I’m Victor and I’m an ESR at the University of Oslo within the INSIGHTS network. My supervisor is Alexander Lincoln Read, he is a Professor at UiO and an expert in Higgs physics and Statistics within the ATLAS collaboration.
I was born in Kyiv, a capital city of Ukraine, and there my scientific trip has started. My acquaintance with Natural Sciences traces its origin to the high school — KNSL #145 where I spent 4 amazing years of being continuously distracted from computers and learning to entertain myself with only math, physics, pen and paper. Nevertheless, I kept spending my free time on programming (that time web technologies were at the level where C++ is now in comparison to Python). Keeping both science and computers together became a sophisticated task for me to stay social 🙂
After school, I entered Taras Shevchenko National University of Kyiv to learn Physics. I was not sure what I would do for living that time so I chose the most promising sphere for me to grow and develop. I did my Bachelor and Master in Kyiv studying Quantum Field Theory in application to High Energy Physics. I also attended courses hosted by Bogoliubov Institute for Theoretical Physics. The warm atmosphere of people discussing Representation theory and Fiber bundles while having tea in the kitchen has bought my attention to advanced math. Is there anything more powerful than coffee breaks that encourage students to attend optional lectures?
Since the last years of the Bachelor program, I got engaged in the Heavy Quarkonia physics project at Mainz University in Germany. Lately, this activity under the supervision of Marc Vanderhaeghen resulted in my Master’s. This period was also an intense traveling time for me and @Artem (yes, we know each other since KNSL where we were classmates and then became groupmates at Univesity), we were attending a variety of winter and summer schools in physics which has widen our understanding of what are the hot topics in science these days. Machine Learning and Advanced statistics have definitely entered the list!
After I finished my Master’s, I was very picky in choosing the Ph. D. program while professors were picky on their side as well for choosing suitable candidates, thus it took a year for me to find the match and get matched. During this year I gave the deserved freedom to my passion for programming and entered the R&D squad of Israeli startup Emedgene. We were developing a platform for automated interpretation of the human genome saving hours of time clinicians spend analyzing patients’ cases. It was not only a software engineering job but also good training in bioinformatics and applied genetics.
Since September 2019 I’m doing a Ph. D. in Experimental Physics as an ESR of INSIGHTS network and an employee of the University of Oslo. As I have already mentioned, Higgs physics will be a sphere of our research. We plan to target the Higgs CP-violating sector and to develop advanced methods in statistics (like ML and Bayesian approaches) in order to approach required efficiency and sensitivity.
I’m very happy to join the Team! Thank you for providing me with such an opportunity!
On Friday, September 27th, several universities and research centers across Europe hosted outreach activities for the European Researchers’ Night. In Rome, nearby the buildings of the Math and Geology department of the University of Roma Tre, one of the events that welcomed visitors was organized by Pangea Formazione: “It’s raining cats and dogs“.
To visitors of the stand, mainly targeted at kids, it was given the chance to get in touch with some of the core ideas that advanced machine learning solutions are based upon, through a pair of board games.
One of the activities dealt with the basics of convolutional neural networks and image classification via deep learning. Kids were divided in teams and assigned one (sketchy) drawings each, with the goal to help the other team to guess their image through a series of subsequent elements. At each round, the host was presenting a new ‘feature’ (a particular curve line, a corner, or some other shape) that members of each team had to search inside their images. If such a feature was present, they shall draw it on a thin sheet of paper. Through addition of multiple features, a more and more complete picture was composed and it was easier for the opponent team to guess the subject of the drawings, but scoring progressively fewer points.
This procedure mimics quite closely the inner functionality of a trained CNN classifier that first learns a series of abstract patterns (through the different filters that get trained in the sequence of layers of the neural network), which in our game were represented by the lines and patterns proposed by the host of the game, and then searches for them in any new picture that is fed for classification.
activity consisted of a card game about updating probability estimates, based
on different levels of information. During subsequent rounds of a game, one or
two players want to guess the current presence of a specific weather conditions
(rainy, cloudy, sunny, windy, etc.), while being unable to directly obtain this
information e.g. because they cannot just look outside the window or use a
weather forecast app.
Hence, they can decide to guess blindly about it (having a certain low probability to guess correctly) or to play additional information cards in order to gain further evidences in support to their guess. Examples of information cards are: the current season, which can increase chance to guess right the weather for some conditions and decrease it for other conditions, the city in which the player’s character currently is (e.g. Palermo, Rome, Milan, etc.), or the fact that the player’s character is a person who has spent part of her life in a chosen city, which increases the chance of a correct guess if the same city card has been played as well.
Players therefore take turns either by trying to guess their answer, or by adding an information card to their advantage (if such cards turn odds in their favor) or by putting obstacles on the opponent’s guess (if the cards give negative points for the weather condition that the other player is trying to guess).
This mechanism about accounting for every available information before evaluating the probability of the event shall remind you about the description of the subjective probability given in a previous blog post. Along the same lines, the game helped to convey the idea that in real situations we must be flexible enough to update our belief in presence of new evidences.
Several components of the Pangea Formazione work team participated to the event, helping visitors to grasp the rules of the games and illustrating the underlying principles that really made the games close to the actual machine learning algorithms we often see in action in everyday life. When we see a mobile phone capable to recognize our faces as a security mechanism, or when translation apps can identify and translate texts that the camera focus on, we seldom have the knowledge needed to understand how such complex tasks are accomplished. Even if a casual observer could believe some magic is involved, in fact it is just the (complex) combination of simpler elements, whose understanding luckily does not need particular studies.
Kids (and their parents and grandparents as well, in fact) were very curious and wanted to have a glimpse of the actual ideas that lie behind common applications of machine learning.
