I'm Jeffrey, a technologist applying AI concept and representation learning to real-world systems.
I'm experienced in startups, specializing in systems engineering, product strategy, and platform development for AI-enabled hardware-software systems. I’ve led innovation across industries including automotive, clean energy, and developer tooling.
I've completed my Master of Science in Artificial Intelligence at UT Austin, and have enrolled in the Berkeley Haas CoBE executive program focusing on technology strategy and leadership. I’m passionate about leveraging AI to drive business innovation.
I am now actively seeking roles centered on deep and generative models ... data curation for model improvement, deep learning model development and tuning, and/or rigorous model evaluation ... for applications in industries where data-driven modeling can transform real-world systems, including in foundational AI, healthcare and life sciences, financial services, enterprise technology, retail and e-commerce, culture and entertainment.
I created a dataset of deep learning concept embeddings, extracted hierarchical topic structure, and created text and concept indexes across 158 books about deep learning comprising 50k book-pages.
An intuitive exploration of why Stochastic Gradient Descent often outperforms traditional gradient descent in machine learning optimization, from data efficiency, a focus on progress, and leveraging randomness.
I used prompt engineering and RAG to have Perplexity's Default LLM design a process for a demographics fairness audit of the Stable Diffusion v2.1 text-to-image model.
I used prompt engineering and RAG to get LLMs to pose as ethical theorists and react to a contemporary AI ethics dilemma, the potential adoption of social robots within public facilities for vulnerable and special needs children.