Image: Dr Kapal Dev
MTU’s Dr Kapal Dev explains the importance of effective communication network protocols, and the freedom he has in academia to pursue his research interests.
It’s fair to say that Dr Kapal Dev is curious and tenacious when it comes to research. With more than 65 research papers and over €1.2m in funding, he was recently recognised as a top young researcher by the Institute of Electrical and Electronics Engineering for his significant contributions to his field.
“The scale and pace of Dr Dev’s research achievements since joining MTU indicates a bright academic future ahead,” said Dr Seán McSweeney, head of the computer science department at Munster Technological University (MTU).
Dev joined MTU in 2021, having worked in a number of previous roles including in Trinity College Dublin and at the SFI Connect Centre.
Dev’s passion for research developed in 2010 when he was completing his undergraduate degree at Mehran University in Jamshoro, Pakistan. His final year project on a wireless patient monitoring system made him realise that he wanted to work on connectivity, particularly high-speed networks such as 5G and then 6G.
‘We as researchers are driven by a passion for knowledge and discovery, a desire to make a difference in the world, and a curiosity about the world around us’
Tell us about the research you’re currently working on.
My research focuses on the design of effective and efficient communication network protocols and techniques that help improve the performance and sustainability of such services, with the aim of reducing the computational complexity and increasing the energy efficiency while improving the communication efficiency for 5G and 6G.
In your opinion, why is your research important?
It’s important to focus on the progress and efficiency of communication networks as these networks affect all our everyday lives and are necessary infrastructure for applications such as autonomous driving, e-healthcare systems and clinical care, education technology, virtual reality, artificial intelligence (AI), streaming services and other digital technologies.
My research also encompasses the techniques proposed for data and model privacy preservation, which is one of the emerging problems in recent times. Furthermore, the problem increases manifold as the usage of AI services increase.
Considering that AI is used for almost every smart service, the data as well as model privacy preservation cannot only help in reducing cybercrimes but also help in gaining the trust of users concerning automated services.
What inspired you to become a researcher?
My teachers always motivated me to go in depth to understand everything I could about a topic and I believe this consistent, curious nature developed over time which led me to become a researcher.
We as researchers are driven by a passion for knowledge and discovery, a desire to make a difference in the world, and a curiosity about the world around us. I’m always motivated to find solutions to pressing problems or to advance our understanding of a particular issue. For example, recently we are looking into one of the most-talked-about topics around the world, ChatGPT. Stay tuned for our evaluation!
I also enjoy working on a variety of activities in academia. I enjoy lecturing because it gives me the freedom to work on topics of my own interest. Specifically, freedom is a major reason for staying in academia. We all know that you can get very high salaries in industry with more benefits, but this freedom allows me to work on different topics and apply for a range of funding.
What are some of the biggest challenges or misconceptions you face as a researcher in your field?
The biggest challenge we face is benchmark datasets which are rarely available because they require huge infrastructure which not everyone can afford, especially in academia.
In the world of AI and machine learning, benchmarking is the practice of comparing tools and platforms to identify the best-performing technologies in the industry. Benchmarking is used to measure performance using a specific indicator resulting in a metric that is then compared to other machine learning methods.
Good computer vision benchmark datasets will reflect the setting of the real-world application of the model you are developing. ObjectNet is an example of an image repository purposefully created to avoid the biases found in popular image datasets. The intention behind ObjectNet’s creation was to reflect the realities that AI algorithms face in the real world.
Do you think public engagement with science has changed in recent years?
Yes, it has changed. The Covid-19 lockdowns gave people time to think about things for which we never had time. People started engaging with science via different sources and, as they became aware of many scientific developments such as vaccines, they started to realise how important scientific discovery is for all of us.
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