About

Adam L. is a highly skilled machine learning scientist with a focus on deep learning and computer vision for science and AI. He has a strong background in mathematics, having completed a Doctor of Philosophy (Ph.D.) Program in Mathematics and Mathematical Logic at the University of California, Berkeley, and an Honors Bachelor of Arts (A.B.) Program in Mathematics and Philosophy at Harvard University. Adam has over 6 years of relevant experience in the field of machine learning and has worked on problems related to creating optimal data sets and learning with limited labels using techniques from active learning, uncertainty in AI, and Bayesian techniques. Currently working as a Machine Learning Scientist at NVIDIA, Adam is responsible for developing new deep active learning policies, methods to estimate model uncertainties, and generative models for active learning. His work has applications in autonomous vehicles and other domains. Prior to his current role, Adam was a Co-founder and ML Head at Safewalk, a startup where he was in charge of developing ML, DL, and modeling techniques and heading research. He started the deep convolutional neural network project to build their hazard database through computer vision techniques for street view and other image data sources. He designed, built, and tuned their backend routing engine in Numpy and Pandas that found optimal paths by minimizing the path integral through their pdf. He also developed their hazard probability density function based on kernel methods over discrete data points and oversaw the development and implementation of their cloud pipeline in bash, Python, and bs4 to continuously collect pedestrian hazard incidents that feed data into their models. Adam has a strong tech stack that includes ML, AI, Research Scientist, Deep Learning, Computer Vision, Python, Pandas, Neural Networks, and Backend. He is a highly motivated individual with a passion for solving complex problems using machine learning techniques.