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E
Ethan Q.

Ethan Q.

Summer Intern (Molecular Biology Research)

Australia flagN/A, Australia

Key Skills

Software

Other

Top Subject Matter

Spatial transcriptomics
Bioinformatics Domain Expertise
machine learning

Top Data Types

ImageImage
Computer Code ProgrammingComputer Code Programming
DocumentDocument

Top Task Types

ClassificationClassification
Evaluation/RatingEvaluation/Rating
Computer Programming/CodingComputer Programming/Coding

Freelancer Overview

Summer Intern (Molecular Biology Research). Brings 4+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other. Education includes Bachelor of Advanced Computing, University of Sydney (2027) and Bachelor of Science, University of Sydney (2027). AI-training focus includes data types such as Computer Code and Programming and labeling workflows including Computer Programming and Coding.

Labeling Experience

Denison Scholarship Project — Charles Perkins Centre (Jan 2025 – Mar 2025)

Other

Developed computational pipelines to process large-scale genomic data on the NCI Gadi supercomputer. Implemented a distance autocorrelation (DAC) analysis workflow to evaluate a novel model of chromatin architecture and nucleosome spacing. While this is primarily analytics rather than annotation, it constitutes AI/data-model evaluation work central to model assessment. • Built Python and Bash pipelines for genomic data processing at scale. • Implemented DAC analysis to measure dependencies in chromatin architecture. • Ran and validated computations on the supercomputing environment. • Produced results for downstream interpretation of chromatin/nucleosome spacing.

2025 - 2025

Research Assistant — Centenary Institute (Feb 2024 – Jun 2024)

Other

Performed ML-based analysis for lipidomics experiments in collaboration with a PhD researcher. Conducted LC-MS lipidomics preprocessing and analytical work to support model-driven interpretation of lipid profiles. The role included developing the data representations used by machine learning methods, though explicit human annotation was not described. • Processed blood and liver samples for LC-MS lipidomics workflows. • Applied machine-learning-based analysis to lipidomics datasets. • Collaborated with a PhD researcher to interpret ML results. • Contributed to scholarly outputs including a manuscript under review.

2024 - 2024

Summer Student — Children’s Medical Research Institute (Dec 2023 – Jan 2024)

Other

Benchmarked unsupervised machine learning methods to cluster spatial transcriptomics datasets using R and Python bioinformatics libraries. This involved preparing and analyzing gene expression feature representations so that clustering outputs could be evaluated as part of the ML workflow. No manual data labeling was explicitly stated, but the work directly supported training/evaluation of unsupervised models for spatial transcriptomics. • Prepared and processed spatial transcriptomics data inputs in R/Python. • Evaluated unsupervised clustering approaches for spatial transcriptomics. • Compared clustering quality across methods as part of ML benchmarking. • Supported analysis as a precursor to downstream interpretation and model selection.

2023 - 2024

Education

U

University of Sydney

Bachelor of Science, Biochemistry and Molecular Biology

Bachelor of Science
2023 - 2027
U

University of Sydney

Bachelor of Advanced Computing, Data Science

Bachelor of Advanced Computing
2023 - 2027

Work History

C

Children's Medical Research Institute

Summer Intern (Molecular Biology Research)

N/A
2025 - 2026
C

Charles Perkins Centre

Research Scholar (Computational Genomics)

N/A
2025 - 2025