I am 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, in partnership with Fortune 500 and 100 companies, I have worked on diverse computational modeling problems, including predicting human perception, which has aided in the creation of new food, beverage, and personal care products. For more details as well as media coverage, see my publications. I have served as the Chief Technology Officer of a company that built AI/machine learning platforms to discover safe, natural, and effective replacements for chemicals in everyday products, a company that has since undergone a merger. 

Additionally, I have worked on the evolution and development of nervous systems and have investigated the mechanisms of tissue regeneration and repair, specifically in the context of nerve injuries and cancer metastasis. Interestingly, metastasis is a process where abnormal gene regulation results in a reversion to the stem cell-like properties that characterize cells very early in development. Among other things, this increases motility (or cells’ capacity to move long distances). How living systems develop is therefore a useful model for cancer progression. 

Though I am a scientist, my first love was actually art, drawing, painting, and designing. Science and art nevertheless intersect in interesting ways. Nature is fiercely creative, in its elaborate, diverse patterns; however, patterns emerge from the convergence of parts and relationships operating at a low level of analysis (e.g. chemistry, physics, and, foundationally, mathematics). Subsequently, nature generates artistic forms or patterns, inspiring the world’s great artists, through a clearly principled or 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 look to history, and the masters of art and literature, to express nature’s grandeur inclusively. 

Admittedly, an unsettling narrative has been unfolding with the popularization of AI/Machine Learning. 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?