Name: Ajeeta Khatiwada
Pronouns: she/her/hers
Biography:
Dr. Khatiwada is a staff scientist at Los Alamos National Lab. She is an experimental high-energy physicist by education, with an expertise in the application of machine learning and uncertainty quantification in ‘big’ complex data. She got her PhD from Florida State University in 2016 working on CERN’s Large Hadron Collider’s CMS experiment. She did consecutive postdocs at Purdue University (based at Fermilab) and LANL working on high energy physics research at CMS (2016-2018) and heavy-ion physics at PHENIX experiment (2018-2021) respectively. Since being at LANL, she’s been engaged in various mission relevant activities on radiography/tomography, gamma spectroscopy, and most recently on nuclear data. She is a coauthor to approximately 600 peer-reviewed publications, out of which she led some world class publications in high-energy physics. Her most recent interests lie in the application of machine learning and other advanced statistical tools to solving problems in nuclear data. Dr. Khatiwada is an executive committee member of the American Physical Society’s Four Corners Section. She believes in making STEM field diverse, inclusive and equitable for all, and has been engaged in various outreach activities throughout her career.
Institution/Lab: Los Alamos National Laboratory
Website: https://www.linkedin.com/in/ajeeta-khatiwada-9a67b7121
SRP Collaboration Topic/Title: Application of Machine Learning in Nuclear Data Evaluation
Field or research area: Nuclear Physics
Please select all the topical areas that apply to your project:
Data Science (i.e., data analytics, data management & storage systems, visualization); Machine Learning and AI
Brief Abstract:
Nuclear Data libraries, which contain information about the interaction of particles with nuclei, are carefully curated from experimental data and theoretical predictions. This data includes details about nuclear reactions, such as their reaction probability (cross section), decay yields, spectra of the outgoing particles etc. and are used to understand/predict the behavior of particles in nuclear systems, such as nuclear reactors, astrophysical processes, radiography, gamma-based interrogation techniques etc. As such, any inaccuracies and imprecision in the nuclear data gets propagated to the uncertainties in the application of interest. Until recently, most general purpose nuclear data libraries have utilized Bayesian approach to tune the theory model input parameters to fit the experimental data. In this project, the student(s) will explore machine learning based approach to combine the theoretical models with experimental data to come up with evaluated nuclear data for specific reaction channels and physics observables. Upon the successful completion of this work, the work will be published in peer reviewed journals.
Desired relevant skills, background, or interests:
- Hard working * Friendly * Team player * Preference to someone with background in physics and/or math * Familiarity with coding (preference to Python) * Familiarity with/interest in Machine Learning algorithms
Other comments:
Do any special requirements apply? Minimum GPA (specify what GPA in comments below); In-Person Only; U.S. Citizen Only
Other, specify:
Keywords:
Nuclear Physics; Machine Learning; Data Analysis: Nuclear Data; Statistics
Lightning Talk Title: Application of Machine Learning (ML) in Nuclear Data Evaluation