Nicolas Gioanni
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HomeExperienceResearchProjectsRecommendationsResume

Experience

My experience spans engineering with the DoW Chief Digital & Artificial Intelligence Office (CDAO) and the U.S. Treasury, research software and machine learning at the University of Washington, and teaching core computer science courses.

© 2026 Nicolas Gioanni. All rights reserved except where otherwise stated.

Nicolas Gioanni

Software Engineer

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Experience

My experience spans engineering with the DoW Chief Digital & Artificial Intelligence Office (CDAO) and the U.S. Treasury, research software and machine learning at the University of Washington, and teaching core computer science courses.

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DoW Chief Digital & Artificial Intelligence Office (CDAO)

1

Member of Technical Staff

Oct 2026 – Present

Washington, District of Columbia, United States

Current

Details not yet available.

U.S. Department of the Treasury

2

AI Engineer

Aug 2026 – Oct 2026 · 3 mos

Washington, District of Columbia, United States

Tech.Treasury.Gov

UW Bothell School of STEM

3

Research Assistant (Software Engineering)

Aug 2024 – Aug 2026 · 2 yrs 1 mo

Bothell, Washington, United States

Research

Built and deployed CytoCV, an open-source web platform that turns yeast microscopy stacks into reviewable per-cell fluorescence measurements and structured exports.

  • Built for the University of Utah Miller Lab through UW Bothell's SEE Lab.
  • Aligned the platform with biology-facing requirements across ingestion, segmentation, measurement, and result review.
  • Reviewable overlays align DIC segmentation with fluorescence channels before export.
  • Configurable analyses cover puncta distance, CEN-dot localization, biorientation, contour intensity, and nuclear or cell-pair intensity.
  • Authentication and retention controls support repeatable shared-lab use.
  • PostgreSQL persistence keeps experiments, analysis state, and exported results available across sessions.
  • Released CytoCV v2.0.0 with citation metadata, reproducibility documentation, and a versioned software DOI.
  • Continued maintaining the deployed application while the journal manuscript remained in preparation.

University of Washington

4

Teaching Assistant

Jan 2025 – Jun 2025 · 6 mos

Bothell, Washington, United States

Teaching

Supported core computer-science courses through weekly quiz sections, office hours, one-to-one tutoring, grading, and project review.

  • Helped students work through concepts, debug assignments, and build confidence with unfamiliar material.
  • Supported Data Structures & Algorithms, Discrete Mathematics, Operating Systems, and Software Engineering.
  • Evaluated projects for more than 400 students through code review.
  • Balanced correctness, maintainability, and clear explanations when assessing student work.
  • Worked across algorithms and data structures, discrete mathematics, operating systems, and team-based software development.
  • The rating reflects student feedback across the teaching, tutoring, and course-support experience.

University of Washington

5

Undergraduate Researcher (Adversarial Machine Learning)

Sep 2024 – Dec 2024 · 4 mos

Bothell, Washington, United States

Research

Completed an independent study in adversarial machine learning with four attack prototypes and four defensive strategies across image-classification benchmarks.

  • The study covered evasion, model extraction, model inversion, and backdoor poisoning.
  • Defenses covered adversarial-input detection, input preprocessing, output perturbation, and defensive distillation.
  • The prototypes covered distinct evasion, extraction, inversion, and poisoning threat models.
  • Each experiment kept its target model and generated outputs available for inspection.
  • FGM adversarial samples supplied detector training data.
  • Preprocessing, postprocessing, and distillation experiments exposed their effects on downstream predictions.
  • Ran the prototypes across MNIST, Fashion-MNIST, and CIFAR-10.
  • Captured experiment metrics and visual outputs to make attack and defense behavior inspectable.

UW Bothell School of STEM

6

Research Assistant (AI/ML)

Mar 2024 – Aug 2024 · 6 mos

Bothell, Washington, United States

Research

Built Guide Donor Scheduler, a Python tool that converts yeast FASTA sequences and requested amino-acid substitutions into CRISPR/Cas9 guide and donor constructs.

  • The tool reads FSA or FNA records, applies requested mutation rules, and prepares candidate designs.
  • Researchers receive an organized XLS workbook containing the sequence-design decisions.
  • Forward NGG and reverse-strand CCN discovery keeps candidate selection strand-aware.
  • Codon-aware silent PAM or seed edits preserve the requested protein change while reducing re-cutting risk.
  • Configurable rank thresholds and guide-library rules narrow candidate sets when those modes are enabled.
  • Unit tests cover strand inversion, PAM discovery, mutation generation, PAM disruption, and adjacent mutations.
  • Outputs include guides, original PAMs, mutation offsets, cut-site distances, full constructs, and decision rationale.