6years of professional experience, including 2+ years working on generative AI and 3D computer vision systems in PyTorch, alongside prior experience supporting production software systems, CI/CD, monitoring and incident response. Own research and engineering work at Mo-Sys across text-to-video generation, transformer-based pose modelling and PTZ camera calibration, covering literature review, implementation, benchmarking, evaluation tooling and technical communication. MSc in Computer Vision, Robotics & Machine Learning from the University of Surrey. Speaker at AI Summit London on ML reliability in live systems and winner of the AI Encode Hackathon London
Most AI models in production don't fail in isolation; they fail due to dependency failures. This session explored why well-trained models silently degrade in the real world, covering practical approaches to monitoring, evaluation, drift detection, guardrails, and operational reliability for production AI deployments. Built around the through-line: "A model is a photograph of a moving world."
Audience Delegates, AI Leaders, Industry practitioners, researchers, and AI product teams
A hands-on portfolio spanning PyTorch fundamentals, custom DDPM/UNet diffusion training, Stable Diffusion v1.5 fine-tuning across FP32, FP16 and gradient-checkpointing configurations, and LoRA adaptation of BLOOM-7B1. Each experiment is backed by executable notebooks, reproducible training settings and published Hugging Face artifacts.
Developed a novel dual-encoder architecture for multimodal synthesis (Sketch + Image), focusing on interpretability and fine-grained control over generated outputs.
Prototyped a real-time Sign Language Generation proof-of-concept within 48 hours using MediaPipe and pose estimation models for gesture-to-avatar translation.
Built a gender classification model using PCA and SVM, optimizing feature extraction pipelines for high accuracy.
Authored technical analysis of Generative AI and Virtual Try-On Systems, covering model architectures, pipelines, and industry trends.
PyTorch, TensorFlow, Transformers, Diffusion Models, Neural Networks, CLIP, StyleGAN, Model Training, Hyperparameter Tuning, Algorithm Development
Docker, Kubernetes, CI/CD, GPUs (CUDA), GCP, AWS (S3, Lambda), Azure Compute, FastAPI, Flask, Distributed Training
OpenCV, Semantic Segmentation, Vision-Language Models, Video & Trajectory Data, Multimodal Learning, Image Processing
Python, C++, Bash, SQL, NumPy, Pandas, Matplotlib, Scikit-learn, Data Pipelines, Synthetic Data Generation