Ph.D. Candidate in Computer Science
Specializing in Large Language Models & Quantum Machine Learning & Quantitative Finance
My Academic Journey & Professional Experience
I am Yufeng Wang, a passionate Ph.D. candidate in Computer Science Department at Stony Brook University, specializing in LLM, quantum machine learning, and quantitative finance. With a strong foundation in physics from University of Science and Technology of China and research experience at Stanford University, I bring a unique interdisciplinary perspective to solving complex problems in artificial intelligence. My research focuses on developing innovative AI solutions that can be applied to real-world challenges in LLM, quantum computing, quantitative finance, and beyond.
Stony Brook University, New York
Supervised by Prof. Haibin Ling
Expected Graduation: Fall 2026
Focus: LLM, Quantum Machine Learning, Quantitative Finance
Center for Artificial Intelligence in Medicine & Imaging
Stanford University
• Developed AI algorithms for medical image analysis
• Collaborated with healthcare professionals on clinical applications
• Contributed to research on computer vision in healthcare
University of Science and Technology of China (USTC)
Major: Atomic and Molecular Physics
Research: Quantum Computing
Cutting-edge AI/ML Research with Real-world Applications
Developing novel Large Language Models to predict molecular structures from mass spectrometry data using chain-of-thought reasoning. Implementing multi-stage training architecture (SFT, Reward Modeling, RL) inspired by state-of-the-art LLMs like DeepSeek-R1.
Technologies: Large Language Models, Reinforcement Learning, Chain of Thought, Molecular Design
Fine-tuned and combined YOLOv8 and LLAMA3 model backbonesto extract molecular information from scientific literature and predict electrochemical properties using Graph Neural Networks. Achieved R² coefficient exceeding 99.1% in property prediction.
Technologies: Multi-modal LLMs, YOLOv8, LLAMA3, Graph Neural Networks, DFT
Proposed and developed a novel transformer density operator ansatz to efficiently model steady states of dissipative quantum systems. Validated on dissipative Ising model with high accuracy.
Technologies: Transformer Architecture, Quantum Computing, Variational Methods
Constructed extensive database of X-ray Absorption Spectra and protein structures. Developing diffusion model-based multi-modal approach to reconstruct protein structures from XAS spectra.
Technologies: Diffusion Models, Computer Vision, Graph Neural Networks, X-ray Spectroscopy
Developed large vision model-based deep learning pipelines for heart disease diagnosis. Achieved over 99% accuracy in cardiac anomaly detection, surpassing human performance benchmarks.
Technologies: Computer Vision, Deep Learning, Medical Imaging, Large Vision Models
Nature Medicine 30 (5), 1471-1480
Physical Review B 112 (6), 064303
arXiv preprint arXiv:2506.11908
arXiv preprint arXiv:2504.18554
Let's Connect for Opportunities
I'm eager to collaborate on impactful AI/ML research and real-world applications. I enjoy building systems that learn from complex data, exploring new ideas with persistence, and iterating until we get it right. If you're interested in working together—whether it's research, engineering, or productizing ML—let's connect. I'm open to collaborations, internships, and full-time job opportunities.