Skills
Tools, topics, and the work behind them
I prefer depth over breadth. These are the areas I work in day-to-day, not a wish list.
Machine Learning
- PyTorch
- Hugging Face Transformers
- TensorFlow
- Scikit-learn
- Multi-task Learning
- Transfer Learning
- Representation Learning
- Reinforcement Learning
- NLP
Training & Evaluation
- MLflow
- Weights & Biases
- Bootstrap CIs
- Ablation design
- Statistical Modeling
- Pandas / NumPy
- Plotly / Seaborn
- SQL
Engineering & Systems
- Python
- C / C++
- Java
- JavaScript
- Haskell
- FastAPI
- React
- PostgreSQL
- Docker
- CI/CD
- LaTeX
Interests & Reading
- Attention / transformers
- Post-training & RLHF
- Multi-task & curriculum
- RL theory
- Language modeling from scratch
Depth
Where my ML / DL / RL background comes from
Deliberate self-study on top of my university curriculum. I take lectures, notes, problem sets, and exams end-to-end — rather than skim.
Stanford (self-directed)
CS229 Machine Learning · CS230 Deep Learning · CS336 Language Modeling from Scratch · CS224R Deep Reinforcement Learning · CME295/296 Transformers & LLMs.
Appalachian State
Applied Machine Learning · Advanced Reinforcement Learning · Numerical Methods · Computational Mathematics · Statistical Data Analysis · Linear Algebra · Theoretical Computer Science · Data Structures & Algorithms.
Other
DeepLearning.AI Machine Learning Specialization (Andrew Ng) and Advanced Learning Algorithms — credentials on certifications.