At 1910, I focus on improving our CANDID™ suite of predictive ADMET AI/ML models, primarily for small molecules, and expanding to larger therapeutic modalities. CANDID™ models can expedite the hit to lead and lead to candidate stages of drug discovery. I've practiced machine learning (scikit learn, BERT/NLP, Tensorflow/object detection), data science (python, pandas, MongoDB), and software development (UI/UX, SQL, React, node, CSS), generally in the healthcare and pharmaceutical space, typically in start-ups or similar teams.In addition to my drive and professional experience in software and AI/ML, I've academic experience as a former postdoc scientist/engineer in droplet microfluidics for directed evolution, iPSC to neocortical spheroid differentiation, and MDCK cell culture. My PhD work was in protein expression/purification/thermodynamics, DNA-coated colloid physics/chemistry, fluorescence microscopy, and statistical mechanics (theoretical partition functions/entropy calculations/energy landscapes on graphs). I've mentored undergraduate and high school researchers, and served my colleagues as a lab safety officer and group meeting organizer.In my spare time I've worked on a project in drop-based microfluidics design/maker manufacturing (NSF SBIR project pitch ok'd) and software projects including an Electron prototype exploring the use of writing to recall memories, and one for which a patent was granted. I’ve also enjoyed formulating strawberry flavored alginate and co-leading my son’s former daycare class in making it into drops and gels.