Ever wondered how computer science and neuroscience might fit hand in hand? With our AI, Tech and Computation event coming up next week, Swati Rajwal describes her journey, as a computer scientist, into the world of neuroscience and how her skillset has been instrumental in advancing a huge range of projects!
I have always been intrigued by the complexities of the human brain and the technological intricacies of computer science. The intersection of these two fields is where I found my passion and it led me to a wonderful role as a Research Assistant at the University of Cambridge's Engineering Department. Here, I'd like to share how my background in computer science has been instrumental in advancing my work in neuroscience.
Background & Career Path
My earliest memory of using a computer dates back to visiting my dad's office as a 9-year-old and using the Windows Paint app to draw houses. Amazed by the capabilities of a computer to execute my commands, I knew this would grow to become my career of interest. And so, after finishing my bachelor’s in Computer Science & Engineering, I started working as a Software Engineer. A few years later, I took my interest further by pursuing a Masters in Computer Science. During my Masters, I realised the ubiquitous nature of my skills as a computer scientist through various academic endeavours such as an R&D internship at Accenture, a collaboration with Mayo Clinic practitioners (paper), a research project at Saïd Business School and many others. I might not have the subject knowledge (e.g., medicine), but give me a dataset large enough and a machine on which to run it, and I shall move the world (rephrasing Archimedes here)!
Academic Journey at Cambridge
After my Masters, I joined Cambridge University and started with a project investigating statistical learning in chronic back pain and endogenous pain regulation. The role demanded not only a deep understanding of neuroscience, but also strong computational skills. Leveraging my experience in computer science, and with a team consisting of a neuroscience expert and other members, we were able to design and execute a PsychoPy task (article), published on Pavlovia platform, as an open source software that efficiently recorded pain and clinical data. Although there is an intuitive builder interface (drag-and-drop based) to create tasks, one needs to have knowledge of Python and Javascript programming languages, along with Git-based software version control to incorporate features and debug any issues.
Another core aspect of my research involved processing large sets of data using Python and RSTAN (a software package for statistical modeling), based on (for instance) multi-armed bandit algorithm. This kind of data analysis is fundamental in neuroscience research, where understanding patterns and anomalies could lead to breakthroughs in understanding, for instance, pain mechanisms. A significant part of my role was contributing to a grant application for the Engineering and Physical Sciences Research Council (EPSRC). The application was successful, awarding our team substantial computational resources: 1,216,800 CPU (Central Processing Unit) hours and 1,000 GPU (Graphics Processing Unit) hours. CPUs and GPUs are essential for processing large amounts of data quickly, with GPUs providing additional power for tasks involving graphics and simulations. This substantial grant enhanced our lab's capacity to conduct complex simulations and data analyses, accelerating our research and enabling more ambitious projects. This achievement was not just a testament to the quality of our proposed research but also highlighted the critical role of computational expertise in justifying the requirement of securing resources for scientific experiments.
The skills I gained throughout these years in computer science—data analysis, algorithm design, and system integration—proved to be incredibly valuable in my research at Cambridge. They enabled me to contribute effectively to my team's research goals, bridging the gap between theoretical knowledge and practical application. Moreover, these skills facilitated collaboration with other scientists and helped in articulating complex ideas more comprehensively during team meetings and in drafting research manuscripts. This synthesis of computer science and neuroscience is not just about enhancing research capabilities; it's also about improving patient outcomes. The models and systems I have worked on at Cambridge are geared towards understanding, and eventually mitigating, pain—a goal that stands to significantly improve the quality of life for patients suffering from chronic conditions.
What's Next?
To all the women in neuroscience, contemplating a similar integration of skills, I encourage you to embrace the possibilities that such interdisciplinary knowledge can bring. The fusion of computer science and neuroscience is a vivid example of how diverse skills can lead to greater innovation and impactful outcomes in science.
This narrative not only reflects my professional journey, but also demonstrates the profound impact that expertise in a seemingly unrelated field can have on another. As I advance in my academic career, my goal would be to enhance my computational skills further and leverage them to build open-source software that promotes equitable healthcare, in collaboration with domain experts. It is a call to all aspiring scientists to look beyond traditional boundaries and explore how their unique skills can contribute to broader scientific advancements.
Disclosure Statement: The views and opinions expressed in this article are solely my own and do not necessarily reflect the official policy or position of any department or research lab at the University of Cambridge.
This article was written by Swati Rajwal and edited by Rebecca Pope, with graphics produced by Lilly Green. Interested in writing for WiNUK yourself? Contact us through the blog page and the editors will be in touch. If you enjoyed this article, sign up to our emails at the bottom of this page to be notified about new posts!
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