Atanu Pathak

My main field of expertise is Physics, where I gained special recognition in Experimental Particle Physics, having had the opportunity to work in several world-class experiments. The overarching theme of my research is to search for new physics that can explain currently unsolved mysteries, using machine learning techniques and large datasets that are being recorded by modern particle physics detectors.

I am currently working as a postdoctoral fellow on the Compact Muon Solenoid (CMS) experiment, housed at the LHC and located at CERN on the border of France and Switzerland. Meanwhile, I am currently stationed at the Fermi National Laboratory (Fermilab) at Batavia, Illinois, USA. My current research involves contributions to four areas of the CMS experiment research program at LHC Physics Center (LPC) at Fermilab:  1) the construction of CMS Phase 2 Tracker Forward PiXel (TFPX), which will operate in the harsh conditions of the HL-LHC, and will extend the acceptance of the CMS tracking detector; 2) the development of data quality management software as well as data quality monitoring (DQM) GUI for the future LHC Run; 3) novel physics searches i) an expanded search for a beyond the standard model (BSM) leptophobic heavy resonance decaying to BSM intermediate resonance particles; and ii) the search for a heavy resonance decaying to top quark pairs;

During my Ph.D., I have searched for signatures of new physics in lepton flavor violating decays of the Higgs boson using data collected at 13 TeV by the ATLAS experiment at the world’s highest energy accelerator, the Large Hadron Collider at CERN. I also studied properties of tau decays at the Belle II experiment at the world’s highest luminosity accelerator, the SuperKEKB in Japan.

My research is based on large scale data analysis using object-oriented data mining and data analysis framework called ROOT, detector upgrades and development of data quality monitoring techniques. I have expertise in C++, Latex, JavaScript, CSS, HTML, Python, Matplotlib, Pandas, NumPy, SciPy, Scikit-learn amongst other similar tools.