Leader’s Voice: Ayse Bat, Researcher, CERN

Ayse Bat

SDD: Tell us a bit about your journey in science and technology. How you got interested in particle physics to working as a researcher on a large hadron collider at CERN?

Ayse: I have always been interested in physics, but  High Energy Physics is my favorite.  Just imagine a place where the people working there share a common goal which is scientific discovery. They came from all around the world, passionate about physics, science, and technology.  That passion draws me to particle physics. I know that it was a long journey and I also know that it will worth it in the end. So, I joined the CMS experiment at the Large Hadron Collider to work on particle physics for my Ph. D. studies.

SDD: What was your experience like while working on the largest science experiment in the world?

Ayse: CERN just like a huge university, with no undergrads (except summertime) but with most of the top scientists in this field. It is a dream place not only for physicists but also for Engineers, Technicians, Mechanics, and software developers. Because this is a place where scientists can work without boundaries. They have the freedom to think differently, to imagine, etc…  And I think that’s one of the reasons experts come to CERN – to test themselves, push their abilities and help to create history with groundbreaking discoveries.  I think one of the things I really like about the CERN ethos that it doesn’t matter who you are, what matters is what you are good at.

SDD: What type of tools and analysis did you use on data while searching for new particles?

Ayse: One of the primary data provided by CMS  is “Mini Analysis Object Data” or MINIAOD for short. These MINIAOD files are prepared by piecing of raw data collected by various sub-detectors of CMS and contain all the information that is needed for analysis. The files cannot be opened and understood as simple as the data tables but require ROOT in order to be read. ROOT is the framework used by several particle-physics experiments to work with the collected data. It provides all the functionalities needed to deal with big data processing, statistical analysis, visualization, and storage. It is mainly written in C++ but integrated with other languages such as Python and R.We start by applying selection cuts via the configuration file and reduce the MINIAOD files into a format known as PATtuple that stands for Physics Analysis Toolkit and provides easy access to algorithms in the framework of CMS Software (CMSSW), suitable for most CMS analyses. From the point of view of the data analysis, we must select the sample of ”interesting” events and apply some selection criteria to our events depend on our physics analysis. Eventually, these events using the searching for new particles and then we estimate the significance of any new particle in the data. If there isn’t any significant evidence for these new particles, we proceed to set limits for this new particle using a Bayesian approach for most of the study.

SDD: At the CERN, you must have worked with big teams that are highly diverse. How did working as a part of diverse teams enrich your experience?

Ayse: I think it makes you more open-minded,  you will be open to new perspectives for a personal and professional manner. Just in one place, you have an opportunity to know the different cultures that came from all around the world. The countries forget about politics and collaborate to achieve the things that never done before. Everyone here makes an effort to be the best they can be, it doesn’t matter which background they come from.

SDD: There are many women in our community both freshers and women who are looking for career switches who are keen to enter the world of data science. What is your advice for them on how to get into this field?

Ayse: While the idea of data science is attractive, many people are away from this area because they are finding the coding frightening. What really makes a data scientist is to stand out the problems solving and structured thinking skills, tools are just a way to implement this. So,  focus on problem-solving skills, and the tools will come naturally. We learn by doing, practice, practice, practice. The more you apply it, the better you will become at it. I think here, consistency is the key, keep going even if the progress is slow. Nobody starts off being an expert if they have made challenges things at the beginning, what they did you can also make it. The best way to become a data scientist is by doing data science. Find whatever project or area that interests you, get the data set and start working on it. The women are always involved in a challenging and complex task, so don’t give up working on to be a data scientist. 

SDD: What do you think of the position of data science, ML and AI in the current education systems around the world? How do you think it can improve?

Ayse: Data Science could give a huge effect on the education system with machine learning (ML), teachers could have access to all of their student’s data in one place. With this data and using machine learning, could helps teachers to improve their lessons by identifying where groups of students are struggling. ML has the ability to predict student performance by “learning” about each student, this technology can identify weaknesses and suggests ways to improve, such as additional practice tests. Teachers also will be able to use the data to see which students need additional assistance. As a result, education could look a lot different a few decades from now.

SDD: What inspires you every day?

Ayse: Work is a big part of the daily life of many of us, so I believe in the importance of engaging in a business that inspires us. This is an area where you can always learn new things and improve yourself and keep yourself dynamic. Always a new sense of learning motivates me. Working with people who believe in innovation, technology and science motivates me.


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