Ardavan Saeedi

1.8k total citations · 1 hit paper
10 papers, 699 citations indexed

About

Ardavan Saeedi is a scholar working on Artificial Intelligence, Molecular Biology and Oncology. According to data from OpenAlex, Ardavan Saeedi has authored 10 papers receiving a total of 699 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 4 papers in Molecular Biology and 2 papers in Oncology. Recurrent topics in Ardavan Saeedi's work include Bayesian Methods and Mixture Models (3 papers), Machine Learning and Algorithms (2 papers) and Topic Modeling (2 papers). Ardavan Saeedi is often cited by papers focused on Bayesian Methods and Mixture Models (3 papers), Machine Learning and Algorithms (2 papers) and Topic Modeling (2 papers). Ardavan Saeedi collaborates with scholars based in United States, United Kingdom and Canada. Ardavan Saeedi's co-authors include Karthik Narasimhan, Joshua B. Tenenbaum, Tejas D. Kulkarni, Gholamreza Safaee Ardekani, Gang Li, Seyed Mehdi Jafarnejad, Lawrence Tan, Daniel C. Alexander, Nathan Silberman and Ryutaro Tanno and has published in prestigious journals such as PLoS ONE, Lecture notes in computer science and PubMed.

In The Last Decade

Ardavan Saeedi

9 papers receiving 679 citations

Hit Papers

Hierarchical deep reinforcement learning: integrating tem... 2016 2026 2019 2022 2016 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ardavan Saeedi United States 6 376 139 116 97 74 10 699
Shaobin Chen China 14 114 0.3× 53 0.4× 113 1.0× 186 1.9× 69 0.9× 54 667
Ali Moeini Iran 16 110 0.3× 46 0.3× 255 2.2× 158 1.6× 68 0.9× 97 977
Minghu Jiang China 16 323 0.9× 192 1.4× 58 0.5× 400 4.1× 98 1.3× 91 1.1k
Stefano Mariani Italy 13 123 0.3× 158 1.1× 32 0.3× 132 1.4× 31 0.4× 85 723
Rongshan Yu Singapore 18 290 0.8× 185 1.3× 250 2.2× 206 2.1× 83 1.1× 103 1.2k
Zemin Liu China 16 261 0.7× 119 0.9× 69 0.6× 271 2.8× 22 0.3× 51 1.1k
Zhihua Zhang China 14 91 0.2× 96 0.7× 72 0.6× 69 0.7× 17 0.2× 57 743
Yuedan Chen China 13 159 0.4× 76 0.5× 87 0.8× 165 1.7× 12 0.2× 44 749

Countries citing papers authored by Ardavan Saeedi

Since Specialization
Citations

This map shows the geographic impact of Ardavan Saeedi'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 Ardavan Saeedi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ardavan Saeedi more than expected).

Fields of papers citing papers by Ardavan Saeedi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ardavan Saeedi. 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 Ardavan Saeedi. The network helps show where Ardavan Saeedi may publish in the future.

Co-authorship network of co-authors of Ardavan Saeedi

This figure shows the co-authorship network connecting the top 25 collaborators of Ardavan Saeedi. A scholar is included among the top collaborators of Ardavan Saeedi 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 Ardavan Saeedi. Ardavan Saeedi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

10 of 10 papers shown
1.
Saeedi, Ardavan, et al.. (2022). Knowledge Distillation via Constrained Variational Inference. Proceedings of the AAAI Conference on Artificial Intelligence. 36(7). 8132–8140. 2 indexed citations
2.
Lovchinsky, Igor, Pouya Samangouei, Ardavan Saeedi, et al.. (2020). Discrepancy Ratio: Evaluating Model Performance When Even Experts Disagree on the Truth. International Conference on Learning Representations. 3 indexed citations
3.
Tanno, Ryutaro, Ardavan Saeedi, Swami Sankaranarayanan, Daniel C. Alexander, & Nathan Silberman. (2019). Learning From Noisy Labels by Regularized Estimation of Annotator Confusion. 11236–11245. 129 indexed citations
4.
Saeedi, Ardavan, Matthew D. Hoffman, Stephen DiVerdi, et al.. (2018). Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models. International Conference on Artificial Intelligence and Statistics. 1309–1317. 2 indexed citations
5.
Kulkarni, Tejas D., Karthik Narasimhan, Ardavan Saeedi, & Joshua B. Tenenbaum. (2016). Hierarchical deep reinforcement learning: integrating temporal abstraction and intrinsic motivation. DSpace@MIT (Massachusetts Institute of Technology). 29. 3682–3690. 318 indexed citations breakdown →
6.
Batmanghelich, Kayhan, et al.. (2016). Nonparametric Spherical Topic Modeling with Word Embeddings. PubMed. 2016. 537–542. 41 indexed citations
7.
Huggins, Jonathan H., Karthik Narasimhan, Ardavan Saeedi, & Vikash K. Mansinghka. (2015). JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. International Conference on Machine Learning. 693–701. 1 indexed citations
8.
Batmanghelich, Kayhan, Ardavan Saeedi, Michael Cho, Raúl San Jośe Estépar, & Polina Golland. (2015). Generative Method to Discover Genetically Driven Image Biomarkers. Lecture notes in computer science. 24. 30–42. 6 indexed citations
9.
Ardekani, Gholamreza Safaee, Seyed Mehdi Jafarnejad, Lawrence Tan, Ardavan Saeedi, & Gang Li. (2012). The Prognostic Value of BRAF Mutation in Colorectal Cancer and Melanoma: A Systematic Review and Meta-Analysis. PLoS ONE. 7(10). e47054–e47054. 181 indexed citations
10.
Saeedi, Ardavan & Alexandre Bouchard‐Côté. (2011). Priors over Recurrent Continuous Time Processes. Neural Information Processing Systems. 24. 2052–2060. 16 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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