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Machine Learning Engineer & Statistician
Machine learning engineer and Ph.D. statistician building production AI systems for noisy, high-dimensional, and time-dependent data.
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Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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Blog Post number 2
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Production AI/ML Systems for Complex Data Workflows
AI/ML systems that transform noisy, unstructured, multimodal, and time-dependent inputs into structured outputs for downstream products and decision workflows.
Retrieval and Reasoning Systems
Workflows that combine language models, retrieval, validation, tool execution, and structured actions across heterogeneous data sources.
Neural, Sensor, and Time-Series Modeling
Statistical modeling, deep learning, and signal-processing methods for noisy biological, neural, and sensor time-series data.
Computer Vision Retrieval for Production Search
Deep learning retrieval pipeline for person re-identification — 96% rank-1 accuracy, 33% revenue lift.
Bayesian Forecasting for Large-Scale Time-Series Data
Bayesian time-series forecasting model for national cell-tower traffic — 36% accuracy improvement over the production baseline.
publications
Dynamic compensation mechanism gives rise to period and duty-cycle level sets in oscillatory neuronal models
Published in Journal of Neurophysiology, 2016
Study of dynamic compensation mechanisms producing period and duty-cycle invariance in oscillatory neuronal models.
Recommended citation: Rotstein, H., Olarinre, M., & Golowasch, J. (2016). "Dynamic compensation mechanism gives rise to period and duty-cycle level sets in oscillatory neuronal models." Journal of Neurophysiology.
Population burst propagation across interacting areas of the brain
Published in Journal of Neurophysiology, 2022
Statistical analysis of how population bursts propagate across interacting brain areas in large-scale neural recordings.
Recommended citation: Chen, Y., Douglas, H., Medina, B., Olarinre, M., Siegle, J. and Kass, R. (2022). "Population burst propagation across interacting areas of the brain." Journal of Neurophysiology.
Identification of interacting neural populations: methods and statistical considerations
Published in Journal of Neurophysiology, 2023
Methods and statistical considerations for identifying interacting neural populations in multi-area recordings.
Recommended citation: Kass, R., Bong, H., Olarinre, M., Xin, Q., and Urban, K. (2023). "Identification of interacting neural populations: methods and statistical considerations." Journal of Neurophysiology.
Relative timing and coupling of neural population bursts in large-scale recordings from multiple neuron populations
Published in Frontiers in Computational Neuroscience, 2024
Statistical methods for estimating relative timing and coupling of neural population bursts in large-scale recordings.
Recommended citation: Olarinre, M., Siegle, J., Kass, R. "Relative timing and coupling of neural population bursts in large-scale recordings from multiple neuron populations." Frontiers in Computational Neuroscience.
talks
Subject-to-subject variability in the relative timing and coupling of population bursts in mouse visual cortex
Published:
The Allen Brain Observatory Visual Coding Neuropixels data set contains simultaneous recordings from hundreds of neurons across many brain areas in multiple mice. We study the variation, across mice, of the functional connectivity among brain visual areas.
teaching
36-236: Probability and Statistical Inference II (Spring 2023)
Course, Statistics and Datascience Department, CMU, 2023
This is the second half of a two-semester, calculus-based course sequence that introduces theoretical aspects of probability and statistical inference to students. The course covers specific probability distributions and their inferential applications, starting with the normal distribution and continuing with the binomial and Poisson distributions, etc., and their use in point and interval estimation, hypothesis testing, and regression. Also covers topics related to multivariate distributions: marginal and conditional distributions, covariance, and conditional distribution moments.
36-225: Introduction to Probability Theory (Fall 2023)
Course, Statistics and Datascience Department, CMU, 2023
This is the first half of a year-long course which provides an introduction to probability and mathematical statistics for undergraduate students in the data sciences. Topics include elementary probability theory, conditional probability and independence, random variables, distribution functions, joint and conditional distributions, law of large numbers, and the central limit theorem.