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      <div class="brand">Akshatha Mohan</div>
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      <img class="avatar" src="images/avatar.jpg" alt="Akshatha Mohan profile photo" />
      <h1 class="author-name">Akshatha Mohan</h1>
      <p class="author-role">Graduate Research Assistant, WashU Medicine MIR/CIRC</p>
      <p class="author-meta"><i class="fa-solid fa-location-dot"></i> St. Louis, Missouri</p>
      <p class="author-meta"><i class="fa-solid fa-stethoscope"></i> Medical Imaging AI, Computational Imaging</p>
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        <a href="mailto:akshatha.mohan@tamu.edu" title="Email"><i class="fa-regular fa-envelope"></i></a>
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      <h2 class="page-title">I build clinically grounded AI for medical imaging.</h2>
      <p class="lead">
        I work in the Medical AI Lab at Washington University in St. Louis (MIR/CIRC), led by
        <a href="https://www.mir.wustl.edu/employees/shinjini-kundu/" target="_blank" rel="noreferrer">Dr. Shinjini Kundu, MD, PhD</a>.
        My research focuses on robust imaging pipelines, task-driven evaluation, and translational medical AI systems.
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      <p class="lead">
        In Duke collaborative work, I contributed to a task-driven evaluation of 13 image quality metrics across NLST, VLST, and DLCS datasets
        for lung nodule detection, lesion-centric cancer classification, and COPD quantification with CT harmonization.
      </p>
      <p class="lead"><a href="Akshatha_Mohan_PhD_applicant.pdf" target="_blank" rel="noreferrer">Curriculum Vitae (PDF)</a></p>

      <div class="section" id="news">
        <h2>News</h2>
        <ul class="news-list">
          <li><strong>2026:</strong> Continuing medical imaging AI research in MIR/CIRC with focus on clinically meaningful evaluation.</li>
          <li><strong>2025:</strong> Contributed to Duke collaborative manuscript on task-driven IQA and CT harmonization analysis.</li>
          <li><strong>2024:</strong> Publications across CVPRW, ICMLA, and SPIE on texture analysis and interpretable computer vision.</li>
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      <div class="section" id="impact">
        <h2>Impact Summary</h2>
        <ul class="clean-list">
          <li>Integrated structural, perceptual, and statistical IQA perspectives to evaluate cross-cohort CT reliability.</li>
          <li>Connected image quality behavior directly to downstream clinical tasks instead of relying on generic visual metrics alone.</li>
          <li>Built end-to-end AI systems, including retrieval-augmented LLM applications for practical decision-support workflows.</li>
        </ul>
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      <div class="section" id="selected-publications">
        <h2>Selected Publications</h2>

        <article class="paper-box">
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            <span class="badge">Manuscript in Progress</span>
            <img src="images/project-2.jpg" alt="Lung CT IQA manuscript visual" />
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            <h3>Task-Driven Evaluation of Image Quality Metrics for Lung CT AI Workflows</h3>
            <p class="paper-authors"><strong>A. Mohan</strong>, collaborators</p>
            <p class="paper-meta">Duke collaboration spanning NLST, VLST, and DLCS analyses for detection, classification, and harmonization tasks.</p>
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        <article class="paper-box">
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            <span class="badge">CVPRW 2024</span>
            <img src="images/lacunarity.png" alt="Lacunarity pooling visual" />
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            <h3>Lacunarity Pooling Layers for Plant Image Classification Using Texture Analysis</h3>
            <p class="paper-authors"><strong>A. Mohan</strong>, J. Peeples</p>
            <p class="paper-meta"><a href="https://openaccess.thecvf.com/content/CVPR2024W/Vision4Ag/papers/Mohan_Lacunarity_Pooling_Layers_for_Plant_Image_Classification_using_Texture_Analysis_CVPRW_2024_paper.pdf" target="_blank" rel="noreferrer">Paper link</a></p>
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