Sara Sameer

I am a Research Engineer at Singapore Institute of Technology, where I work on Knowledge-based Deep Learning models for battery analytics under the guidance of Prof. Wei Zhang. My research interests are desigining deep learning models for multivariate and irregularly sampled time-series data.

Previously, I was a Machine Learning (ML) Intern at the University of California, Los Angeles, where I worked with Prof. Tan Minh Nguyen and industry partners from Toyota North America on building physics informed neural network for lifecycle prediction of Lithium-ion batteries.

Before that, I completed my Bachelor's degree in Computer Science from FAST NUCES Karachi.

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Publications

'*' denotes equal contribution

We propose GiNet, a gated recurrent units enhanced Informer network, for predicting battery's capacity. The novelty and competitiveness of GiNet lies in its capability of capturing sequential and contextual information from raw battery data and reflecting the battery's complex behaviors with both temporal dynamics and long-term dependencies.

A hybrid approach combining a physics-based equation with a self-attention model to predict the cycle lifetimes of commercial Li-ion Batteries via early-cycle data.