ESB AI Lab
A California 501(c)(3) Nonprofit Research Organization
ESB AI Lab 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
| Oct 03, 2026 | ESB AI Lab receives IRS determination letter granting 501(c)(3) tax-exempt status. The Internal Revenue Service has recognized ESB AI Lab as a tax-exempt public charity under Section 501(c)(3) of the Internal Revenue Code, classified as a 509(a)(2) public charity. The determination is effective retroactive to the date of incorporation, April 22, 2026. All contributions to ESB AI Lab are tax-deductible to the extent permitted by law. The determination follows the Form 1023 application filed in July 2026 with the assistance of Foundation Group. |
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| Oct 02, 2026 | ESB AI Lab releases HapSolo2, a GPU-accelerated tool for removing secondary haplotigs from diploid genome assemblies. HapSolo2 was developed with lab alumni Juan Yin, Mansi Agrawal, and Prashansa Goel from Dr. Solares’ research at UC Davis and UC San Diego, and the manuscript was completed at ESB AI Lab. HapSolo2 reproduces the published results of the original HapSolo while adding GPU acceleration, new optimizers, and up to 7.6 times faster run times, with a wrapper that simplifies the pipeline into commands such as align, search, train, and classify. Across four diploid assemblies, HapSolo2 reduced duplicated orthologs from 52.0% to 5.6% in avocado, with ortholog completeness decreasing by at most 1.3 percentage points. The preprint is available on bioRxiv, and the software is available on GitHub. |
| Sep 11, 2026 | ESB AI Lab hosts its first public webinar, “Leveling Up Research with AI.” The free virtual session covered practical applications of artificial intelligence in scientific research. The recording is available on the ESB AI Lab YouTube channel. |
| Jul 24, 2026 | ESB AI Lab submits Form 1023 application for federal 501(c)(3) tax-exempt status. The application, prepared by Foundation Group under the SureStart Complete package, was filed with the Internal Revenue Service seeking recognition as a tax-exempt public charity under Section 501(c)(3) of the Internal Revenue Code. Upon favorable determination, contributions to ESB AI Lab are expected to be tax-deductible retroactive to the date of incorporation, April 22, 2026. The organization was incorporated as a California Nonprofit Public Benefit Corporation for scientific, educational, and charitable purposes. Processing times for Form 1023 applications typically range from three to six months. |
| 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. |