Fall 2025 Lecture in Climate Data Science: ZHOU YU

LEAP’s primary research strategy is to improve near-term climate projections by merging physical modeling with machine learning across a continuum from expertise in climate science and climate modeling to cutting-edge machine learning algorithms. The benefits will be significant for both the climate and data sciences communities. Climate scientists and modelers struggle to fully integrate the wealth of existing datasets into their models, while machine learning algorithms have been good at emulating and interpolating but have difficulties extrapolating or predicting extremes. By combining both approaches, LEAP will trigger a significant advancement for data science algorithms applied to physical problems. LEAP will incorporate physics and causal mechanisms into machine learning algorithms for better generalization and extrapolation, while optimally using the wealth of data available to climate science, in order to better predict the future.

LEAP Center

JOIN THE LEARNING THE EARTH WITH ARTIFICIAL INTELLIGENCE + PHYSICS (LEAP) CENTER AT COLUMBIA FOR A SEMINAR ON CLIMATE DATA SCIENCE.

Columbia Innovation Hub - Tang Family Hall

<h3><strong><strong>TITLE: "FROM POC TO PROFIT: LEVERAGING SYNTHETIC USERS FOR AUTOMATED TESTING + EVALUATION"</strong></strong></h3><h3><strong><strong>SPEAKER: ZHOU YU (Columbia University)</strong></strong></h3><p><br></p><h3>Date: November 13, 2025</h3><h3>Time: 12:00 p.m.</h3><h3>Format: Hybrid</h3><h3>Virtual: Zoom link provided upon registration</h3><h3>In-person: Columbia Innovation Hub, 2276 12th Avenue, Second Floor, Room 202, New York, NY 10027</h3><p>*Please note that in-person space is limited.*</p><p>__________________________________________________________________________________</p><p><strong><strong>Abstract</strong></strong>: 95% of the enterprise AI agents are stuck in POC (proof-of-concept). We don't know when agents are good enough to deploy without systematic testing and evaluation process. We'll share how to leverage AI to automate this process. In addition, we'll talk about how to improve agents and make them self-learn through reinforcement learning (RL).</p><p><strong><strong>Bio</strong></strong>: <a href="https://www.cs.columbia.edu/~zhouyu/" title="https://www.cs.columbia.edu/~zhouyu/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Dr. Zhou(Jo) Yu</a> is an Associate Professor in the Department of Computer Science at Columbia University and Founder of Arklex.ai. She obtained her Ph.D. from Carnegie Mellon University. Dr. Yu has received several best paper awards in top NLP conferences (such as ACL 2024) and has won Forbes 30 under 30 in 2018. Dr. Yu has developed various AI systems that have had a real impact, including winning the Amazon Alexa Prize. Dr. Yu co-founded Arklex.ai, democratizing AI agent development through an enterprise-grade tool optimized for cross-team collaboration, low latency, security, and robustness.</p><p><br></p><p>Learn More: <a href="https://leap.columbia.edu" title="https://leap.columbia.edu" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer"><strong><strong>LEAP</strong></strong></a></p>

<p><strong><strong>ZHOU YU</strong></strong>Columbia University</p>

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Fall 2025 Lecture in Climate Data Science: ZHOU YU

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Fall 2025 Lecture in Climate Data Science: ZHOU YU

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