About me

  • I am the final-year (fifth-year) CS PhD student co-advised by Prof. Srijan Kumar and Prof. Mustaque Ahamad homed in School of Computer Science at Georgia Institute of Technology.

  • My foucs of research area is Machine Learning and Natural Language Processing (NLP). My research topic is to design advanced NLP methods to characterize, detect, and mitigate incorrect and harmful contents generated by: (1) human, e.g., misinformation, fake news, hate speech, rumor, and fraud; (2) machines, e.g., hallucinations in/alignment of/evaluation of Large Language Models (LLMs).
    • Methodology: Multi-task Learning, Reinforcement Learning, Few-shot Learning, Representation learning
    • Application: Text Classification, Text Generation, Computational Social Science
  • I am looking for full-time research scientist/data scientist/applied scientist/machine learning engineer positions or similar roles. Please message me if you are hiring or know good positions!
  • Contact: bhe46@gatech.edu

Selected Publications [Google Scholar]

  1. Bing He, Sreyashi Nag, Limeng Cui, Suhang Wang, Zheng Li, Rahul Goutam, Zhen Li and Haiyang Zhang, “Hierarchical Query Classification in E-commerce Search”, ACM Web Conference (ACM WWW) 2024 (Acceptance Rate: 21.3%)
  2. Bing He, Mustaque Ahamad, Srijan Kumar, “Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation”, code and data, ACM Web Conference (ACM WWW) 2023 (Acceptance Rate: 365/1900=19.2%)
  3. Bing He, Mustaque Ahamad, Srijan Kumar, “PETGEN: Personalized Text Generation Attack on Deep Sequence Embedding-based Classification Models”, code and data, ACM SIGKDD 2021 (Acceptance Rate: 238/1541=15.4%)
  4. Bing He, Caleb Ziems, Sandeep Soni, Naren Ramakrishnan, Diyi Yang, Srijan Kumar, “Racism is a virus: Anti-asian Hate and Counterspeech in Social Media during the Covid-19 Crisis”, code and data, IEEE/ACM ASONAM 2021
  5. Nicholas Micallef∗, Bing He∗, Srijan Kumar, Mustaque Ahamad, Nasir Memon, “The Role of the Crowd in Countering Misinformation: A Case Study of the COVID-19 Infodemic”, code and data, IEEE BigData 2020 (∗ equal contribution)

Internship Experiences

  • 2023.05 - 2023.08: Amazon, Palo Alto, California
    • Applied Scientist Intern, Search Mission Understanding Team, Amazon Search/A9.com
    • Hosts: Ms. Sreyash Nag, Dr. Limeng Cui, Dr. Zheng Li
  • 2022.05 - 2022.08: Amazon, San Diego, California
    • Applied Scientist Intern, Machine Learning Accelerator Team, SPS
    • Hosts: Dr. Shobeir Fakhraei, Mr. Akshay Kulkarni, Dr. Na Zhang, Dr. Chris Jones
  • 2021.05 - 2021.08: Amazon, San Diego, California
    • Applied Scientist Intern, Machine Learning Accelerator Team, SPS
    • Hosts: Ms. Veena Padmanabhan, Dr. Hyun Ah Song, Dr. Na Zhang, Dr. Chris Jones

Academic Services

Program Committee/Reviewer: IJCAI 2023, ACM SIGKDD 2023, ACM FAccT 2023, AAAI AI4SG 2023, AAAI ICWSM 2023, TheWebConf (ACM WWW) 2023, TheWebConf CySoc 2023, PACIS 2023, COLLA 2023, CMC(J) 2023, INFOCOMP 2023, ACM CSCW 2023, DIS 2023, ISMAR 2023, IEEE/ACM ASONAM 2023,AAAI ICWSM 2022, ACM CSCW 2022, CySoc 2022, Cyberc 2022, JENRS 2022, CMC(J) 2022, AAAI ICWSM 2021, ACII 2021, AAAI ICWSM 2020, MAISoN 2020, TTO 2020, IEEE ICPADS 2018, ACM CIKM 2017

I am open to peer reviews in the field of machine learning, natural language processing, and graph mining with its applications in misinformation, fake news, social network analysis, and etc.

I am always open to collaborations, both academia and industry. If you have some interesting ideas or want to have a chat, feel free to contact me! Let’s work together to create a safer online ecosystem.