Budding data scientist looking to make an impact in healthcare.

Based in 🗽 New York City.
I am a biomedical engineering graduate & budding data scientist looking to use machine learning to make an impact in the field of healthcare - with the goal of improving prevention, diagnosis and treatment.

After my degree at Imperial College London, I first worked in academia to better understand the fundamental research needed to improve health outcomes in the real world. I then interned at Pathos, a biotech startup leveraging big data and machine learning to bring precision medicines to market.

I've always loved helping people, and I'm currently looking for an opportunity to translate clinical research into a product or process that would help improve the lives of people living with debilitating diseases.

I believe we're only just beginning to witness the impact machine learning will have on healthcare, with many more breakthrough discoveries to come from the intersection of those two fields.

Education

MEng Biomedical Engineering
Imperial College London
2015 - 2019
As a high school student who loved the maths and sciences, I chose to study biomedical engineering as it offered an opportunity for a career in healthcare without following the traditional path of becoming a doctor.

This was an integrated degree, combining a bachelor's and master's into four years of study. It was in my fourth and final year that I completed rewarding projects, including one that applied machine learning to neural data.

Experience

Data Science Intern
Pathos
2023 - 2024
I had the opportunity to intern for six months at Pathos, a startup leveraging big data and machine learning to bring precision medicines to market. This internship allowed me to work with and be mentored by brilliant minds. I was also able to delve into a new field - drug development in cancer.

With access to real-world oncology and genomic data, Pathos can uncover key insights into the drug development process. One of the projects I was involved in aims to identify and validate a biomarker in ovarian cancer.
Research Assistant
The Francis Crick Institute
2019 - 2022
My two years spent in the Kohl Lab, a neurophysiology lab at the Francis Crick Institute, were extremely rewarding. This experience allowed me to gain an understanding of the many concepts, methods and tools needed to perform quality research. It also gave me the opportunity to collaborate with people from different backgrounds and learn new skills along the way.

The lab investigates the neural circuits underlying instinctive behaviours such as parenting and aggression, and how physiological state shapes information processing in these circuits.

For parenting, a major physiological state to investigate is pregnancy, and this is studied in female mice. It was the basis of my projects - looking at how pregnancy affected individual neurons, and brain structure.
Summer Research Student at the American University of Beirut
2018
In the summer between my third and fourth year at Imperial, I had my first taste of academic research with the Daou Lab at the American University of Beirut. The lab focusses on identifying neural mechanisms for vocal learning. During my short stint in the lab, I was tasked with analysing and improving a neuron model trained on songbird neural data.
Summer Intern at CardioDiagnostics
2017
In the summer between my second and third year at Imperial, I completed my first machine learning project as an intern with CardioDiagnostics in Lebanon. The company develops proprietary solutions to monitor patients with heart arrhythmias remotely. During my internship, I was tasked with training and improving a neural network to detect anomalous heartbeats.

Coding Courses

Google Machine Learning Crash Course (Ongoing)
I have recently started this Machine Learning Crash Course to further develop my skills and understanding of ML. I will be following this guide for further learning.
Data Science and Machine Learning with MIT Institute for Data, Systems, and Society
In May 2022, I was eager to learn more about machine learning and applied to Data Science and Machine Learning by MIT Institute for Data, Systems and Society. This online 12-week course includes recorded video lectures from MIT faculty, weekly mentored sessions, graded assessments, three industry-relevant projects and 50+ case studies. Topics covered include regression, deep learning, recommendation systems, graphical models and more. I was given a certificate upon course completion.
Web Development with Code First Girls
While at Imperial I was introduced to Code First Girls, a UK organisation that provides free coding courses to women. I completed their introductory Web Dev course, learning the fundamentals of HTML and CSS for front-end development. A year later, I completed their Python & Apps course which delved into back-end development in Python. In the final weeks, we teamed up to build a web application with Flask and APIs, with my team winning best website!