Daniel Agyapong

I am a Ph.D. student in the School of Informatics, Computing, and Cyber Systems at Northern Arizona University. I work on developing new statistical models, optimization algorithms, interactive systems, and software for machine learning.

My research interests are focused on fast, accurate, and interpretable algorithms for learning from large data, using continuous optimization (clustering, regression, ranking, classification). The main application domains for these algorithms are genomics, medicine and microbiome data analysis.

One of the major problems in ML is tuning hyperparameters. Selecting the right hyperparameters from a combination of hyperparameters is a challenging task and time consuming. I address this issue by using High-Performance Computing (HPC) to parallelize the hyperparameter search process. At NAU, our HPC (Monsoon) could potentially run 1000s of jobs in parallel, which makes it possible to search for the best hyperparameters in a reasonable amount of time.

I am currently working on training and testing ML models for predicting associations between bacteria (Positive and Negative Association).