About

Nyla Worker is a Latina data scientist currently working as a Data Scientist II at NVIDIA, where she specializes in simulation and deep learning within embedded devices. With 3.44 years of relevant experience, she has extensive knowledge in developing accelerated inference pipelines for embedded devices and generating synthetic data to enhance the performance of Deep Neural Networks. Before joining NVIDIA, Nyla worked on accelerating the inference pipeline of eBay's image recommendation system as a Data Science Intern in the Hardware team. Her responsibilities included cross-comparing different hardware with accelerators, finding the most optimal hardware for eBay’s needs with regards to throughput, power, and latency, and generating an automated hardware test of inference for different hardware (GPU P100, V100) with Docker images and Jenkins. She also worked across multiple teams such as computer vision, AI platform, and hardware team gathering information about latency and throughput needs for different deep neural network tasks. At the University of Minnesota, Nyla worked on distributed training of Deep Neural Networks. She has also worked as a data scientist in the solution architecture organization at NVIDIA, providing accelerated deep learning solutions within automotive, robotics, and retail. Nyla holds a Bachelor of Arts (B.A.) in Physics from Carleton College and completed her schooling from Colegio Alemán Mariscal Braun. She has a strong technical background and is proficient in Data Science, Research Science, Deep Learning, Neural Networks, Recommendation System, Computer Vision, AI, test, Docker, TensorFlow, and Jenkins. Overall, Nyla is a skilled data scientist with a passion for developing deep learning edge applications for robotics and autonomous vehicles. Her expertise in generating synthetic data to accelerate the deployment and enhance the performance of Deep Neural Networks makes her a valuable asset to any team.