Raha Moraffah

612 total citations
15 papers, 321 citations indexed

About

Raha Moraffah is a scholar working on Artificial Intelligence, Signal Processing and Information Systems. According to data from OpenAlex, Raha Moraffah has authored 15 papers receiving a total of 321 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 4 papers in Signal Processing and 2 papers in Information Systems. Recurrent topics in Raha Moraffah's work include Topic Modeling (4 papers), Adversarial Robustness in Machine Learning (4 papers) and Bayesian Modeling and Causal Inference (3 papers). Raha Moraffah is often cited by papers focused on Topic Modeling (4 papers), Adversarial Robustness in Machine Learning (4 papers) and Bayesian Modeling and Causal Inference (3 papers). Raha Moraffah collaborates with scholars based in United States, Hong Kong and China. Raha Moraffah's co-authors include Huan Liu, Ruocheng Guo, Adrienne Raglin, Mansooreh Karami, Qianru Wang, K. Selçuk Candan, Lu Cheng, Amrita Bhattacharjee, Nitin Agarwal and Vineeth Rakesh and has published in prestigious journals such as Knowledge and Information Systems, IEEE Transactions on Artificial Intelligence and ACM SIGKDD Explorations Newsletter.

In The Last Decade

Raha Moraffah

13 papers receiving 313 citations

Peers

Raha Moraffah

AI

SP

IS

SP

CNC

Adrienne Raglin United States

Zhixuan Chu United States

James Jordon United Kingdom

Emily Jia United States

Dylan Slack United States

Ghazanfar Farooq Siddiqui Pakistan

Nicolas Langrené Australia

Aleksander Øhrn Norway

Mathias Bauer Germany

Alessandro Antonucci Switzerland

Adrienne Raglin United States

Raha Moraffah

153 ×0.8

AI

25 ×0.6

SP

27 ×0.7

IS

26 ×0.8

SP

26 ×1.1

CNC

Citations per year, relative to Raha Moraffah Raha Moraffah (= 1×) peers Adrienne Raglin

Countries citing papers authored by Raha Moraffah

Since Specialization

Citations

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

Fields of papers citing papers by Raha Moraffah

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Raha Moraffah

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

All Works

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15 of 15 papers shown

1.

Moraffah, Raha, et al.. (2024). Causality Guided Disentanglement for Cross-Platform Hate Speech Detection. 626–635. 7 indexed citations

2.

Moraffah, Raha, et al.. (2024). Glue pizza and eat rocks - Exploiting Vulnerabilities in Retrieval-Augmented Generative Models. 1610–1626. 3 indexed citations

3.

Bhattacharjee, Amrita, Raha Moraffah, Joshua Garland, & Huan Liu. (2024). Zero-shot LLM-guided Counterfactual Generation: A Case Study on NLP Model Evaluation. 1243–1248. 5 indexed citations

4.

Moraffah, Raha, et al.. (2024). Causal Inductive Biases for Cognitive Machine Learning. 14–16.

5.

Moraffah, Raha, et al.. (2023). How Reliable Are AI-Generated-Text Detectors? An Assessment Framework Using Evasive Soft Prompts. 1337–1349. 4 indexed citations

6.

Bhattacharjee, Amrita, et al.. (2023). ConDA: Contrastive Domain Adaptation for AI-generated Text Detection. 598–610. 13 indexed citations

7.

Raglin, Adrienne & Raha Moraffah. (2023). Data Integrity and Artificial Reasoning. 93–96.

8.

Moraffah, Raha & Huan Liu. (2022). Query-Efficient Target-Agnostic Black-Box Attack. 368–377. 1 indexed citations

9.

Cheng, Lu, et al.. (2022). Evaluation Methods and Measures for Causal Learning Algorithms. IEEE Transactions on Artificial Intelligence. 3(6). 924–943. 40 indexed citations

10.

Moraffah, Raha, et al.. (2021). Causal inference for time series analysis: problems, methods and evaluation. Knowledge and Information Systems. 63(12). 3041–3085. 71 indexed citations

11.

Richmond, Christ D., et al.. (2020). METRIC-Bayes: Measurements Estimation for Tracking in High Clutter using Bayesian Nonparametrics. 1518–1522. 1 indexed citations

12.

Moraffah, Raha, Mansooreh Karami, Ruocheng Guo, Adrienne Raglin, & Huan Liu. (2020). Causal Interpretability for Machine Learning - Problems, Methods and Evaluation. ACM SIGKDD Explorations Newsletter. 22(1). 18–33. 145 indexed citations

13.

Rakesh, Vineeth, Ruocheng Guo, Raha Moraffah, Nitin Agarwal, & Huan Liu. (2018). Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects. 1679–1682. 20 indexed citations

14.

Sarwat, Mohamed, et al.. (2017). Database System Support for Personalized Recommendation Applications. 34. 1320–1331. 10 indexed citations

15.

Yu, Jia, Raha Moraffah, & Mohamed Sarwat. (2017). Hippo in Action: Scalable Indexing of a Billion New York City Taxi Trips and Beyond. 1413–1414. 1 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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