Data Science Poster Fair Archives

The Data Science Poster Fair is held at the end of the fall and spring semesters. 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. 

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

Expand all

Fall 2023

The Fall 2023 Data Science Poster Fair featured six posters. All submitted posters are detailed below.

Aviral Bhatnagar
Advisor:  Jaideep Srivastava
"Human-in-the loop Approach to enhance Urban Living"
Jingran Cao
Advisor: Kristin Palmsten
"Managing depression during pregnancy"
Colin Ornelas
Advisor: Jie Ding
"Using Machine Learning Methods to Predict Quarterback Fantasy Football Scores"
Zhecheng Sheng
Advisor: Serguei Pakhomov
"Explore Gender Bias in Dementia Detection with BERT"
Jonah Shi
Advisor: Sisi Ma
"Applying machine learning methods to identify predictors and causes for trauma related outcomes"
Nicole Sullivan
Advisor: Erich Kummerfeld
"It was all YELLOW: predicting depression in teenage students using a bi-Yearly Ensemble Learner with an Optimization Workflow"

Spring 2023

The Spring 2023 Data Science Poster Fair featured 21 posters. All submitted posters are detailed below.

John Carruth 
Advisor: Chang Ge 
"Secure Multiparty Top-k"
Mohammed Guiga 
Advisor: Julian Wolfson 
"Measuring Physical Distancing and Mask-Wearing Behavior in Public Spaces using Computer Vision and Deep Learning Techniques"
Mark Jokinen 
Advisor: Erich Kummerfeld 
"Identifying At-Risk Students and Analyzing Achievement Decline with Causal Analysis"
Navanshu Khare 
Advisor: Rui Zhang 
"Synergistic effects of PI and NPI for AD/ADRD"
Anisha Khetan 
Advisor: Sisi Ma 
"Modeling SRT liver data using machine learning methods"
Shashank Magdi 
Advisor: ​​​​​​Erich Kummerfeld 
"How are the student behaviors, health conditions, the classes they opt & MCA test scores affecting the Hopkins District student's High school GPA's?"
Kelsey Neis 
Advisor: Dongyeop Kang 
"An Analysis of Reader Engagement in Literary Fiction Through Eye Tracking"
Minh Nguyen 
Advisor: Jie Ding 
"Photovoltaic Electricity Generation Forecasting"
Noah Rissman 
Advisor: Erich Kummerfeld 
"The Impact of Free and Reduced Lunch Programs on BIPOC Education Disparities in the Hopkins School District"
Nan Wang 
Advisor: Rui Zhang 
"Extracting SBDH concepts"
Destiny Ziebol 
Advisor: Gyorgy Simon 
"Synthetic raw EHR data generation with preserved causal structure" 
Avinash Akella 
Advisor: Joseph Konstan 
"Beyond Accuracy: Understanding user perception of diversity and serendipity in online movie recommenders"
SriHarshitha Anuganti 
Advisor: Rui Zhang 
"A causal analysis to investigate factors associated with bariatric surgery"
Aviral Bhatnagar 
Advisor: Jaideep Srivastava 
Raj Vaibhav Gude 
Advisor: Erich Kummerfeld 
"Identifying education and health outcome disparities in MN K12 BIPOC using Causal Inference"
Silas Swarnakanth Kati 
Advisor: Erich Kummerfeld 
"Understanding Institutional and Systemic Factors Contributing to Achievement Gaps in Education and Strategies for Mitigation"
Rahul Mehta 
Advisor: Erich Kummerfeld 
"Causal Discovery Analysis of Bipolar Disorder Patients"
Steven Moore 
Advisor: Galin Jones 
"Stellar Nucleosynthesis"
Gavin Schaeferle 
Advisor: Moein Enayati 
"Identifying Unmet Needs for Phenotyping Using Deep NLP Algorithms"
Shifa Siddiqui 
Advisor: Rui Zhang 
"Leveraging natural language processing to analyze healthcare data"
Xiaobing Wang 
Advisor: Xiaotong Shen 
"A Performance Evaluation of Group-Specific Recommender Systems"

