Presenter: Y.P. Peter Di, PhD

Despite many prevention measurements and successful development of vaccines against SARS-CoV2, the COVID-19 pandemic continuously affects our life likely for many years to come. The emerging delta variant from the evolving virus showed the critical role of the mutations in infections. Viruses and bacteria become successful pathogens by finding ways to adapt to the host environment. Pseudomonas aeruginosa (P.

Presenter: Xia Ning, PhD

Due to the rapid growth of information available about individual patients, most physicians suffer from information overload and inefficiencies when they review patient information in health information technology systems.

Presenter: Anne-Ruxandra Carvunis, PhD


Presenter: Pardeep Raamana, PhD

Mental health is a major public health challenge currently costing trillions of dollars. Neuroscientific approaches are key to understanding the underlying risk factors as well as developing biomarkers and treatments.

Presenter: Carrie Benson, MEd
Presenter: Ben Raphael, PhD

Tumors are heterogeneous mixtures of normal and cancerous cells with distinct genetic and transcriptional profiles.  In this talk, I will present several computational approaches to quantify tumor heterogeneity and reconstruct tumor evolution using data from bulk, single-cell, and spatial sequencing technologies.  For bulk and targeted single-cell DNA sequencing, I will describe methods that reconstruct tumor evolution using both somatic single-nucleotide mutations

Presenter: Rayid Ghani, PhD

Can AI, ML and Data Science help help prevent children from getting lead poisoning? Can it help reduce police violence and misconduct? Can it increase vaccination rates?  Can it help cities better prioritize limited resources to improve lives of citizens and achieve equity? We’re all aware of the potential of ML and AI but turning this potential into tangible social impact, and more importantly equitable social impact, is not straightforward.

Presenter: Christopher Horvat, MD

Real world data (RWD), meaning data generated by providers and payors during the delivery of patient care, are increasingly looked to as a source of truth for the development of practice-changing real-world evidence (RWE).  Uses include the development of predictive models, observational cohort analyses and even individual-level data collection to support randomized clinical trials.  RWE generated using RWD holds promise for being substantially more time and cos

Presenter: Manisha Desai, PhD

Data scientists have been at the forefront of helping to resolve the COVID pandemic. Their roles to address numerous critical questions have been integral to finding solutions to the pandemic. Our team has responded on numerous fronts but has primarily focused efforts on the design and analysis of clinical trials to establish optimal treatments for COVID-19 in the outpatient setting.