Improve Data Reproducibility Using Machine Learning and Cloud-based Image Analysis: ZEISS Intellesis and APEER

Wednesday, January 27 | 2:00 pm EST


Webinar Description

The journey from image to insights can be challenging and hard to reproduce. According to a recent survey by Nature, more than 70% of researchers have tried and failed to reproduce another scientist's experiments. Many factors contributing to irreproducibility can be addressed via automation and collaborative work. Cloud offers the right infrastructure to automate image analysis tasks and to provide a collaborative environment. Cloud also enables location-independent access to tools whose importance is even more emphasized during the current COVID-19 pandemic. This webinar introduces the audience to the APEER platform that facilitates automation and collaborative work with microscopy images. The presentation provides an overview of APEER, including its new Machine Learning toolkit that makes deep learning accessible to all researchers. It also provides an example use case in which APEER helped automate a complex cell segmentation task.

What you will learn in the webinar:
  • Why cloud-based image analysis?
  • ZEISS APEER: a free cloud platform
  • How APEER makes deep learning accessible for everyday use by researchers 
  • Extending ZEN capabilities via APEER
  • Case study: How APEER addressed the automation of complex cell segmentation


Sreenivas Bhattiprolu

Dr. Sreenivas Bhattiprolu (Sreeni) is the head of digital solutions at Carl Zeiss Microscopy. His team focuses on solving tough microscopy challenges by leveraging the latest advancements in digital technology and artificial intelligence. Sreeni has over 25 years of experience in microscopy in a variety of fields, including life sciences, materials sciences, geosciences, electronics, and semiconductor technologies. Sreeni received his Ph.D. in materials sciences and engineering from Michigan Technological University and earned his master’s degree in physics from the University of Hyderabad.

Julia Mack

Julia Mack is an assistant adjunct professor in the Department of Medicine, Division of Cardiology at UCLA. Julia received her Ph.D. in chemistry and completed her postdoctoral training in vascular biology in the Department of Molecular, Cell & Developmental Biology at UCLA. In July 2018, Julia started her research group at the David Geffen School of Medicine. She was awarded a Career Development Award from the American Heart Association and serves as a mentor for the AHA STEM Goes Red Program. Her research focuses on the mechanobiology of endothelial cell signaling using high-resolution imaging techniques to interpret the role of blood flow forces in cardiovascular health.