My name is Shaoliang Nie. I am PhD candidate in the Computer Science department at North Carolina State University. My advisor is Christopher Healey. My current research focuses on visual analytics solutions to understand deep learning models. Deep learning has achieved remarkable results at many intelligent tasks in areas of image, speech and natural language. This means that these models have found a solution for a certain task. However, the question what the solution is remains unanswered. The challenge of anwswering this question comes partly from the fact that we don’t know what we are looking for. Sometimes we don’t even have a clear picture of how human solves the same problems, such as recognizing an image or understanding language, which makes it hard to formulate or validate theories of how a neural network solves these problems. Answers to this question can faciliate training, debugging exisiting models, creating better models and inspiring ideas of how human brain works. Visual analytics marries the powers of automatic data analysis and human visual perception to support exploration and dicovery of large datasets and complex processes. I believe that effective and creative design of visual analytical systems can enable us to see the patterns hiding in deep neural networks.

I also have strong interests in learning, understanding and creating. In order to learn, understand or create, we need to know about how we learn, understand and create. One prominent element enabling these capacities is the cognitive medium available. While visualization is widely used as an approach to explore and discover patterns from large dataset, it can also be seen as a congitive medium that auguments human intelligence by combining the power of human brain and technology. Studying learning, understanding and creating also has a reciprocal relationship with artificial intelligence. Knowledge of how the brain works inspires new AI paradigms and knowledge of AI models sheds lights on how the brain works.