Quilee Simeon

Curriculum vitae

CV

Applied Scientist at Amazon FAI. Previously MIT ORCD, Numenta, and the MIT McGovern Institute.

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Experience

  1. Applied Scientist · Amazon FAI (formerly Amazon AGI), Boston, MA May 2026 – Present
    • Build agent-based pipelines that automatically assess the quality of STEM expert data (math, physics, chemistry, biology, computer science), cutting the time experts spend on manual review.
    • Develop LLM-as-a-judge graders with critical and non-critical rubric checks, calibrated against expert labels (Cohen's κ, MCC), that catch defective expert data before it reaches training.
    • Build RL environments and evaluation harnesses (Harbor, Docker) spanning software engineering, STEM reasoning, and agentic tool use, graded by automated verifiers.
    • Write task rubrics and guidelines with STEM domain experts and deliver quality feedback to data vendors.
  2. Research Computing Technical Staff (part-time) · MIT Office of Research Computing and Data, Cambridge, MA Nov 2025 – May 2026
    • Supported MIT's 3,000+ cluster users on Slurm scheduling, Apptainer containers, and software environments; advised researchers on parallel computing and resource allocation.
    • Built tooling for running LLM agents on MIT's supercomputers and wrote the reference recipe for deploying OpenClaw on HPC (Apptainer isolation, SSH tunneling, Lmod).
    • TA'd 6 workshops on parallel programming, GPU computing (CUDA), and distributed deep learning.
  3. Machine Learning Intern · Numenta, Menlo Park, CA Jun – Sep 2025
    • Designed a knowledge-preserving method to grow pretrained LLMs from 70M to 1.4B parameters, with distributed multi-GPU training, W&B tracking, and automated evaluation; projected 1.5–2× compute savings.
    • Explored sparse training for efficient LLM inference, reaching ~2× speedup on CPU and wafer-scale hardware.
  4. Graduate Researcher · MIT Brain & Cognitive Sciences (Boyden Lab, McGovern Institute), Cambridge, MA Sep 2021 – Dec 2025
    • Published scaling laws for neural-network models of the C. elegans nervous system (IEEE SoutheastCon 2024), then released the training data, 11 public datasets (~900 animals) unified on Hugging Face.
    • Built end-to-end HPC pipelines for high-dimensional neural data, covering preprocessing, multimodal alignment, simulation, and forecasting.
    • Rebuilt light-sheet and diSPIM microscopes for whole-brain imaging (Onshape CAD, optomechanics) and a 35-script analysis pipeline for 1 TB+ recordings.
  5. Bioinformatics & Software Development Intern · Triplet Therapeutics, Cambridge, MA 2019, 2021
    • Automated RNA-sequence analysis on AWS EC2 across 10+ concurrent instances, cutting processing time 40%.
    • Deployed neural networks predicting siRNA and ASO knockdown efficacy, improving candidate-selection accuracy by 15%.
  6. Quality Assurance Analyst · Saint Lucia Distillers, Castries, St. Lucia 2016 – 2017

Education

  1. M.S., Computational Neuroscience · Massachusetts Institute of Technology 2025
    • Thesis: C. elegans as a Platform for Multimodal Neural Data Integration
  2. B.S., Computation & Cognition · Massachusetts Institute of Technology 2021
    • Minor in Statistics & Data Science · GPA 4.9/5.0

Publications

  1. Octopus Protocol: One-Shot Hardware Discovery and Control for AI Agents via Infrastructure-as-Prompts2026
  2. Recovering Sparse Neural Connectivity from Partial Measurements: A Covariance-Based Approach with Granger-Causality Refinement2026
  3. C. elegans as a Platform for Multimodal Neural Data Integration2025
  4. Homogenized C. elegans Neural Activity and Connectivity Data2024
  5. Scaling Properties for Artificial Neural Network Models of a Small Nervous System2024
  6. The Time Is Ripe to Reverse Engineer an Entire Nervous System: Simulating Behavior from Neural Interactions2023

Honors

  1. 1st Place, Neural Decoding Challenge, BrainStorm BCI Hackathon · Precision Neuroscience2026
  2. Featured in MIT Spectrum: “Unlocking AI’s Mysteries Using Insights from Brain Research”2026
  3. K. Lisa Yang ICoN Graduate Fellowship · MIT2025–26
  4. Honorable Mention (twice), Envisioning the Future of Computing Prize · MIT SERC2025, 2026
  5. Graduate Research Spotlight, The Bridge · IEEE-HKN2024
  6. Henry E. Singleton (1940) Fellowship · MIT2023–24
  7. Rhodes Scholarship Finalist · Jamaica & Commonwealth Caribbean2021

Talks & posters

  1. Homogenized C. elegans Neural Activity and Connectivity Data · Analysis and Modeling of Connectomes, HHMI Janelia Research Campus2025
  2. C. elegans as a Platform for Multimodal Neural Data · NeuroLunch, MIT Brain & Cognitive Sciences2025
  3. Unified C. elegans Neural Activity and Connectivity Datasets for Building Foundation Models of a Small Nervous System · Bernstein Conference on Computational Neuroscience, Goethe University2024
  4. Scaling Properties for Artificial Neural Network Models of a Small Nervous System · Fifth International Convention on the Mathematics of Neuroscience and AI2024
  5. Scaling Properties For ANN Models of C. elegans · Yang Tan Collective, MIT McGovern Institute2023
  6. Neural network modeling of the C. elegans brain · Cog Lunch, MIT Brain & Cognitive Sciences2022

Teaching

  1. Teaching Assistant, ORCD workshops on GPU computing, CUDA & distributed deep learning · MIT Office of Research Computing and Data2025 – 2026
  2. Teaching Assistant, Emergent Computations Within Distributed Neural Circuits (9.53) · MIT Brain & Cognitive Sciences2023
  3. Teaching Assistant, Principles of Neural Computation in Brains and Machines · MIT Brain & Cognitive Sciences2023
  4. Instructor, SPISE computer programming · Caribbean Science FoundationSummer 2020
  5. Instructor, MIT Global Teaching Labs · Cardiff, WalesJan 2020
  6. Instructor, MIT Global Teaching Labs · Johannesburg, South AfricaJan 2019
  7. Lab Assistant, Introduction to Neural Computation · MIT Brain & Cognitive SciencesSpring 2019
  8. Tutor & Lab Assistant, Fundamentals of Programming · MIT EECSFall 2018

Skills

Evaluation
LLM-as-judge, rubric design, inter-annotator agreement (κ, MCC), benchmark and task design, error analysis
Agents & LLMs
Claude Code and coding agents, MCP, multi-agent workflows, Anthropic / OpenAI / Gemini / Bedrock APIs
ML engineering
Python, PyTorch, Hugging Face, distributed multi-GPU training, Weights & Biases, scikit-learn
Infrastructure
Docker, Harbor, AWS (Bedrock, S3, EC2, Lambda, Batch), Slurm/HPC, Apptainer, Linux, Git, CI

Community

  • Builder, Sundai Club weekly AI hackathons (2025 – Present)
  • Co-President, IEEE-HKN Beta Theta Chapter at MIT (2020 – 2022)
  • Secretary, Beta Chapter of Theta Chi at MIT (2017 – 2021)
  • Social Chair, MIT Black Student Union (2018 – 2019)