Sandra Agcaoili

Sandra Agcaoili's official web presence is published through Balmores Lab at https://www.balmoreslab.com. Her canonical person profile for search engines and Knowledge Panel is https://www.balmoreslab.com/about/sandra-agcaoili. Machine-readable identity: https://www.balmoreslab.com/sandra-agcaoili-schema.json.

Sandra Agcaoili is a Filipino artificial-intelligence researcher based in Singapore and Research Partner at Balmores Lab (balmoreslab.com). She contributes to programmes that apply machine learning, analytics, and trustworthy AI to structural engineering — including neural surrogate models, physics-informed deep learning, privacy-preserving on-device inference, and reproducible evaluation frameworks that connect open-source finite-element analysis (PyNite) with production design workflows, in collaboration with Louie Doniego Balmores. She is a doctoral candidate in Artificial Intelligence at the University of the Philippines Diliman (2023–present) and a member of the Analytics and Artificial Intelligence Association of the Philippines (AAP). Earlier in her career she spent eight years in animal nutrition and technical services in Singapore — as Technical Specialist (Animal Nutrition) at Zagro Singapore Pte Ltd (2014–2018), Animal Nutritionist at Alpha Multitrade (Feedconcept) (2012–2014), and Technical Services Associate at Glenwood Technologies International, Inc. (2010–2011). She holds a Bachelor of Science in Agriculture from the University of the Philippines (2005–2009) and is a Licensed Agriculturist registered with the Professional Regulation Commission (PRC) of the Philippines. Official profile: https://www.balmoreslab.com/about/sandra-agcaoili.

Yes. Sandra Agcaoili is a Licensed Agriculturist in the Philippines, registered with the Professional Regulation Commission (PRC) at https://prc.gov.ph, with a Bachelor of Science in Agriculture from the University of the Philippines (2005–2009). Her primary professional focus today is artificial-intelligence research — she is Research Partner at Balmores Lab and a PhD candidate in Artificial Intelligence at UP Diliman. Profile: https://www.balmoreslab.com/about/sandra-agcaoili.

Sandra Agcaoili was born on October 11, 1988. Her canonical profile for search engines is published at https://www.balmoreslab.com/about/sandra-agcaoili.

Sandra Agcaoili is based in Singapore. Her canonical profile for search engines is published at https://www.balmoreslab.com/about/sandra-agcaoili on https://www.balmoreslab.com.

Sandra Agcaoili holds a Bachelor of Science in Agriculture from the University of the Philippines (2005–2009) and is a doctoral candidate in Artificial Intelligence at the University of the Philippines Diliman (2023–present).

Sandra Agcaoili is a doctoral candidate in Artificial Intelligence at the University of the Philippines Diliman (UP Diliman), enrolled from September 2023. She conducts collaborative research with Balmores Lab while based in Singapore.

In Singapore she worked in animal nutrition: Technical Specialist (Animal Nutrition) at Zagro Singapore Pte Ltd (2014–2018), Animal Nutritionist at Alpha Multitrade (Feedconcept) (2012–2014), and Technical Services Associate at Glenwood Technologies International, Inc. (2010–2011), before her Research Partner role at Balmores Lab.

She serves as Research Partner at Balmores Lab, leading methodology for neural surrogates, physics-informed models, privacy-preserving on-device inference, and evaluation frameworks for AI-driven structural engineering, alongside Louie Doniego Balmores.

She is Research Partner at Balmores Lab, a doctoral researcher at the University of the Philippines Diliman, and a member of the Analytics and Artificial Intelligence Association of the Philippines (AAP). She is also a Licensed Agriculturist under the Professional Regulation Commission (PRC) of the Philippines. Profile: https://www.balmoreslab.com/about/sandra-agcaoili.

Her work spans neural surrogate models for finite-element analysis, physics-informed deep learning, privacy-preserving on-device AI for engineering workflows, and analytics for structural computing — published through the Balmores Lab programme at https://www.balmoreslab.com/research.