<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Jacques | snel.ai</title><link>https://snel.ai/author/jacques/</link><atom:link href="https://snel.ai/author/jacques/index.xml" rel="self" type="application/rss+xml"/><description>Jacques</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><image><url>https://snel.ai/media/icon_hu_57db69234da5e938.png</url><title>Jacques</title><link>https://snel.ai/author/jacques/</link></image><item><title>Brandon Jacques, PhD</title><link>https://snel.ai/author/brandon-jacques-phd/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://snel.ai/author/brandon-jacques-phd/</guid><description>&lt;p&gt;Dr. Brandon Jacques was a Postdoctoral Fellow researching the neural processes underlying speech-related movements. Following his postdoctoral position in the lab, he took a position at &lt;a href="https://cogsc.ai/" target="_blank" rel="noopener"&gt;CogSc.AI&lt;/a&gt;, an AI startup that uses the technology he developed during his PhD.&lt;/p&gt;
&lt;p&gt;Dr. Jacques received his PhD from the University of Virginia in 2023 in Cognitive Psychology, and his undergraduate degree from The Ohio State University in 2015 in Electrical Engineering. During his doctoral studies, Dr. Jacques focused on bridging the gap between neural mechanisms of memory and machine learning models. His thesis showcased the applicability of cognitive principles to enhance machine learning algorithms, particularly evident in his pioneering work on the Scale-Invariant Temporal History (SITH) representation. Through this research, he demonstrated how insights from cognitive psychology can significantly contribute to advancing machine learning methodologies.&lt;/p&gt;</description></item></channel></rss>