Akhil Mathur

2.2k total citations
65 papers, 1.3k citations indexed

About

Akhil Mathur is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Akhil Mathur has authored 65 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Computer Vision and Pattern Recognition, 19 papers in Artificial Intelligence and 13 papers in Signal Processing. Recurrent topics in Akhil Mathur's work include Context-Aware Activity Recognition Systems (14 papers), Mobile Crowdsensing and Crowdsourcing (12 papers) and ICT in Developing Communities (9 papers). Akhil Mathur is often cited by papers focused on Context-Aware Activity Recognition Systems (14 papers), Mobile Crowdsensing and Crowdsourcing (12 papers) and ICT in Developing Communities (9 papers). Akhil Mathur collaborates with scholars based in United Kingdom, United States and India. Akhil Mathur's co-authors include Fahim Kawsar, Chulhong Min, Nicholas D. Lane, Alessandro Montanari, Claudio Forlivesi, Sourav Bhattacharya, John Canny, Matthew Kam, Anuj Kumar and Afra Mashhadi and has published in prestigious journals such as IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Mobile Computing and IEEE Pervasive Computing.

In The Last Decade

Akhil Mathur

64 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Akhil Mathur United Kingdom 21 501 337 263 231 228 65 1.3k
Chulhong Min South Korea 18 464 0.9× 175 0.5× 302 1.1× 253 1.1× 93 0.4× 75 1.1k
Jonathan Lester United States 16 711 1.4× 221 0.7× 298 1.1× 212 0.9× 159 0.7× 33 1.5k
Jane Yung-jen Hsu Taiwan 24 564 1.1× 492 1.5× 261 1.0× 330 1.4× 357 1.6× 140 1.7k
Vangelis Metsis United States 16 511 1.0× 439 1.3× 212 0.8× 127 0.5× 397 1.7× 60 1.6k
Christos Efstratiou United Kingdom 17 518 1.0× 183 0.5× 246 0.9× 339 1.5× 267 1.2× 48 1.4k
Yao‐Jen Chang Taiwan 24 598 1.2× 194 0.6× 210 0.8× 321 1.4× 134 0.6× 98 2.1k
Mario Muñoz-Organero Spain 25 299 0.6× 292 0.9× 141 0.5× 85 0.4× 278 1.2× 105 1.7k
Reza Rawassizadeh United States 17 331 0.7× 169 0.5× 133 0.5× 149 0.6× 145 0.6× 56 991
Henk Muller United Kingdom 16 421 0.8× 139 0.4× 406 1.5× 442 1.9× 207 0.9× 67 1.4k
Matthias Kranz Germany 22 565 1.1× 95 0.3× 244 0.9× 519 2.2× 182 0.8× 99 1.5k

Countries citing papers authored by Akhil Mathur

Since Specialization
Citations

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

Fields of papers citing papers by Akhil Mathur

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Akhil Mathur

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

All Works

20 of 20 papers shown
1.
Spathis, Dimitris, et al.. (2024). CroSSL: Cross-modal Self-Supervised Learning for Time-series through Latent Masking. 152–160. 7 indexed citations
2.
Ding, Aaron Yi, et al.. (2023). Tiny, Always-on, and Fragile: Bias Propagation through Design Choices in On-device Machine Learning Workflows. ACM Transactions on Software Engineering and Methodology. 32(6). 1–37. 10 indexed citations
3.
Min, Chulhong, Akhil Mathur, Utku Günay Acer, Alessandro Montanari, & Fahim Kawsar. (2023). SensiX++: Bringing MLOps and Multi-tenant Model Serving to Sensory Edge Devices. ACM Transactions on Embedded Computing Systems. 22(6). 1–27. 3 indexed citations
4.
Yang, Jing, Chulhong Min, Akhil Mathur, & Fahim Kawsar. (2022). SleepGAN: Towards Personalized Sleep Therapy Music. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 966–970. 4 indexed citations
5.
Cho, Hyunsung, Akhil Mathur, & Fahim Kawsar. (2022). FLAME. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies. 6(3). 1–29. 23 indexed citations
6.
Lei, Jie, Rishad Shafik, Alex Yakovlev, et al.. (2021). Low-Power Audio Keyword Spotting Using Tsetlin Machines. Journal of Low Power Electronics and Applications. 11(2). 18–18. 21 indexed citations
7.
Mathur, Akhil, et al.. (2021). Characterising the Role of Pre-Processing Parameters in Audio-based Embedded Machine Learning. Zenodo (CERN European Organization for Nuclear Research). 439–445. 5 indexed citations
8.
Wang, Chongyang, Temitayo Olugbade, Akhil Mathur, et al.. (2021). Chronic Pain Protective Behavior Detection with Deep Learning. arXiv (Cornell University). 2(3). 1–24. 11 indexed citations
9.
Mathur, Akhil, et al.. (2021). FRuDA: Framework for Distributed Adversarial Domain Adaptation. IEEE Transactions on Parallel and Distributed Systems. 1–1. 1 indexed citations
10.
Mathur, Akhil, Nadia Bianchi‐Berthouze, & Nicholas D. Lane. (2020). Unsupervised Domain Adaptation Under Label Space Mismatch for Speech Classification. 1271–1275. 2 indexed citations
11.
Agarwal, Ankur, et al.. (2019). EDUQA: Educational Domain Question Answering System Using Conceptual Network Mapping. arXiv (Cornell University). 8137–8141. 9 indexed citations
12.
Wang, Chongyang, Temitayo Olugbade, Akhil Mathur, et al.. (2019). Automatic Detection of Protective Behavior in Chronic Pain Physical Rehabilitation: A Recurrent Neural Network Approach.. arXiv (Cornell University). 3 indexed citations
13.
Mathur, Akhil, et al.. (2019). Situation-Aware Conversational Agent with Kinetic Earables (demo). 657–658. 1 indexed citations
14.
Min, Chulhong, Alessandro Montanari, Akhil Mathur, & Fahim Kawsar. (2019). A closer look at quality-aware runtime assessment of sensing models in multi-device environments. 271–284. 14 indexed citations
15.
Min, Chulhong, Akhil Mathur, Alessandro Montanari, & Fahim Kawsar. (2019). An early characterisation of wearing variability on motion signals for wearables. 166–168. 13 indexed citations
16.
Mathur, Akhil, et al.. (2019). AudiDoS: Real-Time Denial-of-Service Adversarial Attacks on Deep Audio Models. 978–985. 4 indexed citations
17.
Min, Chulhong, Alessandro Montanari, Akhil Mathur, Seung‐Chul Lee, & Fahim Kawsar. (2018). Cross-Modal Approach for Conversational Well-being Monitoring with Multi-Sensory Earables. 706–709. 9 indexed citations
18.
Mashhadi, Afra, et al.. (2016). A case study on capturing and visualising face-to-face interactions in the workplace. 575–584. 4 indexed citations
19.
Mathur, Akhil, et al.. (2015). Quantified Workplace. 37–41. 10 indexed citations
20.
Mathur, Akhil, et al.. (2012). LifeView. 371–380. 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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