Sean (Xiang) Ren
Andrew and Erna Viterbi Early Career Chair and Associate Professor of Computer Science
Education
- 2017, Doctoral Degree, Computer Science, University of Illinois at Urbana-Champaign
- 2015, Master's Degree, Computer Science, University of Illinois at Urbana-Champaign
Biography
Xiang Ren is an assistant professor and Viterbi Early Career Chair at the USC Computer Science Department, a Research Team Leader at USC ISI, and the director of the Intelligence and Knowledge Discovery (INK) Lab at USC. Priorly, he spent time as a research scholar at the Stanford University and received his Ph.D. in Computer Science from the University of Illinois Urbana-Champaign. Ren's research seeks to build generalizable natural language processing (NLP) systems which can handle a wide variety of language tasks and situations. Dr. Ren's research leads to over 120 research publications in top conferences and journals, was covered in over 10 conference tutorials (KDD, WWW, NAACL). His research work has received several best paper awards in top NLP and AI conference venues. Dr. Ren has been awarded a NSF CAREER Award, multiple faculty research awards from Google, Facebook, Amazon, JP Morgan and Sony, and the 2018 ACM SIGKDD Doctoral Dissertation Award. He was named Forbes' Asia 30 Under 30 in 2019.
Research Summary
Xiang Ren's research interests span natural language processing (NLP) and machine learning, with a focus on building generalizable NLP systems---i.e., systems that can handle a wide variety of language tasks and situations---by broadening the scope of "model generality". He works on new algorithms and datasets to make NLP systems cheaper to develop and maintain, arm machine models with common sense, and improve model's transparency and reliability to build user trust. In Dr. Ren's recent research, he focus on (1) creating evaluation methods and datasets that expose the state-of-the-art NLP systems in various commonsense reasoning scenarios; (2) building novel learning algorithms and model architectures to augment NLP systems with common sense; (3) developing scalable graph neural network methods for relational reasoning; and (4) verifying and enhancing the robustness of NLP models.
Awards
- 2022 NAACL Outstanding Paper Award
- 2022 Google Research Scholar Award
- 2022 Andrew and Erna Viterbi Early Career Chair
- 2021 NSF CAREER Award
- 2020 Sony Faculty Innovation Award
- 2020 The Web Conference (WWW) Best Paper Award runner-up
- 2019 Amazon Faculty Research Award
- 2019 JP Morgan AI Research Award
- 2019 Google Faculty Research Award
- 2019 Adobe Data Science Research Award
- 2019 Forbes' Asia 30 Under 30
- 2018 SIGKDD Doctoral Dissertation Award
- 2017 Google PhD Fellowship