I am a biotech CTO and an AI/Machine Learning scientist, with a PhD focusing on computational neuroscience from the University of California, Riverside and a Master’s in Neuroscience from San Diego State University, there I developed computational models using brain imaging data to help diagnose Alzheimer’s disease.

As a contractor, I have worked on diverse computational modeling problems in partnership with Fortune 500 and 100 companies, including predicting human perception, which guided the development of new food, beverage, and personal care products.  For more details as well as media coverage, see my publications section. 

I previously served as the CTO of a biotech company that builds AI/machine learning platforms to discover safe, natural, and effective replacements for chemicals in everyday products, a company undergoing a merger. I later became the CTO of a stealth mode biotech company, where I am currently leading a team of scientists leveraging AI algorithms and molecular experiments to address longstanding challenges in the neurosciences.   

It was not by way of an easy and predictable path. By the time I moved into industry full-time, I had studied the evolution and development of nervous systems, neurodegenerative diseases, stem cell biology, and cancer, and attended 5 different universities. In laboratories, I would watch cells revert to the stem cell-like properties that characterize both embryonic development and cancer. And I would watch neural networks as they build-up or breakdown, countless memories formed and fading. In life as in nature, there is this inescapable dance between chaos and order — that I have known in various ways.

While scientists with similar experiences might conveniently leave chaos and complexity to art — making science the desire for explanations and art the desire to destroy them — that was never an option for me. It is from my first love of drawing, painting, and designing that science, rather than providing a complementary perspective to art, became the art of relationships. 

Nature is fiercely creative in its elaborate, diverse patterns — patterns that emerge from abstract and often hidden relationships (for example, what chemistry, physics, and, foundationally, mathematics expose). While the traditional artist often recreates these patterns without explicitly mastering physics or mathematics, nature acts on both scientist and artist alike through an algorithmic process. Whereas the traditional artist decodes the world of visible and aesthetically pleasing patterns, scientists can now use math and computer programming to unveil nature’s generative algorithms. That is, how the patterns come into existence in the first place. Scientists are therefore nature’s storytellers and must embrace art and literature as foundational to scientific progress. 

Admittedly, an unsettling narrative has been unfolding with the popularization of AI technologies. Discoveries in neuroscience, physics, computer science, and mathematics are already challenging historical ideas on uniqueness and intelligence—blurring well established lines between the real and the artificial. It is unclear where this path might lead. But today’s uncertainty implies possibility; the exciting possibilities of a future that is not yet written. What world will we create?