Omid Khazaie headshot

Omid Khazaie

Adjunct Professor

Biography

Omid is a data scientist with extensive experience in statistical analysis, machine learning, and predictive modeling. Throughout his career, he has been driven by challenge. He has enjoyed a dynamic career in IT leadership roles developing team performance, analyzing large and complex data sets to identify trends, patterns, and opportunities that support strategic decision-making and drive business growth.

Expertise

  • Machine learning
  • Statistical computing
  • Natural language processing
  • Data analytics of genomics data
  • Databases
  • Business Intelligence analytics

Research Areas

  • Locus specific databases
  • Clinical natural language processing
  • Machine learning

Education

  • University of San Francisco, MS in Health Informatics, 2019
  • Federation University Australia, Graduate Diploma of Information Technology Management, 2011
  • Azad University of Arak, BE in Computer Software Engineering, 2002

Prior Experience

  • Data Science Manager, Ultragenyx
  • Data Scientist, Mednition Inc.
  • Program Manager, St. Anthony’s
  • IT Project Manager, RNET

Selected Publications

  • Daugherty, Sean, Vanessa Rangel Miller, Roberto Giugliani, Omid Khazaie Japalaghi, Deborah Marsden, Heather McLaughlin, Andrew Willcock, Madhuri Hegde, and Nicole Miller. "Early development of a locus specific database for GUSB, the gene associated with mucopolysaccharidosis type VII: Hints of a higher predicted prevalence." Molecular Genetics and Metabolism 138, no. 2 (2023): 107068.
  • Japalaghi, Omid K., Heather McLaughlin, Nicole Miller, Prameela Ramesan, Eric Rush, Jillian Yong, and Kathryn Dahir. "OR13-3 Hypophosphatemia Gene Panel Sponsored Program: A High Yield of Molecular Diagnoses from Clinically Confirmed XLH and Suspected Genetic Hypophosphatemia." Journal of the Endocrine Society 6, no. Suppl 1 (2022): A192.
  • Sharif, Omar, Matthew Domingo, Jie Han, Michael Chang, Omid Khazaie, and Anil Kemisetti. "Forecasting Model for Disease Propensity Using EHR Data." (2019).