CCB is happy to provide computational and data science resources for HMS graduate students, postdoctoral fellows, research staff, and faculty across all departments of the HMS quadrangle.
Please email us for additional information on project collaboration, tool/technology development, educational resources, or any general inquiries.
CCB hosts weekly help sessions for analyzing and visualizing data in R, as well as providing statistical advice on research questions. HMS students, postdocs, research staff, and faculty can book a specific time slot by emailing Andrew Ghazi or are welcome to drop-in throughout the 2 hour session to ask for help using R or […]
Location: In-Person at Countway L1-032 (Please note, New Location for this week and the New Zoom Link) Zoom, https://harvard.zoom.us/j/92093238424?pwd=eEdJSjA2WEczL1NvdnN0MysyM1JoQT09 Add to Calendar: https://drive.google.com/file/d/1E2iIsla_TzdTzS_3YLcj6gr-DHO-KP7v/view?usp=sharing Speaker: Mariana Gabitto, PhD Abstract: Since their inception, single-cell technologies have revolutionized biology, providing exciting opportunities to characterize cellular populations and track their changes with disease. These technologies enable the […]
CCB hosts weekly help sessions for analyzing and visualizing data in R, as well as providing statistical advice on research questions. HMS students, postdocs, research staff, and faculty can book a specific time slot by emailing Andrew Ghazi or are welcome to drop-in throughout the 2 hour session to ask for help using R or […]
CCB hosts weekly help sessions for analyzing and visualizing data in R, as well as providing statistical advice on research questions. HMS students, postdocs, research staff, and faculty can book a specific time slot by emailing Andrew Ghazi or are welcome to drop-in throughout the 2 hour session to ask for help using R or […]
CCB is excited to announce an upcoming interactive workshop on Deep Learning Environments led by Phil Bowsher, the Director of Life Sciences and Healthcare at Posit (formerly RStudio). Registration is closed for this workshop. ---
Office hours CANCELLED for Wednesday, April 3. CCB hosts weekly help sessions for analyzing and visualizing data in R, as well as providing statistical advice on research questions. HMS students, postdocs, research staff, and faculty can book a specific time slot by emailing Andrew Ghazi or are welcome to drop-in throughout the 2 hour […]
CCB hosts weekly help sessions for analyzing and visualizing data in R, as well as providing statistical advice on research questions. HMS students, postdocs, research staff, and faculty can book a specific time slot by emailing Andrew Ghazi or are welcome to drop-in throughout the 2 hour session to ask for help using R or […]
Title: To Transformers and Beyond: Large Language Models for the Genome Speaker: Micaela Consens, Computer Science Ph.D. candidate at University of Toronto with a Focus in Machine Learning Applications for Healthcare in the Moses and Wang Lab. Abstract: In the rapidly evolving landscape of genomics, deep learning has emerged as a useful tool for tackling […]
Join us in-person on Monday, April 15th at 3 PM as Dr. Glenn Ko shares his experience founding a company as a Harvard academic and the technical details behind his enterprise-ready AI system, Stochastic AI. Talk Title: From Harvard Lab to Global Enterprises: The Journey of Stochastic's Personalized AI Speaker: Glenn Ko, PhD […]
Register to Attend: http://bit.ly/4ap8g4Q Excited to share the second seminar in the IM-Tech series, Innovation in Medical Technology and Entrepreneurship Cross Harvard. This series is co-sponsored by the Tananbaum […]
CCB hosts weekly help sessions for analyzing and visualizing data in R, as well as providing statistical advice on research questions. HMS students, postdocs, research staff, and faculty can book a specific time slot by emailing Andrew Ghazi or are welcome to drop-in throughout the 2 hour session to ask for help using R or […]
Aimed to motivate, explain, and demonstrate mixed effects models in biomedical research Learn how mixed effects models connect experimental design to statistical model structure Learn how to construct, run, and evaluate these models in R with lme4 and brms