Wenlong Zhao



I’m a second-year Computer Science PhD student at UMass Amherst, working with Prof. Andrew McCallum at Information Extraction and Synthesis Lab (IESL). I completed a Computer Science master’s degree also at UMass, prior to which I graduated with a major in Mathematics and Economics, a major with distinction in Cognitive Science, a minor in Computer Science, and a minor in Philosophy from UC San Diego.

My recent research focuses on probabilistic box embeddings and energy-based models, their learning and inference algorithms, and efficiency-accuracy trade-offs. I am also enthusiastic about building systems. I am part of a multi-university AI-for-science project series led by Prof. Subhransu Maji and Prof. Dan Sheldon on developing machine learning algorithms to detect and track bird and bat roosts in weather radar data for conservation biology and ecology research. I worked with Prof. Mohit Iyyer and Prof. Brendan O’Connor on developing a crowdsourcing platform for collecting coreference resolution annotations. I have also contributed to AI-for-sociology research about using computational models to study peer review processes.

I am excited to see AI become useful tools in industry. I have worked with Adobe collaborators to develop an authoring assistant that generates texts according to intent guidance. I have collaborated with Amazon Alexa as a Summer 2022 intern and via the UMass Industry Mentorship Program to investigate computational efficiency of large language models.

In a longer term, I am interested in both AI applications and the possibility to build AI that learns to interact with the world based on reliable common sense, knowledge, and reasoning. Relevant work include my Spring 2023 collaboration with Allen AI mentors about editing memory in transformer models.


  1. NLP
    Collecting Crowdsourced Annotations for Coreference Resolution
    Ankita Gupta, Marzena Karpinska, Wenlong Zhao, Kalpesh Krishna, Jack Merullo, Luke Yeh, Mohit Iyyer, and Brendan O’Connor
    Findings of ACL: EACL 2023
  2. CV
    Long-term analysis of persistence and size of swallow and martin roosts in the US Great Lakes
    Maria Carolina Belotti, Yuting Deng, Wenlong Zhao, Victoria F. Simons, Zezhou Cheng, Gustavo Perez, Elske Tielens, Subhransu Maji, Daniel Sheldon, Jeff Kelly, and Kyle Horton
    Remote Sensing in Ecology and Conservation 2023
  3. CV
    Using Spatio-Temporal Information in Weather Radar Data to Detect and Track Communal Bird Roost
    *Gustavo Perez, *Wenlong Zhao, Zezhou Cheng, Maria Belotti, Yuting Deng, Victoria Simons, Elske Tielens, Jeffrey Kelly, Kyle Horton, Subhransu Maji, and Daniel Sheldon
    Under review 2022
  4. CV
    Quantifying long-term phenological patterns of aerial insectivores roosting in the Great Lakes region using weather surveillance radar
    Yuting Deng, Maria Belotti, Wenlong Zhao, Zezhou Cheng, Gustavo Perez, Elske Tielens, Victoria Simons, Daniel Sheldon, Subhransu Maji, Jeff Kelly, and Kyle Horton
    Global Change Biology 2022
  5. ML
    Structured Energy Network As a Loss
    Jay-Yoon Lee, Dhruvesh Patel, Purujit Goyal, Wenlong Zhao, Zhiyang Xu, and Andrew McCallum
    NeurIPS 2022
  6. ML
    Toward Compact Parameter Representations for Architecture-Agnostic Neural Network Compression
    Yuezhou Sun, Wenlong Zhao, Lijun Zhang, Xiao Liu, Hui Guan, and Matei Zaharia
    arXiv preprint arXiv:2111.10320 2021
  7. NLP
    IGA: An intent-guided authoring assistant
    Simeng Sun, Wenlong Zhao, Varun Manjunatha, Rajiv Jain, Vlad Morariu, Franck Dernoncourt, Balaji Vasan Srinivasan, and Mohit Iyyer
    EMNLP 2021
  8. NLP
    Compressing transformer-based semantic parsing models using compositional code embeddings
    *Prafull Prakash, *Saurabh Kumar Shashidhar, *Wenlong Zhao, Subendhu Rongali, Haidar Khan, and Michael Kayser
    Findings of ACL: EMNLP 2020
  9. NLP
    A Thorough Examination of the RACE Machine Reading Comprehension Task
    Wenlong Zhao
    UC San Diego Cognitive Science Undergrad Honors Thesis 2019
  10. NLP
    Rethinking exposure bias in language modeling
    Yifan Xu, Kening Zhang, Haoyu Dong, Yuezhou Sun, Wenlong Zhao, and Zhuowen Tu
    arXiv preprint arXiv:1910.11235 2019


  • Mentor for UMass Amherst COMPSCI 696: Independent Study, Fall 2022
  • Mentor for UMass Amherst COMPSCI 696DS : Industry Mentorship Program, Spring 2022
  • Grader for UMass Amherst COMPSCI 611: Advanced Algorithm, Spring 2021
  • Teaching assistant for UCSD COGS 118A: Introduction to Machine Learning, Winter 2018
  • Teaching assistant for UCSD COGS 10: Cognitive Consequences of Technology, Spring 2017


  • Organizer: UMass Machine Learning and Friends Lunch (MLFL) series.
  • Reviewer: COLING 2022.