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My research focuses extensively on time series analysis, with a particular emphasis on classification and forecasting tasks. In the initial phase of my doctoral research, I explored methods for data augmentation and the synthetic generation of time series to address the challenges of extensive labeling and data cleaning. Progressing further, my focus shifted towards adversarial attacks, where I developed cutting-edge attack strategies alongside robust defense mechanisms. Subsequently, I focused on time series forecasting, particularly investigating the limitations of transformers in this context and strategizing on enhancing their performance. This effort led to the development of SAMformer, a new state-of-the-art model in time series forecasting, excelling in both performance and training time. Currently, my research is centered on the development of foundation models for multivariate time series classification. My work, while centered on time series classification and forecasting, can be readily adapted to other fields like computer vision or NLP.
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2024 IEEE 40th International Conference on Data Engineering Workshops (ICDEW) (2024): 128-139
PROCEEDINGS OF THE 2023 WORKSHOP ON RECENT ADVANCES IN RESILIENT AND TRUSTWORTHY ML SYSTEMS IN AUTONOMOUS NETWORKS, ARTMAN 2023pp.17-28, (2023)
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#Papers: 4
#Citation: 0
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Activity: 13
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