AI Insights

How I Pursued My Computer Science Master’s in the USA and Gained Real-World Machine Learning Skills

December 27, 2022

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Amal Mathew from India completed several Omdena Challenges ranging from preventing road crashes using computer vision to predicting cardiac arrest. He became an Omdena Top Talent to work on paid gigs with companies. During his bachelor’s, Omdena helped him pursue and pave the way for a master’s. Also, through Omdena’s experience, he has gained leadership and problem-solving skills, and he overcame his communication barriers.

Amal, what is your professional background? 

I am currently enrolled in the MSCS master’s program at Northeastern University, Boston, USA. I have varied practical and research experiences in the Python ecosystem specializing in different deep learning integrated computer vision fields.

From the days of my bachelor’s, I have been interested in exploring the vast and booming fields of data science through working in multiple startup environments, open source contributions, and developing and deploying ML solutions to various real-world problems.

The most satisfying part of Omdena was being part of it, contributing to solving real-world applications with the utilization of gained knowledge and creating social impact in society. 

When and how did you enter Omdena? What was the reason?

I came to know about Omdena through a close friend, who was at that time already a part of Omdena, and he recommended to join Omdena’s challenges to brush up on my skills. Each project of Omdena, starting from 2020 till the present, was an exciting journey for me. The excitement is because each project provides an opportunity to interact with various ML experts, whose constructive criticism and guidance helped me improve myself as a person and also my technical skills.

Amal took part in 15 projects! These are some of them:

Malaria is a mosquito-born disease, claiming over 400,000 lives each year, mainly children under 5. By targeting the water bodies where mosquitoes lay eggs, the disease can be controlled or even eliminated altogether.

Combining satellite images, topography data, population density, and other data sources, a team of 40 AI changemakers built an algorithm that identifies the areas in which stagnant water bodies likely exist. The model helps to identify breeding sites more accurately and quickly.

Highlighted grids have a higher risk of containing water bodies

Highlighted grids have a higher risk of containing water bodies

I have been an Omdena School instructor as well.

I have delivered a lecture to more than 190 international students. I have taught them from the basics of deep learning to advanced concepts and helped them deploy Models to categorize plant diseases: Identifying Diseases in Plants with Image Categorization in Edge Devices 

My experience as Omdena India Chapter Lead:

I’m one of the founding chapter leads for the India chapter. It was a great experience for me, from designing a logo for the chapter, organizing multiple impactful projects, and scheduling meetings to connect with  AI for good startup partners who could join the chapter for potential projects.  During the covid outbreak, I coordinated an AI challenge/project, Leveraging AI to Analyse the Socio-Economic Impact of Covid19 in India. As the name suggests a dashboard that identifies and predicts various economical impacts of the virus on the Indian economy, Using live API to show analysis of vaccine intake and covid cases in states, etc. AI enthusiasts from throughout the country joined the collaborative project, and Omdena gave me the experience of leading, organizing, and managing the team and the project end-to-end.

What technical solution did you build recently in a project?

In collaboration with IMMA on the project to improve health monitoring, I have developed software that is capable of analyzing the follicle present in the ovaries. It also able to create and visualize the 3D point cloud to represent the cells. The solution involved extensive use of computer vision techniques and deep learning to segregate, classify, and display the relevant information from the follicle through 3D reconstruction techniques.

Amal at work

Amal at work

What was the biggest obstacle you overcame?

After completing more than ten projects with Omdena, I could boldly say – Omdena taught me the true essence of workplace skills such as leadership and conflict resolution, problem-solving, and communication skills. Direct communication with clients to understand the problem at a deeper level and collaborating with diverse and brilliant individuals couldn’t have been more enjoyable.

“I feel Omdena is an advancement to the skillset needed for a successful AI career!”

How did the Omdena Top Talent experience help you in your career?

While each Omdena project was exciting on its own, Omdena’s Top Talent Project was even more exciting and an upgrade for me. With a very selective number of collaborators and an elaborative problem statement, each talent project gave me an opportunity to explore deep and improve my ML skills, both conceptually and technically. Each talent project helped me add something new to my skillset.

“The best thing about Omdena is that it gives hands-on experience on real-world impact problems. The journey is truly innovative, inspirational, and a true career booster.”

Computer vision Summit in Boston, USA

Computer vision Summit in Boston, USA

Ready to test your skills?

If you’re interested in collaborating, apply to join an Omdena project at:

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Leveraging AI to Drive Positive Change: How Becoming an Omdena Top Talent Shaped My Career in AI