Deep Learning for Single Cell Analysis

Project Summary

Bob Ziyang Ding (Math/Stats) and Daniel Chaofan Tao (ECE) spent ten weeks understanding how deep learning techniques can shed light on single cell analysis. Working with a large set of single-cell sequencing data, the team built an autoencoder pipeline and a device that will allow biologists to interactively visualize their own data.

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Themes and Categories
Paul Bendich

Disciplines Involved: Biology, Biomedical Engineering, PreHealth/PreMed, Biostatistics, all Quantitative STEM

Project Lead: Cliburn Chan

Project Manger: Kuei Yueh Ko

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In this project, we are interested in creating a cohesive data pipeline for generating, modeling and visualizing basketball data. In particular, we are interested in understanding how to extract data from freely available video, how to model such data to capture player efficiency, strength and leadership, and how to visualize such data outcomes. We will have four separate teams as part of this project working on interrelated but separate goals:

Team 1: Video data extraction

This team will explore different video data extraction techniques with the goal of identifying player locations, ball location and events at any given time during a basketball game. The software developed as part of this project will be able to generate a usable dataset of time-stamped basketball plays that can be used to model the game of basketball.

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The two teams will explore different models for the game of basketball. The first team will concentrate on modeling offensive plays and try to answer questions such as: How does the ball advance? What leads to successful plays? The second team will concentrate on defensive plays: What is an optimal strategy for minimizing opponent scoring opportunities? How should we evaluate defensive plays?

Team 4: Visualizing basketball data

This team will work on dynamic and static visualization of elements of a basketball game. The goal of the visualization is to capture information about how players and the ball move around the court. They will develop tools to represent average trajectories be in these settings that can also capture uncertainty about this information.

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