AI Optimization & LLM Evaluation
Experience evaluating and improving Large Language Models (LLMs) at Outlier AI.
Beyond traditional physics research, I have hands-on experience with cutting-edge AI and machine learning technologies, particularly in the domain of Large Language Models (LLMs).
Work at Outlier AI
As part of the Outlier AI team, I contributed to:
- Model Evaluation: Systematic assessment of LLM outputs across various domains
- Quality Assurance: Rigorous testing of model responses for accuracy, coherence, and safety
- Performance Optimization: Identifying patterns in model behavior to guide improvements
- Domain Expertise: Applying physics and mathematical knowledge to evaluate technical content
Key Skills
This experience has strengthened my abilities in:
- Critical evaluation of AI-generated content
- Understanding of modern NLP architectures and their limitations
- Interdisciplinary problem-solving combining domain expertise with AI/ML
- Quality metrics and evaluation frameworks
Bridging Physics and AI
The intersection of high-energy physics and artificial intelligence is particularly exciting. Modern HEP analyses increasingly rely on machine learning for:
- Event classification and particle identification
- Anomaly detection in detector systems
- Optimization of trigger algorithms
- Fast simulation techniques
My experience with both traditional physics analysis and modern AI systems positions me well to contribute to this evolving landscape.
Technical Proficiency
- Programming: Python, C++, ROOT
- ML Frameworks: PyTorch, TensorFlow, scikit-learn
- HEP Tools: Coffea, Uproot, Awkward Array
- Data Analysis: Pandas, NumPy, SciPy
- Version Control: Git, GitHub