# Talks & Podcasts — Scott Clark

40+ documented conference and podcast appearances since 2013.

## AI as Science's Greatest Translator

**2026-03-03** · SAIR Podcast (with Chuck Ng)

[Video](https://www.youtube.com/watch?v=7qDx8PdljiA)

Conversation with SAIR co-founder Chuck Ng on collaboration, observability, and AI's role as a translation layer for scientific work.

## AI Testing and Evaluation

**2025-07-13** · GenAI Week Silicon Valley 2025

Speaker at GenAI Week Silicon Valley 2025 (Santa Clara Convention Center, July 13–17) on the AI observability landscape and the importance of behavioral signals in AI evaluation systems for enterprise reliability.

## Coffee with a Founder: Scott Clark, Distributional

**2025-06-03** · BAM Podcast

[Video](https://www.youtube.com/watch?v=qYcphOXSSEE)

Founder-interview format conversation on building Distributional, the enterprise AI reliability thesis, and the path from SigOpt through Intel to founding again.

## Building AI Systems You Can Trust

**2025-05-23** · AI + a16z Podcast

[Video](https://podcasts.apple.com/us/podcast/building-ai-systems-you-can-trust/id1740178076?i=1000709586075)

Co-hosted conversation with Matt Bornstein (a16z partner) on enterprise AI reliability, agentic systems, and how Distributional approaches behavioral testing of production AI.

## Distributional Co-Founder & CEO Interview

**2025-02-18** · CEO.com Podcast

[Video](https://www.youtube.com/watch?v=I5btboBQIIk)

Founder-CEO conversation on building Distributional, the enterprise AI testing thesis, and lessons from founding two ML companies.

## The Hidden Signal in Production AI Logs

**2025-01-01** · Jason Liu Podcast

[Video](https://www.youtube.com/watch?v=FKL918FgxAw)

Long-form conversation with Jason Liu on production AI observability, what behavioral signals to extract from production logs, and the testing thesis behind Distributional.

## NYSE Distributional Spot

**2024-01-01** · NYSE

[Video](https://www.youtube.com/watch?v=RBCbinZ_UV0)

Short NYSE-branded clip introducing Distributional and the enterprise AI testing thesis.

## Power Consumption and AI Are Key Priorities for Dell and Intel in the Supercomputing Space

**2022-11-15** · SuperComputing 22 (theCUBE panel, Dallas)

[Video](https://www.youtube.com/watch?v=-B-vQ0koA3Y)

theCUBE panel at SuperComputing 22 in Dallas with David Schmidt of Dell Technologies. Discussion of Intel's oneAPI open-ecosystem strategy across heterogeneous hardware. Speaking as Intel's VP & GM of AI and HPC Supercomputing Application Level Engineering.

## Fireside Chat with Sam Charrington: Experimentation in ML

**2022-05-18** · TWIML AI Podcast Fireside Chat

[Video](https://www.youtube.com/watch?v=GGpImwvgezA)

Wide-ranging fireside conversation with Sam Charrington on the state of experimentation in machine learning, drawing on the SigOpt era and post-acquisition Intel work.

## How to Optimize Your Models with Intelligent AI Experimentation

**2021-10-12** · MLconf 2021 Webinar

[Video](https://www.youtube.com/watch?v=xqp7v3qTo0U)

MLconf-hosted webinar on intelligent AI experimentation for model optimization, presented as SigOpt GM during the Intel era.

## Experimenting with AI Optimizations

**2021-07-22** · Intel Conversations in the Cloud (Episode 250)

[Video](https://podcasts.apple.com/us/podcast/experimenting-with-ai-optimizations-citc-episode-250/id552020357?i=1000529697823)

Intel-produced podcast conversation with host Jake Smith on intelligent AI experimentation and how SigOpt's platform fits into Intel's broader AI software stack post-acquisition.

## Building the Better, More Scalable Algorithms

**2021-04-29** · IT Visionaries (Mission.org)

[Video](https://www.youtube.com/watch?v=RY7CF1W4SKQ)

Post-acquisition conversation on Mission.org's IT Visionaries podcast about building scalable optimization algorithms, leading SigOpt under Intel, and the broader landscape of intelligent experimentation.

## Intelligent AI Experimentation

**2021-01-01** · Ai4 2021

[Video](https://www.youtube.com/watch?v=dYnTfVdPPAI)

Talk at the Ai4 2021 conference on intelligent AI experimentation as a foundation for production model development, presented as SigOpt GM during the Intel era.

## Boost AI Experimentation to Design, Explore, and Optimize Your Models

**2021-01-01** · SigOpt Summit 2021 (keynote)

[Video](https://www.youtube.com/watch?v=1BDa42BOwKo)

Keynote at the virtual SigOpt Summit 2021 on accelerating AI experimentation across design, exploration, and optimization workflows. Co-speakers included Subutai Ahmad of Numenta.

## Scott Clark of SigOpt

**2020-06-22** · AI at Work Podcast (PJC), Season 2 Episode 2

[Video](https://podcasts.apple.com/us/podcast/s2-episode-2-scott-clark-of-sigopt/id1519875578?i=1000479142061)

Conversation on SigOpt's history and the Y Combinator origin story, covering the path from Yelp's MOE project to building an optimization-as-a-service company.

