Hezekiah J. Branch
M.S., Data Science
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B.S., Cognitive Brain Science
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Tufts University
Email:
Location:
Boston, MA
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A Bit About Me
As a kid, I was always obsessed with uncovering stories. At six years old, I was obsessed with Charmed. I would wake up around 7AM and flip through channels until a repeat aired on TNT. I loved the kickboxing demon-slaying sisters, but I was deeply engaged with knowing the lore and predicting the outcome of each episode. And beyond the stories of Phoebe, Piper, and Prue, I wanted to know what stories were happening around me. As a Roxbury kid, Boston had no shortage of stories from the barbershop, to the community center, to my mom bumping into old friends on the T.
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​As I got older, I realized that there wasn't a story I was more interested in than the human brain. I wanted to know how people think, how we make decisions, and what's going on under-the-hood when engaging with the real world. However, it was extremely challenging to find mentors and science role models as a young Black kid living in a tough neighborhood. So, I poured into libraries, checked out tens of books at a time, and absorbed as much as I could to one day achieve what, back then, felt like an impossible dream.
Fast forward to now, I am now living that dream to the fullest. I spend each day exploring the rich universe around us. I've been privileged enough to learn from the greatest minds in the world including NASA pioneers, world-renowned neuroscientists, and expert mathematicians and physicists. I'm excited to build on the work of the scientists who have come before me as I blaze new trails over the next several decades. And most importantly, I am committed to opening doors for future scientists of all backgrounds to continue doing this important work.
Work Experience
June 2023 - January 2025
August 2022 - May 2023
January 2022 - August 2022
May 2021 - August 2021
As a scientist in the MIT Department of Brain and Cognitive Sciences and the Massachusetts General Hospital Department of Functional Neurosurgery, I architected data pipelines, held clinical oversight over our functional epilepsy intake, wrote data policies, and contributed to research in functional neuroscience from SEEG recordings collected in the EMU. I also co-designed a novel course on Human Intracranial Neuroscience alongside several MIT and Harvard faculty over the course of 8 months. I later pitched this course to the MIT Education Committee where I presented an interactive demo for analyzing brain recordings and building deep learning models as part of a graduate seminar course.
During my time in the NEWDIGS FoCUS (Financing and Reimbursement of Cures in the US) Project under the Institute for Clinical Research and Health Policy Studies (ICRHPS), I tracked the development of clinical trials in the U.S. market from 2000 to 2023, modeling the adoption of trials into market. My work included constructing and administering multiple time series databases, a foundational understanding of cancer epidemiology and clinical workflows, meticulous data cleaning of patient data, complex statistical modeling, and data analysis of large datasets. Our work received massive valuation from global stakeholders. At the end of my time in NEWDIGS, I was grateful to lead the end-to-end development of a pharmaceutical dashboard put into production for the Children's Hospital of Philadelphia.
At the MIT Center for Biomedical Innovation, I had the opportunity to work on projects that evolve the future of medical research. I enabled statistical forecasts on massive amounts of clinical trial data, predicting the market success of curative drug therapies. I led the creation of a natural language processing (NLP) utility to parse clinical trial attributes and map identified oncogenes to their respective disease class. In addition to rigorous data analysis and modeling, I administered multiple time-series databases while running complex queries in PostgreSQL. This effort enabled our team to monitor and create custom clinical trial datasets. As a result of hands-on, meticulous research experience, I developed a deep specialization of state-of-the-art machine learning/statistical modeling techniques, mechanisms and networks of oncology, and data visualization of pharmaceutical trends.
My work at IBM Research involved training deep triplet networks to create new food recipes. We leveraged advances in optimal transport theory to create "food-sensitive" distance measures. I launched experiments to improve training loss over large amounts of "food embeddings", contributed to end-to-end model development, and pitched an AI-driven research app. I also had the privilege of speaking to the entire company on DEI efforts in tech as part of a company panel.
January 2021 - May 2021
As a Research Assistant in the Cochran Lab at Tufts School of Medicine, I built interpretable machine learning models for predicting tumor growth in glioblastoma multiforme (GBM). This was an exciting experience researching canonical signalling pathways of glioblastoma multiforme (GBM). I successfully identified and pitched the relevance of dual inhibitors of MEK (Yu et al., 2019) for minimizing tumor growth in GBM, gave regular 1-hour research presentations on canonical signalling pathways to our team, and implemented the CellBox algorithm (Yuan et al., 2021) to predict tumor growth with messy data including RNA-seq data.
November 2020 - March 2021
At the Tufts COVID Command Center, I led the computer vision development of our site's COVID-19 pandemic response strategy. In my role, I spearheaded the text detection and localization of patient health data from over 7000 patient images with an LSTM neural network, defined key milestones, and managed our project backlog with GitHub Issues for a 5-person team. In addition, I investigated methods for medical image registration to improve object recognition and presented these findings at our team's weekly meetings.
June 2019 - August 2020
As an undergrad, I interned at Prudential Financial under the Global Business and Technology Solutions division contributing to enterprise data pipelines. I later interned in the Office of the CIO as a Product Management Intern in Silicon Valley. I deeply valued my time at Prudential. I had the chance to experience data engineering in-industry for the first time, worked directly with C-suite leadership to design new products, and pitched enterprise-scale designs that springboarded me to the Office of the CIO.
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