Jennifer Piscionere

Jennifer Piscionere, PhD, has extensive work experience in data science and research. Jennifer is currently the Genomic Data Product Lead at the Murdoch Children's Research Institute, with previous roles as the Lead Data Scientist and Data Scientist. In these positions, they have led projects to develop open data access platforms and delivered data from data warehouses to the front end.

Prior to this, Jennifer worked at Swinburne University of Technology as a Postdoctoral Researcher and Data Scientist. Their research involved web content development, analysis pipelines, and clustering analysis for galaxy catalogues. Jennifer also conducted natural language processing and data translation using various packages and cloud-based platforms.

Additionally, Jennifer has served as a Data Science Fellow at the Open Science Data Cloud, where they focused on cloud computing for gene mutation analysis, model development, and Hadoop MapReduce algorithms.

Jennifer's educational background includes a PhD in Computational Astrophysics from Vanderbilt University, where they developed statistical pipelines, predictive analytics, and Bayesian models for large-scale astronomical surveys. Jennifer also gained research experience as an Undergraduate Research Assistant at Columbia University.

Overall, Jennifer Piscionere, PhD, has a diverse background in data science, research, and computational astrophysics.

Jennifer Piscionere, PhD obtained their Bachelor of Arts (B.A.) degree in Astronomy and Astrophysics from Columbia University in 2012. Jennifer then pursued their Doctor of Philosophy (PhD) degree in Computational Astrophysics at Vanderbilt University from 2008 to 2015. Additionally, they hold certifications including Certified Health Informatician Australasia (CHIA) from the Australasian Institute of Digital Health (AIDH) and has completed the Kaggle R Tutorial on Machine Learning from DataCamp.