Spring 2022

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

Bob Sturm
Advisor: Lucy Fortson
"Towards Efficient IACT Calibration with Deep learning-based Muon Identification"
Hexuan Zhang
Advisor: Charles Doss
"Tax Prediction and Inference with Time Series"
Miguel Miguélez Díaz
Advisor: Dongyeop Kang
"Sentiment Analysis using NLP: Contextual Saliency Variation" 
Tiruo Yan
Advisor: Mochen Yang 
"Efficiently Increase Anchor Customers by Scoring and Prioritizing Sysco Leads"
Qi Le
Advisor: Jie Ding
"Privacy-Preserving Personalized Recommender Systems with Federated AutoEncoders"
Sai Sharan Sundar
Advisor: Gregory Pawloski 
"Isolating Supernova Neutrinos using Machine Learning Techniques"
Rui Zhou
Advisor: Galin Jones
"Statistics of Supernova Siblings in Host Galaxies from 1900 through 2022"
Linxi Zhang
Advisor: Qian Qin 
"MCMC and its applications"
Rebecca Dura
Advisor: Gregory Pawloski 
"Identifying Neutrinos Associated with Supernova Events"
Sam Walczak
Advisor: Adam Rothman
"U.S. Severe Thunderstorm Activity Over Time"
Yue Liang
Advisor: Chih-Lin Chi 
"Patient Centered Prescription using Counterfactual Prediction Optimization Algorithms for Statin Users"
Samuel Johnson
Advisor: Galin Jones
"Analysis of the Demographics of Supernovae within Host Galaxies"
Rosalind Hong
Advisor: Jeff Calder
"Graph learning techniques in hyper spectral image classification"
Xianjian Xie
Advisor: Jie Ding
"Client selection in federated learning with adversarial clients"


Fall 2021

The fall 2021 Data Science Poster Fair was held virtually via GatherTown. All submitted posters are detailed below.

Won Joon Choi
Advisor: Daniel Boley
"Patterns in Genomic Variation of SARS-CoV2"
Ramanish Singh
Advisor: Ilja Siepmann
"Prediction of Unary Adsorption Isotherms in Zeolites Using Neural Networks"
Abby Slater
Advisor: Daniel Boley
"Grain Quality Predictive Modeling"
Miao Yang
Advisor: Arindam Banerjee
"Machine Learning and Equity Investment"


Spring 2021

The spring 2021 Data Science Poster Fair was held virtually via Zoom. All submitted posters are detailed below.

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


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.

Sai Kumar Kayala
Advisor: Daniel Boley
"DeepFake Detection using Deep Learning"
Alexander Long
Advisor: Shashi Shekhar
"Assessing Sensitivity of Income Inequality Measurements to Gerrymandering"
Suhail Alnahari
Advisor: Lucy Fortson
"Zooniverse Raccoon Project: Accelerating Crowd-Sourcing using Machine Learning"
Daniel Baxter
Advisor: Serguei Pakhomov
"Automatic Generation of Training Data for Acronym Disambiguation"
Shreya Datar
Advisor: Serguei Pakhomov
"Modelling the Physiological Markers of Stress"
Aditya Gaydhani
Advisor: Serguei Pakhomov
"Natural Language Conversational Agent for Daily Living Assessment Coaching"
Zachary Gilfix
Advisor: Ed McFowland
"Exploring the Causal Impact of Movie Piracy on Box Office Revenue"
Nanxun Huang
Advisor: Daniel Boley
"Value of poster analysis for predicting click action in movie recommender system"
Sheng Huang
Advisor: Daniel Boley
"Deep Learning Networks for Intracranial Hemorrhage Detection"
Tatiana Lenskaia
Advisor: Daniel Boley
"Prokaryote autoimmunity in the context of self-targeting by CRISPR-Cas systems"
Samuel Naden
Advisor: Rui Kuang
"Cerebral atrophy, anticoagulants, and the risk for developing chronic subdural hematoma"
Shriya Rai
Advisor: Michael Steinbach
Anchit Sharma
Advisor: Daniel Boley
"Land cover classification using remote sensing data"
Patrick Skoglund
Advisor: Jeffrey Lortie
"Creating a Mortality Table for an Aging Block of Life Insurance"
Karthik Unnikrishnan
Advisor: Daniel Boley
"Investment Portfolio Optimization using Reinforcement Learning"