## From argmax f(x) to an International Business

**2019-12-06** · Cornell CAM Notable Alumni Speaker Series

[Video](https://www.youtube.com/watch?v=EUXRJs4GVg4)

76-minute career-arc talk at Cornell's Center for Applied Mathematics on how applied mathematics research in Bayesian optimization led from a Cornell PhD through Yelp's MOE project to founding SigOpt as an international optimization-as-a-service business.

## Automated Model Tuning

**2019-11-01** · TWIML AI Podcast (Episode 324)

[Video](https://twimlai.com/network/scott-clark/)

Deep dive into automated model tuning techniques, covering the state of the art in Bayesian optimization and how it applies to production machine learning workflows.

## Supporting Rapid Model Development at Two Sigma

**2019-06-15** · TWIML AI Podcast (Episode 273)

[Video](https://twimlai.com/network/scott-clark/)

Discussion on how SigOpt's optimization platform supports rapid model development workflows at scale, with a case study from Two Sigma.

## Best Practices for Scaling Modeling Platforms

**2019-04-17** · O'Reilly AI Conference, New York 2019

[Video](https://www.youtube.com/watch?v=wL9BhmsG0sE)

Co-presented with Matt Greenwood, Chief Innovation Officer at Two Sigma. Lessons from scaling SigOpt's intelligent experimentation platform alongside a Two Sigma case study on running a modeling platform at quantitative-finance scale.

## Modeling at Scale in Systematic Trading

**2019-04-01** · Quantitative Finance Conference

[Video](https://www.youtube.com/watch?v=CBwA6FodNxM)

54-minute talk on running model-development platforms at scale across algorithmic trading firms, drawing on work with funds representing $300B AUM and a Two Sigma case study.

## Tuning the Un-Tunable

**2019-03-18** · NVIDIA GTC Silicon Valley 2019

[Video](https://www.youtube.com/watch?v=G00fVTKbmZE)

Strategies for optimizing deep learning models with long training cycles using Bayesian optimization. Presented at NVIDIA GTC Silicon Valley 2019.

## A Conversation with Scott Clark

**2017-10-16** · Voices in AI with Byron Reese, Episode 12

[Video](https://podcasts.apple.com/us/podcast/episode-12-a-conversation-with-scott-clark/id1291540809?i=1000495733050)

56-minute long-form conversation with Byron Reese covering algorithms, transfer learning, the nature of human intelligence, and broader philosophical territory in AI.

## Bayesian Optimization for Hyperparameter Tuning

**2017-08-01** · TWIML AI Podcast (Episode 50)

[Video](https://twimlai.com/network/scott-clark/)

Podcast discussion on Bayesian optimization approaches to hyperparameter tuning, the theory behind optimal experiment design, and practical applications in production ML systems.

## Bayesian Global Optimization

**2017-05-01** · MLconf Seattle 2017

[Slides](https://www.slideshare.net/SessionsEvents/scott-clark-ceo-sigopt-at-mlconf-seattle-2017)

Deep dive into Bayesian global optimization methods, covering theory, algorithms, and practical applications for machine learning hyperparameter tuning at scale.

## Tuning Machine Learning Algorithms

**2017-02-12** · The AI in Business Podcast (Emerj)

[Video](https://podcasts.apple.com/us/podcast/tuning-machine-learning-algorithms-with-scott-clark/id670771965?i=1000381100550)

Earliest documented podcast appearance, on Emerj's AI in Business Podcast (then 'AI in Industry'). Discussion of Bayesian optimization for tuning machine learning algorithms in production.

## Using Bayesian Optimization to Tune Machine Learning Models

**2016-11-01** · MLconf San Francisco 2016

[Slides](https://www.slideshare.net/SessionsEvents/scott-clark-cofounder-and-ceo-sigopt-at-mlconf-sf-2016)

Talk on applying Bayesian optimization techniques to efficiently tune machine learning model hyperparameters, drawing on experience building SigOpt's optimization platform.

## Adaptive Sequential Experimentation Techniques for A/B Testing and Model Tuning

**2015-05-01** · The Web Conference (WWW 2015)

Presentation on adaptive sequential experimentation methods that improve upon traditional A/B testing by dynamically allocating resources to the most promising alternatives.

## Introducing the Metric Optimization Engine (MOE)

**2014-11-01** · MLconf San Francisco 2014

[Slides](https://www.slideshare.net/SessionsEvents/scott-clark-software-engineer-yelp-at-mlconf-sf)

Introduction of MOE, an open-source Bayesian optimization framework built at Yelp for optimizing real-world metrics through intelligent experimentation.

## CMU Silicon Valley TOCS Colloquium

**2013-04-09** · Carnegie Mellon University Silicon Valley

[Video](https://www.youtube.com/watch?v=bOQqOL2en9M)

Earliest documented public talk, given at CMU Silicon Valley's Talks on Computer Science colloquium during the Yelp tenure. Predates the 2014 MLconf SF MOE talk.

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Source: https://scottclark.io/talks