About me

Hi there! 👋 I’m a senior undergraduate student majoring in Electrical Engineering (Electronic Packaging Technology) at Huazhong University of Science and Technology (HUST). I recently completed my exchange program at UC Berkeley, where I took courses in Computer Science.

🌟 Exciting News! 🌟
I am thrilled to announce that I have officially joined Westlake University and am now collaborating with Dr. Jerry Wang at CELab—a cutting-edge biology research lab led by Zitong (Jerry) Wang. Learn more about CELab here.

🎉 Past News 🎉

• Our project at Grapedata received the Cloud Computing Application Award in the UCB Data Science Discovery Program, standing out among over 100 teams! You can find more details on my blog post.
• I completed my internship at Grapedata, where I worked as a data science research intern.
• I also wrapped up my exchange program at UC Berkeley, where I took various CS courses and participated in the Data Science Discovery Program.

🔬 Current Open Source Projects

1. Temporal Inference of Cells (TIC)
   TIC is a computational framework for analyzing cellular micro-environments using graph-based pseudo-time analysis. It integrates tools for graph construction, embedding preparation, pseudo-time trajectory computation, and biomarker trend visualization. The framework facilitates both pseudotime ordering and causal inference, enabling comprehensive analyses of how cellular states evolve over time. I am collaborating on TIC with Dr. Jerry Wang.

2. LLM-DepthEval: Evaluating GPT-4o for Monocular Depth Estimation
   This project investigates whether GPT-4o inherently possesses the ability to estimate depth from single RGB images without specific training in monocular depth estimation. More broadly, we aim to evaluate if GPT-4o’s multimodal training has led to an emergent “world model”—allowing it to implicitly grasp physical relationships, particularly spatial and depth relations, through joint learning from RGB images and textual information.

My research interests include:

• Fintech: Utilizing LLMs for textual analysis in asset pricing and portfolio management.
• Machine Learning and AI: Exploring the potential of advanced LLMs to process real-time physical signals (with potential applications in the Internet of Things, IoT) and discovering the possibilities of LLMs as decision-making brains for AI agents.

I’m fortunate to be advised by Prof. Weijie at HUST. Previously, I was advised by Prof. Chaowei at the University of Wisconsin, Madison, and worked with his doctoral student Fangzhou Wu and Qingzhao Zhang at the University of Michigan.

You can find my CV here: Jiahao’s Curriculum Vitae.

⚠️ Note: The “Projects” and “CV” sections are still in the process of being updated, and the “Publications” and “Blog Posts” sections will be added soon. I’m working hard on my first paper, so stay tuned!

Feel free to reach out: Email / edu Email / Github

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