<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Machine Learning on Mohammad Movahedi</title><link>https://m-movahedi.com/tags/machine-learning/</link><description>Recent content in Machine Learning on Mohammad Movahedi</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Sat, 30 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://m-movahedi.com/tags/machine-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Agentic AI as a Research Tool</title><link>https://m-movahedi.com/scratchpad/research-agents/</link><pubDate>Sat, 30 May 2026 00:00:00 +0000</pubDate><guid>https://m-movahedi.com/scratchpad/research-agents/</guid><description>&lt;p&gt;This scratchpad explores how agentic AI can be used as a practical research tool. It focuses on understanding how AI agents can support literature review, data analysis, writing, coding, workflow automation, and knowledge organization.&lt;/p&gt;</description></item><item><title>Local LLMs</title><link>https://m-movahedi.com/scratchpad/local-llms/</link><pubDate>Thu, 28 May 2026 00:00:00 +0000</pubDate><guid>https://m-movahedi.com/scratchpad/local-llms/</guid><description>&lt;p&gt;This scratchpad collects my notes on running and working with local large language models. It is meant to better understand model deployment, inference tools, hardware requirements, quantization, fine-tuning, evaluation, and practical workflows for using LLMs outside cloud-based platforms.&lt;/p&gt;</description></item><item><title>Introduction to LLM</title><link>https://m-movahedi.com/scratchpad/llm/</link><pubDate>Fri, 01 May 2026 00:00:00 +0000</pubDate><guid>https://m-movahedi.com/scratchpad/llm/</guid><description>&lt;p&gt;This scratchpad collects the &lt;strong&gt;LLM introduction series&lt;/strong&gt;, an eight-part walkthrough from transformer basics to responsible deployment. Many examples use evacuation and transportation planning as a running case, but the concepts apply broadly.&lt;/p&gt;</description></item><item><title>Advanced Estimation Models for Demolition Waste</title><link>https://m-movahedi.com/research/demolition-waste-estimation/</link><pubDate>Wed, 20 Mar 2024 00:00:00 +0000</pubDate><guid>https://m-movahedi.com/research/demolition-waste-estimation/</guid><description>&lt;div style="background-color: #fff3cd; border-left: 6px solid #ffc107; padding: 15px 20px; border-radius: 4px; margin-bottom: 30px; box-shadow: 0 2px 4px rgba(0,0,0,0.05);"&gt;
 &lt;h4 style="margin-top: 0; color: #856404; display: flex; align-items: center;"&gt;&lt;span style="font-size: 1.5em; margin-right: 10px;"&gt;🏗️&lt;/span&gt; The Demolition Data Gap&lt;/h4&gt;
 &lt;p style="margin-bottom: 0; color: #533f03;"&gt;In the United States, construction and demolition waste (CDW) accounts for roughly 67% of the total solid waste stream, with a staggering &lt;strong&gt;90% originating directly from demolition sites&lt;/strong&gt;. For a transition toward a circular economy, accurately estimating the &lt;em&gt;recoverable&lt;/em&gt; portion of this waste is crucial. Yet, traditional estimation models remain heavily focused on total volume rather than recyclable potential.&lt;/p&gt;</description></item></channel></rss>