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January 25, 2024

Empowering Healthcare with Machine Learning: A Hands-On Approach

About This Video

This tutorial offers an insightful journey into machine learning (ML) in healthcare, tailored for the Women in Data Science community. We’ll explore ML’s fundamental concepts, delve into its transformative role in healthcare, and highlight real-world applications. A key feature is the hands-on session, where participants apply ML techniques in a practical healthcare scenario. Designed to be both informative and interactive, this tutorial aims to empower attendees with a deeper understanding of ML’s impact on healthcare and inspire further exploration in the field.

In This Video
Data & Applied Scientist, Microsoft

Millicent Ochieng is a seasoned Data & Applied Scientist currently spearheading significant research initiatives at the Microsoft Africa Research Institute (MARI) in Nairobi, Kenya. With an acute focus on Multilingual Natural Language Processing (NLP) and developing AI solutions for low-resource languages.

A proud recipient of the IUCEA Masters Scholarship Award, Millicent completed her MSc in Data Science at the University of Rwanda, where she explored transfer learning techniques for precision agriculture under the mentorship of Dr. Weiwei Pan, Harvard University and Dr. Melanie Fernandez, Microsoft Research.

As an ardent advocate for gender equality and representation in STEM fields, she continually seeks opportunities to foster inclusivity and innovation in the industry. Millicent’s commitment to excellence and passion for bridging the technological divide with AI advancements are evident in her contributions as a Women in Data Science (WiDS) Ambassador for both Africa and Kenya.

Associate Data Scientist, Roche

Diana is an Associate Data Scientist at Roche. As an associate, she supports a Post Doctorate Data Scientist in a project to Predict Cardiac Adverse Events using Machine Learning Algorithms and Electronic Health Records Data. Before this, Diana worked with healthcare start-ups on AI projects to find a correlation between symptoms, triggers and Morbidity for Hashimoto’s disease and another building predictive models using EMG signals and patient data to detect and predict painful behaviours for lateral elbow patients.

She is also an instructor at Code First Girls UK where she gets to teach and guide other women into a career in Data Science. Diana enjoys organising events and workshops related to Tech, AI and Data and thus volunteers as a Google Women Techmaker Ambassador and Women in Data Science Kenya Community Organizer. She is currently at the onset of her MSC in Big Data Analytics at the University of Liverpool and looks forward to publishing papers on research in Healthcare and AI.