<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Haibin Ren on Haibin Ren - Personal Website</title><link>http://haibinren.com/</link><description>Recent content in Haibin Ren on Haibin Ren - Personal Website</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Mon, 06 Jul 2026 00:00:00 +0000</lastBuildDate><atom:link href="http://haibinren.com/index.xml" rel="self" type="application/rss+xml"/><item><title>CausticFlow: a machine-learning proposal engine for binary microlensing</title><link>http://haibinren.com/works/causticflow/</link><pubDate>Mon, 06 Jul 2026 00:00:00 +0000</pubDate><guid>http://haibinren.com/works/causticflow/</guid><description>CausticFlow converts irregular light curves into posterior proposals for conventional optimization, recovering about 80% of simulated events and 7 of 10 real events in the paper&amp;rsquo;s test set.</description></item><item><title>KMT-2025-BLG-1314 and KMT-2025-BLG-1392: two microlensing planetary/brown-dwarf candidates analyzed with differentiable code</title><link>http://haibinren.com/works/differentiable-modeling-microlensing/</link><pubDate>Mon, 02 Mar 2026 00:00:00 +0000</pubDate><guid>http://haibinren.com/works/differentiable-modeling-microlensing/</guid><description>Using the differentiable microlensing package microlux, we analyze two highly degenerate events. Accurate derivatives enable efficient gradient-based inference, and for the Point/Finite sub-degeneracy in KMT-2025-BLG-1314, HMC mixes more robustly than traditional MCMC.</description></item><item><title>A differentiable binary microlensing model using adaptive contour integration method</title><link>http://haibinren.com/works/microlux/</link><pubDate>Tue, 21 Jan 2025 00:00:00 +0000</pubDate><guid>http://haibinren.com/works/microlux/</guid><description>We develop a differentiable binary microlensing model with adaptive contour integration. The code extends VBBinaryLensing and uses JAX to compute accurate light curves and derivatives for optimization and inference.</description></item></channel></rss>