Deriving actionable insights from real-world data
Our research focuses on understanding machine learning models and real-world data through quantitative metrics and analyses. We work on interdisciplinary and crossdisciplinary research projects in biomedical, life, health and social sciences and Computer Science education. Below are ongoing projects in our research group.
Many real-world applications of machine learning are multi-objective in nature, yet they are solved using single-objective formulation. We are developing new evoluationary-based approaches to solving such multi-objective problems in multiple domains.
Data-driven artificial intelligency and machine learning are rapidly changing the process of scientific discovery and development of solutions to real-world problems. We are interested in understanding and quantifying the value of the real-world data used in training of such models and their impact on the reliability, stability and fairness of the trained models.
Meta-research is the process of organizing, producing and communicating scientific research. Its overall aim is to contribute to the scientific ecosystem by identifying gaps in knowledge as well as in transparency, rigor and reproducibility. We are particularly interested in understanding the trends and interconnections of scholarly works at a large scale.
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Nathan is developing federated learning workflows for the analysis of single-cell RNA-sequencing data.
Andrea is developing collaborative human-AI approaches to text to image conversion.
Shelton is developing multi-objective evoluationary approaches for mining high-dimensional, noisy and biased data.
Get in touch, if you are interested in collaborations, have project ideas, or want to discuss our research.