Haibin Ren

Profile

I am Haibin Ren (任海滨), a Ph.D. student working with Wei Zhu in the Department of Astronomy at Tsinghua University. Before that, I received my B.S. in physics from Beihang University in 2023.

My research centers on gravitational microlensing, computational methods, and statistical inference for astrophysical data. More broadly, I am interested in machine learning for astronomy and in building research software that is both scientifically reliable and practically useful.

This website collects selected research projects, software, and short academic notes related to my work.

Research areas

I work at the intersection of astrophysical modeling, inference, and scientific software.

Microlensing
Binary-lens modeling
Statistical inference
Differentiable programming
Research software
Machine learning

Selected research

Highlighted work

Methods, software, and results that are central to my current research.

July 2026

CausticFlow: a machine-learning proposal engine for binary microlensing

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’s test set.

March 2026

KMT-2025-BLG-1314 and KMT-2025-BLG-1392: two microlensing planetary/brown-dwarf candidates analyzed with differentiable code

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.

January 2025

A differentiable binary microlensing model using adaptive contour integration method

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.