New York University

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. Check out the SLATTIC Group.

Research

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] [code coming soon] [abstract] [old-BibTeX]

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]

Another completed research project is on approximate inference for structured prediction. Please email me to learn more.

Presentation

  • Poster presentation on Learning Criteria and Evaluation Metrics for Textual Transfer between Non-Parallel Corpora (nonarchival); 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

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

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 in September 2019. More materials coming soon. Get in touch at yzpang at _ dot edu (where _ is uchicago or nyu)!