Melissa R. Dale, PhD
Machine Learning Research Scientist
Modeling complex systems under uncertainty
ABOUT
I’m a machine learning researcher focused on building predictive models under uncertainty. My work centers on combining multiple data sources to improve decision-making in complex, real-world systems. I’m particularly interested in applying these methods to scientific problems where models guide real-world exploration and discovery.
EDUCATION
Michigan State University
2016 – 2024
PhD: Computer Science and Engineering
Information Fusion, Machine Learning, AutoML, Computer Vision
Montana State University
2011 – 2014
Master of Science: Computer Science
Montana State University
2006 – 2011
Undergraduate, Magna Cum Laude
BS in Computer Science
BA in Modern Languages (Spanish)
Minor in Physics
PUBLICATIONS
To Impute or Not: Recommendations for Multibiometric Fusion
WIFS 2023
Dale M., Singer E., Borgström B., Ross A.
IEEE International Workshop on Information Forensics and Security, Germany, December 2023.
On the Design of the MIT LL Trimodal Dataset for Identity Verification
IWBF 2023
Singer E., Borgström B. J., Alperin K., Nguyen T., Dagli C., Dale M., & Ross A.
IEEE 11th International Workshop on Biometrics and Forensics, Barcelona, April 2023.
Addressing Missing Scores in Evolving Multibiometric Systems
ICPR 2022
Dale M., Jain A., Ross A.
26th International Conference on Pattern Recognition, Canada, August 2022.
Fusing AutoML Models: A Case Study in Medical Image Classification
ICPRAI 2022
Dale M., Ross A., Shapiro E.
3rd International Conference on Pattern Recognition and Artificial Intelligence, France, June 2022.
Impacts of Design Pattern Decay on System Quality
ESEM 2014
Dale M., Izurieta C.
8th ACM-IEEE International Symposium on Empirical Software Engineering and Measurement, Torino, September 2014.
EXPERIENCE
Machine Learning Researcher
2016 – Present
iPRoBe Lab @ Michigan State University
I design and evaluate machine learning systems that integrate multiple data sources to improve
predictive performance under uncertainty. My work focuses on understanding when additional data
improves predictions and how to build robust models across heterogeneous datasets.
Technologies: Python, Scikit-learn, Pandas, NumPy, Matplotlib, AutoML, R
Teaching Assistant, Instructor
2014 – 2020
Montana State University, Michigan State University
Taught and supported courses in programming, algorithms, data structures, biometrics,
discrete mathematics, web design, and ethics in computing.
Software Engineering Research Assistant
2012 – 2016
Software Engineering Lab @ Montana State University
Developed analytical tools to study software architecture, design pattern degradation,
technical debt, and long-term maintainability in large codebases.
Technologies: Java, R
XBRL Services Software Engineer Intern
2013 – 2014
WebFilings / Workiva
Refactored nearly 10,000 lines of legacy code across 30 files into a more maintainable,
modular structure with minimal regression.
Technologies: XBRL, Java, Python
PROJECTS
Map Generation Pipeline
2025
Built a geospatial data pipeline using OSMnx and GeoPandas to extract, process, and visualize
OpenStreetMap data. Designed configurable rendering workflows for feature extraction,
visualization, and large-scale custom map generation.
Technologies: Python, OSMnx, GeoPandas, Matplotlib
Score Fusion App
2020
Framework for evaluating and comparing score-level fusion strategies across biometric datasets,
including normalization, distribution analysis, and performance visualization.
Technologies: Python, Kivy, Pandas, Scikit-learn, Matplotlib
Grime Injector
2014
Java-based research tool for simulating architectural decay and measuring the impact of
design pattern erosion on software quality.
Technologies: Java, SonarQube, JAD, Javassist
BEYOND THE RESUME
I care about mentoring the next generation of STEM students and making technical material more approachable. Outside of research, I enjoy creative technical projects that combine programming, design, and real-world problem solving.