Kai Stewart
About Kai Stewart
Kai Stewart is an Associate Computational Biologist II at the Broad Institute of MIT and Harvard, specializing in the integration of multi-modal data to study disease drug resistance and relapse. He holds a Bachelor of Applied Science from Tufts University and an Associate of Arts from Bunker Hill Community College.
Work at Broad Institute
Kai Stewart has been serving as an Associate Computational Biologist II at the Broad Institute of MIT and Harvard since 2022. In this role, Stewart focuses on integrating multi-modal data, which includes single-cell, bulk RNA, and genomic data. This work aims to address critical questions related to disease drug resistance and relapse. The Broad Institute is known for its innovative research in genomics and computational biology, providing a collaborative environment for scientists from various disciplines.
Education and Expertise
Kai Stewart holds a Bachelor of Applied Science (BASc) degree from Tufts University, where studies were completed from 2018 to 2020. Prior to this, Stewart earned an Associate of Arts (AA) degree from Bunker Hill Community College, studying from 2015 to 2017. This educational background has equipped Stewart with a strong foundation in both applied sciences and the life sciences, enhancing expertise in computational biology.
Background
Kai Stewart has a keen interest in the intersection of computing and life sciences. This fascination has driven Stewart's career path, leading to a focus on computational biology. The experience gained through education and current professional roles has fostered a commitment to collaborative research, engaging with scientists from diverse disciplines to tackle complex biological questions.
Research Interests
Stewart specializes in integrating various types of biological data, including single-cell, bulk RNA, and genomic data. This specialization is crucial for addressing significant challenges in understanding disease mechanisms, particularly drug resistance and relapse. The ability to synthesize multi-modal data is essential in advancing research in the life sciences.