Summary
Amor Ines is a Principal Scientist and agro‑climate modeling leader with over 25 years of research and academic experience applying ML/AI, remote sensing, and high‑performance computing to crop and water management. Currently leading crop and climate modeling at PepsiCo while holding adjunct and research roles at Columbia, Texas A&M, and University of Minnesota, he bridges enterprise R&D with academic innovation. He builds and maintains production tools (CAMDT, FResampler, GCM Bias Correction, predictWTD) and codes in Modern Fortran, Python and C/C++, demonstrating rare expertise in both legacy scientific computing and modern data science. His work spans stochastic weather generators, bias correction and data assimilation to create decision‑ready climate‑agriculture products at scale. A persistent tinkerer, he founded AI{vm] Consulting and has turned long‑standing Fortran roots into scalable open toolchains for agricultural decision support. He is also pursuing applied AI credentials and cloud competency, signaling a shift toward integrating generative and cloud technologies into agro‑climate solutions.
11 years of coding experience
23 years of employment as a software developer
Certificate, Python for Data Science, Certificate, Python for Data Science at Emeritus
Generative AI Bootcamp, Generative AI Bootcamp at Business Science University
B.Sc., Agricultural Engineering, Magna Cum Laude & Valedictorian, B.Sc., Agricultural Engineering, Magna Cum Laude & Valedictorian at Mariano Marcos State University (MMSU)
Certificate, Applied Machine Learning, Certificate, Applied Machine Learning at Columbia Engineering
Ph.D., Water Resources, Ph.D., Water Resources at Asian Institute of Technology
Postdoc, Climate and Agriculture, Postdoc, Climate and Agriculture at Columbia University