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Meet Malvika Kharbanda

Visiting Masters student, Bioinformatics division. Malvika wants to make the world a better place, one code at a time.

Why did you choose WEHI for your Masters?

In my first semester, WEHI researchers Professor Melissa Davis and Dr Dharmesh Bhuva gave a lecture about a bioinformatics package that they developed called ‘singscore’, which uses a simple rank-based method to distinguish cancer samples with certain mutations.

They described a project using ‘singscore’ to explore single cell RNA-seq data. This appealed to me as they were revolutionising cancer research. I wanted to be a part of it.

Malvika Kharbanda profile
Above: For her Masters research, Malvika developed a computational benchmarking study that used cell type identification as a higher order molecular phenotyping technique to evaluate different methods to assess molecular phenotypes.

What was your Masters research about?

I developed a computational benchmarking study that used cell type identification as a higher order molecular phenotyping technique to evaluate different methods to assess molecular phenotypes.

I demonstrated that a simple rank-based method, such as ‘singscore’, which is computationally efficient, has a similar performance to complex methods, and is therefore suitable for studies with atlas-scale data.

The methods with good performance in my benchmarking study will be helpful in studying cancer systems by identifying cancer cells that are resistant or responsive to treatments. We can use these methods to identify rare cell types or subpopulations of cells.

Precise dissection of a patient’s cellular heterogeneity will help them in getting individualised treatment plans based on how unique characteristics dictate the disease response and progression.

“One day I aim to be like one of the great scientists around me at WEHI, able to say that I have contributed enough to make the world a better place, one code at a time.”
– Malvika Kharbanda

What are you passionate about?

Apart from science, I love embroidering and knitting; it helps me focus when work gets overwhelming. One day I aim to be like one of the great scientists around me at WEHI, able to say that I have contributed enough to make the world a better place, one code at a time.

What does a typical working day involve?

A typical working day involves at least one meeting/seminar, and the rest of the day is spent balancing coursework and my project.

How has WEHI supported your studies?

I joined WEHI during COVID-19 when Melbourne was in lockdown and everyone was working from home. I met Professor Davis and the rest of the lab team after six months of working with them online. They were all so welcoming.

I grew more confident because I was given the opportunity to collaborate with other researchers and my contribution was always validated. This exposure provided the opportunity to push my intellectual boundaries.

“There were a lot of challenges along the way; there were times when I felt like an imposter, but reassurance from supervisors and lab members kept me going.”
– Malvika Kharbanda

WEHI awards Masters students with the Alan W Harris Scholarship, which financially kept me afloat during COVID-19. With their employee assistance program, WEHI makes sure that students have mental health support.

Taking part in the fun WEHI student association events kept me sane when I was so far away from home.

What did you do before starting your post-graduate studies?

Before my Masters, I did my Bachelors in Computer Science and Engineering, with specialisation in Bioinformatics from Vellore Institute of Technology in India. I came to Australia to do my Bachelors project in Bioinformatics at ANU with Dr Allen Rodrigo. Whilst doing the project, it became clear that I wanted to pursue a career in this field. I applied for a Masters in Bioinformatics at The University of Melbourne.

What are you doing now and what your plans for the future?

I have just finished my Masters Degree and am working as a research assistant in Melissa Davis’s lab at WEHI. I’m also applying for my PhD in Computational Biology and Machine Learning.

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