Hi there! I am Sitong An (安思同 in Chinese), Marie Skłodowska-Curie Fellow at CERN with project INSIGHTS and PhD student at Carnegie Mellon University (CMU). Originally from China, I left at the age of 16 and travelled the world for education. Currently, I’m working at CERN, Geneva, Switzerland, under the supervision of Dr. Sergei Gleyzer and Dr. Lorenzo Moneta. My Ph.D. advisor from CMU is Prof. Manfred Paulini. From September 2018, I will be working on Machine Learning/Deep Learning for Particle Physics for three years. I am immensely grateful to INSIGHTS and to my supervisors for giving me such a great opportunity to work in this exciting subfield.
A bit of background about me: I was born and raised in a small, nondescript city in northeastern China. As a kid, the thought of venturing overseas for education never crossed my mind. That was the case until 2009, when I was offered a scholarship (SM1) by the Singaporean government to attend high school in Singapore. It was a once-in-a-life opportunity, a rare window to the world outside, and yet it was also a daunting choice to go to a foreign country and learn to survive on my own. Eventually, this became the decision that changed the path of my life. I spent four intense and memorable years at Singapore, attending Catholic High School and Hwa Chong Institution. Till this day, I still feel a strong affinity for the dear “Little Red Dot”.
After my A-Levels there, I moved to U.K. for my undergraduate education at University of Cambridge, partially supported by scholarships from both the University and my college, Wolfson College Cambridge. I graduated in 2018 with a Bachelor of Arts and a Master of Natural Sciences (Physics). During my journey I was fortunate enough to have the opportunity to visit many places around the globe, including MIT for an exchange year abroad, and Weizmann Institute (Rehovot, Israel) and DESY (Hamburg, Germany) for internships. The coursework at Cambridge could feel gruesome and never-ending at times, but it was a privilege to wander about on the paths walked by Newton and Maxwell. Looking back, the three years I spent there were bittersweet, but still dream-like.
To work at CERN has been my dream and goal since high school. I remember the naive but passionate excitement I felt about the Higgs discovery while I was still a high school student. I remember seeing the advertisement on the CERN career website for the INSIGHTS position and thinking “this is exactly what I want to do!” I also remember attending the interview nervously, fully aware of the competitiveness of the position, and telling my future supervisors how much I care about making an impact in this field that I love, to the fullest of my abilities. And…voila, now I am here. As I sit in my office and type this blog post to tell you my story, I still can’t help but feel amazed at how these ten years passed by, and how that dream came true.
For these three years, I will devote roughly half of my time here to the development of deep learning algorithms for particle physics experiments. Specifically, currently I’m investigating the use of Graph Neural Network for event reconstruction at the new and upcoming High Granularity Calorimeter (HGCal) for the CMS Experiment. Reconstruction algorithms are an important step in the workflow of high energy physics experiments. They take raw data from the detectors and convert them into physical objects that physicists understand – like particles for example. Because of the sheer complexity of our detectors, deep learning holds promises in greatly enhancing the pattern recognition of our future reconstruction algorithms and empowering our detectors to make more precise measurements. This is, of course, a very brief and simplistic explanation, and I will describe this project in greater details in another technical blog post in the future.
The other half of my time will be spent on developing software tools in support of HEP-ML community – particle physicists who are developing and applying Machine Learning algorithms to their work. I am part of the ROOT team in the CERN EP-SFT group. ROOT is a data analysis framework widely used in the data workflow of high energy physics, and I will be contributing to ROOT-TMVA (Toolkit for Multivariate Data Analysis), the machine learning project within ROOT. My work will focus on modernisation of ROOT-TMVA, aiming to allow physicists develop and deploy machine learning models more easily with ROOT data. More details upcoming about this too.
Apart from my technical work, I also care deeply about public engagement. High energy physics is a costly enterprise and what we’re doing would not be possible without public support. I am a CERN guide as well as a qualified guide to both CMS and ATLAS experiments. It is always an enjoyable experience to show visitors around and share our passion; to explain why we are doing this, why curiosity-driven fundamental research is important; and to see the awe-struck expressions of the visitors when they see the underground detectors for the first time. I also volunteer actively in CERN public activities, like CERN Opendays and TEDxCERN.
If you’re interested in learning more about me, welcome to visit my website/blog by clicking here. It is still very simple and lacks much content at the moment, but I will furnish it with more details as my work progresses. You can also find ways to contact me there – feel free to reach out to me with questions or opportunities in Machine Learning.
If you’re a student or a teacher from a high school and interested in organising a virtual visit to CMS [more details], please do not hesitate to contact me for help too. (in Chinese) 如果你是来自中国或新加坡的初/高中老师或学生，并对组织远程虚拟访问活动来参观CERN地下实验感兴趣的话，我愿意帮忙协调组织和华语讲解 – 如有需要请联系我。关于远程虚拟参观，你可以点击这里了解更多（页面仅英文）。
Looking forward to sharing more of my journey here – stay tuned!