Om Thakkar
Impact in
- Artificial Intelligence top 10%
- Privacy-Preserving Technologies in Data
- Cryptography and Data Security
- Stochastic Gradient Optimization Techniques
- Adversarial Robustness in Machine Learning
- Topic Modeling
Papers in
-
- Privacy-Preserving Technologies in Data 10
- Speech Recognition and Synthesis 5
- Cryptography and Data Security 4
- Stochastic Gradient Optimization Techniques 4
- Topic Modeling 3
- Intelligent Tutoring Systems and Adaptive Learning 1
- Adversarial Robustness in Machine Learning 1
-
- Mobile Crowdsensing and Crowdsourcing 2
- Co-authors
- Abhradeep Thakurta (5 shared papers)Galen Andrew (1 shared paper)H. Brendan McMahan (1 shared paper)Lun Wang (2 shared papers)Roger Iyengar (1 shared paper)Joseph P. Near (1 shared paper)Dawn Song (1 shared paper)Rajiv Mathews (5 shared papers)
- Journals
- International Conference on Artificial Intelligence and Statistics (1 paper)International Conference on Machine Learning (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)IEEE Conference Proceedings (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesCanadaChina
In The Last Decade
Om Thakkar
14 papers receiving 178 citations
Peers
Comparison fields: 5 of 31
- Health Informatics 9
- Artificial Intelligence 165
- Computer Science Applications 19
- Computer Vision and Pattern Recognition 15
- Information Systems 15
Countries citing papers authored by Om Thakkar
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
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-authors
The 21 scholars most cited alongside Om Thakkar, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 67 | |
| 2 | Differentially Private Learning with Adaptive Clipping | 2021 | 42 |
| 3 | 2021 | 25 | |
| 4 | 2022 | 9 | |
| 5 | Evading the Curse of Dimensionality in Unconstrained Private GLMs | 2021 | 7 |
| 6 | Characterizing Private Clipped Gradient Descent on Convex Generalized Linear Problems. | 2020 | 7 |
| 7 | 2022 | 5 | |
| 8 | 2022 | 5 | |
| 9 | Differentially Private Matrix Completion Revisited | 2018 | 4 |
| 10 | 2024 | 3 | |
| 11 | 2022 | 3 | |
| 12 | 2024 | 2 | |
| 13 | 2024 | 1 | |
| 14 | 2024 | 1 |
About Om Thakkar
Om Thakkar is a scholar working on Artificial Intelligence, Computer Science Applications, Control and Systems Engineering, Computational Mechanics and Hardware and Architecture, having authored 14 papers that have together received 181 indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (10 papers), Speech Recognition and Synthesis (5 papers), Cryptography and Data Security (4 papers), Stochastic Gradient Optimization Techniques (4 papers), Topic Modeling (3 papers), Mobile Crowdsensing and Crowdsourcing (2 papers), Intelligent Tutoring Systems and Adaptive Learning (1 paper) and Adversarial Robustness in Machine Learning (1 paper). The work is most often cited by research in Health Informatics (9 citations), Artificial Intelligence (165 citations), Computer Science Applications (19 citations), Computer Vision and Pattern Recognition (15 citations) and Information Systems (15 citations). Om Thakkar has collaborated with scholars based in United States, Canada and China. Frequent co-authors include Abhradeep Thakurta, Galen Andrew, H. Brendan McMahan, Lun Wang, Roger Iyengar, Joseph P. Near, Dawn Song, Rajiv Mathews, Françoise Beaufays and Gautam Kamath. Their work appears in journals such as International Conference on Artificial Intelligence and Statistics, International Conference on Machine Learning, Proceedings of the AAAI Conference on Artificial Intelligence, IEEE Conference Proceedings and arXiv (Cornell University).
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.