2024 schedule

Author: fredhohman

Diff for: index.html

@@ -139,8 +139,8 @@ <h2>7<sup>th</sup> Workshop on <br> <b>Visualization for AI Explainability</b></
         <h2 id="dates">Important Dates</h2>
         <pre>
 <s>July 30, 2024</s> August 06, 2024, anywhere: Submission Deadline
-September 10, 2024: Author Notification
-October 1, 2024: Camera Ready Deadline
+<s>September 10, 2024:</s> Author Notification
+<s>October 1, 2024:</s> Camera Ready Deadline
 <b>October 13, 2024 - Morning Session (ET)</b>
     </pre>
 
@@ -155,92 +155,116 @@ <h2 id="program">Program Overview</h2>
 </p>
 
 <table style="padding: 5pt;">
-    <!-- <tr>
-        <td class="schedule">8:00</td>
+    <tr>
+        <td class="schedule">8:30am</td>
         <td><b>Welcome from the Organizers</b></td>
     </tr>
     <tr>
-        <td class="schedule">8:00 -- 8:30</td>
-        <td><b>Session I</b>
+        <td></td>
+        <td><b>Session I (75 minutes)</b>
+    </tr>
+    <tr>
+        <td class="schedule">8:35 -- 9:15</td>
+        <td><b>Opening Keynote: David Bau - <a href="https://x.com/davidbau" target="_blank">@davidbau</a></b>
+            <br>
+            <b>Resilience and Human Understanding in AI</b>
+            <br>
+            What is the role of human understanding in AI?  As increasingly massive AI systems are deployed into an unpredictable and complex world, interpretability and controllability are the keys to achieving resilience. We discuss results in understanding and editing large-scale transformer language models and diffusion image synthesis models, and how these are part of an emerging research agenda in interpretable generative AI. Finally, we talk about the concentration of power that is emerging due to the scaling up of large-scale AI, and the kind of infrastructure that will be needed to ensure broad and democratized human participation in the future of AI.
+        </td>
+    </tr>
+
+    <tr>
+        <td class="schedule">9:15 -- 9:45</td>
+        <td><b>Lightning Talks I</b>
+            
             <br>
-            <a href="https://conformal-prediction.streamlit.app/">
-                Conformal Prediction: A Visual Introduction
+            <a href="https://pair.withgoogle.com/explorables/patchscopes/">
+                Can Large Language Models Explain Their Internal Mechanisms?
             </a>
-            -- Mihir Agarwal, Lalit Chandra Routhu, Zeel B Patel, Nipun Batra
+            -- Nada Hussein, Asma Ghandeharioun, Ryan Mullins, Emily Reif, Jimbo Wilson, Dr. Nithum Thain, Dr Lucas Dixon
             <br>
-            <a href="https://jku-vds-lab.github.io/amumo">
-                Understanding and Comparing Multi-Modal Models
+            <a href="https://yizhe-ang.github.io/matrix-explorable/">
+                The Matrix Arcade: A Visual Explorable of Matrix Transformations
             </a>
-            -- Christina Humer, Vidya Prasad, Marc Streit, Hendrik Strobelt
+            -- Yi Zhe Ang
             <br>
-            <a href="https://www.moritzdueck.com/neighborhood-traces/">
-                Neighborhood traces: When your neighborhood changes one layer at a time
+            <a href="visexplain.com">
+                Explaining Text-to-Command Conversational Models
             </a>
             -- Moritz Dück, Johannes Knittel, Hendrik Strobelt, Mennatallah El-Assady
             <br>
-            <a href="http://jku-vds-lab.at/hoxai-at-visxai/">
-                Of Deadly Skullcaps and Amethyst Deceivers: Reflections on a Transdisciplinary Study on XAI and Trust
+            <a href="http://talktoranker.njitvis.com/">
+                TalkToRanker: A Conversational Interface for Ranking-based Decision-Making
             </a>
-            -- Andreas Hinterreiter, Christina Humer, Benedikt Leichtmann, Martina Mara, Marc Streit
+            -- Conor Fitzpatrick, Jun Yuan, Aritra Dasgupta
             <br>
-            <a href="https://observablehq.com/@visforpinns/visualization-for-understanding-pinns">
-                VisForPINNs: Visualization for Understanding Physics Informed Neural Networks
+            <a href="https://murphyka.github.io/information_explorable/">
+                Where is the information in data?
             </a>
-            -- Viny Saajan Victor, Manuel Ettmüller, Andre Schmeißer, Heike Leitte, Simone Gramsch
+            -- Kieran Murphy, Dani S. Bassett
+            <br>
+            <a href="https://explainability-vit.ivia.ch/">
+                Explainability Perspectives on a Vision Transformer: From Global Architecture to Single Neuron
