Teaching
My teaching vision is simple: teach with verve and enthusiasm, be yourself, and most importantly be humble and kind. My teaching mission is to inspire students to be impactful in what they love to do most (and this may not be statistics, computer science, or machine learning). For undergraduates, I want to show them the powerful nature that lies within the statistical core and its connections with social sciences and other disciplines. For graduate students and postdoctoral students, I want to emphasize the connection between the foundations of statistics and how these relate to modern and applied topics today that have impacts beyond statistics, often in the social sciences, medical research, human rights, or public policy. I strive to be a mentor to students and empower them to be independent researchers and make their own contributions and impact in society.
Course taught at Duke University
- STA 490/690 - Almost All of Entity Resolution
- STA 790 - Some of Entity Resolution (PhD Mini Course)
- Fall 2020, Spring 2023 :: SOURCE ON GITHUB</ small>
- STA 360/602 - Bayesian Methods and Modern Statistics (Combined Undergraduate/Graduate Course)
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STA 325 - Machine Learning and Data Mining (Undergraduate Course)
- STA 794 - Advances in Modern Record Linkage (Graduate Course)
Courses taught at CMU
- STA 36-786,787: Introduction to Bayesian Theory (Graduate Course)
- STA 36-464: Introduction to Multivariate Methods and Machine Learning (Combined Undergraduate/Graduate Course)
Courses taught at University of Florida
- STA 4930: Introduction to Bayesian Statistics (Honors Undergraduate Course)
Courses taught at Clemson University
- MTHSC 203: Elementary Statistical Inference (Undergraduate Course)
- Spring 2005
- MTHSC 104: Precalculus and Introductory Differential Calculus (Undergraduate Course)
- Fall 2005