Scott Clark

Scott has been applying optimal learning techniques in industry and academia for years, from bioinformatics to production advertising systems. Before SigOpt, Scott worked on the Ad Targeting team at Yelp leading the charge on academic research and outreach with projects like the Yelp Dataset Challenge and open sourcing MOE. Scott holds a PhD in Applied Mathematics and an MS in Computer Science from Cornell University and BS degrees in Mathematics, Physics, and Computational Physics from Oregon State University. Scott was chosen as one of Forbes’ 30 under 30 in 2016.

Posts by Scott Clark

Data Science

Optimizing End-to-End Memory Networks Using SigOpt and GPUs

Natural language systems have become the go-between for humans and AI-assisted digital services. Digital assistants, chatbots, and automated HR systems all rely... 18 MIN READ
SigOpt Optimization Loop
Simulation / Modeling / Design

Deep Learning Hyperparameter Optimization with Competing Objectives

In this post we’ll show how to use SigOpt’s Bayesian optimization platform to jointly optimize competing objectives in deep learning pipelines on NVIDIA... 15 MIN READ