As an AI practitioner, I’m focused on applied machine learning by putting the best research from academia to use on solving real world problems. I’m fluent in machine learning best practices for large scale distributed systems, robust model training, high-speed model inference and continuous testing and integration of improved models. My work experience is in applied ML for vision, and 3D scene understanding (SLAM) for autonomous robotics at Sarcos and NASA. During my Masters I’m specializing in large scale, distributed ML modeling for scientific applications, working as an AI consultant on a heterogeneous team of scientists to solve cross-discipline problems with AI.
My favorite tools are first and foremost Python, often super-powered by libraries to speed it up like Cython, Dask and multi-processing. PyTorch, Tensorflow and sklearn are home to me. Recently, my paid research is distributed ML training using Pytorch Distributed (DDP) and Tensorflow Distributed (tf.distribute), and I’m very comfortable with 3rd party distributed ML libraries like Ray.io and Horovod. Finally, I often build complex data engineering pipelines in Luigi, Airflow, RabbitMQ, Docker, Kubernetes. And much more!