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<Title>UMBC physicist wins Amazon award for AI-orchestrated scientific assistant</Title>
<Tagline>Physics Co-Pilot to be an AI assistant for physics research</Tagline>
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    <div class="html-content"><p><a href="https://physics.umbc.edu/faculty/anza/" rel="nofollow external" class="bo">Fabio Anza</a>, an assistant professor in UMBC's <a href="https://physics.umbc.edu/" rel="nofollow external" class="bo">Department of Physics</a>, <a href="https://qsi.umbc.edu/" rel="nofollow external" class="bo">Quantum Science Institute</a> and <a href="https://cybersecurity.umbc.edu/" rel="nofollow external" class="bo">Cybersecurity Institute</a>, has won an Amazon Research Award to develop <a href="https://www.amazon.science/research-awards/recipients/fabio-anza" rel="nofollow external" class="bo">Physics Co-Pilot</a>—a scientific AI assistant that combines large language model orchestration with reliable computational algorithms to address critical bottlenecks in modern physics research.<br>Physics Co-Pilot takes a fundamentally different approach from current AI code generation systems. Rather than having language models write code—which can produce unreliable results—the system uses LLMs only for natural language understanding, while delegating all computational work to pre-written, validated algorithms.<br>Read more about his award and project <a href="https://umbc.edu/quick-posts/anza-ai-orchestrated-scientific-assistant/" rel="nofollow external" class="bo">here</a>.</p></div>
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<Summary>Fabio Anza, an assistant professor in UMBC's Department of Physics, Quantum Science Institute and Cybersecurity Institute, has won an Amazon Research Award to develop Physics Co-Pilot—a scientific...</Summary>
<Website>https://umbc.edu/quick-posts/anza-ai-orchestrated-scientific-assistant/</Website>
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<ThumbnailAltText>Fabio Anza headshot</ThumbnailAltText>
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<PostedAt>Fri, 30 Jan 2026 09:52:54 -0500</PostedAt>
<EditAt>Tue, 31 Mar 2026 17:15:16 -0400</EditAt>
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<NewsItem contentIssues="true" id="145495" important="false" status="posted" url="https://beta.my.umbc.edu/groups/umbc-ai/posts/145495">
<Title>Talk: Machine Learning for Earth System Prediction and Predictability, 11/13</Title>
<Tagline>11-12pm Wed., Nov. 13, in-person in Physics 401 at UMBC</Tagline>
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<![CDATA[
    <div class="html-content"><strong>Professor <a href="https://www.google.com/url?q=https://aosc.umd.edu/people/maria-molina&amp;sa=D&amp;source=calendar&amp;usd=2&amp;usg=AOvVaw0-Y0NLuoSCaYdxAEQrXOVM" rel="nofollow external" class="bo">Maria Janeth Molina</a> </strong><span>from the University of Maryland talk on </span><strong>Machine learning for Earth System Prediction and Predictability</strong><span>, 11-12pm on </span><span>Wednesday, November 13</span><span> in Physics 401 at UMBC.</span><br><br><span>Machine learning can be used for Earth system prediction, or to study the uppermost limit of prediction skill theoretically achievable given the system's initial state or other factors, otherwise known as predictability. In traditional numerical weather prediction frameworks, we solve the governing partial differential equations starting from an initial state. This initialized prediction framework usually involves three stages: 1) generating the initial conditions of the Earth system, 2) running the mathematical representation of the system on a computer forward in time, and 3) analyzing the output and converting it into a format that is useful for end users.</span><div><span><br></span></div><div><span>Machine learning can be used to improve each of these individual stages or to circumvent the three-stage framework altogether, and examples of each will be given in this seminar. More time during the seminar will be dedicated to the challenges surrounding subseasonal prediction, which focuses on lead times of three to four weeks, and how we can use machine learning to both uncover potential biases in our initialized prediction systems and how we can bias-correct them.</span></div><div><span><br></span></div>
    <hr><a href="https://ai.umbc.edu/" rel="nofollow external" class="bo"><strong>UMBC Center for AI</strong></a></div>
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<Summary>Professor Maria Janeth Molina from the University of Maryland talk on Machine learning for Earth System Prediction and Predictability, 11-12pm on Wednesday, November 13 in Physics 401 at UMBC....</Summary>
<Website>https://my3.my.umbc.edu/groups/physics/events/135831</Website>
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<Sponsor>UMBC Pysics Department</Sponsor>
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<PostedAt>Sun, 10 Nov 2024 19:32:49 -0500</PostedAt>
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