He He

5.4k total citations · 3 hit papers
83 papers, 2.5k citations indexed

About

He He is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering. According to data from OpenAlex, He He has authored 83 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Artificial Intelligence, 15 papers in Computer Vision and Pattern Recognition and 10 papers in Electrical and Electronic Engineering. Recurrent topics in He He's work include Topic Modeling (33 papers), Natural Language Processing Techniques (25 papers) and Multimodal Machine Learning Applications (10 papers). He He is often cited by papers focused on Topic Modeling (33 papers), Natural Language Processing Techniques (25 papers) and Multimodal Machine Learning Applications (10 papers). He He collaborates with scholars based in United States, China and Japan. He He's co-authors include Dongrui Wu, Wan-Chi Siu, Percy Liang, Hal Daumé, Jason Eisner, Zhiliang Hong, Yejin Choi, Mark Yatskar, Mohit Iyyer and Eunsol Choi and has published in prestigious journals such as Nature, IEEE Access and IEEE Transactions on Biomedical Engineering.

In The Last Decade

He He

77 papers receiving 2.3k citations

Hit Papers

QuAC: Question Answering in Context 2018 2026 2020 2023 2018 2019 2024 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. QuAC: Question Answering in Context
    2018 343 citations He He et al. profile →
  2. Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach
    2019 301 citations He He et al. profile →
  3. Solving olympiad geometry without human demonstrations
    2024 95 citations He He et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
He He United States 23 1.4k 626 363 257 238 83 2.5k
Pengjiang Qian China 25 942 0.7× 722 1.2× 388 1.1× 92 0.4× 155 0.7× 101 2.2k
Amardeep Singh India 20 378 0.3× 226 0.4× 211 0.6× 235 0.9× 182 0.8× 63 1.6k
Rodolfo Zunino Italy 23 883 0.6× 786 1.3× 173 0.5× 242 0.9× 248 1.0× 167 1.9k
Tong Zhang China 23 977 0.7× 1.1k 1.7× 972 2.7× 124 0.5× 228 1.0× 111 2.9k
Mounir Boukadoum Canada 23 434 0.3× 311 0.5× 398 1.1× 501 1.9× 60 0.3× 185 1.9k
Ashish Kapoor United States 37 1.8k 1.3× 1.6k 2.6× 338 0.9× 114 0.4× 145 0.6× 96 4.0k
Hua Yang China 31 745 0.5× 1.4k 2.2× 480 1.3× 116 0.5× 175 0.7× 218 2.9k
N.B. Karayiannis United States 27 1.1k 0.8× 693 1.1× 345 1.0× 235 0.9× 149 0.6× 98 2.3k
Karim Faez Iran 34 863 0.6× 2.4k 3.8× 318 0.9× 560 2.2× 640 2.7× 335 4.2k
Thomas Navin Lal Germany 7 1.5k 1.1× 1.3k 2.1× 341 0.9× 116 0.5× 242 1.0× 10 2.9k

Countries citing papers authored by He He

Since Specialization
Citations

This map shows the geographic impact of He He's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by He He with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites He He more than expected).

Fields of papers citing papers by He He

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by He He. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by He He. The network helps show where He He may publish in the future.

Co-authorship network of co-authors of He He

This figure shows the co-authorship network connecting the top 25 collaborators of He He. A scholar is included among the top collaborators of He He based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with He He. He He is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
He, He, et al.. (2025). Multi-Agent Reinforcement Learning for Efficient Resource Allocation in Internet of Vehicles. Electronics. 14(1). 192–192. 2 indexed citations
2.
Yi, Xun, et al.. (2025). Safety evaluation of ILaris: a real-world analysis of adverse events based on the FAERS database. Expert Opinion on Drug Safety. 1–13.
3.
Saparov, Abulhair, et al.. (2024). LLMs Are Prone to Fallacies in Causal Inference. 10553–10569. 2 indexed citations
4.
5.
Chakrabarty, Tuhin, Vishakh Padmakumar, He He, & Nanyun Peng. (2023). Creative Natural Language Generation. 34–40.
6.
7.
Si, Chenglei, et al.. (2023). Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations. 11289–11310. 2 indexed citations
8.
Pang, Richard Yuanzhe, Vishakh Padmakumar, Thibault Sellam, Ankur P. Parikh, & He He. (2023). Reward Gaming in Conditional Text Generation. 4746–4763. 1 indexed citations
9.
Zhao, Chen, et al.. (2023). On the Relation between Sensitivity and Accuracy in In-Context Learning. 17 indexed citations
10.
Pang, Richard Yuanzhe, Alicia Parrish, Nikita Nangia, et al.. (2022). QuALITY: Question Answering with Long Input Texts, Yes!. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 5336–5358. 21 indexed citations
11.
Chakrabarty, Tuhin, Vishakh Padmakumar, & He He. (2022). Help me write a poem: Instruction Tuning as a Vehicle for Collaborative Poetry Writing. 6848–6863. 18 indexed citations
12.
Wang, Tianshu, Faisal Ladhak, Esin Durmus, & He He. (2022). Improving Faithfulness by Augmenting Negative Summaries from Fake Documents. 11913–11921. 3 indexed citations
13.
He, He, et al.. (2021). IRM---when it works and when it doesn't: A test case of natural language inference. Neural Information Processing Systems. 34. 5 indexed citations
14.
15.
He, He, Sheng Zha, & Haohan Wang. (2019). Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual. 132–142. 86 indexed citations
16.
Khandelwal, Urvashi, He He, Peng Qi, & Dan Jurafsky. (2018). Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context. 284–294. 149 indexed citations
17.
Chang, Kai-Wei, He He, Stéphane Ross, Hal Daumé, & John Langford. (2016). A Credit Assignment Compiler for Joint Prediction. Neural Information Processing Systems. 29. 1705–1713. 5 indexed citations
18.
Liu, Xiangyang, He He, & John S. Baras. (2015). Crowdsourcing with multi-dimensional trust. International Conference on Information Fusion. 574–581. 2 indexed citations
19.
He, He, Hal Daumé, & Jason Eisner. (2014). Learning to Search in Branch and Bound Algorithms. Neural Information Processing Systems. 27. 3293–3301. 72 indexed citations
20.
He, He, Jason Eisner, & Hal Daumé. (2012). Imitation Learning by Coaching. Neural Information Processing Systems. 25. 3149–3157. 46 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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