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I'm a Ph.D. candidate at New Mexico Institute of Mining and Technology (NMT). My general research interests are on deep learning, time series classification, and neural architecture search. To learn more about me, you can find my most recent CV [here].

I'm currently working on my Ph.D. dissertation on Smart Meter Data Analytics using Deep Learning methods, More specifically, I am using Neural Architecture Search (NAS) approach to build an automated pipeline searching for a better model on time series classification. Many smart meter data analytics applications are within the domain of time series classification such as building occupancy detection, socio-demographic information identification, and nonintrusive load monitoring. The need for providing many services through the Smart Grid while protecting users' privacy is quite challenging. A non-intrusive approach via smart meter data through smart meter infrastructures attracted many attentions in the last decade.

Deep learning is really good at image recoginition and classification. However, a lot more works needs to be done to unleash higher potential of deep learning technique on more complicated, non-structured, and noisy real-world data.