Xiang Ren

xiangren [at] usc.edu

Assistant Professor, USC Computer Science
Research Team Leader, USC ISI
Director, USC INK Research Lab
Information Director, SIGKDD
Forbes' Asia 30 Under 30

Natural Language Processing,
Machine Learning, Knowledge Graph

Follow @xiangrenNLP
Publications / Group / Research
Google Scholar / GitHub

I'm an Assistant Professor at USC in Computer Science and a Research Team Leader in Information Sciences Institute (ISI). I'm a member of the USC NLP Group, USC Machine Learning Center and ISI Center on Knowledge Graphs. At USC CS I'm the PI of the Intelligence and Knowledge Discovery (INK) Research Lab. Previously I was a Data Science Advisor at Snapchat. Prior to USC, I did my PhD work in computer science at UIUC. I've also spent time with the NLP group and the SNAP group at the Stanford University.

I work on new algorithms and datasets in natural language processing and machine learning to make our AI systems cheaper (less labeled data), transparent (explainability) and reliable (incorporating human knowledge, constraints, and decision rationales). My group (INK Lab) focuses on developing label-efficient, prior-informed models that extract machine-actionable knowledge from natural language data, perform neural-symbolic knowledge reasoning for intelligent applications, and learning (to adapt and improve) from human explanations and instructions. Please check out the our group website for more information.

A summary of my PhD work on label-efficient NLP can be found in the book "Mining Structures of Factual Knowledge from Text: An Effort-Light Approach". Our research work is funded by NSF (CAREER award, SciSIP #1829268), DARPA (MCS, INCAS, SCORE, GAILA, SAIL-ON), IARPA (BETTER, SAGE), and gift awards from industry partners including Google, Amazon, JP Morgan, Adobe, Sony, and Snapchat.

News

11/ 2021 - Excited to co-organize three workshop in 2022 *CL conferences: Workshop on Commonsense Representation and Reasoning and FL4NLP at ACL 2022, and DeepLo at NAACL 2022
10/ 2021 - I will serve as an action editor (area chair) for ACL Rolling Review.
09/ 2021 - INK lab has 3 papers accepted at NeurIPS 2021 (one spotlight, two posters), presenting new results on explanation-based for NLP and continual learning.
08/ 2021 - INK lab has 9 papers accepted at EMNLP 2021 and 3 papers accepted to Findings of EMNLP. Congratulations to all authors!
07/23/ 2021 - Our lab received a research award from USC + Amazon Center on Secure and Trusted Machine Learning to work on federated learning for NLP -- media coverage by Amazon and USC.
07/ 2021 - Serve as area chair for EMNLP 2021, ICLR 2022, and AAAI 2022.
06/ 2021 - USC covers our K-12 outreach activities on explaining to high school kids how AI can be applied to help combat online hate speech.
06/ 2021 - Congratulations to Dongho, Ravi and the other authors for their paper "AutoTriggER: Named Entity Recognition with Auxiliary Trigger Extraction" wining the Best Paper Award at NAACL TrustNLP workshop!
05/ 2021 - INK lab has 7 papers accepted to ACL 2021 and its findings, spanning over analysis of language models regarding common sense, question answering, and explanation-based learning.
04/ 2021 - Our group won a NSF CAREER award to support the work on teaching machine through human explanations.
04/ 2021 - Our work on probing languages models (NumerSense) and knowledge-aware graph networks (KagNet) got covered by Communications of the ACM.
04/ 2021 - Giving an invited talk at Amazon Alexa AI.
03/ 2021 - INK lab has 4 papers accepted at NAACL 2021, with topics spanning over bias mitigation, open-ended commonsense reasoning, and cross-lingual learning
03/ 2021 - Very happy to participate at USC Viterbi's K-12 outreach with Theodore Roosevelt High School and STEM Academy Hollywood.
02/ 2021 - Excited to give an invited talk at UIUC NLP Seminar on "Teaching Machine through Human Explanations". Slides are available now.
01/ 2021 - Check out our new papers on pre-training for concept-centric common sense and deceiving knowledge graph-augmented models; both accepted to ICLR 2021.
11/ 2020 - Our EMNLP work CommonGen and NumerSense got covered by Tech Xplore, Radio.com, EurekAlert and ScienceDaily.
11/ 2020 - Our team got selected to participate in the 4th Alexa Prize Socialbot Grand Challenge.
10/ 2020 - I'm serving as Area Chair for ACL 2021, NAACL 2021 and IJCAI 2021.
09/ 2020 - INK lab has 12 papers accepted to EMNLP 2020 (7 to the main conference and 5 to Findings of EMNLP).
08/ 2020 - I'm serving as Senior Area Chair for AAAI 2021.
08/ 2020 - I will join a penal in DaSH@KDD 2020 to discuss open challenges in human-computer cooperation in data science, with Azza Adouzied, AnHai Doan, and Marti Hearts.
07/ 2020 - Our ACL work on examining and reducing biases in hate speech detection algorithms is featured by Digital Trends, ScienceDaily, EurekAlert, Unite.AI, and USCViterbi.
Jul, 2020 - Will serve as area chair in ICLR 2021.
May, 2020 - INK lab members Ryan Moreno and Lily Cao won Undegraduate Outstanding Student Award from USC. Congratulations!
May, 2020 - Our paper "NERO: A Neural Rule Grounding Framework for Label Efficient Relation Extraction" received Best Paper Award Runner-up at The Web Conference 2020!
Apr, 2020 - INK Lab has four papers accepted at ACL 2020.
Apr, 2020 - Our LEAN-LIFE system for label-efficient, explanation-based annotation has been accepted to ACL 2020 demo track.
Mar, 2020 - Excited to receive a Sony Faculty Research Award to support our work on learning from natural language explanations.
Mar, 2020 - Will serve as area chair for ML on NLP in EMNLP 2020.
Feb, 2020 - Give invited talk about "Fast Learning with Explanation and Prior Knowledge" at CMU LTI colloquium and UT Austin.
Dec, 2019 - INK lab has two papers (spotlight & poster) accepted at ICLR 2020.
Nov, 2019 - Invited talk at CMU LTI Colloquium in Feb, 2020.
Sep, 2019 - Excited to receive a data science research award from Adobe Research to work on neural symbolic learning for recommendation.
Aug, 2019 - INK lab members have 10 papers accepted at EMNLP 2019. Congratulations!
June, 2019 - We're excited to receive a gift award from Snapchat to work on modular neural networks for interpretable NLP!
June, 2019 - We received a DARPA GAILA grant to work on building AI to mimic children language learning.
May, 2019 - Serve as area chair for EMNLP 2019, ACL 2019; as senior PC for AAAI 2020.
Mar, 2019 - Excited to receive a Google Faculty Award for supporting our research on explainable recommendation.
Mar, 2019 - Our research on interpretable knowledge reasoning is funded by JP Morgan AI Research Award.
Feb, 2019 - As part of the USC/ISI team, we received DARPA award to work on Machine Commonsense and Learning with Less Data.
Jan, 2019 - Our research on neural-symbolic deep learning for NLP is funded by an Amazon Research Award.
Dec, 2018 - Organizing the ICLR 2019 LLD Workshop on learning from limited labeled data.
Dec, 2018 - Organizing the RepL4NLP Workshop at ACL 2019 on representation Learning for NLP. We're soliciting submissions.
Nov, 2018 - Organizing the DeepLo Workshop at EMNLP 2019 on deep learning for low-resource NLP.

