Hi, my name is
Kipngeno Koech.
I build bridges between minds & machines.
I'm an AI Engineer and BCI Researcher passionate about deep learning, agentic AI systems, and brain-computer interfaces. Currently exploring how intelligent agents and neural networks can push the boundaries of what's possible.
01.About Me
Hello! I'm Kipngeno, a researcher and engineer passionate about deep learning, agentic AI systems, and brain-computer interfaces. My interest in technology started when I first discovered the power of code to solve complex problems.
I love building things—from neural networks from scratch to autonomous AI agents that can reason and act. While BCI research remains my north star, I'm equally excited about pushing the boundaries of what's possible with large language models, retrieval-augmented generation, and multi-agent systems.
I believe in rigorous thinking, elegant code, and learning in public. When I'm not coding or researching, you'll find me hiking, traveling, reading, or contributing to the IEEE community.
Here are a few technologies I've been working with recently:
- ▹Python
- ▹PyTorch
- ▹Deep Learning
- ▹Agentic AI
- ▹LLMs & RAG
- ▹Brain-Computer Interfaces
- ▹TypeScript
- ▹React
02.Education
Master of Science - Engineering Artificial Intelligence
Advanced study in AI engineering, focusing on building production-ready AI systems and research methodologies.
Master of Engineering - Artificial Intelligence
Smart Africa Scholar. Graduate TA for Introduction to Deep Learning (11-785) and Bridge Program. Research in computational neuroscience and BCI.
Bachelor of Science - Software Engineering
Best Club of the Year 2022. Class Representative throughout. Vice Chair of MMU Tech Community. Foundation in software engineering and algorithms.
Software Engineering Bootcamp
Intensive software engineering program covering full-stack development, system design, and professional engineering practices.
02.Where I've Worked
Graduate Teaching Assistant
Teaching Intro to Deep Learning, Computational Materials Science, and Bridge Program. Guiding students through PyTorch, EEG/PPG analysis, and ML-based materials modeling.
Graduate Research Assistant
Multimodal Biointerfacing & Catalysis research. Performed computational analyses linking material structures to experimental outcomes using Python and CrystalMaker.
Graduate Student Fellow & Treasurer
Leading industry engagement for IEEE Africa, managing finances, and driving innovation through IoT training programs.
2025 Summer School Fellow
Intensive training in computational neuroscience and machine learning at University of Zambia.
Founding Engineer
Built the technical foundation for a carbon offset platform as part of Microsoft for Startups.
Founder
Founded an initiative mentoring K-12 students in web development through hands-on workshops at schools.
03.Some Things I've Built
Featured Project
npmlp-core
A modular deep learning framework built from scratch using NumPy. Implements neural network components including forward/backward propagation, vectorized optimization, and regularization techniques without PyTorch or TensorFlow. Features linear layers, multiple activations (ReLU, Sigmoid, Tanh, GELU, Swish, Softmax), Batch Normalization, and pre-built MLP architectures. Published on PyPI with compiled binary wheels supporting Python 3.9-3.12 on Linux, macOS, and Windows.
- Python
- NumPy
- SciPy
- Cython
- GitHub Actions
Featured Project
custom-cnn
A convolutional neural network library built from scratch using NumPy. Implements Conv1d, Conv2d, transposed convolution, max/mean pooling, upsampling/downsampling, 7 activation functions, and loss functions — all with hand-derived forward and backward passes. Includes pre-built CNN and scanning MLP architectures. Published on PyPI as Cython-compiled binary wheels with no source code exposed.
- Python
- NumPy
- SciPy
- Cython
- GitHub Actions
Featured Project
custom-rnn
A recurrent neural network library built from scratch using NumPy. Implements RNN and GRU cells with full BPTT, CTC forward-backward algorithm with posterior computation, CTC loss, and greedy/beam search decoders — all with hand-derived forward and backward passes. Includes pre-built RNN phoneme classifier and GRU character predictor. Published on PyPI as Cython-compiled binary wheels with no source code exposed.
- Python
- NumPy
- Cython
- GitHub Actions
Featured Project
custom-transformer
A transformer library combining NumPy attention primitives with PyTorch encoder-decoder models. Implements scaled dot-product and multi-head attention from scratch with hand-derived gradients, Pre-LN Transformer architecture with encoder and decoder layers, CTC + cross-entropy joint training for ASR, greedy/beam/sampling decoding, and progressive layer unfreezing. Supports both decoder-only language modeling and full encoder-decoder speech recognition. Published on PyPI as Cython-compiled binary wheels.
- Python
- PyTorch
- NumPy
- Cython
- GitHub Actions
Featured Project
Phoenix RAG
A Retrieval-Augmented Generation system for code refactoring assistance. Features a ReAct-style reasoning agent with think-act-observe loop, vector-based semantic search using ChromaDB, hybrid document chunking, multi-provider LLM support (Ollama, Groq, Anthropic, OpenAI), and groundedness verification to reduce hallucinations. Includes code smell detection, complexity metrics, and Pytest test stub generation.
- Python
- LangChain
- ChromaDB
- Streamlit
- Pydantic
Featured Project
Phoenix Agent
An autonomous AI agent that analyzes, refactors, and verifies code through a 7-phase control loop (OBSERVE → REASON → PLAN → DECIDE → ACT → VERIFY → UPDATE) with human-in-the-loop approval. Features a crew-style multi-agent system with parallel CoderAgents for concurrent file modifications, real-time WebSocket streaming, GitHub-style diff review, and a 3-layer memory system (Redis + PostgreSQL + Neo4j). Supports Anthropic, OpenAI, Groq, and Ollama.
- Python
- FastAPI
- Next.js
- LangChain
- Redis
- PostgreSQL
Featured Project
PhoenixGitHub
An always-on GitHub issue automation agent that watches label-triggered issues, plans and implements code changes, runs validation checks, and creates pull requests for human review. Includes revise/failure loops, screenshot-aware planning support, and release automation to PyPI with trusted publishing.
- Python
- GitHub API
- LangChain
- PyPI
- GitHub Actions
Featured Project
Improving SSVEP BCI Spellers with Data Augmentation and Language Models
Published at IEEE SiPS 2025. A hybrid framework that integrates domain-specific data augmentation with a language model to enhance SSVEP-based Brain-Computer Interface speller performance. Decodes scalp-recorded EEG signals to identify characters a user gazes at, enabling hands-free communication for individuals with disabilities. Addresses challenges of high EEG variability and poor generalization to unseen subjects using deep neural networks.
- Python
- EEG
- Deep Learning
- Signal Processing
- NLP
Other Noteworthy Projects
Deep Learning Library
Custom deep learning framework with automatic differentiation and GPU support.
- Python
- CUDA
- NumPy
Signal Processing Toolkit
Real-time signal processing tools for EEG and neural data analysis.
- Python
- SciPy
- DSP
05.Latest Writing
Building Neural Networks from First Principles
A deep dive into implementing backpropagation, gradient descent, and activation functions without any ML frameworks.
The Future of Brain-Computer Interfaces
Exploring current BCI technology, challenges in neural signal processing, and what the next decade might bring.
EEG Signal Processing for Beginners
A practical guide to preprocessing, filtering, and feature extraction from electroencephalogram data.
04. What's Next?
Get In Touch
I'm always interested in collaborations at the intersection of technology and neuroscience. Whether you have a question, want to discuss research ideas, or just want to say hi—my inbox is always open.
Say Hello