present training epoch: optimizing and updating artificial neural network weights, including in my own biological neural network.
recent training epoch: graduated from the University of New Haven's M.S. in Data Science program.
current local minima: computer vision, NLP, reinforcement learning, and potential applications of AI in relation to creative applications, biotechnology, and healthcare sectors.
favorite architectures: variational autoencoders and generative adversarial networks.
fascinated by: computational neuroscience, brain-computer interfaces, and epigenetics.
tools: python, scikit-learn, Keras, TensorFlow, and actively learning PyTorch.