I work across the full machine learning lifecycle: data pipelines, distributed training and finetuning, evaluation, and models running in production. I train and finetune language models, and I build the systems that decide whether one is ready for real users: the infrastructure that serves it, and the evaluation harness that measures whether it behaves as intended. What I find most interesting is the practical side of model safety, getting a model to stay reliably inside the behaviour you specified, and being able to prove it before a failure reaches anyone.
Competitive programmer and side-project maniac by night. I built a dual RTX 5090 workstation so the experiments can run at home.
Toolkit. PyTorch · Hugging Face · vLLM · FSDP-v2 / DeepSpeed ZeRO · LoRA / QLoRA · DPO / GRPO · Axolotl · MLflow / Weights & Biases · AWS (SageMaker, Lambda, Bedrock) · Terraform · SLURM · Docker · Python · C++ · familiar with JAX
A language model for Sudanese Arabic, trained from scratch. Trains its own tokenizer, pretrains a decoder-only transformer (4 layers, 512-dim, 8 heads, 1k context) on Arabic text, then finetunes on Sudanese dialect chat. Config-driven runs with pluggable model and dataset classes, MMLU checks and temperature-swept generation samples during training.
A PyTorch implementation of BERT pretraining and finetuning, written to understand the architecture and the masked-language-modelling objective from the ground up rather than calling into a library.
A multi-task ResNet-34 that wakes on the trigger word "on", then classifies the speaker's gender and the digit (1–9) they spoke. Trigger-word detection trained on SpeechCommands, the classification heads on Speech-MNIST.
A PyTorch implementation of the SRCNN super-resolution model from Dong et al. (2015), alongside a critical analysis of how later state-of-the-art approaches attack the same problem.
A hybrid agent for Pommerman combining rule-based play with Monte Carlo tree search. Against the framework's strongest built-in agent it wins 48.2% and draws 25% — it plays for power-ups and survival, so it reaches the endgame as the strongest player.
A deep Q-network trained with Q-learning to play Atari Breakout from pixels, built to work through value-based reinforcement learning hands-on.
Pong written entirely in NASM assembly and booted in a virtual machine with no operating system, so the code handles keyboard interrupts and draws to the screen itself. Two-player and one-player modes.
A small Unity game built as an excuse to compare search algorithms (BFS, DFS and A*) on a live grid. The shipped version routes the waiter with BFS.
Happy to talk about LLM training, distillation, evaluation, or how any of the projects above were built.