2020 Data Science Poster Fair

This year's Data Science M.S. Poster Fair will be a virtual event in light of the state of Minnesota's current stay at home orders. Students will submit a 5 minute video highlighting their capstone project under the guidance of a faculty advisor. In the coming weeks, videos will be reviewed by a panel of faculty judges to identify a winning submission as well as honorable mentions. Watch for more information on the winning selections to come.
 
This year's presenters are:
 
Name Capstone Project Title Advisor
Suhail Alnahari Zooniverse Raccoon Project: Accelerating Crowd-Sourcing using Machine Learning Lucy Fortson
Daniel Baxter Automatic Generation of Training Data for Acronym Disambiguation Serguei Pakhomov
Shreya Datar Modelling the Physiological Markers of Stress Serguei Pakhomov
Aditya Gaydhani Natural Language Conversational Agent for Daily Living Assessment Coaching Serguei Pakhomov
Zachary Gilfix Exploring the Causal Impact of Movie Piracy on Box Office Revenue Ed McFowland
Nanxun Huang Value of poster analysis for predicting click action in movie recommender system Joseph Konstan
Sheng Huang Deep Learning Networks for Intracranial Hemorrhage Detection Daniel Boley
Sai Kumar Kayala Deep learning Approach for DeepFake Detection Daniel Boley
Tatiana Lenskaia Prokaryote autoimmunity in the context of self-targeting by CRISPR-Cas systems Daniel Boley
Alexander Long Assessing Aggregation Affects on Inequality Measurements in the US Census Shashi Shekhar
Samuel Naden Cerebral atrophy, anticoagulants, and the risk for developing chronic subdural hematoma Rui Kuang
Shriya Rai TBD Michael Steinbach
Anchit Sharma Land cover classification using remote sensing data Daniel Boley
Patrick Skoglund Creating a Mortality Table for an Aging Block of Life Insurance Jeffrey Lortie
Karthik Unnikrishnan Investment Portfolio Optimization using Reinforcement Learning Daniel Boley
 
The M.S. in Data Science program provides a strong foundation in the science of Big Data and its analysis by gathering in a single program the knowledge, expertise, and educational assets in data collection and management, data analytics, scalable data-driven pattern discovery, and the fundamental concepts behind these methods. Jointly sponsored by the College of Science and Engineering, College of Liberal Arts, and School of Public Health.
 
 

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