Ammara Ahmad
Dr. ammara ahmad Post Doctoral Fellow Physics

Contact Information
ammara.ahmad@ku.ac.ae +971 544116874

Biography

Dr. Ammara Ahmad is a particle physicist and data scientist specialising in experimental high-energy physics, detector calibration, and advanced data analysis. She received her Ph.D. in Physics in 2023 from the Universitat Autònoma de Barcelona (Spain), where her doctoral research focused on the calibration of the ATLAS central hadronic Tile Calorimeter at the CERN Large Hadron Collider (LHC). She was selected as a CERN master student, where she contributed to the calibration of detector channels and performance analysis of photomultiplier tubes (PMTs) at the ATLAS experiment. During her PhD, she was selected as a CERN Doctoral Student, where she significantly contributed to the development of the calibration frameworks, analysed Photomultiplier tubes response contributing extensively to detector performance studies and calibration strategies for present and future collider operations.

Following her Ph.D., Dr. Ammara worked on machine learning models for Dark Photon searches and contributed to ATLAS Tile Calorimeter software development. She is currently a Postdoctoral Fellow at Khalifa University of Science and Technology, Abu Dhabi, working with Dr. Rachik’s Soualah High Energy Physics Group at the ATLAS Collaboration at CERN.

Her research interests span experimental collider physics tackling beyond the Standard Model searches, detector calibration and performance, and applications of machine learning in high-energy physics. She has presented her work at several international conferences, and continues to integrate advanced computational techniques and experimental R&D at Colliders. 

 


Education
  • PhD in Physics, Universitat Autònoma de Barcelona, Spain
  • MPhil in Physics, Quaid-i-Azam University, Pakistan
  • MSc in Physics, Quaid-i-Azam University, Pakistan


Affiliated Centers, Groups & Labs

Research
Research Interests
  • Collider Physics
  • Detector Calibration and Performance studies
  • Physics Beyond the Standard Model (Dark Photons, Higgs Physics)
  • Computing in High Energy Physics
  • Machine Learning and Data Science Applications in High-Energy Physics