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Name: Tri Nguyen

Type: User

Company: MIT

Bio: I am a PhD student at MIT with an interest in applying machine learning and data science to astrophysics.

Twitter: TriAstroML

Location: Cambridge

Blog: https://trivnguyen.github.io/

Tri Nguyen's Github Profile

Hello hello! I'm a Ph.D. candidate at the MIT Kavli Institute for Astrophysics and Space Research. I'm also a Pre-Doctoral Researcher at the Center for Computational Astrophysics (CCA) at the Flatiron Institute. I'm interested in using machine learning to understand structure formation in the Universe.

I'm currently working with Prof. Lina Necib at MIT and Prof. Rachel Sommerville at CCA on various projects:

Applying graph-based simulation-based inference to infer the dark matter density profiles of dwarf galaxies [1].
Using kinematics of accreted stars to characterize the galaxy accretion history of the Milky Way.
Constructing synthetic Gaia DR3 surveys from Milky Way-like galaxies in the FIRE simulation.
Generating galaxy merger trees with flow-based generative models.

🔭 Current Projects

JeansGNN: A simulation-based inference framework for Jeans modeling based on Nguyen et al. (2023) [1] and Chang & Necib (2021) [2]
FLORAH: Generating galaxy merger trees using flow-based recurrent graph neural network. Repo coming soon!

📫 How to reach me

Thanks for stopping by!

References

[1] Tri Nguyen, Siddharth Mishra-Sharma, Reuel Williams, Lina Necib, "Uncovering dark matter density profiles in dwarf galaxies with graph neural networks", Physical Review D (PRD), vol. 107, no. 4, article no. 043015, Feb. 2023, https://doi.org/10.1103/PhysRevD.107.043015
[2] Laura J Chang, Lina Necib, Dark matter density profiles in dwarf galaxies: linking Jeans modelling systematics and observation, Monthly Notices of the Royal Astronomical Society, Volume 507, Issue 4, November 2021, Pages 4715 4733, https://doi.org/10.1093/mnras/stab2440

Tri Nguyen's Projects

academic-kickstart

Easily create a beautiful website using Academic and Hugo

agama

Action-based galaxy modeling framework

ananke_dr3

Ananke DR3

bbhnet_inference

Inference repo for BBHnet

bbhnet_output_analysis

Code to analyze output of BBHnet on timeslides data

bbhnet_simulation

Simulation code and tutorial for BBHnet training data

bbhnet_training

Training package for BBHnet

course

Main course repo for IAP 2022

csc231n

CSC231n Stanford

deep-learning-with-cats

Deep learning with cats (^._.^)

deepclean_prod

Production pipeline for DeepClean: LIGO non-linear noise regression algorithm

diffusion_model_tutorial

dsphs_gnn

Sampling the dark matter density profiles posterior with graph neural network and normalizing flows

dsphs_jeans

dsphs_utilities

Utilities for dwarf galaxies analysis

fire_ananke_analysis

A collection of scripts and notebook for analyzing FIRE and Ananke

florah

Generate galaxy merger tree with machine learning

florah-tree

florah-tree-simulate

gaia-dr3-agama-tutorial

gaia-star-classification

gaia_merger_classification

Use kinematics of stars to classify mergers in the Milky Way

gans

Generative Adversarial Networks implemented in PyTorch and Tensorflow

graphgdp

Implementation for the paper: GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation

graphrnn

interpretabilitydemos

jax-conditional-flows

Normalizing flow models allowing for a conditioning context, implemented using Jax, Flax, and Distrax.

jeansgnn

Neural Simulation-based Inference with GNN for Jeans Modeling

jgnn

logger

Pytorch Logger implementation

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