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  <title>Education - Rita Ionides</title>
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    <div class="title">Rita Ionides</div>
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    <h1 class="headline"><center>education</center> </h1>

    <div class="card">
        <h2> University of Michigan </h2>
        <p><strong> Working my way through the Statistics PhD program.<br>
          B.S. Honors Mathematics, B.S. Data Science. Expected May 2027.</strong>  </p>

        <p>Graduate Courses:</p>
        <ul>
          <li> Stats 610: Statistical Theory </li>
          <li> Stats 600: Regression </li>
          <li> EECS 598: Computational Symmetry in AI</li>
          <li> Math 555: Complex Analysis </li>
          <li> Math 526: Stochastic Processes</li>
          <li> Math 525: Probability Theory</li>
        </ul>

        <p>Undergraduate Courses:</p>
        <ul>
          <li>Stats 451: Bayesian Statistics</li>
          <li>EECS 281: Data Structures & Algorithms</li>
          <li>Math 295: Real Analysis</li>
          <li>Math 452: Multivariate Analysis </li>
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        <h2> University of Michigan </h2>
        <p><strong> High school dual enrollment 2020-2023. </strong></p>
        <p>Undergraduate Courses:</p>
        <ul>
          <li>Stats 415: Data Mining</li>
          <li>Stats 426: Intro. Statistical Theory</li>
          <li>Stats 425: Intro. Probability </li>
          <li>Stats 306: Statistical Computing </li>
          <li>Stats 315: Statistics and AI </li>
          <li>EECS 203: Discrete Math </li>
          <li>Math 217: Linear Algebra </li>
          <li>Math 215: Calculus III</li>
          <li>Math 176: Real Analysis</li>
          <li>Stats 206 (now DATASCI 101): Intro. Data Science </li>
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        <h2>Privacy-Preserving Generative Models via Latent Diffusion</h2>
        <p>A differentially private autoencoder and diffusion model pipeline. Trained on CIFAR-10 with Opacus for strict noise accounting. Demonstrates high utility despite privacy constraints.</p>
        <img src="images/project-diffusion.jpg" alt="DP Diffusion" class="project-img">
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      <div class="card">
        <h2>Privacy-Preserving Generative Models via Latent Diffusion</h2>
        <p>A differentially private autoencoder and diffusion model pipeline. Trained on CIFAR-10 with Opacus for strict noise accounting. Demonstrates high utility despite privacy constraints.</p>
        <img src="images/project-diffusion.jpg" alt="DP Diffusion" class="project-img">
      </div>

      <div class="card">
        <h2>Privacy-Preserving Generative Models via Latent Diffusion</h2>
        <p>A differentially private autoencoder and diffusion model pipeline. Trained on CIFAR-10 with Opacus for strict noise accounting. Demonstrates high utility despite privacy constraints.</p>
        <img src="images/project-diffusion.jpg" alt="DP Diffusion" class="project-img">
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