Elior Rahmani

Elior Rahmani is an Adjunct Assistant Professor in the Department of Computational Medicine at the University of California, Los Angeles (UCLA). He develops novel machine-learning methods and statistical models for analyzing high-dimensional genomic and clinical data, focusing on robustness against unknown confounding effects.

• Education:
  • BSc in Computer Science and Biology, Tel Aviv University
  • MSc in Computer Science, Tel Aviv University
  • PhD in Computer Science, UCLA

Rahmani’s research spans computational biology, machine learning, and AI/ML in healthcare. His work enables cell-type-specific analysis of genomic data without requiring reference panels, advancing applications in epigenetics and single-cell biology.

His software tools include Keris (single-cell variation analysis), TCA (tensor decomposition for heterogeneous data), BayesCCE (Bayesian cell-type estimation), GLINT (DNA methylation analysis), and ReFACTor (reference-free correction methods).

• Scientific Awards:
  • Travel fellowship, RECOMB 2022
  • Charles J. Epstein Trainee Award, ASHG 2021
  • LSRF Fellowship finalist, 2021
  • Google outstanding graduate research award, UCLA 2019
  • Best poster award, UCLA Human Genetics Retreat 2019
  • Northrop-Grumman award, UCLA 2018
  • Edmond J. Safra prizes, Tel Aviv University 2013-2017