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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. Contact: sarasameer991 [at] gmail [dot] com |
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News |
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Dec 2025 Our paper on PACE got accepted at ACM SAC 2026. Mar 2025 Our paper on GiNet got accepted at IEEE VTC 2025. |
Publications'*' denotes equal contribution |
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Pace: Physics-Aware Attentive Temporal Convolutional Network for Battery Health Estimation
Sara Sameer*, Wei Zhang*, Kannan Dhivya Dharshini, Xin Lou, Qingyu Yan, Terence Goh, Yulin Gao ACM Symposium On Applied Computing (SAC), 2026 paper / code A lightweight deep learning framework for accurate and efficient battery SoH monitoring. PACE combines temporal convolutional networks with physics-informed features from equivalent circuit models and chunked attention mechanisms to achieve superior performance while maintaining computational efficiency. |
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GINET: Integrating Sequential and Context-Aware
Learning for Battery Capacity Prediction
Sara Sameer*, Wei Zhang*, Xin Lou, Qingyu Yan, Terence Goh, Yulin Gao IEEE Vehicular Technology Conference (VTC), 2025 paper / code / talk / slides 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. |
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Optimizing Cycle Life Prediction of Lithium-ion Batteries via a Physics-Informed Model
Constantin-Daniel Nicolae, Sara Sameer, Nathan Sun, Karena Yan Transactions of Machine Learning Research (TMLR), 2025 Also presented at Joint Mathematics Meetings (JMM), 2024 paper / code / poster 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. |
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