Accelerating Drug Discovery with AI and Network Science
When:
Friday, Mar 01, 2024 1:00p -
1:45p
Friday, Mar 01, 2024 1:00p -
1:45p
Where:
Online event
Admission:
FREE
Hosted by:
Categories:
Business & Professional, Innovation, Tech, University
Event website:
https://bit.ly/3T8b7c5
Join us online on Friday, March 1, 2024 at 1pm EDT for an exciting AI + Life Sciences webinar titled, “Accelerating Drug Discovery with AI and Network Science” with Sam Scarpino, our Director of AI + Life Sciences and guest Giulia Menichetti, Principal Investigator and Junior Faculty at Harvard Medical School and Brigham and Women’s Hospital.
Sam and Giulia will discuss:
- What network science is and how it is advancing AI applications in medicine and biology
- What is needed to realize the potential of AI in diagnosing and treating complex diseases
- What we should expect in AI and life sciences in 2024
- How our Institute advances diagnostics, drug discovery, and wet-lab-in-the-loop
About our Speakers:
Sam Scarpino
Samuel V. Scarpino, PhD, is the Director of AI + Life Sciences at the Institute for Experiential AI at Northeastern University and a Professor of the Practice in Health and Computer Sciences. He holds appointments in the Institute for Experiential AI and the Network Science, Global Resilience, and Roux Institutes.
Prior to joining Northeastern in November 2022, Scarpino was the Vice President of Pathogen Surveillance at The Rockefeller Foundation and Chief Strategy Officer at Dharma Platform (a social impact, technology startup). Outside of these roles, he has over 10 years of experience translating research into decision support and data science/AI tools across diverse sectors from public health and clinical medicine to real estate and energy.
Giulia Menichetti
Giulia is a Principal Investigator and Lecturer at Harvard Medical School. She received her Ph.D. (“Doctor Europaeus” or European Doctorate) in Physics from the University of Bologna. Her background is in network modeling of biological information, with a focus on the characterization of network and multilayer network ensembles as null models, to identify non-random patterns in real data and quantify the information content. She is currently supervising the Foodome project to which she is contributing the Foodome Knowledge Graph and research on individual eating pattern variability.
