Ting Qian

Ting Qian
Teaching Faculty
325 Peretsman Scully Hall
Ph.D., University of Rochester
Curriculum Vitæ (53.83 KB)

I am a method-oriented "Data Scientist" who focuses on designing the optimal statistical and machine learning models to discover hidden patterns in data. I work with small and big data alike, using adaptive methods that scale from typical experimental data consisting of tens of participants to real-world datasets with millions of rows.

My (mostly dormant) academic research focuses on uncovering how humans and animals understand "the order of things". For example, how come we understand the concept of "seasons"? Why did the discrete concepts of Spring, Summer, Fall, and Winter arise from continuous weather changes? Does the order of events influence our ability to categorize them? I find these questions fascinating.

Representative Publications
  • Cocos, A., Qian, T., & Masino, A. J. (2017). Crowd Control: Effectively Utilizing Unscreened Crowd Workers for Biomedical Data Annotation. Journal of Biomedical Informatics, 69, 86-92. 
  • Qian, T., Jaeger, T. F., & Aslin, R. N. (2016). Incremental Implicit Learning of Bundles of Statistical Patterns. Cognition, 157, 156-173. 
  • Qian, T., & Masino, A. J. (2016). Latent Patient Cluster Discovery for Robust Future Forecasting and New-patient Generalization. PLoS ONE, 11(9): e0162812. doi:10.1371/journal.pone.0162812 
  • Qian, T. & Aslin, R. N. (2014). Learning bundles of stimuli renders stimulus order as a cue, not a confound. Proceedings of the National Academy of Sciences of the United States of America, 111(40), 14400-14405. 
  • Fine, A. B., Jaeger, T. F., Farmer T., & Qian, T. (2013). Rapid Expectation Adaptation During Syntactic Comprehension. PLOS One, 8(10):e77661. doi:10.1371/journal.pone.0077661 
  • Qian, T., Jaeger, T. F., & Aslin, R. N. (2012). Learning to Represent a Multi-Context Environment: More than Detecting Changes. Frontiers in Psychology, 3:228.