JGI Student Experience Profiles: Maciej Glowacki

Maciej Glowacki is a 2nd year PhD student in the School of Physics at the University of Bristol

JGI Student Experience Profiles: Maciej Glowacki (Ask-JGI Data Science Support 2021-22)

What made you decide to apply to join the Ask-JGI team? 

Applying to be a part of the Ask-JGI team was an easy choice. Even though I wasn’t actively searching, I always wanted to be a part of a more diverse data science community. I guess I was curious as to how my know-how from particle physics would transfer and be perceived in the wider landscape of working with data, so when the opportunity presented it seemed like the natural fit that would put these questions to rest. 

Looking back, my decision to take a step out of my little corner of the room to discover the huge scope of data science projects was for sure validated! Along the way, I met some fascinating people making headway on challenging and relevant problems.  

What did you find most rewarding about your Ask-JGI experience? 

The hallmark of joining the Ask-JGI cohort is the people you work and interact with. The impressive breadth of talent across the Ask-JGI student team makes it the ideal place to develop and establish really valuable connections in the process. The class of 2022 will stay in touch long after the program’s conclusion! 

What sort of work did you do as a part of your Ask-JGI experience? 

Over the course of the past six months I immersed myself in some really captivating projects, ranging from statistics and machine learning to data visualisation and network analyses. The line of work the JGI is involved in comes in all shapes and sizes; from assisting graduate students with their research programmes to cross-disciplinary endeavours with professional researchers. 

The most substantial piece of work I was involved with during my time with the JGI was a collaboration with the Political Science department aiming to interrogate hierarchy structures within organisations. That is, it looked to quantify how an individual’s network within an institution impacted their progression potential. The prototype for this focused on academic circles, and quantified the connections between individuals based on their network and reach. Connections between two individuals were based on a “hierarchical structure”. Whereby, the edges between two nodes (individuals) are weighted proportionally based on either presenting at or chairing a conference panel, thus identifying connection strength between individuals to expose the formation of patterns and recognise “gate-keepers”.  

Would you recommend this experience to other students? 

I would recommend joining the JGI team for anyone interested in the wide reach of data science. On top of this, you’ll meet cool people, coordinate various initiatives, contribute towards live events, and develop skills you’ll be thankful for in the future!