Yan Lin's Projects

## Primary Projects

Fundamental Research Funds for the Central Universities of China

##### Research on Prediction of User Travel Destination and Travel Time Based on Trajectory Representation Learning

Applies representation learning to trajectory data to transform original features into high-level information, improving the performance of downstream tasks such as travel time and destination prediction.

Personal Interest Project

Home Install

##### Development of OverleafCopilot - Empowering Academic Writing in Overleaf with Large Language Models

This project aims to develop a Browser extension to seamlessly integrate Large Language Models (such as ChatGPT) into the popular online academic writing platform, Overleaf.

Personal Interest Project

Website Code

##### Development of PromptGenius - All-purpose prompts for LLMs

This project focuses on developing a website that offers a wide range of prompt categories, enhancing the versatility of LLMs for various tasks and improving their output quality.

## Secondary Projects

Villum Foundation

##### Research on Inverse Design of Materials Using Diffusion Probabilistic Models

This project focuses on developing diffusion probabilistic models to first understand the relationship between chemistry/structure and material properties, then enable the inverse design of new materials with specific properties. This project currently supports my postdoctoral research position.

National Natural Science Foundation of China

##### Research on Pre-training Representation Learning Methods of Spatial-temporal Trajectory Data for Traffic Prediction

This project aims to propose pre-training representation learning methods for spatial-temporal trajectory data, modeling multiple features to improve traffic prediction tasks. It demonstrates how trajectory representation learning can enhance traffic data mining.

National Natural Science Foundation of China

##### Research on Spatial-temporal Trajectory Generation and Representation Learning Methods for Sparsity Problems

This project explores how to generate high-quality spatial-temporal trajectory data and corresponding representations to address sparsity-related issues, thereby supporting a variety of downstream tasks.