Om Thakkar

514 total citations
14 papers, 170 citations indexed

About

Om Thakkar is a scholar working on Artificial Intelligence, Computer Science Applications and Control and Systems Engineering. According to data from OpenAlex, Om Thakkar has authored 14 papers receiving a total of 170 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 2 papers in Computer Science Applications and 1 paper in Control and Systems Engineering. Recurrent topics in Om Thakkar's work include Privacy-Preserving Technologies in Data (10 papers), Speech Recognition and Synthesis (5 papers) and Cryptography and Data Security (4 papers). Om Thakkar is often cited by papers focused on Privacy-Preserving Technologies in Data (10 papers), Speech Recognition and Synthesis (5 papers) and Cryptography and Data Security (4 papers). Om Thakkar collaborates with scholars based in United States, China and Canada. Om Thakkar's co-authors include Abhradeep Thakurta, H. Brendan McMahan, Galen Andrew, Joseph P. Near, Dawn Song, Lun Wang, Roger Iyengar, Rajiv Mathews, Françoise Beaufays and Xi He and has published in prestigious journals such as arXiv (Cornell University), International Conference on Machine Learning and International Conference on Artificial Intelligence and Statistics.

In The Last Decade

Om Thakkar

14 papers receiving 167 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Om Thakkar United States	6	155	18	14	13	13	14	170
Mikhail Yurochkin United States	6	138 0.9×	24 1.3×	13 0.9×	15 1.2×	20 1.5×	19	169
Natalia Ponomareva United States	5	139 0.9×	12 0.7×	28 2.0×	12 0.9×	5 0.4×	21	165
Yunhui Long United States	5	122 0.8×	7 0.4×	12 0.9×	15 1.2×	16 1.2×	9	133
Rajiv Mathews United States	7	90 0.6×	6 0.3×	11 0.8×	12 0.9×	6 0.5×	13	103
Bargav Jayaraman United States	5	141 0.9×	15 0.8×	17 1.2×	17 1.3×	5 0.4×	7	158
Haoxuan Che Hong Kong	4	117 0.8×	26 1.4×	19 1.4×	13 1.0×	15 1.2×	9	148
Kallista Bonawitz United States	3	60 0.4×	8 0.4×	14 1.0×	11 0.8×	10 0.8×	3	75
Vasileios Iosifidis Germany	5	134 0.9×	9 0.5×	16 1.1×	11 0.8×	11 0.8×	7	176
Prashan Madumal Australia	4	155 1.0×	5 0.3×	7 0.5×	4 0.3×	16 1.2×	7	186

Countries citing papers authored by Om Thakkar

Since Specialization

Citations

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

Fields of papers citing papers by Om Thakkar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Om Thakkar

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

All Works

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14 of 14 papers shown

Shejwalkar, Virat, Om Thakkar, & Arun Narayanan. (2024). Quantifying Unintended Memorization in BEST-RQ ASR Encoders. 2905–2909. 1 indexed citations

Chien, Steve, et al.. (2024). Training Large ASR Encoders With Differential Privacy. 102–109. 1 indexed citations

Jagielski, Matthew, Om Thakkar, & Lun Wang. (2024). Noise Masking Attacks and Defenses for Pretrained Speech Models. 4810–4814. 2 indexed citations

Wang, Lun, Om Thakkar, & Rajiv Mathews. (2024). Unintended Memorization in Large ASR Models, and How to Mitigate It. 4655–4659. 3 indexed citations

He, Xi, et al.. (2022). The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection. Proceedings of the AAAI Conference on Artificial Intelligence. 36(7). 7806–7813. 9 indexed citations

Amid, Ehsan, Om Thakkar, Arun Narayanan, Rajiv Mathews, & Françoise Beaufays. (2022). Extracting Targeted Training Data from ASR Models, and How to Mitigate It. Interspeech 2022. 2803–2807. 3 indexed citations

Thakkar, Om, et al.. (2022). A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter IT. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 4338–4342. 5 indexed citations

Chien, Steve, et al.. (2022). Detecting Unintended Memorization in Language-Model-Fused ASR. Interspeech 2022. 2808–2812. 5 indexed citations

Thakkar, Om, Galen Andrew, & H. Brendan McMahan. (2021). Differentially Private Learning with Adaptive Clipping. arXiv (Cornell University). 34. 41 indexed citations

10.

Thakkar, Om, et al.. (2021). Understanding Unintended Memorization in Language Models Under Federated Learning. 1–10. 22 indexed citations

11.

Steinke, Thomas, et al.. (2021). Evading the Curse of Dimensionality in Unconstrained Private GLMs. International Conference on Artificial Intelligence and Statistics. 2638–2646. 5 indexed citations

12.

Song, Shuang, Om Thakkar, & Abhradeep Thakurta. (2020). Characterizing Private Clipped Gradient Descent on Convex Generalized Linear Problems.. 6 indexed citations

13.

Iyengar, Roger, Joseph P. Near, Dawn Song, et al.. (2019). Towards Practical Differentially Private Convex Optimization. 299–316. 63 indexed citations

14.

Jain, Prateek, Om Thakkar, & Abhradeep Thakurta. (2018). Differentially Private Matrix Completion Revisited. International Conference on Machine Learning. 2215–2224. 4 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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