Research and Publications

Research Experiences & Interests:

My research interests have always been driven by following interesting problems that combine deep mathematical theory with real-world applications. My current research interests include:

📊 Machine Learning/Deep Learning/Statistics:

LLMs (Multi-Agent Systems, Mechanistic Interpretability, and Applications),
Causal Inference and Machine Learning,
Mathematical/High-dimensional Statistics, and
Time-Series Models and Analysis.

Differential Topology:

Genericity, Transversality, and Applications (to math/data science/machine learning).

These have evolved from earlier work as a graduate and an undergraduate student, where I have worked on projects involving:

Hyperbolic PDEs and Conservation Laws: Analysis & Applications.
Mathematical general relativity,
Bayesian modeling in nuclear astrophysics, and
Numerical methods in quantum field theory.

Throughout, I think of myself as a “full stack mathematician” — I take projects end-to-end, from theory and modeling through implementation and simulation to production-scale systems whenever possible!

In Progress:

📋 Causal Lag Structure Discovery in Confounded Time Series via Orthogonalized Adaptive Estimation
       (submitted for a conference)
       Tags: Causal Machine Learning, Time Series Analysis.
       📊 GitHub Repo.

📋 Generic Structural Stability for n x n Systems of Hyperbolic Conservation Laws
Tags: Analysis of PDEs, Differential Topology, Functional Analysis, Numerical Analysis.

Publications:

* := Equal Contribution
^† := First Author(s)
Underline := Myself

📜 Circuit Oracle: Automating Attribution Graph Analysis via Natural-Language Queries
       Work done as a Research Fellow under SPAR.
       Hong Kiat Tan^*†, Shariar Kabir^*†, Swastik Agrawal, Sai V R Chereddy, Sriram Balasubramanian.
       Accepted at ICML 2026 Workshop on Compositional Learning: Safety, Interpretability, and Agents (Poster).
       Tags: Interpretability, LLMs, Multi-agent Systems, Causal Inference.

📜 Macroscopic Traffic Flow Network Modeling for Wildfire Evacuation: A Game-Theoretic Junction Optimization Approach with Application to the Lahaina Fire
       Annie Lu^*†, Hong Kiat Tan^*†, Alex Xue^*†, Alice Koniges, and Andrea L. Bertozzi.
       https://arxiv.org/abs/2603.29055.
       Tags: Optimization, PDEs on Graphs, Numerical Analysis, Mathematical Modelling.
       🚦 GitHub Repo.

📜 Beyond Statistical Changepoint Detection: Semantic Interpretation of Time Series via LLMs
       Work done as an intern at Amazon,
       Hong Kiat Tan^†, Trilokya Akula, Akash Tonne, Thomas Blake.
       ICLR 2026 Time Series in the Age of Large Models Workshop (Poster).
       Tags: Mathematical Statistics, LLMs, Multi-agent Systems.

📜 Generic Structural Stability for 2 x 2 Systems of Hyperbolic Conservation Laws
       Hong Kiat Tan^†, Andrea L. Bertozzi.
       SIAM J. Math. Analysis, 58(2) (2026).
       Tags: Analysis of PDEs, Differential Topology, Functional Analysis, Numerical Analysis, Fluid Dynamics.
       📋 Slides.

📜 Weak Cosmic Censorship Conjecture for the Spherically Symmetric Einstein-Maxwell-Charged Scalar Field System
       Hong Kiat Tan^*†, Xinliang An^*†.
       arXiv:2402.16250 (2024).
       Tags: Analysis of PDEs, General Relativity.

📜 Regularization of Complex Langevin Method
       Zhenning Cai^*†, Yang Kuang^*†, Hong Kiat Tan^*†.
       Phys. Rev. D 105, 014508 (2022).
       Tags: Numerical Analysis, Statistics, Quantum Mechanics.

📜 Hierarchical Bayesian Thermonuclear Rate for 7Be(n,p)7Li Big Bang Nucleosynthesis Reactions
       Rafael S de Souza^†, Hong Kiat Tan, Alain Coc, and Christian Iliadis.
       The Astrophysical Journal 894, 132 (2020).
       Tags: Bayesian Statistics, Astrophysics.

You can access my Google Scholar here.