Our focus is developing AI models for early detection of Acute Myeloid Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL). The group is actively involved in reproducing results published by various researchers, and developing new models and algorithms for the same.
In addition to helping students progress in their education, our initiative helps gain recognition for student's involvement in the fight against leukemia. Through our roles as Intel Innovators, students may also get opportunities to present their work at technology events & conferences.
Our students communicate with our team remotely as our student program is an online initiative, Research Interns can participate from anywhere in the world.
The Peter Moss AML/ALL AI Student Program is entirely free! Approved students will learn how to develop AI for medical use cases using open source tools. We use free and open tools for our work like Google Colaboratory and Microsoft Azure Notebooks. The students get to work with latest deep learning frameworks like TensorFlow, PyTorch, MxNet etc.
PLEASE NOTE: We are currently not accepting applications for the student program. Applications will be opened up again later in the year.
Meet The Mentors
The Peter Moss Leukemia AI Research Student Program is headed by Adam Milton-Barker, Juan Carlos Carrasco-Giménez & Shriya Narang, students are also able to communicate with other team members who mentor the group and give advice.
If you would like to volunteer to the research project or student program as a professional, please use the application form on our Join Us page.
At this moment we are not accepting new applications for students. Please do not use our profesionaly volunteer application form to apply to the student program.
President & Founder
Student Program Lead
Student Program Lead
Meet The Students
Find out about our current students.
There are currently no active students
Meet The Student Alumni
Find out about past students.
Taru JainPre-final year undergrad B.Tech in IT
Researches into Convolutional Neural Networks & Generative Adversarial Networks for clinical systems