Skills
More than 8 years:
Python, Machine Learning, Optimization & Sampling, Bayesian Inference, Code Design, Version Control, Automatic Differentiation
More than 5 years:
Deep Learning, Generative Models, Computer Vision, Anomaly Detection, Time Series Analysis, Tensorflow, Keras, Pytorch, Pandas, SKlearn, Mentoring, Communication
1 to 3 years:
CI/CD, MLOps, AWS, GCP, SQL
About
I am a Data Scientist and Physics PhD with expertise in machine learning, high-dimensional optimization and analytical modeling. My work experience combines academia and industry and covers a variety of domains. As a senior data scientist at Nautilus Labs I worked with a cross-functional team to reduce CO2 emissions in the maritime industry. My contributions ranged from building physics-based machine learning models of vessels from sensor and weather data to leading R&D initiatives for voyage optimization and vessel performance monitoring.
As a postdoctoral researcher at UC Berkeley, I developed scalable and differentiable simulation codes, solved optimization problems in 10^6 dimensions and created anomaly detection schemes based on generative neural networks.
While a physicist by training, I enjoy applying my skills to impactful problems in many domains and have successfully contributed to and led projects in Earth Science, Epidemiology and Sociology. My works have been published in competitive scientific journals, including Nature Communications and Nature Astronomy, and presented at NeurIPS workshops.
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