Loading…
Thursday, October 3 • 4:35pm - 4:55pm
Data Automation- Automated Science Education and AI Driven Closed Loop Experimentation

Sign up or log in to save this to your schedule and see who's attending!

Automated Science Education and AI Driven Closed Loop Experimentation
Until recently, the primary mechanism for training scientists in the use of automation was by providing experience working with automated systems in research labs currently using automation technology. At Carnegie Mellon University, we have started the first Automated Science Master’s Degree program in the country. Students in this program are trained to design and implement AI-driven closed-loop experimentation processes. They receive hands-on experience using modern automation equipment to perform experiments. They are trained to use machine learning and related methods for data analysis and they are trained to use artificial intelligence methods to automatically decide what experiments to run next in a campaign. To this end, we have spent the last year identifying automation equipment supplier(s) for our Automation Teaching Lab. This effort has put us in a unique position to have surveyed the current state of a lot of hardware and software in industry from the perspective of both AI-driven automation and education. For this presentation, we will discuss our findings from the perspective of education and AI-driven experimentation. From an educational perspective, we were primarily concerned with the ability to have multiple students use the system and its software in parallel. Furthermore, we considered the ability for software to be used to simulate protocols developed by students. The ability to test in simulation is particularly crucial when there are throughput or consumables limitations. Lastly, since extensive machine learning/AI computation typically cannot be performed locally on hardware included with most integrated automation systems, we also considered the ability for remote control through an application program interface (API). Related to this, we considered to what extent we could automatically move data generated on the system to an external environment for processing. To the best of our knowledge, no single company currently offers a comprehensive package addressing all of these needs. As such, we will be discussing strengths and weaknesses of industry offerings in these areas (and more) during the presentation, but we will not be discussing the offerings of specific companies.

Co-Authors: Christopher J. Langmead,  Robert F. Murphy, Ph.D.

Speakers
avatar for Joshua Kangas, PhD

Joshua Kangas, PhD

Assistant Teaching Professor, Carnegie Mellon University
Joshua Kangas is an Assistant Teaching Professor at Carnegie Mellon University. The courses he teaches are focused primarily on teaching students laboratory methods through wet-lab experience. Students in his courses design their own experiments, generate their own experimental data... Read More →

Chairs
JM

Jeff Milton

Associate Director, Ionis Pharmaceuticals
Jeff Milton has over 20 years of bioinformatics and software development experience in both academic and industry settings. He began his career at Molecular Simulations Inc (now Biovia) developing bioinformatics tools before the first draft of the Human Genome. In 2005 he moved to... Read More →


Thursday October 3, 2019 4:35pm - 4:55pm
Theater Ballroom (5th Fl) - Courtyard Boston Downtown 275 Tremont Street, Boston, MA 02116