John Chenxi Song
Research Assistant | Network and System Administorator | Software Engineering
Hello! I’m John Song, a Master’s graduate in Information Science with 1.5+ of research experience in bioinformatics. I’ve implemented a Java-based Bayesian Network Model for flu diagnosis and contributed to projects involving AI and machine learning, including prompt design for large language models and pathology image analysis using pre-trained models. I’m passionate about leveraging AI to benefit to everyone.
My Journey
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Bayesian Network Modeling in Java Developed a user-friendly package implementing a Bayesian Network Model for flu disease diagnosis. This tool assists healthcare professionals in making accurate diagnoses by analyzing complex data patterns.
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Large Language Models for Healthcare Provided code support for projects utilizing Large Language Models (LLMs) to design prompts for flu disease diagnosis. This work pushes the boundaries of how AI can interpret and generate human-like text to aid in medical assessments.
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Pathology Image Analysis with Pre-trained Models Contributed to pathology projects by supporting code development for analyzing whole slide images using the pre-trained model Prov-Gigapath. This enhances the ability to perform downstream tasks like detecting anomalies in medical images.
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Data Preprocessing and Management As a research assistant in the bioinformatics department, I handled preprocessing of unstructured data (text, whole slide images) and tabular data (CSV files), efficiently uploading and organizing them into SQL databases.
My Vision
- The rapid advancement of AI techniques, such as LLMs and machine learning models, has unlocked new possibilities in AI-driven scientific research (AI4Science). I am eager to investigate foundational structures, gain a deep understanding of the underlying theories, and expand the frontiers of AI research to drive societal progress.
What’s Next?
- I’m currently applying for a PhD in Computer Science to further my research in AI-driven solutions. My goal is to continue pushing the envelope in AI, machine learning, and large language models to create innovative tools that benefit everyone.
News
Sep 20, 2024 | Transfer Learning with Clinical Concept Embeddings from Large Language Models has been posted on arXiv |
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Sep 01, 2024 | AMIA 2025 Informatics Summit |
Jun 03, 2024 | Online Transfer Learning for RSV Case Detection –Published in: 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI) |
May 01, 2024 | Online Transfer Learning for RSV Case Detection has been posted on arXiv |
Recent Posts
Dec 23, 2024 | Data Shapley Implementation |
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Dec 09, 2024 | Monte Carlo Sampling |
Sep 10, 2024 | CUDA 8.0 Setup |