Scott Clark


Scott Clark, CEO & Co-Founder of SigOpt, is passionate about empowering experts to achieve their full potential with optimization solutions. He conceived of the idea for SigOpt while completing his Applied Mathematics PhD at Cornell and proceeded to build and open source  the Metric Optimization Engine at Yelp to help solve this problem. This process taught him that optimization needed to be productized to be effective for enterprises, which led him to found SigOpt in 2014, which has subsequently been funded by Y-Combinator, Andreessen  Horowitz, Blumberg Capital, DCVC, In-Q-Tel, and others. SigOpt now helps firms and academics around the world accelerate and amplify their research through optimization in fields from machine learning to algorithmic trading and beyond. 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. He was chosen as one of Forbes’ 30 under 30 in 2016.

Tuesday, October 1
10:50 am
11:15 am
Robertson 2
As teams scale their AI platforms, they must decide which capabilities to build versus buy. Whether balancing standards and flexibility or differentiation and scale, there is a playbook teams should run to make these decisions effectively. Join SigOpt Co-Founder & CEO Scott Clark’s session at TWIMLcon to learn how AI leaders weigh these tradeoffs. During this talk, Scott will draw on experience implementing SigOpt with cross sections of large companies who represent over $500B in market capitalization and nimble algorithmic trading firms with over $300B assets under management. This talk will touch on the end-to-end modeling process, but focus on Experiment Management. In particular, Scott will apply this decision making framework to different parts of Experiment Management in a customer case study format and discuss tradeoffs in methodologies for technical tasks like cluster management, distributed tuning and hyperparameter optimization in greater detail.
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