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Biography
"Correlation does not imply causation"
I am a Ph.D. student in computer science at George Mason University. Before joining the GMU, I was an MS student in the systems engineering department at the University of Virginia, where I researched applying machine learning models to problems ranging from human activity recognition (HAR) to COVID-19 forecasting.
I enjoy working with data and building data-driven applications. I am an innovative, avid learner, team player, and detail-oriented person. I always strive to learn and tackle challenging problems.
Some of my Skills: Python, R, SQL, PyToch, Tensorflow, Keras, GCP, AWS
News
Nov 2023
Our paper "Leveraging Deep Learning to Improve COVID-19 Forecasting Using Wastewater Viral Load" was accepted at the 2023 IEEE International Conference on Big Data (IEEE BigData 2023).
June 2023
Our paper "Exercise and Sedentary Activity Recognition Using Late Fusion: Building Adaptable Uncertain Models" was presented at the 26th International Conference on Information FUSION.
Apr 2023
Our paper "Competitive Pricing Under Local Network Effects" was accepted by the European Journal of Operational Research.
Mar 2023
I got admitted to the Ph.D. program in computer science at George Mason University. I will start my Ph.D. in Fall 2023.
Dec 2022
The pre-print of our paper, "Leveraging Wastewater Monitoring for COVID-19 Forecasting in the US: a Deep Learning study" is now available on Arxiv.
Jun 2022
I joined Wayfair as a "Data Science and Machine Learning Intern" for the summer.
We submitted our paper "Competitive Pricing Under Local Network Effects" to The International Journal of Management Science (OMEGA).
Feb 2022
Our paper "Wastewater-Based Epidemiological Modeling for Continuous Surveillance of COVID-19 Outbreak" was published at the 2021 IEEE International Conference on Big Data.