Poorvika Negi

What were you doing before you came to study at City?

Before studying at City, I was pursuing my Bachelor’s in Technology, Computer Science and Engineering.

During my undergrad, I developed a keen interest in image processing and gesture recognition technology and published papers focusing on how it can be used in healthcare systems such as brain tumour detection and segmentation for gene therapy.

What has been your favourite module on the course, and what have you enjoyed most about your time at City?

My favourite module in the course has been Computational Cognitive Systems. It is the perfect confluence of artificial intelligence with cognitive science and psychology.

Apart from the computational aspect of the module, the metaphorical connection of the models and the concepts associated to the human mind have been inspiring.

What I have most enjoyed at City has been the exposure with world-renowned researchers, diverse cohort, and the beautiful infrastructure (especially the labs and the library!).

Everyone has been very helpful and welcoming, which has made me feel at home even when I am so far away from it.

What do you plan to do after you graduate?

After I graduate, I plan to pursue my doctorate in Artificial Intelligence, focusing on its applications in healthcare.

There is a wide scope in the field of artificial intelligence that can be utilised for the benefit of the human society. I aim to work on healthcare systems and genomic medicines.

With the abundance of data that exists, I will try to develop new computational approaches that will be able to solve high dimensional datasets.

The human body is highly fascinating, to an extent that even a single cell comprises of several attributes and can be defined by many measurements.

They are inherently complex, and therefore with the help of artificial intelligence and deep learning techniques, I want to pursue research and application to further discover new insights.

Once the data is collected and pre-processed, with the help of Artificial Neural Networks, the cleaned data can be used to train a model to discover relationships and make predictions, without explicitly specifying the rules to carry on such tasks.

Moreover, artificial neural networks and deep neural networks can help in discovering patterns in a particular disease and refine our understanding of the disease.

What would be your top tip for our applicants?

As a STEM professional, my top tip for all the applicants would be to have a basic a knowledge of programming.

In the age of digital era, all the sectors are making use of technology. A good grasp of programming concepts goes a long way.

Explore what is out there. You never know what strikes inspiration. All fields are intertwined and multidisciplinary in nature.

Be true to yourself, follow your passion and never be scared to put in the hard work and effort to make things true.

Good things take time, but they are worth it.

What work experience or activities have you taken part in relating to Artificial Intelligence?

I have thoroughly enjoyed the seminars and events organised by the Artificial Intelligence Research Centre (CitAI) and the School of Science & Technology (SST).

To be able to interact with researchers that are at the top of their fields is inspiring.

I attended the COP26@CITY event which is one of the great initiatives where I gained perspective about the impact of technology on the environment, and how we can use technology ethically.

What is one way we can engage other women and girls in STEM from India?

I think there are still numerous misconceptions about the scope and learning difficulty of STEM programmes.

One way of engaging other women and girls in STEM is exposure to the opportunities in these fields.

Initiatives can be launched to empower women and girls with skills like problem solving, critical thinking, and entrepreneurship by offering hands-on work so that they can apply the theoretical knowledge learnt in real-life applications.

This will boost their confidence to overcome misconceptions that women are not good in science, technology, engineering or maths.

Is there anything else you would like to add?

Think big, innovate, work hard and be consistent.