrated Bioimaging Initiative, September 4, 2013,
- Download File: NGBILDRDLSD.pdf (pdf: 24 MB)
NextGen Bioimaging (NGBI) requires a reliable and flexible solution for multi-modal, high-throughput and high-performance image processing and analysis. In order to solve this challenge, we have developed an OMERO-based modular and flexible platform that integrates a suite of general-purpose processing software, a set of custom-tailored algorithms, specific bio-imaging applications and NERSC's high performance computing resources and its science gateways.
This under-development platform provides a shared scalable one-stop-shop web-service for producers and consumers of models built on imaging data to refine pixel data into actionable knowledge resources.
Fox W., Correa J., Cholia S., Skinner D., Ophus C., "NCEM Hub, A Science Gateway for Electron Microscopy in Materials Science", LBNL Tech Report on NCEMhub, May 1, 2014,
Electron microscopy (EM) instrumentation is making a detector-driven transition to Big Data. High capability cameras bring new resolving power but also an exponentially increasing demand for bandwidth and data analysis. In practical terms this means that users of advanced microscopes find it increasingly challenging to take data with them and instead need an integrated data processing pipeline. in 2013 NERSC and NCEM staff embarked on a pilot to prototype data services that provide such a pipeline. This tech report details the NCEM Hub pilot as it concluded in May 2014.
Joaquin Correa, David Skinner, "BIG DATA BIOIMAGING: Advances in Analysis, Integration, and Dissemination", Keystone Symposia on Molecular and Cellular Biology, March 24, 2014,
A fundamental problem currently for the biological community is to adapt computational solutions known broadly in data-centric science toward the specific challenges of data scaling in bioimaging. In this work we target software solutions fit for these tasks which leverages success in large scale data-centric science outside of bioimaging.