📧 cyingjung@gmail.com | 💼 LinkedIn | 📍 S.F. Bay area, CA | 🇺🇸 US Citizen

I am a passionate Applied Scientist and Machine Learning Engineer with expertise in GenAI, deep learning, computer vision, and natural language processing. My focus is on developing and deploying state-of-the-art models for large-scale datasets, particularly in healthcare, finance, and environmental issues with spatio-temporal applications. I thrive on leading cross-functional teams, publishing cutting-edge research, and driving innovation in AI/ML + Environmental Science domains.

• Programming: Python (Scikit-learn, TensorFlow, PyTorch, Jax, Dask), C++, R, SQL, Bash
• AI/ML & Data Science: Large Language Models (GPT4, llama3, Claude 3.5), Regression and Classification Models, Anomaly Detection, Time Series Analysis, NLP, Computer Vision
• Cloud & Tools: Azure (CosmosDB, AI Foundry), AWS (S3/Lambda, Batch, Sagemaker, CDK), Google Cloud Platform (BigQuery, Anthos, Cloud Run), Git/GitHub, Huggingface, Apache Spark
• Soft Skills: Strategic Planning, Scrum and Agile Methodologies, Training & Mentoring, Cross-Team Collaboration, Leadership

• Independent Consultant (2021-2025): Reduced property evaluation time by 50% using ML-powered dashboards.
• USFS (2024-2025): AI system desgin and RAG chatbot development
• Open Geospatial Consortium (2024): Developed and fine-tuned a recommendation system for OGC engineering reports using Low-Rank Adaptation (LoRA), increasing report accuracy by 20%.
• Descartes Labs (2021-2023): Delivered time series-based deep learning solutions for crop yield prediction.
• Bank of America (2021): Refactored macroeconomic model pipelines in Python, achieving a 30% reduction in processing time.
• The Climate Corporation (2020-2021): Developed computer vision models for crop growth detection.

• M.S. in Computer Science, Georgia Institute of Technology
• M.A./Ph.D. in Environmental Data Science, University of California Santa Barbara

1. "Employing the strengths of Generative AI supports the execution of time series analysis and forecasting." Scipy Conference, 2024.
2. "Robustly modeling the nonlinear impact of climate change on agriculture by combining econometrics and machine learning." ICLR 2023 Workshop.

• 1st place at OpenAI hackathon for Climate Change
• PyData impact scholar (2021, 2023)

Feel free to reach out via email or connect with me by email!