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Bioinformatics Training and Education Program

MACHINE LEARNING AND AI: COMPUTATIONAL SCIENCE IN IMMUNO-ONCOLOGY

MACHINE LEARNING AND AI: COMPUTATIONAL SCIENCE IN IMMUNO-ONCOLOGY

 When: Sep. 15th, 2022 1:00 pm - 2:00 pm

This class has ended.
To Know
  • Where: Online Webinar
  • Organized By: SITC-NCI Computational IO Series

About this Class

In partnership with the National Cancer Institute (NCI) Cancer MoonshotSM, the Society for Immunotherapy of Cancer (SITC) and the Big Data and Data Sharing Committee are excited to announce our second "SITC-NCI Computational Immuno-Oncology Webinar Series" throughout 2022. These nine, hour-long webinars will feature a moderator and faculty speaker leading instruction on a range of topics that cover a host of computational challenges of analyzing and integrating diverse assay data across the spectrum of immuno-oncology. Meant for scientists early in their career or those who want to remain abreast of the latest technologies, the goal of this series is to help foster better communication concerning data science technologies and analyses between cancer immunotherapy researchers and clinicians in order to fuel translational immunotherapy research.
Speakers:
Olivier Elemento, Ph.D
Professor of Physiology and Biophysics, Weill Cornell Medicine
The Elemento lab combines Big Data analytics with experimentation to develop entirely new ways to help prevent, diagnose, understand, treat and ultimately cure cancer. Our research involves routine use of ultrafast DNA sequencing, proteomics, high-performance computing, mathematical modeling, and artificial intelligence/machine learning.
Santosh Putta, PhD (Moderator)
CEO and co-founder, Qognit Inc.
He has over 20 years of experience in creating software and data science solutions in the life sciences industry. Prior to Qognit, he was VP of Computational Sciences at Nodality, where he was responsible for building and leading Computational Biology, Biostatistics and Software functions. Dr. Putta directed statistical analysis and design on multiple clinical studies and guided the software platform architecture to design and manage single cell proteomics data and client facing data software.