Rowan Zellers

(email / github / blog / twitter)

I'm a first-year graduate student at the University of Washington Paul G. Allen School for Computer Science and Engineering, where I study computer vision, natural language processing, and artificial intelligence. I'm very fortunate to be advised by Yejin Choi.

In the past, I was a student at Harvey Mudd College, graduating in 2016 with a major in Computer Science and Mathematics. As an undergraduate, I worked on several research projects: with Louis-Philippe Morency, on deep multimodal machine learning, and with Jacqueline Dresch, on computational biology.

Research

Here's a list of my publications. You can also check out my Google Scholar profile or read my cv.

Zero-Shot Activity Recognition with Verb Attribute Induction
Rowan Zellers, Yejin Choi EMNLP 2017 [paper] [code, data, BibTeX] [poster]

We investigate zero-shot multimodal learning where the topics of classification are verbs, not objects. We crowdsource verb attributes and build a model to learn them from text (word embeddings and dictionary definitions). We also use these verb attributes alongside word embeddings for action recognition in images.

Multimodal Sentiment Intensity Analysis in Videos: Facial Gestures and Verbal Messages
Amir Zadeh, Rowan Zellers, Eli Pincus, Louis-Philippe Morency. IEEE Intelligent Systems 2016 [paper]

We provide a study of sentiment analysis applied on video data, not just text. We present a model that exploits the dynamics between gestures and verbal messages, which improves over the the conventional early fusion representation.

Nucleotide Interdependency in Transcription Factor Binding Sites in the Drosophila Genome
Jacqueline Dresch, Rowan Zellers, Daniel Bork, Robert Drewell. Gene Regulation and Systems Bio 2016. [paper]

MARZ: an algorithm to combinatorially analyze gapped n-mer models of transcription factor binding
Rowan Zellers, Robert Drewell, Jacqueline Dresch. BMC Bioinformatics 2015. [paper+code]

We present a model for modeling the specificity with which regulatory proteins bind to DNA sequences during embryonic development. We then use this model to study binding sites for 15 distinct regulatory proteins in the Drosophila (fruit fly) genome.

Teaching

Deep Learning Workshop

In November 2015 at Harvey Mudd, I organized a workshop for students in CS158: Machine Learning and CS151: Artificial Intelligence on Deep Learning. Slides are available here.

Miscellaneous