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
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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" |
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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 "GeoAI" | 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" |
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Fall 2022
The Fall 2022 Data Science Poster Fair featured seven posters. All submitted posters are detailed below.
| Pui Ying Yew Advisor: Chih-Lin Chi "Association of the 2013 blood cholesterol guidelines and generic statin availability with statin treatment in the U.S. population" | Shuai An Advisor: Daniel Boley "Projecting rice yield in the Mekong River Delta under climate change" | Linxin Li Advisor: Qian Qin "Performance of MCMC in different scenario" | Sarthak Jain Advisor: Sandra Safo "Disease classification using multi-view longitudinal data with Deep IDA" |
| Fafa Hoshyargar Advisor: Ilja Siepmann "Neural networks for automated soft-material mesophase discovery from simulation and experimental microscope data" | Anthony Cotter Advisor: Michael Steinbach "Risk model development for peer to peer sports betting" | Yizhe Wang Advisor: Qian Qin "Exploring time series stock market forecasting with CPI, PPI and Nonfarm data" |
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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" |
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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" |
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Spring 2021
The spring 2021 Data Science Poster Fair was held virtually via Zoom. All submitted posters are detailed below.
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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" |