UConn Student Wins Top Honors at the 2021 ExploreCSR Workshop

Congratulations to UConn Senior Kiera Wolfe on her first place win at the 2021 Tri-State ExploreCSR Workshop! Kiera’s team, including students from Fordham University and New York City College of Technology, was led by UConn Assistant Professor Caiwen Ding. The team’s project titled Utility Monitoring in Data Centers explored the accuracy of two different forecasting models created using statistical machine learning and deep learning techniques in order to predict CPU usage workloads of virtual machines. “Ultimately, we implemented both an ARIMA model and an LSTM model,” said Kiera. “We found that our experimental results were in accordance with our hypothesis that the more advanced LSTM model would outperform the ARIMA model.”

The poster competition highlighted the completion of an 8-week computer science research experience sponsored by Google Research, Fordham University, University of Connecticut and Stevens Institute of Technology. “During this two-month workshop, my teammates and I learned so much about machine learning and gained hands-on experience designing, implementing, and testing forecasting models.” Kiera reflected. “On top of getting to meet and work with students from other universities, I was able to perform some very interesting research in the field of machine learning.”


Kiera was not the only UConn student to earn recognition at the competition! Awards were also presented to the following students:

Second Place:

Ashely Tang, “Quantum Deep Learning for Binary Classification”

Millenia Polanco, “Preference Detection using Eye Movement Gaze Cascade and Combinatorial   Fusion”

Third Place:

Ayushi Patel, Khori Francis, Kathryn Dyson and Nathan Choi, “Predicting COVID-19 Transmission in Connecticut Using Deep Learning”

Kate Piotrowski, “Quantum Convolutional Neural Network”

Saumya Shah, “Efficient Pruning for Deep Neural Networks”

Kayla Alston, “Spam Detectives: SMS Spam Detection”

Michaela Felipe and Aditya Kulkarni, “Recognition on Food Images”

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