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    <loc>https://www.obagoren.com/portfolio-2/neural-radiance-fields-nerfs</loc>
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    <lastmod>2022-09-07</lastmod>
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      <image:title>Projects - State Estimation and Filtering Methods</image:title>
      <image:caption>Extended Kalman Filter State Estimation for Pendulum System</image:caption>
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      <image:caption>3rd Order Gauss-Hermite Kalman Filter for State Estimation</image:caption>
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      <image:caption>5th Order Gauss-Hermite Kalman Filter for State Estimation</image:caption>
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      <image:caption>Unscented Kalman Filter for state estimation</image:caption>
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      <image:caption>Particle Filter vs. UKF for state estimation, displaying the empirical distribution that the particle filter is capable of representing.</image:caption>
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      <image:title>Projects - State Estimation and Filtering Methods - Filtering and State Estimation</image:title>
      <image:caption>This project was completed for AEROSP 567: Statistical Inference, Estimation and Learning. Implemented a variety of Gaussian Filters along with a particle filter with a bootstrap and optimal proposal. Relevant Skills and Topics: Python Gaussian Filters EKF UKF Gauss-Hermite KF Particle Filters</image:caption>
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    <loc>https://www.obagoren.com/portfolio-2/mobile-robotics</loc>
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    <lastmod>2021-12-26</lastmod>
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      <image:caption>This project was for ROB550: Robotics Systems Lab at the University of Michigan. The task was to implement the controls and perception to an autonomous mobile robot in order to explore and map previously unobserved environments. I was part of a 3 person team for this project. Relevant Skills and Topics: C++ LCM SLAM Path Planning Motion Control</image:caption>
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      <image:title>Projects - Mobile Robotics - Make it stand out</image:title>
      <image:caption>Map generated from a maze that the robot followed with given waypoints. Brown indicates the odometry readings, and blue represents the SLAM pose, in order to highlight the necessity for SLAM and sensor fusion for mobile robotics applications.</image:caption>
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      <image:caption>Correlation between samples and autocorrelation metric over a lag of 100, indicating a good sampling process</image:caption>
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      <image:caption>Posterior distribution of the parameters for the SIR model</image:caption>
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      <image:caption>Model representation after regression on model parameters</image:caption>
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      <image:title>Projects - Bayesian Regression for Nonlinear Models + MCMC - Make it stand out</image:title>
      <image:caption>Bayes’ Rule, posterior P(Θ|D) expressed in terms of the likelihood P(D|Θ), the prior P(Θ) and the evidence P(D). Sometimes written as the second line, as the evidence is difficult to compute; hence, the posterior is proportional to the likelihood and prior.</image:caption>
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    <lastmod>2022-09-07</lastmod>
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    <loc>https://www.obagoren.com/portfolio-2/linear-bayes</loc>
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    <lastmod>2021-12-26</lastmod>
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      <image:title>Projects - Bayesian Inference for Linear Models and Decision Making - Bayesian Inference for Linear Models and Decision Making</image:title>
      <image:caption>This project was completed for AEROSP 567: Statistical Inference, Estimation and Learning at the University of Michigan. Relevant Skills and Topics: Python Gaussian Processes Lineat GPR Hyperparameter Tuning and Kernel Design</image:caption>
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