Teaching

Fall 2020-2021: Representation Learning for NLP
Fall 2019: Advanced Topics in Representation Learning for NLP (CSCI 699)
Spring 2019: Machine Learning for Knowledge Extraction and Reasoning (CSCI 699)
Fall 2018: Introduction to Information Extraction

Awards

NSF CAREER Award, 2021
Amazon Secure and Trusted ML Grant, 2021
Best Paper Award, TrustNLP @NACCL 2021
Sony Faculty Innovation Award, 2020
Best paper award runner-up, The Web Conference 2020
Forbes’ Asia 30 Under 30, Healthcare & Science, 2019
JP Morgan AI Research Award, 2019
Amazon Faculty Research Award, 2019
Adobe Data Science Research Award, 2019
Google Faculty Research Award, 2018
ACM SIGKDD Doctoral Dissertation Award, 2018
Best poster award runner-up, The Web Conference 2018
David J. Kuck Outstanding Thesis Award, 2017
Google PhD Fellowship, 2016
Yelp Dataset Challenge winner, 2016
C. W. Gear Outstanding Research Award, 2016

Talks

Commonsense Reasoning in the Wild, 2021 [slides]. Invited talk at AKBC CSR Workshop; Intel AI Lab.
Teaching Machines through Human Explanations [slides], 2021. Invited talks @UIUC, Amazon Alexa AI.
Applying AI for Fighting Online Hate Speech [slides], 2021. @USC Viterbi SHINE, STEM Academy Hollywood
Fast and Faithful Knowledge Graph Construction [video], 2020. @Pinterest Knowledge Graph summit
Commonsense Reasoning: Models and New Challenges [slides], 2020. @Google X
Fast Learning with Explanation and Prior Knowledge [slides], 2020. @CMU LTI seminar, UT Austin
Predicting User Engagement for Social Media Apps [video], 2020. Lecture @USC K-12 SHINE program.
From Data to Model Programing: Injecting Structured Priors for Knowledge Extraction [slides], 2019.
Effort-Light StructMine: Turning Massice Text Corpora into Structures [video], 2018
Scalable Construction and Reasoning of Massive Knowledge Bases [slides], 2018

Services

K-12 Outreach: Guest lecture @USC Viterbi SHINE program (07/ 2020); guest lecture & project pitch panel @Theodore Roosevelt High School, Los Angeles (02/ 2021); project feedback @STEM Academy Hollywood (03/ 2021). Media coverage by USC.
Appointed chair: ACM SIGKDD Information Director
Organizer: TrustworthyNLP@NAACL 2020, LLD@ICLR 2019, RepL4NLP@ACL 2019, DeepLo@EMNLP 2019, KBCOM 2018
Co-chair: KDD 2019/2020 Media and Publicity Co-chair, SDM 2020: Publicity Co-chair, AKBC 2019 workshop Co-chair, ICDM 2018 Data Challenge co-chair,
Area Chair/Senior PC: NAACL 2021, IJCAI 2021, AAAI 2021, ICLR 2021, ACL 2020, EMNLP 2020, ACL 2019, EMNLP 2019, EMNLP 2018, COLING 2018
PC/Reviewer: KDD (2015-present), ACL (2017-present), NeurIPS (2018-present), ICML (2018-present), EMNLP (2015-17), WWW (2017-present), NAACL (2018), SIGIR (2017), IJCAI (2018-2020), WSDM (2017-present), TACL, TKDE, TKDD, TIST

Contact

Office: ~~RTH 305~~ (work from home)

xiangren [at] usc.edu by email

Twitter: @xiangrenNLP

Prospective students I'm actively recruiting graduate students who are excited about doing fun research. Please check out this page for more information before emailing me. I may not be able to respond your email.