Join us to explore the McCormick School of Engineering’s strategic vision and its vital role in advancing Northwestern’s top priorities. From groundbreaking research to transformative education, Northwestern Engineering is shaping solutions to the world’s most pressing challenges and working to advance Northwestern. During this presentation, you will learn directly from Dean Christopher Schuh on how our alumni community can come together to amplify this impact—through engagement, mentorship, philanthropy, and industry connections. Learn how your involvement can help unlock bold ideas and ensure Northwestern remains a global leader in engineering and beyond.

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NAA Alumni Education
alumnieducation@northwestern.edu

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The Open SkAI 2025 conference is the U.S. National Science Foundation and Simons Foundation-funded SkAI Institute's inaugural conference. The main aim of the conference is to enhance and generate new Astro-AI research directions. Conference themes will include: Astro-AI research across survey astronomy, from stars and transients to galaxy formation, evolution, and the dark sector. We encourage researchers from all levels to apply to attend.

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Do you need to identify a set of themes from within a large collection of text documents? If so, topic modeling can help. For example, topic modeling can be used to identify recurring themes in news articles, discover research trends in scientific publications, or analyze public sentiment across social media posts. In this workshop, you will get a high-level overview of different existing techniques used for topic modeling which focuses on modern AI-driven approaches. You will also have ample time to work through step-by-step hands-on exercises to learn how to leverage AI-based topic modeling analysis techniques using real data through Python.

Prerequisites: Basic familiarity with Python.

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The Open SkAI 2025 conference is the U.S. National Science Foundation and Simons Foundation-funded SkAI Institute's inaugural conference. The main aim of the conference is to enhance and generate new Astro-AI research directions. Conference themes will include: Astro-AI research across survey astronomy, from stars and transients to galaxy formation, evolution, and the dark sector. We encourage researchers from all levels to apply to attend.

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Do you have text data you need to classify? Are you lacking the time or money for research assistants to label a large number of examples to train a custom model? This workshop covers how to “fine-tune” an existing Large Language Model (LLM) to classify your own research text data. Fine-tuning helps you take advantage of a large model that other people with more resources and machine learning expertise already trained and use it for your project with relatively few labeled examples. That way you can accurately, reproducibly, and cost-effectively classify text data by topic, sentiment, or another concept.

Prerequisites: Basic familiarity with Python.

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The Open SkAI 2025 conference is the U.S. National Science Foundation and Simons Foundation-funded SkAI Institute's inaugural conference. The main aim of the conference is to enhance and generate new Astro-AI research directions. Conference themes will include: Astro-AI research across survey astronomy, from stars and transients to galaxy formation, evolution, and the dark sector. We encourage researchers from all levels to apply to attend.

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Retrieval-Augmented Generation (RAG) is an approach to build Large Language Model (LLM) based systems which are grounded on an external knowledge base, such as a collection of academic papers, clinical data, books, or websites. In research, RAG is applied to a plethora of tasks, from improving medical diagnoses, to summarizing legal documents, to generating novel research ideas grounded on well-vetted and trusted sources. In this hands-on workshop, we will start by learning the basic framework and core elements of RAG, including embedding models, vector databases, indexing techniques, and generative models. We will then build a RAG system step by step and test it on provided datasets.

Prerequisites: Basic familiarity with Python.

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The Open SkAI 2025 conference is the U.S. National Science Foundation and Simons Foundation-funded SkAI Institute's inaugural conference. The main aim of the conference is to enhance and generate new Astro-AI research directions. Conference themes will include: Astro-AI research across survey astronomy, from stars and transients to galaxy formation, evolution, and the dark sector. We encourage researchers from all levels to apply to attend.

Related Info

Motion tracking is a powerful tool for understanding and analyzing animal behavior. DeepLabCut is a state-of-the-art, open-source, AI software tool designed for highly accurate pose estimation and motion tracking of animals (and even humans) in video data. This hands-on workshop will introduce participants to the fundamentals of DeepLabCut: you will learn to install and configure DeepLabCut, get acquainted with the software interface, label video data to train a model, and extract pose data from videos. Attendees will also learn how to select model parameters, fine-tune training settings, and evaluate model performance. By the end of this workshop, participants will have a strong foundation in using DeepLabCut for animal behavior analysis, and the confidence to apply it to their own research questions.

Prerequisites: Basic familiarity with Python.

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Enjoy a welcome from Dean Christopher A. Schuh and other McCormick leaders, and receive a Northwestern Engineering T-shirt. A free light breakfast on the Tech East Plaza will follow.