https://event.on24.com/wcc/r/4903163/6F8CE852E22C58540A57022C7495ED52?partnerref=pe-website

This course is also offered in an on-campus format, meeting simultaneously with the live online cohort.

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Lead Instructor(s)
Date(s)
Jun 02 - 04, 2025
Registration Deadline
Location
Live Online
Course Length
3 Days
Course Fee
$3,600
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AI is transforming processes across countless disciplines—and the sciences are no exception. In this high-impact three-day course, you’ll master a range of practical AI skills—including predictive modeling, large language models, and AI-driven experiment planning—to streamline and enhance your scientific research and uncover new insights.

Course Overview

From biology to chemistry to physics, AI is now ubiquitous across all corners of the sciences, helping researchers enhance workflows and accelerate the innovation process. For scientific researchers and professionals, knowledge of AI is rapidly transitioning from a helpful asset to an essential skill for success.

In this intensive three-day course taught by computer science expert Professor Wojciech Matusik, you’ll gain the practical skills you need to apply AI within your field of scientific research. Through a blend of interactive lectures and labs you’ll master a range of foundational and advanced machine learning techniques, including neural networks, large language models, predictive models, and experimental planning methods such as Bayesian optimization. Specifically, you’ll gain the practical skills you need to handle high-dimensional datasets, build and optimize AI models, and apply advanced methodologies for a range of tasks—from text analysis to hypothesis generation to data-driven decision-making. Equipped with these new skills, you’ll be ready to leverage AI to optimize your experiments, build both generative and predictive models, streamline your data analysis, and uncover new insights more efficiently.

With a strong emphasis on practical applications, you’ll also examine real-world case studies in fields such as molecular discovery and materials science—as well as participate in hands-on labs where you’ll implement AI methods in realistic scenarios. Ultimately, you’ll emerge from the experience with the actionable tools and strategies you need to harness AI’s transformative potential—and drive the next breakthrough in your field.

Learning Outcomes

•    Gain a strong foundation in AI techniques, including neural networks, transformers, and generative models, with a focus on their applications in scientific research
•    Acquire hands-on experience with tools and methods such as predictive modeling, large language models, and Bayesian optimization for planning and conducting experiments
•    Explore practical applications of AI in areas like molecular discovery, materials science, and other scientific disciplines
•    Learn to effectively integrate AI tools into scientific workflows to accelerate innovation and enhance R&D efficiency
•    Understand and address ethical considerations in AI research, including issues of transparency, bias, and reproducibility

Core Skill
•    Understanding how AI enhances workflows and drives innovation across various scientific disciplines.
•    Applying techniques for managing and analyzing large, complex datasets.
•    Building and optimizing AI models tailored to scientific research needs.
•    Designing and implementing machine learning models for advanced pattern recognition and predictive analysis.
•    Utilizing Bayesian optimization to improve experiment design and increase efficiency.
•    Integrating diverse AI tools to streamline and optimize experimental workflows.

Core Competencies
•    Leveraging AI to make data-driven decisions in scientific research, accelerating breakthroughs and innovation.
•    Applying AI techniques across diverse scientific disciplines, such as molecular discovery and materials science.
•    Identifying and addressing ethical challenges in AI applications, including issues of transparency, bias, and reproducibility.
•    Applying AI in laboratory settings to tackle complex scientific problems effectively.

Who Should Attend

This program is ideal for entry- to mid-level professionals or researchers working at the intersection of science and technology. Participants should have a bachelor’s degree or higher in a relevant field and a basic understanding of programming and data analysis, including python or similar tools. Experience with scientific workflows or data-driven problem-solving is recommended, but not mandatory.

AI for Scientific Discovery is particularly relevant for: 
•    Research and development (R&D) professionals looking to harness AI to advance their research and pioneer breakthrough solutions and innovations
•    Engineers seeking to enhance product design, simulation, or testing with the latest AI-powered tools
•    Product management leaders working with AI-powered tools, technologies, or workflows who are seeking a deeper understanding of AI in order to maximize their investments
•    Strategy and operations managers in research-focused organizations looking to leverage AI solutions to optimize workflows 

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