<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Adaptive Experimentation |</title><link>https://tiao.io/tags/adaptive-experimentation/</link><atom:link href="https://tiao.io/tags/adaptive-experimentation/index.xml" rel="self" type="application/rss+xml"/><description>Adaptive Experimentation</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Mon, 01 Sep 2025 00:00:00 +0000</lastBuildDate><image><url>https://tiao.io/media/icon_hu_9c2a75fde2335590.png</url><title>Adaptive Experimentation</title><link>https://tiao.io/tags/adaptive-experimentation/</link></image><item><title>Ax: A Platform for Adaptive Experimentation</title><link>https://tiao.io/publications/ax-platform/</link><pubDate>Mon, 01 Sep 2025 00:00:00 +0000</pubDate><guid>https://tiao.io/publications/ax-platform/</guid><description/></item><item><title>💼 Joined Meta CAS Adaptive Experimentation</title><link>https://tiao.io/posts/joined-meta-cas/</link><pubDate>Mon, 05 Aug 2024 00:00:00 +0000</pubDate><guid>https://tiao.io/posts/joined-meta-cas/</guid><description>&lt;p&gt;Started as a Research Scientist at Meta on the Adaptive Experimentation (AE)
team within Central Applied Science (CAS), based in New York City. The team
develops and maintains the open-source
and
frameworks for Bayesian optimization and
adaptive experimentation at scale.&lt;/p&gt;</description></item><item><title>Ax</title><link>https://tiao.io/projects/ax/</link><pubDate>Thu, 01 Aug 2024 00:00:00 +0000</pubDate><guid>https://tiao.io/projects/ax/</guid><description>&lt;p&gt;
is an open-source platform for
developed by Meta&amp;rsquo;s Adaptive
Experimentation team. It provides a unified interface for
,
, multi-objective, and
constrained optimization, built on top of
.&lt;/p&gt;
&lt;p&gt;I contribute to Ax as part of my work at Meta, with a particular focus on
sample-efficient methods for
, capacity management, and
scaling-law-based modeling. Co-first author on
(AutoML 2025).&lt;/p&gt;</description></item></channel></rss>