Data Science Talk

Vassilis Tsotras (organizer)

Multidisciplinary Research Building (MRB) Seminar Room,

Sponsored by the Data Science Center, the purpose of the Data Science Seminar is to foster collaborations between the core Data Science faculty (CSE/ECE/STAT) and faculty from other sciences that face Data Science problems in their research. After the talk, there will be time for discussions.The talk will be given by Mst. Shamima Hossain, Dept. of Computer Science and Engineering, UCRTITLE:"Electronic Bee-Veterinarian: A Data Centric Approach to Monitor Honeybee Health" ABSTRACT:Honeybees are vital for pollination and food production. Among many factors, extreme temperature (e.g., due to climate change) is particularly dangerous for bee health. Anticipating such extremities would allow beekeepers to take early preventive action. Thus, given sensor (temperature) time series data from beehives, how can we find patterns and do forecasting? Forecasting is crucial as it helps spot unexpected behavior and thus issue warnings to the beekeepers. In that case, what are the right models for forecasting? ARIMA, RNNs, or something else?We propose the EBV (Electronic Bee-Veterinarian) method, which has the following desirable properties: (i) principled: it is based on a) diffusion equations from physics and b) control theory for feedback-loop controllers; (ii) effective: it works well on multiple, real-world time sequences, (iii) explainable: it needs only a handful of parameters (e.g., bee strength) that beekeepers can easily understand and trust, and (iv) scalable: it performs linearly in time. We applied our method to multiple real-world time sequences, and found that it yields accurate forecasting (up to 49% improvement in RMSE compared to baselines), and segmentation. Specifically, discontinuities detected by EBV mostly coincide with domain expert's opinions, showcasing our approach's potential and practical feasibility. Moreover, EBV is scalable and fast, taking about 20 minutes on a stock laptop for reconstructing two months of sensor data. (See paper at: https://epubs.siam.org/doi/epdf/10.1137/1.9781611978032.34) Joint work with Christos Faloutsos (CMU), Boris Baer (UCR), Hyoseung Kim (UCR), and Vassilis Tsotras (UCR) *** Pizza and refreshments will be provided! ***

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Data Science Talk

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Data Science Talk

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