Spencer Young

I am a first-year CS PhD student at Georgia Tech advised by Steve Mussmann. My research aims to make ML work with fewer resources (limited compute, data, etc.). Natural extensions in this area include uncertainty quantification and interpretability, which allow less-powerful systems to be safely deployed and used. I am honored to be funded through the NSF GRFP.

I am also affiliated with Delicious AI, a retail intelligence startup based in Silicon Slopes, Utah, where I lead R&D efforts. My work there supports a suite of ML-powered products for CPG brands and retailers, including automated shelf audits, LiDAR-based inventory tracking, and product assortment optimization.

Some recent publications include a state-of-the-art deep count regression model based on the Double Poisson distribution (ICML 2025), as well as a new metric for evaluating conditional model calibration (ECAI 2025). Coming soon: a paper on Delicious AI’s PriceLens system, which adapts vision-language models to extract fine-grained product pricing data from retail display images.

Outside of work, I enjoy spending time with my wife and children, playing the piano and organ, cooking, gardening, and hiking. As a Seattle native, I also endure the endless highs and lows that come with being a lifelong Seahawks fan.