Hello. I am a first-year Ph.D. student in the Courant Institute of Mathematical Sciences at New York University. I am a member of the Machine Learning for Language (ML²) Group (subset of the CILVR Group). I am advised by Prof. He He and Prof. Kyunghyun Cho.

My research focuses on natural langauge processing and machine learning. Specifically, I have been working on structured prediction algorithms, generation, and neural machine translation.

I graduated from the University of Chicago in June 2019, and I majored in mathematics and computer science. In Chicago, my advisor/collaborator has been Prof. Kevin Gimpel at Toyota Technological Institute at Chicago (TTIC) and the University of Chicago.

Research

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Improving Joint Training of Inference Networks and Structured Prediction Energy Networks
Lifu Tu, Richard Yuanzhe Pang, Kevin Gimpel
[paper] [abstract] [BibTex]

Consistency of a Recurrent Language Model With Respect to Incomplete Decoding
Sean Welleck, Ilia Kulikov, Jaedeok Kim, Richard Yuanzhe Pang, Kyunghyun Cho
[paper]

The Daunting Task of Real-World Textual Style Transfer Auto-Evaluation
Richard Yuanzhe Pang
Extended abstract in EMNLP 2019 Workshop on Neural Generation and Translation (WNGT); abstract in Workshop on Noisy User-generated Text (W-NUT)
[paper] [abstract] [poster]

Unsupervised Evaluation Metrics and Learning Criteria for Non-Parallel Textual Transfer
Richard Yuanzhe Pang, Kevin Gimpel
EMNLP 2019 Workshop on Neural Generation and Translation (WNGT)
[paper] [supplementals] [abstract] [poster] [BibTeX]

Presentations

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• Poster presentation and lightning talk on The Daunting Task of Real-World Textual Style Transfer Auto-Evaluation; EMNLP 2019 WNGT workshop (poster) and W-NUT workshop (lightning talk) in Hong Kong, China; November 2019 [poster]
• Poster presentation on Unsupervised Evaluation Metrics and Learning Criteria for Non-Parallel Textual Transfer; EMNLP 2019 WNGT workshop in Hong Kong, China; November 2019 [poster]
• Poster presentation on Learning Criteria and Evaluation Metrics for Textual Transfer between Non-Parallel Corpora; NAACL 2019 NeuralGen workshop in Minneapolis, USA; June 2019
• Talk titled Learning Approximate Inference Networks and Structured Prediction Energy Networks with Lifu Tu and Kevin Gimpel; Midwest Speech and Language Days (MSLD) 2019 in Chicago, USA; May 2019
• Poster presentation on Learning Criteria and Evaluation Metrics for Textual Transfer between Non-Parallel Corpora; UChicago STEM Research Symposium in Chicago, USA; October 2018

Teaching

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Spring 2017, Course Assistant, MATH 15910: Intro to Proofs in Analysis
Winter 2017, Course Assistant, MATH 15910: Intro to Proofs in Analysis [Sol]
Winter 2017, Grader, CMSC 15200: Intro to Computer Science II
Autumn 2016, Teaching Assistant, MATH 15300: Calculus III

Relevant Coursework

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At the University of Chicago (2015-2019)

CMSC 27230 - Honors Theory of Algorithms
CMSC 25025 / STAT 37601 - Machine Learning and Large-Scale Data Analysis (grad level, Lafferty)
CMSC 35400 / STAT 37710 - Machine Learning (grad level, Kondor)
TTIC 31020 - Statistical Machine Learning (grad level, Shakhnarovich)
TTIC 31190 - Natural Language Processing (grad level, Gimpel)
TTIC 41000 - Spectral Techniques (grad level, Stratos)
MATH 20300-20500 - Accelerated Real Analysis I, II, III
MATH 20250, 25400-25500 - Abstract Linear Algebra; Abstract Algebra I, II
BIOS 10602-10603 - Multiscale Modeling of Biological Systems I, II (computational biology)

Last updated: November 7, 2019. Get in touch at yzpang at _ dot edu (where _ is uchicago or nyu)!