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.
Recurrent Marked Temporal Point Processes
2016279 citationsHanjun Dai, Rakshit Trivedi et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Hanjun Dai'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 Hanjun Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hanjun Dai more than expected).
This network shows the impact of papers produced by Hanjun Dai. 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 Hanjun Dai. The network helps show where Hanjun Dai may publish in the future.
Co-authorship network of co-authors of Hanjun Dai
This figure shows the co-authorship network connecting the top 25 collaborators of Hanjun Dai.
A scholar is included among the top collaborators of Hanjun Dai 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 Hanjun Dai. Hanjun Dai is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ren, Hongyu, Hanjun Dai, Bo Dai, et al.. (2021). LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs. International Conference on Machine Learning. 8959–8970.20 indexed citations
3.
Sun, Ruoxi, Hanjun Dai, Li Li, Steven Kearnes, & Bo Dai. (2021). Towards understanding retrosynthesis by energy-based models. Neural Information Processing Systems. 34.13 indexed citations
Dai, Hanjun, et al.. (2019). Meta Particle Flow for Sequential Bayesian Inference.. arXiv (Cornell University).1 indexed citations
10.
Dai, Bo, Hanjun Dai, Arthur Gretton, et al.. (2019). Kernel exponential family estimation via doubly dual embedding. UCL Discovery (University College London). 2321–2330.1 indexed citations
11.
Dai, Hanjun, Zornitsa Kozareva, Bo Dai, Alexander J. Smola, & Le Song. (2018). Learning Steady-States of Iterative Algorithms over Graphs. International Conference on Machine Learning. 1106–1114.59 indexed citations
12.
Si, Xujie, Hanjun Dai, Mukund Raghothaman, Mayur Naik, & Le Song. (2018). Learning Loop Invariants for Program Verification. Neural Information Processing Systems. 31. 7751–7762.19 indexed citations
13.
Si, Xujie, Yuan Yang, Hanjun Dai, Mayur Naik, & Le Song. (2018). Learning a Meta-Solver for Syntax-Guided Program Synthesis. International Conference on Learning Representations.9 indexed citations
14.
Dai, Bo, Hanjun Dai, Niao He, et al.. (2018). Coupled Variational Bayes via Optimization Embedding. Neural Information Processing Systems. 31. 9690–9700.9 indexed citations
15.
Dai, Hanjun, Yingtao Tian, Bo Dai, Steven Skiena, & Le Song. (2018). Syntax-Directed Variational Autoencoder for Structured Data. International Conference on Learning Representations.8 indexed citations
Dai, Bo, Niao He, Hanjun Dai, & Le Song. (2016). Provable Bayesian Inference via Particle Mirror Descent. International Conference on Artificial Intelligence and Statistics. 985–994.7 indexed citations
20.
Tian, Fei, Hanjun Dai, Jiang Bian, et al.. (2014). A Probabilistic Model for Learning Multi-Prototype Word Embeddings. International Conference on Computational Linguistics. 151–160.68 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.