[ds-undergrads] [UCR_DataScience] Data Science talk by Mst. Shamima Hossein, Friday April 26th, 12-1pm, MRB Seminar Room

[tsotras at cs.ucr.edu]

Dear DS majors,
please find below the info for the next DS seminar this Friday at noon in
the MRB seminar room. Come and see how we used sensor temperature data to
predict if a beehive's health is in danger and alert the bee-keeper.

Please use the link below to register (pizza and refreshments will be
provided)

best,
V. Tsotras


---------------------------- Original Message ----------------------------
Subject: [UCR_DataScience] Data Science talk by Mst. Shamima Hossein,
Friday April 26th, 12-1pm, MRB Seminar Room
From:    "tsotras--- via DataScience" <datascience at lists.ucr.edu>
Date:    Mon, April 22, 2024 11:56 am
To:      datascience at lists.ucr.edu
--------------------------------------------------------------------------

We will have a Data Science seminar this Friday, April 26th, 12:00-1:00pm
at the MRB Seminar Room (1st floor). This talk presents a recent
collaboration that started out of the Data Science Seminar series.

**** Pizza and refreshments will be provided ****

To keep track of the number of attendees, please *register* at:
https://www.eventbrite.com/e/data-science-talk-tickets-887808578357

The talk will be given by Mst. Shamima Hossain, Dept. of Computer Science
and Engineering, UCR

TITLE:
"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)


------------------------------------
Sponsored by the UCR Data Science Center, the purpose of the Data Science
Seminars is to foster collaborations between "core" Data Science faculty
(from CSE/ECE/Stat Departments) and faculty/visitors from other sciences
that face Data Science problems in their research. These informal
gatherings are open to interested faculty and graduate students. Each
meeting will start with a talk describing research problems and then a
discussion will follow for questions, open problems, ideas for possible
collaborations etc.

A full list of previous seminars appears at:
http://datascience.ucr.edu/seminars

Forward this email to other colleagues or graduate students in your lab
that may be interested. Moreover, if you are interested in giving a Data
Science related talk, please contact me (tsotras at cs.ucr.edu).

Sincerely,
Vassilis Tsotras
Professor, CSE Department
co-Director, Data Science Major
Director, MS in Computational Data Science


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