1. Gabe Mersy
(612) 916 5973 • gabemersy@gmail.com
linkedin.com/in/gabemersy/ • gmersy
Education
University of Minnesota May 2021 (anticipated)
Bachelor’s Degree
Computer Science, Emphasis in Statistics and Applied Mathematics
Research Experience
Undergraduate Research Assistant | TNTLab | Dr. Richard Landers Ongoing
○ Developing novel data engineering and feature engineering methodology using Revelian Inc.’s mouse click data
○ Applying machine learning algorithms to predict game score from engineered features
Research Assistant | ChatBot Research Group | Dr. Maria Gini, Dr. Pakhomov Ongoing
○ Rewriting a photoplethysmography (PPV) signal processing algorithm that will be incorporated into a stress
monitoring wearable technology system
Data Science Intern | The Burr Project, Inc. June 2019 - August 2019
○ Built, trained and tested a feed forward Artificial Neural Network (ANN) in Keras that accurately predicted
state-level political polarization from census data
Undergraduate Research Assistant | Dr. Glen Meeden April 2019 - August 2019
○ Developed computational methods used in Dr. Meeden’s Bayesian statistical sampling methodology research
Publications
Auer, E. M., Marin, S., Landers, R. N., Mersy, G. A., Mujcic, S., Blaik, J. A. (2020). You Are What
You Click: Trace Data as Big Data in Psychological Testing and Measurement. International Journal of
Testing (Manuscript under review).
Santore, V., Mersy, G., Rand, I. et al. (2019). The Application of Deep Learning in the Prediction of
State Legislature Polarization from Demographic Data. (Manuscript under review).
Work Experience
Code Curator | kite.com [contract] January 2020 – Present
○ Technical writing and coding of answers to frequently-asked Python questions as a reference for Software Engineers,
Machine Learning Engineers and Data Scientists
Machine Learning Engineer | Craft Music [contract] September 2019 – December 2019
○ Built a Gaussian Machine Learning algorithm into the mobile application that is used to rank users
Skills
○ Nontechnical: teamwork, leadership, empathy, integrity, creativity, interpersonal communication,
interdisciplinary problem solving, patience, dependability, critical thinking
○ Technical: machine learning, artificial intelligence, deep learning, data analytics, Bayesian statistics, data
engineering, natural language processing, agile software development, data analysis, data visualization
○ Programming: Python, R, Java, C/C++, SQL, MongoDB
○ Libraries: TensorFlow, scikit-learn, Keras, Pandas, PyTorch, MatplotLib, NumPy
Memberships
Association for Computing Machinery [ACM]; American Statistical Association [ASA]
Competitions
○ 2nd Place University of Minnesota Undergraduate Data Analytics Competition [2019]
○ MinneMUDAC Student Data Science Challenge [2019]