Data Science Poster Fair Archives

The Data Science Poster Fair is held annually in at the end of the fall and spring semester. As a part of their degree requirements, Data Science M.S. students conduct research under the direction of a faculty advisor culminating with a capstone project and poster presentation. Students presenting at this event will discuss details of their capstone project, including their driving question, methods, and results, and will be available to address questions and connect with guests during their time slot. Faculty from numerous departments across the University are affiliated with the data science graduate program, therefore a variety of areas are represented with each student’s capstone project. 

Poster fairs are generally open to the public and all interested undergraduate and graduate students, alumni, staff, faculty, and industry professionals are encouraged to attend.  During the pandemic, the poster fair was hosted virtually.

Some of the more recent poster fair entries and winners are detailed below. Additionally, check out this list of all previously presented posters

Poster Fair Entries and Winners

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Fall 2022

The Fall 2022 Data Science Poster Fair featured seven posters. There was no best poster or people's choice award at the event. All submitted posters are detailed below.

Pui Ying Yew
Advisor: Chih-Lin Chi, Institute for Health Informatics

"Association of the 2013 blood cholesterol guidelines and generic statin availability with statin treatment in the U.S. population"

Shuai An
Advisor: Daniel Boley, Department of Computer Science & Engineering

"Projecting rice yield in the Mekong River Delta under climate change"

Linxin Li
Advisor: Qian Qin, School of Statistics

"Performance of MCMC in different scenario"

Sarthak Jain
Advisor: Sandra Safo, School of Public Health

"Disease classification using multi-view longitudinal data with Deep IDA"

Fafa Hoshyargar
Advisor: Ilja Siepmann, Department of Chemistry

"Neural networks for automated soft-material mesophase discovery from simulation and experimental microscope data"

Anthony Cotter
Advisor: Michael Steinbach, Department of Computer Science & Engineering

"Risk model development for peer to peer sports betting"

Yizhe Wang
Advisor: Qian Qin, School of Statistics

"Exploring time series stock market forecasting with CPI, PPI and Nonfarm data"


Spring 2022

The spring 2022 Data Science Poster Fair featured 14 posters and was the first event held in person since the pandemic. 

People's Choice Award: Bob Sturm
Capstone project title: Towards Efficient IACT Calibration with Deep learning-based Muon Identification
Advisor: Lucy Fortson

Fall 2021

The fall 2021 Data Science Poster Fair was held virtually via GatherTown. There was no best poster or people's choice award at the event. All submitted posters are detailed below.

Poster 1: Won Joon Choi
Advisor: Daniel Boley
Capstone title: Patterns in Genomic Variation of SARS-CoV2

Poster 2: Ramanish Singh
Advisor: Ilja Siepmann
Capstone title: Prediction of Unary Adsorption Isotherms in Zeolites Using Neural Networks
Poster 4: Abby Slater
Advisor: Daniel Boley
Capstone title: Grain Quality Predictive Modeling

Poster 5: Miao Yang
Advisor: Arindam Banerjee
Capstone title:  Machine Learning and Equity Investment


Spring 2021

The spring 2021 Data Science Poster Fair was held virtually via Zoom. There was no best poster or people's choice award at the event. All submitted posters are detailed below.

“A Causal Analysis of Bipolar Disorder”
Hunter Chavis-Blakely
Advisor: Erich Kummerfeld
“Detecting mental state using Machine Learning”
Mingqian Duan
Advisor: Ju Sun
“Modeling COVID-19 Case Counts with Long Short-Term Memory Networks”
Brandon Voigt
Advisor: Tracy Flood
“Exploring User Engagement in An Online Health Community”
Ruyuan Wan
Advisor: Lana Yarosh, Maria Gini
“Federated Learning approach to crop identification from satellite data”
Anubha Agrawal
Advisor: Kevin Silverstein
“Defining and Monitoring Patient Clusters Based on Therapy Adherence in Sleep Apnea Management”
Mourya Karan Reddy Baddam
Advisor: Jaideep Srivastava
“Inferring level of social connectedness from observed online/offline behavior”
Ayushi Rastogi
Advisor: Lana Yarosh
“Deep Learning Approaches for Breast Cancer Related Concepts Extraction from Electronic Health Records”
Sicheng Zhou
Advisor: Rui Zhang
"Using electronic health records to understand the effects of dietary supplements among patients with Mild cognitive impairment"
Jiyang Chen
Advisor: Rui Zhang
“Machine Learning in Stock Trading”
Yuanyuan Qiu
Advisor: Paul Schrater
“Deep Learning for Morphology Detection of Self-Assembly in Atomistic Simulation”
Zhengyuan Shen
Advisor: Ilja Siepmann
“Prediction Model for Mortality in Patients with Rib Fractures Based on ICU Timeline”
Qixian Zhao
Advisor: Christopher Tignanelli
“Implementing GAN-based method for real-valued medical time series data generation”
Anushree Choudhary
Advisor: Jaideep Srivastava
“Fraud Detection Using Machine Learning Methods”
Sheng Huang
Advisor: Daniel Boley
“Anomaly detection in Ship Trajectories”
Divya Shrinivasa Nairy
Advisor: Shashi Shekhar
“Deep Neural Network Diagnosis of Autism Spectrum Disorder Through Visual Image Eye Movements”
Connor Theisen
Advisor: Catherine Qi Zhao
“Fake Chest Radiograph Generation”
Rutvij Umesh Bora
Advisor: Daniel Boley
“Hazard Detection for the Visually Impaired”
Jason Moericke
Advisor: Paul Schrater
“Leveraging Machine Learning to Predict Inherited Variants Associated with Chronic Lymphocytic Leukemia”
Raphael Mwangi
Advisor: Cavan Reilly

"Chest X-Ray Images Generation Using GAN"

Iris Yan
Advisor: Daniel Boley


Spring 2020

Instead of submitting posters for in-person evaluation, the spring 2020 capstone projects were submitted via video. Each student prepared and submitted a five-minute video, highlighting their research under the guidance of their faculty advisor. All submissions were reviewed by a panel of faculty judges, and the winning projects were selected.

Best video award: Sai Kumar Kayala
Capstone project title: DeepFake Detection using Deep Learning
Advisor: Daniel Boley

Runner-up: Alexander Long
Capstone project title: Assessing Sensitivity of Income Inequality Measurements to Gerrymandering
Advisor: Shashi Shekhar

Runner-up: Suhail Alnahari
Capstone project title: Zooniverse Raccoon Project: Accelerating Crowd-Sourcing using Machine Learning
Advisor: Lucy Fortson