ESB AI Lab
A California 501(c)(3) Nonprofit Research Organization
ESB AI Lab Corporation is an independent nonprofit research organization developing computational methods in artificial intelligence, machine learning, and genomics to address challenges in food security, species conservation, climate resilience, human health, and language preservation.
Research Focus
AI-Directed Breeding and Aquaculture
Computer vision and genomic approaches for sustainable agriculture and aquaculture, including AI-assisted sex determination in sturgeon.
Computer Vision for Conservation
Computational methods supporting the conservation and recovery of threatened and endangered species.
Climate and Disease Ecology
Climate-driven changes in vector-borne disease distribution and vector genomics.
Genome Evolution and RNA Biology
Biological adaptation to environmental change, RNA regulatory mechanisms, and structural variation in crops and model organisms.
Computational Pathology and Cancer Genomics
Machine learning and genomic analysis applied to human health.
Endangered Language Preservation
NLP, speech recognition, and AI-assisted OCR for endangered languages.
Edge AI for Field Research
Multi-sensor edge AI systems for real-time data collection in field and farm settings.
Open Science and Education
Accessible AI tools and educational programs broadening scientific participation.
Funding
Our research is supported by:
- NSF ACCESS Computing Allocations
Get Involved
We welcome collaborators, volunteers, and supporters at all levels. Whether you are a researcher, student, or community member, there are ways to contribute to our mission.
| Learn more about opportunities | Support our work |
Contact
news
| Jul 07, 2026 | Executive Director Edwin Solares’s research on AI-assisted sex determination in endangered red abalone is featured by the San Diego Supercomputer Center. The study developed deep learning models trained on thousands of ultrasound images to identify abalone sex non-invasively, achieving approximately 86% accuracy with near-instantaneous results. Traditional methods require removing abalone from their shells, causing stress and potential injury. The work, conducted at UC San Diego using NSF ACCESS allocations on SDSC’s Expanse and Pittsburgh Supercomputing Center’s Bridges-2 systems, supports conservation hatcheries in selecting mature breeding candidates more efficiently. This research was completed prior to Dr. Solares’s appointment as Executive Director of ESB AI Lab. |
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| Jul 06, 2026 | ESB AI Lab launches its Education Programs page, featuring 22 hands-on training programs across four domains: bioinformatics, machine learning, infrastructure, and hardware. Offerings range from free public seminars to multi-day bootcamps in AI, scientific computing, computer vision, and genomics. Programs are designed for working researchers with contact hours ranging from 4 to 32 hours. An interest form is now live for prospective participants to indicate which programs they would like to see scheduled first. Program dates will be set based on demand, and scholarship support is available. |
| Jun 26, 2026 | ESB AI Lab Corporation is now a registered entity in the U.S. federal government’s System for Award Management (SAM.gov). The registration assigns ESB AI Lab a Unique Entity ID (UEI: D1SDZLS12KK6), enabling the organization to apply for and receive federal grants, contracts, and cooperative agreements. This milestone positions ESB AI Lab to pursue funding from agencies including NSF, NIH, USDA, and DOE in support of its mission to advance scientific research through artificial intelligence, machine learning, and genomics. |
| May 22, 2026 | PvGAP paper published in The Journal of Infectious Diseases. This work describes a cost-effective amplicon panel with 80 high-diversity targets for population genomics and 8 targets of epidemiological interest for Plasmodium vivax, validated with Ethiopian field samples and computational assessment across three geographic regions. The panel supports drug resistance tracking, distinguishes infection origins, and helps separate reinfection from recrudescence in efficacy studies. Read the paper |
| May 21, 2026 | Red Abalone deep learning paper published in Frontiers in Artificial Intelligence. This work demonstrates that convolutional neural networks can classify reproductive tissue from ultrasound imagery non-invasively, reducing stress on animals, lowering operational costs for producers, and improving scalability in breeding programs. This is the first application of deep learning to sex determination in abalone and opens a pathway for AI-assisted species assessment in aquaculture more broadly. Read the paper |