Technical Co-Founder, Vectorspace AI
Kasian got his start coding up search engines and then pattern recognition systems at Genentech. After spending over a decade consulting in Silicon Valley he landed in the bioinformatics industry in 2000 with the job of creating a system that would associate human genes with one another based on vector embeddings.
In 2002, his work attracted the Life Sciences division at Lawrence Berkeley National Lab where he was asked to help construct a system to better understand how to use scientific literature to analyze genomic pathways in breast cancer, the effects of radiation, or gamma rays found in space, on human chromosomes and the detection of hidden relationships between genes that were found to extend the lifespan of C. Elegans (nematodes) to what is equivalent to about 300 years in human lifespan. Applications in areas of research like this were of interest because they connect to our ability to do things like travel in deep space for extended periods of time on our way to what may be habitable planets. Google, Buck Institute on Aging, SENS Research Foundation, Vitalik Buterin/Ethereum, Human Longevity and few others advance research in this area.
In 2008, the work won the R&D100 award, a few patents and a search and discovery startup called SeeqPod which served 50M users and 250M searches every month before being acquired. After founding a public copmany and advsing a few hedge funds, he's started Vectorspace AI, a company focused on context-controlled Natural Language Processing (NLP) & feature engineering feature vectors for real-time on-demand alternative data and datasets.
Today the company works in the area of life sciences and the financial markets with groups including Lawrence Berkeley National Laboratory and a few internal groups at Google along with a couple hedge funds while enabling its customers to engage in context-controlled correlations of sympathetic, parasitic and symbiotic known and hidden relationships between entities such as public companies correlated to the periodic table of elements, cryptocurrencies, human genes, diseases, phytochemicals, pharmaceuticals and other entities for the purpose training ML and AI systems.