Joel Simonoff is a proficient machine learning engineer who has worked on cutting-edge AI/ML projects, including computer vision, natural language processing, time series analysis, and unsupervised learning. He has a Bachelor of Science in Electrical Engineering and Computer Science from UC Berkeley and has studied at various startup accelerators.

At NASA, Joel has applied his skills to help astronauts locate lost items on the International Space Station using state-of-the-art convolutional neural networks and transformers. Joel has also worked as a machine learning engineer at Connectly.ai and Bungee Tech, a machine learning researcher at Berkeley Artificial Intelligence Research and NASA, and a technical program manager at Machine Learning at Berkeley. Additionally, he has experience in embedded systems engineering and has spearheaded multiple technology-based startups.

Joel is particularly skilled in using models like Residual Networks, Transformer Models, Convolutional Networks, LSTMs, MLP, SVM, Random Forest, Bayesian, and Markov. He has significant experience working with Python and AI/ML toolkits like PyTorch, TensorFlow, OpenCV, and NLTK. Joel also has a deep understanding of robotics, including robustness, ROS, depth cameras/LIDAR, point clouds, segmentation, object detection, pose estimation, and tracking.

In his free time, Joel enjoys snowboarding and is an expert at it. He is also no stranger to caffeine and once consumed 12 cups of coffee in a single day at work. Joel is a member of the Pi Kappa Phi fraternity at UC Berkeley.