About Me
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Haoxiang Zhang 张灏翔
Hello! 好呀 ~ こんにちは！
Hello! I am a passionate data scientist and researcher with a strong interest in machine learning, data analysis, and artificial intelligence. I am currently pursuing my Master of Science in Data Science at University of California San Diego. I am also a visiting student at Stanford University, where I am fortunate to be advised by Prof. Yejin Choi and Prof. James Zou.
My research experienced image restoration, continual learning, out-of-distribution generalization, agentic LLMs. I enjoy solving complex problems and developing innovative solutions that leverage the power of data.
Right now I'm exploiring intellegent decision. I'm looking forward to communicating up-to-date new inspirations!
Education
University of California San Diego
Master of Science in Data Science (Expected)
09/2025 - 06/2027
Shanghai University
Bachelor of Engineering in Data Science & Big Data Technology
09/2021 - 06/2025
Hong Kong University of Science and Technology
Summer Session - Data & Computer Science
08/2024
University of Pennsylvania, Wharton School
Wharton Innovation, Entrepreneurship and Leadership Program & The Global Leadership Program for Young Scholars.
07/2023 - 08/2023
Publication
Avoiding Structural Pitfalls: Self-Supervised Low-Rank Feature Tuning for Graph Test-Time Adaptation
Haoxiang Zhang, Zhuofeng Li, Qiannan Zhang, and 3 more authors
Sci-Bench-AIME25: A Multi-Modal Chain-of-Thought Dataset for Advanced Tool-Intergrated Mathematical Reasoning
Haoxiang Zhang✝Corresponding author, Siyuan Wang✝Corresponding author, Xueji Fang, and 5 more authors
GReF: A Unified Generative Framework for Efficient Reranking via Ordered Multi-token Prediction
Zhijie Lin*Equal contribution, Zhuofeng Li*Equal contribution, Haoxiang Zhang, and 4 more authors
Learning from Novel Knowledge: Continual Few-shot Knowledge Graph Completion
Zhuofeng Li*Equal contribution, Haoxiang Zhang*Equal contribution, Qiannan Zhang, and 2 more authors
Long-term Collaboration
Shanghai Tongliang Intelligent Technology Co., Ltd.
> Research Consultant (Permenant).
Focused on exploring Multi-Agent Adversarial (MAA) strategies for competitive and collaborative dynamics in quantitative trading algorithms. Solved challenges related to high instability of financial data and Out-of-Distribution issues in algorithms.
> Chief Scientist (04/2025 - 06/2025)
During this tenure, managed the team to innovate and validate in medium-frequency live trading various MAA-integrated algorithms across diverse asset classes, including but not limited to Chinese market futures and options, cryptocurrencies, and equity investments.
These algorithms included:
- Adaptive VWAP multi-scale order splitting strategies.
- Q-LEARNING Reinforcement Learning.
- VIX based strategies.
- Automated feature factor screening.
- News information factor mining.
Collaborated with the team to achieve a positive corporate annual profit of 20% (annualized) with a maximum drawdown of 8%.
MAA-TSF: Multi-Agent Adversarial Time Series Forecasting
Ye Qiao, Cheng Chen, Haoxiang Zhang✝Corresponding author, and 2 more authors
Internship
Shanghai Artificial Intelligence Labotary
Multimodal Auto-regressive Generation.
View Detail [Lumina-mGPT]08/2024 - 02/2025
Technical Skills
Programming Languages & Tools
Python (Pandas, NumPy, Scikit-learn, TensorFlow2.x, PyTorch), C/C++, SQL
Frameworks & Technologies
Flask, Git, Spark, MATLAB, Keras, LaTeX, JavaScript