At the same time, the ludic aspects of the games were really appreciated by the kids who stopped by our stand, spanning ages from 6 to 12 years, and they really wanted to remain as long as possible with us.
For adults, a series of posters summarized some of the different technical aspects that are involved both in CNN algorithms for image classification and in defining a flexible definition of probability, like the subjective one, that can go beyond the simple examples with coins and dice we learn at school.
The only downside of an otherwise great evening was the fact that ‘our’ ESR Daria could not attend the events, because she is currently spending her time at University of Edinburgh for her secondment period. But we will welcome her in the outreach group next year for sure!
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!
My name is Serena Palazzo and I am an ESR at The University of Edinburgh
within the Innovative Training Network (INSIGHTS) program. My supervisor
is Michele Faucci Giannelli who gave me the possibility to join this network.
I was born in the very south part of Italy, in Calabria and there I got my degrees
in Physics at the University of Calabria in Cosenza. My high school path was
focused on classical studies but, since that time, I started to be enthusiast
about physics and I decided to start my undergraduate studies in Physics.
During my bachelor path I started to be interested in particle physics and my
first collaboration in this field was within the hadronic calorimeter DREAM
community; I worked in this context for the calibration of this calorimeter.
Then, for my master degree I started working within the ATLAS collaboration.
My first project within the ATLAS collaboration was focused on the Phase-1
upgrade program of the Muon Sprectrometer of the experiment where I
contributed in testing the new MicroMegas chambers. During my master
thesis project I collaborated with researchers of the LNF laboratory (the Italian
laboratory of particle physics). This collaboration gave me the possibility to
learn a lot about the upgrade program of the ATLAS detector and to increase
my knowledges on particle physics. I changed then topic, moving from the
upgrade work to the measurements of cross sections. I started working
within the top quark working group of the ATLAS collaboration where I
contributed doing measurements of differential cross sections of the top
During my studies I won a scholarship (INFN-CERN associate simil fellow)
that gave me the possibility to spend 1 year at CERN. Spending this time at
CERN was very useful from the point of view of my career development; I had
the possibility to meet and collaborate with several reaserchers coming from
all the part of the world.
About my work within the INSIGHT newtwork, while continuing work on top
quark measurements, I am learning new important techniques such as
Machine learning techniques that I am currently using for different projects.
The network is without doubt a great opportunity for first years researchers to
enrich the research paths; it allows to have exachange of knowledges
between the ESRs as well as it offers the possibility to follow useful trainings
to consolidate and widen the knowledges.
See you soon!
My name is Vasyl, and it has been more than a year since I have started this amazing Ph.D. journey as an Early Stage Researcher as part of the INSIGHTS Innovative Training Network. One could say that my introduction is a year overdue, though during this year my research trajectory managed to converge, which means that I can introduce myself with more clarity.
I was raised in a small city located on the western side of Ukraine called Ivano-Frankivsk. For those who are not familiar with eastern Europe, this city is located close to the geographical center of Europe. This part of Ukraine is famous for the special role of national traditions and culture in people’s lives. Being very traditional, yet modern, this region attracts many tourists that want to visit the Carpathian mountains, go hiking, skiing or to eat the best Ukrainian food.
I left Ivano-Frankivsk and moved to Kyiv — the capital of Ukraine — when I turned 17, to pursue a bachelor’s degree in the department of Radiophysics, Electronics, and Computer Systems. It was my dad introducing me to electrical engineering when I was a child that first sparked my interest in physics. What I have always found so appealing about physics is a fundamental way of understanding how our world works. The creativity inherent in crafting and applying simple concepts for an explanation of complicated processes is what inspired me to study physics and to pursue a bachelor’s degree in the subject. It turned out much later that Artem and Vitaliy — which are my INSIGHTS colleagues — started studying there at the same time with me!
In addition to my interest in physics, I have always been fascinated by programming, especially in the context of physics. Many examples of this have fascinated me, such as seeing how the Boltzman or Vlasov equations can be numerically solved for the kinetic simulation of plasma, and seeing the wonders of protein folding obtained from computationally expensive Markov Chain Monte Carlo simulations. This interest never left me, only growing more profound and passionate with every new subject I took at university.
Inspired through my great love for physics and programming, my next step was completing a master’s degree in a program called Atomic Scale Modelling of Physical, Chemical, and Bimolecular Systems organized by the European Commission. My classmates and I have been living and studying in the Netherlands, Italy, and France. This was a remarkable experience that broadened all of our cultural and research horizons tremendously. I completed my final project at the European Center for Atomic and Molecular Calculations in Lausanne, where I have been working on the quantum free energy reconstruction using Langevin-guided Monte Carlo. This stimulated my current interest in Markov chain Monte Carlo methods, which — together with knowledge of all benefits of EU-funded scholarships — led me to apply for my Ph.D. at the INSIGHTS Innovative Training Network.
One can wonder what is my Ph.D. project about? Broadly speaking, it is a mixture of physics, statistics, and programming. I have the privilege of working on these topics at the Max Planck Institute for Physics in Munich, with supervision from Prof. Allen Caldwell and Dr. Oliver Schulz. Working in these conditions has allowed me to develop my research skills tremendously. From spending an overnight shift in the AWAKE control room at CERN collecting experimental data, to using hundreds of CPUs for massively parallel computing, the past year has brought a lot of new experiences to my life. I can keep writing much more about them, but instead, I encourage you to have a look at a paper on parallelization of the Markov chain Monte Carlo technique that we are planning to publish in the near future. There, you will be able to find a more detailed explanation of all the interesting things that we do.