Lab of the Future

The Lab of the Future short course is designed to provide the framework, skills and knowledge to assist participating organizations in the design and implementation of automated laboratory workflows that are tightly integrated with modern AI machine learning strategies.

Under the guidance of a skilled instructor, participants will learn topics ranging from analyzing laboratory layouts to selecting optimal machine learning strategies to achieve their goals.

Who Should Attend?

Those looking to move their labs into the future of laboratory automation and machine learning, including:

• Lab managers
• Principal investigators
• Bench scientists
• Students

How You Will Benefit From This Course

• Evaluate the potential benefits of automation
• Utilize the best automation approach for a successful implementation plan
• Monitor and improve the automation setup
• Determine the best application of machine learning for an experimental procedure
• Determine the appropriate hardware for their experimentation processes
• Identify the potential strengths and weaknesses of a proposed automation solution

Course Topics

• Planning for laboratory automation
• Vendor evaluation
• Designing the optimal lab
• Defining the implementation plan
• Machine learning strategies for the Lab of the Future (classical machine learning vs. active machine learning)
• Interactive case studies

Interested in the course but can’t make it on this date? This course is also available on demand!

Instructors

Donat Elsener
Innoco & Hamilton Storage

Donat Elsener, Managing Director at Innoco GmbH and Senior Sales Manager at Hamilton Storage, has served as a business process analyst and project manager for multi-million laboratory automation projects around the globe. Past projects include high-throughput screening (HTS) systems for major pharmaceutical and biotech companies and development projects for laboratory automation vendors. Elsener is an experienced presenter having delivered hundreds of presentations for laboratory automation projects around the globe.

 

Ian Kerman
LabVoice

Ian Kerman (M.S. in Biology and M.S. in Computer Science) possesses comprehensive data science knowledge from 10+ years of using machine learning in the life and material science industries. Ian has assisted users with using and leveraging machine learning in their high throughput and virtual screening workflows. His experience includes data and feature engineering, model training and evaluation, and deployment and maintenance of chosen models.