- Published papers in prestigious academic conferences such as ACL and EMNLP
Research Experience
- Amazon: Owns a production system focused on domain-specific LLM evaluations; developed and implemented a unified tabular deep learning framework to address internal prediction tasks such as safety assessments and headcount forecasting; built a weakly-supervised topic modeling system capable of analyzing millions of internal documents; designed an advanced agent-based system to replace traditional surveys for internal applications
- Schlumberger: Served as a Senior Data Scientist, co-founded the company's first data science team dedicated to addressing employee attrition challenges through innovative tabular deep learning techniques
Education
- Master's Degree: Master of Arts in Statistics, University of California, Berkeley (2018)
- Bachelor's Degree: Bachelor of Science in Applied Mathematics with a minor in Computer Science, University of Toronto (2017)
Background
- Research Interests: Large Language Model Evaluation, Agent-based Systems
- Professional Field: Machine Learning, Deep Learning, Natural Language Processing
- Brief Introduction: Currently a Senior Applied Scientist at Amazon, focusing on domain-specific LLM evaluations