+            </a>
+            -- Anne Marx, Yumi Kim, Luca Sichi, Diego Arapovic, Javier Sanguino, Rita Sevastjanova, Mennatallah El-Assady
         </td>
     </tr>
     <tr>
-        <td class="schedule">8:30 -- 8:45</td>
+        <td class="schedule">9:45 -- 10:15</td>
         <td><b>Break</b></td>
     </tr>
     <tr>
-        <td class="schedule">8:45 -- 9:15</td>
-        <td><b>Session II</b>
+        <td></td>
+        <td><b>Session II (75 minutes)</b>
+    </tr>
+    <tr>
+        <td class="schedule">10:15 -- 10:45</td>
+        <td><b>Lightning Talks II</b>
             <br>
-            <a href="https://pair.withgoogle.com/explorables/grokking/">
-                Do Machine Learning Models Memorize or Generalize?
+            <a href="https://elanapearl.github.io/blog/2024/the-illustrated-alphafold/">
+                The Illustrated AlphaFold
             </a>
-            -- Adam Pearce, Asma Ghandeharioun, Nada Hussein, Nithum Thain, Martin Wattenberg, Lucas Dixon
+            -- Elana P Simon, Jake Silberg
             <br>
-            <a href="https://poloclub.github.io/diffusion-explainer/">
-                Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion
+            <a href="https://cchen-vis.github.io/Narrative-Viz-for-Neural-Network-Robustness/">
+                A Visual Tour to Empirical Neural Network Robustness
             </a>
-            -- Seongmin Lee, Benjamin Hoover, Hendrik Strobelt, Zijie J. Wang, ShengYun Peng, Austin P Wright, Kevin Li, Haoyang Yang. Haekyu Park, Duen Horng Chau
+            -- Chen Chen, Jinbin Huang, Ethan M Remsberg, Zhicheng Liu
             <br>
-            <a href="https://graphical-models.netlify.app">
-                Learning What's in a Name with Graphical Models
+            <a href="https://visxai-aml.vercel.app/">
+                Panda or Gibbon? A Beginner's Introduction to Adversarial Attacks
             </a>
-            -- Vu Luong, Justin S Selig
+            -- Yuzhe You, Jian Zhao
             <br>
-            <a href="https://mlu-explain.github.io/neural-networks/">
-                Neural Networks: A Visual Introduction
+            <a href="https://visual-intelligence-umn.github.io/GNN-101/">
+                What Can a Node Learn from Its Neighbors in Graph Neural Networks?
             </a>
-            -- Jared Wilber
+            -- Yilin Lu, Chongwei Chen, Matthew Xu, Qianwen Wang
             <br>
-            <a href="https://www.cs.brandeis.edu/~dylan/pac_learning/">
-                PAC Learning Or: Why We Should (and Shouldn't) Trust Machine Learning
+            <a href="https://www.explainprompt.com">
+                ExplainPrompt: Decoding the language of AI prompts
             </a>
-            -- Dylan Cashman
+            -- Shawn Simister
+            <br>
+            <a href="https://visualizing-interpretable-model.vercel.app">
+                Inside an interpretable-by-design machine learning model: enabling RNA splicing rational design
+            </a>
+            -- Mateus Silva Aragao, Shiwen Zhu, Nhi Nguyen, Alejandro Garcia, Susan Elizabeth Liao
         </td>
     </tr>
     <tr>
-        <td class="schedule">9:15 -- 9:30</td>
-        <td><b>Break</b></td>
-    </tr> -->
-    <tr>
-        <!-- <td class="schedule">9:30 -- 10:30</td> -->
-        <td><b>Keynote: Adam Pearce - <a href="https://twitter.com/adamrpearce" target="_blank">@adamrpearce</a></b>
+        <td class="schedule">10:45 -- 11:30</td>
+        <td><b>Closing Keynote: Adam Pearce - <a href="https://twitter.com/adamrpearce" target="_blank">@adamrpearce</a></b>
             <br>
-            <b>Why aren't we using visualizations to interact with AI?</b>
+            <b>Why Aren't We Using Visualizations to Interact with AI?</b>
             <br>
             Well-crafted visualizations are the highest bandwidth way of downloading information into our brains. As complex machine learning models become increasingly useful and important, can we move beyond mostly using text to understand and engage with them?
         </td>
     </tr>
-    <!-- <tr><td></td><td><iframe style="border-radius:0.5em" width="560" height="315" src="https://www.youtube.com/embed/xVxnbfZ2fKs?si=n260v3wYSYA2uGn1" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe></td></tr>
     <tr>
-        <td class="schedule">10:30am</td>
+        <td class="schedule">11:30am</td>
         <td><b>Closing</b></td>
-    </tr> -->
+    </tr>
 </table>
 
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