Nathan Hall

Versatile research scientist with 15+ years of experience in neuroscience, boasting a strong foundation in mathematics, statistics, and software programming. Proficient in collecting and leveraging big datasets to answer questions through data mining and machine learning techniques. Passionate about problem-solving in dynamic, interdisciplinary environments with an approach that fuses scientific logical reasoning with quantitative analyses.

Classification algorithm

• Improved event classification with superior feature selection and noise estimator resulting in 50% reduction in false positive clusters
• Likelihood estimation algorithm improves true positive rate by 20% to allow analysis of correlations

Data mining package

• Combining, aligning, and selecting neuron and behavioral data
• Methods for modeling, statistical analyses and visualization of neural data

• Led design, analysis, and visualization for 6 peer-reviewed publications and 12+ conference presentations/lectures (details available upon request).
• Conceived research, training, and management plans for 2 federally funded NIH training grants