📧 [email protected] | 💼 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), R, SQL, Bash
- AI/ML & Data Science: Large Language Models (GPT3.5, llama2), Regression and Classification Models, Anomaly Detection, Time Series Analysis, NLP, Computer Vision
- Cloud & Tools: 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-present): Reduced property evaluation time by 50% using ML-powered dashboards.
- Descartes Labs (2021-2023): Delivered time series-based deep learning solutions for crop yield prediction.
- 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
- "Employing the strengths of Generative AI supports the execution of time series analysis and forecasting." Scipy Conference, 2024.
- "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!