Melissa Moody

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machine learning data science institute data science open data big data data analytics data engineering university of virginia deep learning python natural language processing open science data visualization wikipedia text analytics capstone data ethics biology text mining tom tom founders festival data finance business analytics biomedical engineering evolution algorithm online harassment gender bias artificial intelligence bias social media science medicine health neural network politics redistricting tom tom founders fest

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Melissa Moody

Präsentationen

Joining Separate Paradigms: Text Mining & Deep Neural Networks to Characterize Cell Type in the Cortex and Hippocampus

A Modified K-Means Clustering Approach to Redrawing US Congressional Districts

How to Beat the House: Predicting Football Results with Hyperparameter Optimization

Modeling the Impact of R & Python Packages: Dependency and Contributor Networks

Assessing the reproducibility of DNA microarray studies

Steering Model Selection with Visual Diagnostics

Ethical Priniciples for the All Data Revolution

Introduction to Social Network Analysis

Collective Biographies of Women: A Deep Learning Approach to Paragraph Annotation

Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations

Wikimedia Foundation, Trust & Safety: Cyber Harassment Classification and Prediction of User Blocks in Wikipedia

Dokumente

Plans for the University of Virginia School of Data Science

Automatic detection of online abuse and analysis of problematic users in wikipedia

Deep Learning Meets Biology: How Does a Protein Helix Know Where to Start and Stop?

Improving Credit Card Fraud Detection: Using Machine Learning to Profile and Predict Behavior

Data Collection Methods for Building a Free Response Training Simulation

Connecting citizens with public data to drive policy change