Talks
Conference talks, podcast appearances, and fireside chats. Ordered most recent first.
- Mar 2026AI as Science's Greatest TranslatorSAIR Podcast (with Chuck Ng)
Conversation with SAIR co-founder Chuck Ng on collaboration, observability, and AI's role as a translation layer for scientific work.
- Jul 2025AI Testing and EvaluationGenAI 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.
- Jun 2025Coffee with a Founder: Scott Clark, DistributionalBAM Podcast
Founder-interview format conversation on building Distributional, the enterprise AI reliability thesis, and the path from SigOpt through Intel to founding again.
- May 2025Building AI Systems You Can TrustAI + a16z Podcast
Co-hosted conversation with Matt Bornstein (a16z partner) on enterprise AI reliability, agentic systems, and how Distributional approaches behavioral testing of production AI.
- Feb 2025Distributional Co-Founder & CEO InterviewCEO.com Podcast
Founder-CEO conversation on building Distributional, the enterprise AI testing thesis, and lessons from founding two ML companies.
- Jan 2025The Hidden Signal in Production AI LogsJason Liu Podcast
Long-form conversation with Jason Liu on production AI observability, what behavioral signals to extract from production logs, and the testing thesis behind Distributional.
- Jan 2024NYSE Distributional SpotNYSE
Short NYSE-branded clip introducing Distributional and the enterprise AI testing thesis.
- Nov 2022Power Consumption and AI Are Key Priorities for Dell and Intel in the Supercomputing SpaceSuperComputing 22 (theCUBE panel, Dallas)
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.
- May 2022Fireside Chat with Sam Charrington: Experimentation in MLTWIML AI Podcast Fireside Chat
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.
- Oct 2021How to Optimize Your Models with Intelligent AI ExperimentationMLconf 2021 Webinar
MLconf-hosted webinar on intelligent AI experimentation for model optimization, presented as SigOpt GM during the Intel era.
- Jul 2021Experimenting with AI OptimizationsIntel Conversations in the Cloud (Episode 250)
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.
- Apr 2021Building the Better, More Scalable AlgorithmsIT Visionaries (Mission.org)
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.
- Jan 2021Intelligent AI ExperimentationAi4 2021
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.
- Jan 2021Boost AI Experimentation to Design, Explore, and Optimize Your ModelsSigOpt Summit 2021 (keynote)
Keynote at the virtual SigOpt Summit 2021 on accelerating AI experimentation across design, exploration, and optimization workflows. Co-speakers included Subutai Ahmad of Numenta.
- Jun 2020Scott Clark of SigOptAI at Work Podcast (PJC), Season 2 Episode 2
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.
- Dec 2019From argmax f(x) to an International BusinessCornell CAM Notable Alumni Speaker Series
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.
- Nov 2019Automated Model TuningTWIML AI Podcast (Episode 324)
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.
- Jun 2019Supporting Rapid Model Development at Two SigmaTWIML AI Podcast (Episode 273)
Discussion on how SigOpt's optimization platform supports rapid model development workflows at scale, with a case study from Two Sigma.
- Apr 2019Best Practices for Scaling Modeling PlatformsO'Reilly AI Conference, New York 2019
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.
- Apr 2019Modeling at Scale in Systematic TradingQuantitative Finance Conference
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.
- Mar 2019Tuning the Un-TunableNVIDIA GTC Silicon Valley 2019
Strategies for optimizing deep learning models with long training cycles using Bayesian optimization. Presented at NVIDIA GTC Silicon Valley 2019.
- Oct 2017A Conversation with Scott ClarkVoices in AI with Byron Reese, Episode 12
56-minute long-form conversation with Byron Reese covering algorithms, transfer learning, the nature of human intelligence, and broader philosophical territory in AI.
- Aug 2017Bayesian Optimization for Hyperparameter TuningTWIML AI Podcast (Episode 50)
Podcast discussion on Bayesian optimization approaches to hyperparameter tuning, the theory behind optimal experiment design, and practical applications in production ML systems.
- May 2017Bayesian Global OptimizationMLconf Seattle 2017
Deep dive into Bayesian global optimization methods, covering theory, algorithms, and practical applications for machine learning hyperparameter tuning at scale.
- Feb 2017Tuning Machine Learning AlgorithmsThe AI in Business Podcast (Emerj)
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.
- Nov 2016Using Bayesian Optimization to Tune Machine Learning ModelsMLconf San Francisco 2016
Talk on applying Bayesian optimization techniques to efficiently tune machine learning model hyperparameters, drawing on experience building SigOpt's optimization platform.
- May 2015Adaptive Sequential Experimentation Techniques for A/B Testing and Model TuningThe 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.
- Nov 2014Introducing the Metric Optimization Engine (MOE)MLconf San Francisco 2014
Introduction of MOE, an open-source Bayesian optimization framework built at Yelp for optimizing real-world metrics through intelligent experimentation.
- Apr 2013CMU Silicon Valley TOCS ColloquiumCarnegie Mellon University Silicon Valley
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.