People Behind the PhDs 04: Dr. Ting Qian: “Small Town” Boy 1-3
Dr. Ting Qian (pronounced Ting Chen) is “small town boy” from the city of Qidong, Jiangsu China with over 1,000,000 inhabitants. With numerous large cities in China, Chinese often define their towns and cities by how large the city center is. A large city center equates to a city and a small city center equates to a small town. Dr. Qian studies patterns in data at Princeton University.
People behind the PhDs 03: Ting Qian – Preview
Growing up Ting was interested in both English and computers. After he made the decision to study in the United States and was accepted by the University of Rochester, he kept working to find ways to combine language and computers. At first, he studied cognitive science with a strong focus on machine translation which despite the straightforwardness of the concept is very complex. Words and the combinations of words have pragmatic implicatures which means that language very difficult to translate. Eventually he became interested in how people order things, such as seasons. As a data scientist, Ting likes to discover hidden patterns in data and solve data inference problems. He doesn’t allow himself to be drawn into frequentist statistics versus Bayesian model* debates (though he admits its existentially fascinating).
Dr. Ting Qian Bio
For Academic Years 2018-2020, I am teaching PhD-level regression and machine learning courses in the Department of Psychology at Princeton University. My academic webpage: https://psych.princeton.edu/person/ting-qian
I am a method-oriented “Data Scientist” who focuses on designing the optimal statistical / machine learning models in order to discover hidden patterns in data. I am NOT a software engineer by trade. I am not interested in positions where the primary responsibility is to create software that implements or uses off-the-shelf machine learning algorithms. I am interested in positions where I, together with coworkers, solve difficult data inference problems.
My specialty is in conducting probabilistic inferences on generative models using Bayesian methods. Specifically, I am specialized in clustering and segmenting seemingly unstructured data.
I also develop statistics software for Bayesian inference utilizing GPU in my free time. Progress is slow, but I’d like to focus on this a bit more. I believe GPU-based Bayesian inference will change how we process streaming data.
I was formally trained as a computational cognitive scientist (my PhD is in Brain and Cognitive Sciences). My research tries to uncover how humans and animals understand “the order of things”. For example, how come we understand the concept of “seasons”? What does it mean for Spring, Summer, Fall, and Winter to rotate in such an order? Is the perception of orderly weather patterns the reason why we understand the concept “seasons”? I still find these questions fascinating.
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