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Nicole Parlett

Experience

Research & Experience

Laboratory and project work spanning molecular biology, genomics, and computational analysis.

Research Assistant Co-Op

Current

Macosko Lab · Broad Institute of MIT and Harvard

July – December 2026 Cambridge, MA

  • Apply spatial transcriptomics methods, including Slide-seq–style approaches, to generate high-resolution gene expression maps from intact tissue sections.
  • Apply single-cell transcriptomic analysis to characterize cell type and state changes in the nervous system relevant to neuropsychiatric disease.
  • Support method development work, testing and refining protocols to improve resolution and reproducibility.
  • Spatial transcriptomics
  • Single-cell RNA-seq analysis
  • Method development
  • Python
  • Data analysis

Research Assistant

Cram Lab · Northeastern University, Department of Biology

January – December 2025 Boston, MA

  • Carried out wet-lab molecular biology supporting ongoing C. elegans projects, including PCR, gel electrophoresis, and DNA purification.
  • Prepared and purified plasmids for cloning workflows, maintaining consistent yield and purity across preparations.
  • Followed and documented detailed experimental protocols, keeping records that allowed results to be reproduced across the lab.
  • C. elegans
  • PCR
  • Gel electrophoresis
  • Plasmid preparation
  • DNA purification
  • Cloning
  • Protocol documentation

Independent Work

Projects

Self-directed builds applying machine learning and software development to biomedical problems.

Brain Tumor Classification (BraTS Dataset)

  • Built a binary tumor-present/absent CNN classifier using real clinical MRI data from the BraTS-GLI dataset (~1,250 patients), independently.
  • Designed a patient-level train/val/test split to prevent data leakage and implemented preprocessing and augmentation pipelines for MRI slice classification.
  • Python
  • CNNs
  • Medical imaging
  • Data preprocessing

Antibody Validation Agent

  • Designed a multi-stage evidence pipeline that aggregates vendor claims, published validation data, and documented failure reports to grade antibody reliability.
  • Built a tiered evidence-grading system with citation tracking, distinguishing corroborated failure reports from single anecdotal claims to reduce false positives.
  • Python
  • Agentic pipelines
  • Evidence evaluation

AI-Powered Clinical Trial Matching App

  • Developed a full-stack application that uses AI to match patients with relevant clinical trials based on condition and eligibility criteria.
  • Integrated the Claude API to generate accessible, plain-language summaries of trial details for non-technical users.
  • Full-stack development
  • Claude API
  • Applied AI

Mindful Makers × AWEAR Hackathon

10th Hackathon for Wellness in Everyday LifeApril 2026Boston, MA

  • Developed a pipeline that processed EEG signal data to detect physiological markers of stress.
  • Integrated a machine learning model into the pipeline to classify stress states from the processed signal.
  • Connected the classifier to an AI-guided breathing exercise interface that responded to the detected state in real time.
  • EEG signal processing
  • Machine learning
  • Python
  • Rapid prototyping
  • Team collaboration

For coursework, campus involvement, and a full skills breakdown, see theCV page, ordownload the PDF.