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      <title>Keynote: AI for Scientific Discovery</title>
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      <pubDate>Sun, 15 Dec 2024 14:00:00 +0000</pubDate>
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      <description>&lt;h2 id=&#34;about-the-talk&#34;&gt;About the Talk&lt;/h2&gt;
&lt;p&gt;Prof. Jane Smith will deliver the opening keynote at the prestigious International AI in Science Symposium, highlighting our lab&amp;rsquo;s pioneering work in computational biology and drug discovery.&lt;/p&gt;
&lt;h3 id=&#34;key-topics&#34;&gt;Key Topics&lt;/h3&gt;
&lt;p&gt;&lt;strong&gt;AI-Powered Drug Discovery&lt;/strong&gt;&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Machine learning platforms for molecular design&lt;/li&gt;
&lt;li&gt;Accelerating clinical translation from years to months&lt;/li&gt;
&lt;li&gt;Real-world case studies and clinical outcomes&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;&lt;strong&gt;Protein Structure Prediction&lt;/strong&gt;&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;DeepFold architecture and breakthrough results&lt;/li&gt;
&lt;li&gt;Comparison with AlphaFold and novel improvements&lt;/li&gt;
&lt;li&gt;Applications in understanding disease mechanisms&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;&lt;strong&gt;Future of AI in Science&lt;/strong&gt;&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Emerging opportunities in materials science and climate research&lt;/li&gt;
&lt;li&gt;Challenges in model interpretability and validation&lt;/li&gt;
&lt;li&gt;Building interdisciplinary research partnerships&lt;/li&gt;
&lt;/ul&gt;
&lt;h3 id=&#34;expected-impact&#34;&gt;Expected Impact&lt;/h3&gt;
&lt;p&gt;This keynote will position our lab as a thought leader in AI-driven scientific research and attract potential collaborators from leading institutions worldwide.&lt;/p&gt;
&lt;h3 id=&#34;conference-details&#34;&gt;Conference Details&lt;/h3&gt;
&lt;p&gt;&lt;strong&gt;Venue&lt;/strong&gt;: Stanford University Memorial Auditorium&lt;br&gt;
&lt;strong&gt;Date&lt;/strong&gt;: December 15, 2024&lt;br&gt;
&lt;strong&gt;Time&lt;/strong&gt;: 2:00-3:00 PM PST&lt;br&gt;
&lt;strong&gt;Audience&lt;/strong&gt;: 500+ researchers, industry leaders, and policymakers&lt;br&gt;
&lt;strong&gt;Format&lt;/strong&gt;: 45-minute talk + 15-minute Q&amp;amp;A&lt;/p&gt;
&lt;h3 id=&#34;media-coverage&#34;&gt;Media Coverage&lt;/h3&gt;
&lt;p&gt;The talk will be livestreamed and recorded, with proceedings published in the Journal of AI in Science special issue.&lt;/p&gt;
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      <title>Machine Learning</title>
      <link>http://localhost:1313/research/machine-learning/</link>
      <pubDate>Thu, 11 Jan 2024 00:00:00 +0000</pubDate>
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      <description>&lt;h2 id=&#34;overview&#34;&gt;Overview&lt;/h2&gt;
&lt;p&gt;We build robust and interpretable ML models to accelerate scientific discovery across domains, with emphasis on generalization, uncertainty, and physical constraints.&lt;/p&gt;
&lt;h2 id=&#34;focus-areas&#34;&gt;Focus Areas&lt;/h2&gt;
&lt;ul&gt;
&lt;li&gt;Physics-informed neural networks (PINNs)&lt;/li&gt;
&lt;li&gt;Graph neural networks for structured scientific data&lt;/li&gt;
&lt;li&gt;Interpretable models and reliability assessment&lt;/li&gt;
&lt;/ul&gt;
&lt;h2 id=&#34;selected-publications&#34;&gt;Selected Publications&lt;/h2&gt;
&lt;ul&gt;
&lt;li&gt;See our recent work in Nature Machine Intelligence and NeurIPS.&lt;/li&gt;
&lt;/ul&gt;
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