Higher Degree by Research Candidate
School of Computer and Mathematical Sciences
Faculty of Sciences, Engineering and Technology
My research area is focused on solving the problem of noisy label classification using generative and probabilistic approaches, with a particular interest in the intersection of GANs, audio processing, and NLP. Through my work, I aim to explore the advantages of these techniques, such as their ability to generate synthetic data and their robustness to label noise, and to apply them to real-world datasets in both NLP and audio processing. I also plan to investigate related topics, such as semi-supervised learning and active learning, and to contribute to the ongoing conversation about the potential of generative and probabilistic approaches for solving complex machine learning problems.
Date Institution name Country Title 2021 University of Adelaide Australia PhD 2019 - 2021 University of Adelaide Australia Master of Data Science 2015 - 2019 Rajasthan Technical University India Bachelors of Technology in Computer Science
Year Citation - Verma, M. A. K. (n.d.). BLOCKCHAIN: AN ANALYSIS ON NEXT-GENERATION INTERNET. International Journal of Advanced Research in Computer Science, 8(8), 429-432.
Year Citation 2023 Garg, A., Nguyen, C., Felix, R., Do, T. -T., & Carneiro, G. (2023). Instance-Dependent Noisy Label Learning via Graphical Modelling. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2023) (pp. 2287-2297). Online: IEEE.
DOI Scopus1 WoS1
2021 Shah, P., Garg, A., & Gajjar, V. (2021). PeR-ViS: Person Retrieval in Video Surveillance using Semantic Description. In Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision Workshops, WACVW 2021 (pp. 41-50). ELECTR NETWORK: IEEE COMPUTER SOC.
DOI Scopus8 WoS3
Lecturer for COMP SCI 7306 - Mining Big Data (Tri 1, 2022)
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