Collaborative Image Analysis in the Cloud for Improved Reproducibility and High Scalability

Date: 12/10/2020 12pm EST

Presented by:



Dr. Peter Bajcsy                                                                                                                        

Project Lead  

National Institute of Standards and Technology (NIST) 

Information Technlogy Laboratory (ITL)   



Dr. Nathan Hotaling 

Vice President Data Science Solutions 

Information Technology Research Branch

National Center for Advancing Translational Science-NIH


Dr. Sreenivas Bhattiprolu 

Head of Digital Solutions

Research Microscopy Solutions



About the Presenters

Peter Bajcsy received his Ph.D. in Electrical and Computer Engineering in 1997 from the University of Illinois at Urbana-Champaign (UIUC) and a M.S. in Electrical and Computer Engineering in 1994 from the University of Pennsylvania (UPENN).  He worked for machine vision, government contracting, and research and educational institutions before joining NIST in June 2011. At NIST, he has been leading a project focusing on the application of computational science in metrology, and specifically live cell and material characterization at very large scales.

Dr. Nathan Hotaling is a Senior Data Scientist within the Information Resources Technology Branch at NCATS where he is retiprolu sponsible for overseeing and developing the next generation of artificially intelligent image analysis tools. He received his PhD in Biomedical Engineering and a masters in clinical research in 2013 from the Georgia Institute of Technology and Emory University. After his PhD, Nathan did post-doctoral research in a joint project between the National Institute of Standards and Technology (NIST) and the National Eye Institute (NEI) where he began to develop a platform to analyze high content image data-sets collected for cell bio-manufacturing. This work led to his transition to his current position where he oversees the development of a scalable image analysis platform to non-invasively assess cell and tissue architecture, functionality, phenotype, consistency, and viability.  Using this platform with novel machine learning and deep learning techniques he intends to unlock the next “omics” of cell analysis, Vis-omics, for both research and clinical projects. Towards this end he has co-authored 22 journal papers, two book chapters, and three patents.

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 & Engineering from Michigan Technological University and earned his master’s degree in Physics from the University of Hyderabad.



Webinar Description

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 towards irreproducibility can be addressed via automation and collaborative work. Computer cloud provides the right infrastructure to automate image analysis tasks, especially for resource intensive applications. Cloud also enables collaborative work with its importance being even more emphasized during this covid-19 pandemic since it makes data and software applications accessible from any location and from any networked device. This webinar further explains the benefits of cloud-based image analysis and introduces the audience to two platforms that facilitate automation and collaborative work with microscopy images. These two platforms have been independently developed by ZEISS and NIST/NIH, respectively. The presentation will go over the main features of the two platforms that run computational workflows formed by software containers that are interoperable. The discussion will include a variety of commercial and open source aspects in developing and using such platforms by ISAC community.   


Learning Objectives

  1. Why WEB/CLOUD-based image software solutions? How does your software work? 3-5 minutes, the audience is not computer scientists but technically savvy, potential users
  2. What are the advantages/disadvantages of your platform compared to traditional/existing approaches? When is best to think about using WIPP or APEER to solve quantitative imaging problems? 3-5 minutes
  3. What does your software do? What functionality does it have? What is the best way for a potential user or group to get started using your platform (i.e. downloading, required hardware, necessary expertise)? 5-7 minutes  
  4. Considering that Web/cloud-based platforms are a newer tool with a much smaller user population compared packages such as ImageJ, Matlab, Cell Profiler, what do you see as the future for these platforms? How will they grow? What are your plans for rolling out your platform and increasing adoption rates?


Who Should Attend:

Microscopy data scientists, bio-medical core facility managers, principal investigators working in microscopy laboratories 



Seminar Information
Date Presented:
December 10, 2020 12:00 PM Eastern
1 hour
Registration Fee:
Collaborative Image Analysis in the Cloud for Improved Reproducibility and High Scalability
Individual topic purchase: Selected
American Society for Clinical Pathology
CMLE: 1.00
On-Demand Only
ISAC Member Price: $0.00
Non-Member Price:$0.00