Victoria Lin

Hello! I am a PhD student in the Department of Statistics and the Machine Learning Department at Carnegie Mellon University, where I am advised by Louis-Philippe Morency and Edward Kennedy. I am broadly interested in problems at the intersection of causal inference and machine learning, particularly in natural language settings.

Previously, I was a master's student in CMU's Language Technologies Institute, where I worked on multimodal machine learning and affective computing (also with Professor Morency and with Jeffrey Girard). Prior to joining CMU, I obtained my AB in Statistics and Molecular & Cellular Biology from Harvard University, then spent a wonderful year as a researcher with Miguel Hernán in the Program for Causal Inference (now CAUSALab) at the Harvard School of Public Health.

If you'd like to chat, please feel free to reach out at vlin2(at)andrew(dot)cmu(dot)edu.

* denotes equal contribution


Toward multimodal modeling of emotional expressiveness [Paper]
Victoria Lin*, Jeffrey Girard*, Michael Sayette, Louis-Philippe Morency
ICMI 2020 (Best Paper nominee)

Context-dependent models for predicting and characterizing facial expressiveness [Paper]
Victoria Lin, Jeffrey Girard, Louis-Philippe Morency
AAAI 2020 Workshop on Affective Content Analysis (Best Paper Award)


gfoRmula: An R package for estimating the effects of sustained treatment strategies via the parametric g-formula [Paper][Software]
Sean McGrath*, Victoria Lin*, Zilu Zhang, Lucia Petito, Roger Logan, Miguel Hernán, Jessica Young
Patterns Volume 1, Issue 3 (2020)

Enhancing GABA signaling during middle adulthood prevents age-dependent GABAergic interneuron decline and learning and memory deficits in ApoE4 mice [Paper]
Leslie Tong, Seo Yeon Yoon, Yaisa Andrews-Zwilling, Alyssa Yang, Victoria Lin, Hanci Lei, Yadong Huang
Journal of Neuroscience Volume 36, Issue 7 (2020)