About me

Hi, I’m Quilee Simeon, a research engineer trained at MIT working at the intersection of machine learning, neuroscience, and scientific computing. I’m currently Research Computing Technical Staff at MIT’s Office of Research Computing and Data, and hold an M.S. in Computational Neuroscience and a B.S. in Computation and Cognition from MIT.

🧠 Research Focus

My work centers on multimodal neural data modeling as a platform for understanding how biological neural systems process information. I integrate data across neural dynamics, connectomics, and transcriptomics using machine learning frameworks including transformer architectures and self-supervised learning.

Key Research Areas:

• Multimodal neural data integration and modeling
• Self-supervised learning for neural systems
• Transformer architectures for time-series neural activity
• Graph-based learning approaches for connectome data
• Neural efficiency and interpretability

💼 Professional Experience

MIT Office of Research Computing and Data — Research Computing Technical Staff (Nov 2025 – Present)

Numenta, Inc. — Machine Learning Research Intern (June 2025 – September 2025)

• Developed distillation methods for transferring sparsity patterns from large to small models
• Designed sparse weight and activation mechanisms for efficient inference
• Contributed to external collaboration on LLM inference optimization

MIT Department of Brain and Cognitive Sciences — Graduate Researcher (2022 – 2025)

• Built large-scale data pipelines for multimodal neural datasets
• Modeled neural population dynamics using graph-based architectures
• Integrated transcriptomic and anatomical data for neuron identity prediction

🛠️ Technical Expertise

Machine Learning & AI: Transformer models, contrastive learning, spectral normalization, diffusion models, reinforcement learning, neural network interpretability

Scientific Computing: Python, PyTorch, Julia, high-performance cluster computing (SLURM-based systems)

Data Engineering: Neural data preprocessing, signal aggregation, connectome-based modeling

Software Tools: NumPy, PyTorch Geometric, OpenAI API, Hugging Face Datasets, Matplotlib, Marimo/Pluto notebooks

🎯 Current Direction

I’m transitioning toward applied ML and AI systems roles that blend algorithmic research with scientific applications. I’m particularly interested in:

• Building generalizable models that bridge biological and artificial intelligence
• Working in collaborative environments that value both research depth and practical engineering
• Expanding expertise in reinforcement learning, generative modeling, and distributed systems

I’m open to Research Engineer, Applied Scientist, or ML Systems positions.

🏅 Leadership & Community

• Co-President, IEEE-HKN (Eta Kappa Nu) Honor Society — MIT Chapter
• MIT Black Student Union — Social Chair
• Secretary, Beta Chapter of Theta Chi at MIT
• Mentor and advocate for open science, data transparency, and interdisciplinary education
• Strong commitment to supporting underrepresented minorities in STEM

🌍 Background

I grew up in St. Lucia and completed my B.S. in Computation and Cognition (minor in Statistics & Data Science) and an M.S. in Computational Neuroscience at MIT. Outside of research, I enjoy reading fiction, hiking, and traveling to understand different cultures.

“I’m driven by the idea that understanding intelligence — biological or artificial — means learning how information transforms meaningfully through systems.”