Mercedes Benz Machine Learning Research

Understanding a model means looking at more than whether its answer was correct. An interview with Rigel Smiroldo recorded at NIPS 2017 in Long Beach California. We discuss data privacy, machine learning use cases, model deployment, and end-to-end machine learning.

Guest

Rigel Smiroldo: At Apple I experiment, develop, deploy, and monitor deep learning and statistical learning models with a focus on user-prediction in order to improve how users can interact with our products. I focus on core end-user facing experiences. Previously I worked as a distinguished machine learning engineer at MBRDNA. I lead a small team of research engineers, and together we developed novel statistical machine learning techniques to solve unique machine learning challenges in the automotive and edge device domain, specifically in the domains of behavior modeling, user identification, multimodal user input (speech, gaze, gesture, etc.), action automation, and dynamic UIs. I am a passionate innovator who thrives in environments which try to solve difficult technical problems by offering high creative agency. I graduated from University of California, Berkeley, in 2009 with an interest in various specialties of artificial intelligence and computer simulation