Artificial intelligence (AI) is rapidly transforming science and laboratory automation. With all the development (and hype) surrounding AI, many scientists and managers have fundamental questions about the technology:
What is AI?
How does it work?
Can it solve my problem?
This workshop offers a gentle introduction to the field of AI.The course will cover the three main branches of AI and machine learning: supervised, unsupervised, and reinforcement learning. Each topic will include a case study to illustrate how AI is used to solve data-intensive problems.
Who Should Attend
The workshop is designed for scientists, technicians and managers and requires only a background in introductory statistics and basic biology/chemistry.
By the end of this workshop, participants will be able to:
Describe the capabilities and limitations of common AI techniques.
Understand the types of data and problems amenable to AI algorithms.
Partner with AI practitioners to solve problems.
The workshop will cover the three main branches of artificial intelligence and machine learning (AI/ML):
Each section will include a case study selected from presentations at the previous year’s SLAS Annual Meeting.
Supervised learning: Predicting chemical reactivity with artificial neural networks
Unsupervised learning: Identifying biomarkers with Principal Component Analysis
Reinforcement learning: Optimizing chemical synthesis through automation
The case study model aims to: Introduce the most common AI methods for each type of learning connect the AI methods to a real-world example of interest to the SLAS community
Paul Jensen, Ph.D.
University of Illinois at Urbana-Champaign
Paul Jensen is an assistant professor in the Department of Bioengineering and the Carl R. Woese Institute for Genomic Biology at the University of Illinois. Paul’s research group applies artificial intelligence and laboratory automation to solve combinatorial problems in biology. He is the PI of an NSF grant to study career expectations in AI and the recipient of an NIBIB Trailblazer Award and an NIGMS Maximizing Investigators’ Research Award. Paul teaches classes on data science, machine learning and the design of experiments.
Mark Hendricks, Ph.D.
Mark Hendricks is an assistant professor in the Department of Chemistry at Whitman College. His research is devoted to understanding the chemistry underlying the synthesis of nanomaterials, with a project devoted to developing automation tools and artificial intelligence to gain insights into the synthesis of metallic nanocrystals. Mark teaches physical, materials and general chemistry at Whitman and has developed lab automation modules for the undergraduate chemistry curriculum.