Research

Research themes and projects — scientific machine learning for weather, climate, and multi-agent dynamical systems.

My research develops machine learning methods for dynamical systems, combining physical and structural knowledge for spatiotemporal forecasting, probabilistic prediction, and extreme-event evaluation — with weather and climate as the primary current application.

research
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FAST-ML: Hybrid Physics–Machine Learning for Tropical Cyclone Intensity Forecasting

hybrid physics-ML framework combining a mechanistic tropical cyclone intensity model with learned corrections from large-scale atmospheric data

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TCG: Physics-Guided ML for Tropical Cyclone Genesis Prediction

physics-guided machine learning for tropical cyclone genesis prediction, localization, and interpretation in the North Atlantic

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A Computational Framework for Multi-Agent Behavior Analysis: Integrating Geometric Vision Extraction and Social Attention Inference

social attention models for multi-agent behavior prediction — trajectory forecasting and social interaction analysis

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Generative AI for Compound Flood Forecasting

physics-based generative emulator for probabilistic hurricane flooding
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Segmentation with Noisy Labels via Diffusion Models

label-efficient segmentation using Stable Diffusion

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OCT Speckle Denoising with Diffusion Models

DDPM-based denoising for OCT medical images
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Musical Note Recognition with OCR

text detection of musical notation using OCR
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PangaeaGPT

large language model applications and evaluation

coursework
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CVPR 2023 BDD100K Challenges

multi-object tracking and object detection for autonomous driving
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LLM Evaluation

evaluating conversational LLMs

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eSports Gaze Behavior Detection

few-shot gaze tracking for personalized eSports assistance
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Health Management System

health code and trip card system with risk evaluation