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index.html

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+          <tr>
+            <td style="padding:20px;width:25%;vertical-align:middle">
+              <img src="images/hackprinceton.gif" alt="clean-usnob" width="160" height="160">
+            </td>
+            <td width="75%" valign="middle">
+              <a href="https://prakash-shekhar.github.io/Bartimaeus/">
+                <papertitle>Bartimaeus:  Stealing Vision Back - <b style="color:red;">HACKPRINCETON 2024 FINALIST + HEALTHCARE TRACK WINNER</b></papertitle>
+              </a>
+              <br>
+              October 2024
+              <br>
+              <a href="https://github.com/prakash-shekhar/Bartimaeus">Github</a>
+              /
+              <a href="https://prakash-shekhar.github.io/Bartimaeus/">Website</a>              
+              <br>
+              <p>Created state-of-the-art pre-trained computer vision models + greedy-based path planning algorithm to navigate indoor enviroments for the visually impaired.</p>
+            </td>
+          </tr>
+          <tr>
           <tr>
             <td style="padding:20px;width:25%;vertical-align:middle">
               <img src="images/hackcmu.gif" alt="clean-usnob" width="160" height="160">
@@ -186,7 +205,7 @@
               <p>Using DC-GANs to create synthetic data that can be used to train + validate the robustness of autonomous vehicles. End goal is to apply to extrapolate the data pool we have for adverse driving scenarios/any situations where limited data is available.</p>
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               <img src="images/lanedetection.png" alt="clean-usnob" width="160" height="160">
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               <br>
               <p>Implemented <a href="https://arxiv.org/pdf/1903.02193.pdf">this paper</a> from scratch with just my knowledge of Python and stackoverflow. Focusing on managing spatio-temporal image data for successful training of lane detection. Lanes don't change every single frame, so why can't we take in a few input frames and be able to successfully predict lane lines? Note: Github repository is not up to date as I have been unable to locate some of the files as part of training but the ConvLSTM model and training loop is created (and formatted nicely!).</p>
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               <img src="images/sdcar.gif" alt="clean-usnob" width="160" height="160">
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               <p>Used my knowledge throughout the Udacity Self-Driving Nanodegree [from perception and sensor fusion of LiDAR and radar sensors to polynomial trajectory generation] to create my capstone project; an autonomous vehicle capable of driving in simulation (using waypoitns). I built <a href="https://srianumakonda.medium.com/learning-to-drive-from-a-self-driving-car-a4dda9eb2307">trajectory algorithms</a>, <a href="https://medium.com/mlearning-ai/who-and-where-are-we-in-this-world-d5636f58455c">localization algorithnms</a>, and <a href="https://medium.com/analytics-vidhya/the-true-beauty-of-extended-kalman-filters-32e0c08d9258">kalman filters</a> to name a few of the projects I built!</p>
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+          <!-- <tr>
             <td style="padding:20px;width:25%;vertical-align:middle">
               <img src="images/advlane.gif" alt="clean-usnob" width="160" height="160">
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               <br>
               <p>Creating a custom lane detection pipeline w/ Gaussian Filters + Polynomial fitting along with object detection using sliding windows w/ SVMs with the use of pure Machine Learning.</p>
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