Logan Tate

Background

About Logan Tate

Designed a real-time MEG classification algorithm in Python that converted brain activity into actionable feedback, powering a neurofeedback rehabilitation system for paralyzed patients (with Japanese hospital partnership)- Designed and executed a pilot project investigating the neurobiological basis of the potential attention-enhancing effects of binaural beat auditory stimulation as the first step of development for an app marketed to individuals with ADHD- Gleaned actionable business insights from the analysis of quantitative and qualitative survey responses from service users, ultimately providing specific suggestions for increasing user satisfaction in a presentation to the VPs- Created roadmaps, defined KPIs, conducted user research (e.g, surveys, focus groups, A/B testing), evaluated success of service offerings, and identified areas for data-backed continuous improvement in an iterative cycle- Used bash and batch processing on HPC clusters to conduct analysis of large, longitudinal, high-dimensional data, including medical imaging data- Published a manuscript in a peer-reviewed journal comparing the effectiveness of independent components analysis (ICA) and spatio-spectral decomposition (SSD) signal processing algorithms for their respective feature extraction abilities- Co-authored a published manuscript using deep learning (YOLOv2-tiny) for image processing (https://doi.org/10.3390/electronics9091408)- Applied masters-equivalent training in research design, quantitative analysis, machine learning, and statistics (e.g, power analysis, principal components analysis, logistic regression, Hidden Markov models) to a decade of academic and real-world research questions- Developed and delivered dozens of trainings in both online and in-person formats and presented at local, national, and international conferences.