Angel Michel Yam
UC Berkeley Physics alumnus specializing in machine learning, quantum data analysis, and AI-driven research methodologies. Bridging theoretical physics with cutting-edge computational solutions.
About Me
Transforming complex physics concepts into innovative data solutions
Strong theoretical background in quantum mechanics, statistical mechanics, and mathematical physics, providing the analytical depth needed for complex problem-solving in data science and machine learning.
Proficient in Python, TensorFlow, PyTorch, and scikit-learn with hands-on experience in supervised and unsupervised learning, achieving 92% accuracy in material property predictions.
Published research in computational physics with focus on quantum oscillations and particle collision analysis, combining experimental data with advanced statistical methods.
Academic Excellence
Education
Academic foundation in physics and mathematics
Comprehensive physics education with strong emphasis on mathematical modeling and computational methods.
Relevant Courses:
Strong foundational education in mathematics and physics, preparing for advanced studies at UC Berkeley.
Featured Projects
Cutting-edge research combining physics and machine learning
Utilized 10,000+ material samples to predict electrical and thermal conductivity with 92% accuracy using RandomForest and Gradient Boosted Trees.
Analyzed quantum oscillation experiments using Fourier Transform and LSTM neural networks to determine electron properties in materials.
Classified subatomic particle collisions using K-Means and DBSCAN clustering, optimized with NumPy and pandas for computational efficiency.
Technical Skills
Comprehensive toolkit for data science and research
Experience
Professional journey in AI and education
- • Revised and edited AI-generated content about scientific disciplines including physics, math, and chemistry
- • Effectively taught AI software to engage in conversations with follow-up referenced style format
- • Increased productivity and accuracy of AI-generated responses by 20% over one month
- • Effectively taught students to identify different factors through graphical representations and principle formulas
- • Helped students double-check guiding ideas to confirm/correct and summarize key points
- • Instructed students in calculus-based subjects including physics and chemistry
- • Guided students' ideas by restructuring questions into specialized units for simplicity
- • Utilized techniques to guide questions, explain logical connections, and recap key points
- • Developed effective teaching methodologies for complex scientific concepts
Let's Connect
Interested in collaborating on machine learning projects, quantum research, or AI applications? Let's discuss how we can work together.
© 2025 Angel Michel Yam. UC Berkeley Physics Graduate & Data Scientist.