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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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NewsDec 2025 Our paper on PACE got accepted for oral presentation at ACM SAC 2026. Apr 2025 Our paper on Optimizing Cycle Life Prediction of Lithium-ion Batteries got accepted at TMLR. Mar 2025 Our paper on GiNet got accepted at IEEE VTC 2025. Aug 2024 Joined Singapore Institute of Technology as a Research Engineer. Jan 2024 Presented poster on Physics-Informed Model for Li-ion Batteries at Joint Mathematics Meetings (JMM) 2024. Jun 2023 Selected for summer research internship at UCLA under RIPS Program. |
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 ensemble model for efficient battery SoH monitoring for edge applications. |
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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 A gated recurrent units enhanced Informer network for predicting battery's capacity using long-term dependencies in battery data. |
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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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