Machine Learning is where data and models are used to train computers to learn like humans.
In her research, Professor Li trains CNNs, or Convolutional Neural Networks, on classifying images, videos, etc. When CNNs are given input data, they can classify them into categories. CNNs scan images to extract their features and identify distinctive patterns. The more data given to a CNN, the higher the accuracy of classifying the image/data into the correct category. Her further research consists of experimenting with RNNs (Recurrent Neural Networks) and deep reinforcement learning. RNNs can be utilized to process audio, text, and speech: for example, an RNN can take in a sentence in English and translate it into a sentence in Spanish as its output. Reinforcement learning is where the computer learns from trial and error to come up with the best possible solution based on rewards or penalties for each of its actions. ✦ Read Professor Li's publications here. ✦
Professor Li has gained huge recognition for her contributions in the artificial intelligence field. Her works have been published in top-tier journals and conferences such as Nature, CVPR, IEEE-PAMI, etc. and featured in the New York Times, Wall Street Journal, MIT Technology Review, and more. In addition, she has won prestigious awards such as the 2018 Women in Tech award by ELLE and the 2017 Awesome Women Award by Good Housekeeping.
Professor Li is a keynote speaker at many influential conferences such as TED, the Grace Hopper Conference, and the World Economics Forum. Here, she talks about artificial intelligence from a general standpoint to increase interest in machine learning among the general population.