Emre Kıcıman

1.2k total citations · 1 hit paper
12 papers, 783 citations indexed

About

Emre Kıcıman is a scholar working on Artificial Intelligence, Statistics and Probability and Computer Networks and Communications. According to data from OpenAlex, Emre Kıcıman has authored 12 papers receiving a total of 783 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 7 papers in Statistics and Probability and 3 papers in Computer Networks and Communications. Recurrent topics in Emre Kıcıman's work include Advanced Causal Inference Techniques (7 papers), Bayesian Modeling and Causal Inference (5 papers) and Software System Performance and Reliability (3 papers). Emre Kıcıman is often cited by papers focused on Advanced Causal Inference Techniques (7 papers), Bayesian Modeling and Causal Inference (5 papers) and Software System Performance and Reliability (3 papers). Emre Kıcıman collaborates with scholars based in United States, India and United Kingdom. Emre Kıcıman's co-authors include Armando Fox, Mike Y. Chen, Amit Sharma, Amit Sharma, Maggie Makar, Sarah Bird, Adith Swaminathan, Ben Hutchinson, Denis Charles and Krishnaram Kenthapadi and has published in prestigious journals such as ACM Transactions on Intelligent Systems and Technology, Frontiers in Big Data and IEEE Transactions on Neural Networks.

In The Last Decade

Emre Kıcıman

12 papers receiving 725 citations

Hit Papers

align trajectories

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.

Pinpoint: problem determination in large, dynamic Internet services

2003 609 citations Mike Y. Chen, Emre Kıcıman et al. profile →

Peers

Emre Kıcıman

CNC

IS

SOFTW

AI

HA

Mohamed Nasser Canada

Julie Symons United States

Catia Trubiani Italy

Randolph Yao United States

Hiep Nguyen United States

Fabian Brosig Germany

Risto Vaarandi Estonia

Chang‐ai Sun China

Soila Kavulya United States

Roozbeh Farahbod Canada

Mohamed Nasser Canada

Emre Kıcıman

509 ×0.8

CNC

437 ×1.0

IS

238 ×0.8

SOFTW

137 ×0.7

AI

20 ×1.1

HA

Citations per year, relative to Emre Kıcıman Emre Kıcıman (= 1×) peers Mohamed Nasser

Countries citing papers authored by Emre Kıcıman

Since Specialization

Citations

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

Fields of papers citing papers by Emre Kıcıman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Emre Kıcıman

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

All Works

Sort: Min cites: Since: Top N: Style:

12 of 12 papers shown

1.

Charles, Denis, et al.. (2022). Causal Inference in the Presence of Interference in Sponsored Search Advertising. Frontiers in Big Data. 5. 888592–888592. 4 indexed citations

2.

Xu, Yanbo, et al.. (2021). Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions. 409–417. 1 indexed citations

3.

Sharma, Amit & Emre Kıcıman. (2020). Causal Inference and Counterfactual Reasoning. 369–370. 5 indexed citations

4.

Kıcıman, Emre & Amit Sharma. (2019). Causal Inference and Counterfactual Reasoning (3hr Tutorial). 828–829. 4 indexed citations

5.

Bird, Sarah, Ben Hutchinson, Krishnaram Kenthapadi, Emre Kıcıman, & Margaret Mitchell. (2019). Fairness-Aware Machine Learning. 3205–3206. 5 indexed citations

6.

Li, Jiuyong, Kun Zhang, Emre Kıcıman, & Peng Cui. (2019). Introduction to the Special Section on Advances in Causal Discovery and Inference. ACM Transactions on Intelligent Systems and Technology. 10(5). 1–3. 1 indexed citations

7.

Makar, Maggie, Adith Swaminathan, & Emre Kıcıman. (2019). A Distillation Approach to Data Efficient Individual Treatment Effect Estimation. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 4544–4551. 3 indexed citations

8.

Kıcıman, Emre & Amit Sharma. (2018). Tutorial on Causal Inference and Counterfactual Reasoning. Knowledge Discovery and Data Mining. 3 indexed citations

9.

Parashar, Manish, Renato Figueiredo, & Emre Kıcıman. (2010). Proceedings of the 7th international conference on Autonomic computing. 2 indexed citations

10.

Kıcıman, Emre & Armando Fox. (2005). Detecting Application-Level Failures in Component-Based Internet Services. IEEE Transactions on Neural Networks. 16(5). 1027–1041. 125 indexed citations

11.

Chen, Mike Y., et al.. (2003). Pinpoint: problem determination in large, dynamic Internet services. 595–604. 609 indexed citations breakdown →

12.

Chen, Mike Y., et al.. (2003). Using runtime paths for macroanalysis. 14–14. 21 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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