Hi, I'm An!
I am a senior at Brown University concentrating in Math and Computer Science. I am an aspiring mathematics researcher with interests in algebraic geometry, number theory, and combinatorics. I am also fascinated by AI research, its recent developments and multi-domain applications. My research experience spans mathematics, AI, and education, and I enjoy bridging these fields to develop new ideas and create meaningful work.
My resume can be found .
Balancedness of Normal Bundles of Rational Curves in Grassmannians
This project extends the Coskun–Larson–Vogt conjecture on the balancedness of normal bundles from projective spaces to Grassmannians. The work analyzes deformation theory and interpolation phenomena for rational curves in Grassmannians.
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Radio Gracefulness of Random Graphs
We study radio labelings in probabilistic graph models, showing that Erdős–Rényi graphs, random bipartite graphs, and dense random regular graphs are radio graceful with high probability. Techniques combine probabilistic combinatorics, Hamiltonicity, and Saha’s theorem on antipodal labelings.
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Radio Gracefulness of Moore Graphs and Beyond
with Aleyah Dawkins, Julian Hutchins, Orlando Luce
We investigate radio labeling in extremal low-diameter graphs, including Moore graphs, bipartite Moore graphs, and (r, g)-cages. The work gives structural characterizations of when bipartite graphs admit radio labelings.
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Formalization of an IMO problem in Lean
In this project, we formalize an IMO problem in Lean and verify its proof.
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Voter Model
This project explores the Voter Model, a foundational framework for understanding opinion dynamics and social influence in populations. Through analysis and simulation, we examine the model's insights into the spread, evolution, and convergence of opinions in social networks.
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VIETify - Vietnamese Diacritics Restoration
An AI tool to restore diacritical marks in Vietnamese text.
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DeepSentiment
This project implements and compares three deep learning approaches - Graph Convolutional Networks (GCN), Multi-Channel Convolutional Neural Networks (MC-CNN), and Adaptive Multi-Channel (AM-GCN) - for sentiment classification in natural language processing. The project evaluates the performance of these models using an RNN to identify the best approach for sentiment analysis applications.
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