Skip to content

Vladislav Li

Email: li.vlad.viktor@gmail.com | Location: London, UK | GitHub: couper64 | Portfolio: vladislav.li

Profile

ML Engineer and PhD graduate in Artificial Intelligence with extensive hands-on experience designing, deploying, and maintaining ML systems in production. Specialises in containerised infrastructure, distributed backend services, generative AI pipelines, and GPU-based inference. Comfortable working across the full stack: from bare-metal server setup and Linux administration through to cloud deployment and ML model integration. Brings both research depth and engineering rigour to building reliable, scalable AI systems. Published across IEEE, Image and Vision Computing, and Journal of Systems Architecture with 75+ citations.

Skills

MLOps & Infrastructure

  • Docker and Docker Compose — multi-stage builds, containerised ML service design, multi-service orchestration
  • CI/CD — GitHub Actions: automated test, build, and deployment pipelines; multi-repo synchronisation
  • ML model serving — inference pipeline design, GPU workload management, throughput optimisation
  • Experiment tracking — Weights & Biases
  • Kubernetes — local kubeadm cluster; workload deployment and basic cluster orchestration
  • Grafana — infrastructure and service monitoring dashboards
  • OpenResty — Lua-scripted reverse proxy for production traffic routing

ML & Computer Vision

  • PyTorch, TensorFlow/Keras — model training, fine-tuning, and evaluation
  • Object detection — YOLOv3, YOLOv5, YOLOv9, YOLOX, Faster R-CNN, CenterNet, RetinaNet; few-shot detection and data augmentation strategies
  • Generative models — StyleGAN2, Stable Diffusion 1.5 with LoRA fine-tuning, TimeGAN (time-series synthesis), ESRGAN (super resolution)
  • Model distillation — PatchTST, MobileNetV3→MobileNetV2; wrapping partner pipelines in production stacks
  • Continuous / lifelong learning — image and time-series domains
  • OpenCV — image processing, augmentation, and evaluation pipelines
  • Few-shot learning — survey-level depth; energy-efficient inference for industrial edge settings

Backend Engineering

  • FastAPI, Celery, Redis — distributed APIs, async task queues, message brokering
  • REST API design and microservices architecture
  • PostgreSQL — schema design, normalisation, query optimisation
  • Streamlit — data dashboards and ML-facing UIs
  • Linux systems administration — Ubuntu, Arch, Fedora, Void (scripting, service management, performance tuning)
  • Networking — switches, structured cabling, 802.1x, IP configuration, system integration

Cloud & Infrastructure

  • AWS EC2 — instance provisioning, service deployment, remote access configuration
  • Azure AI Services — integration into backend processing and production ML workflows
  • On-premise GPU server setup and maintenance
  • MinIO — S3-compatible object storage for self-hosted deployments
  • Keycloak — identity and access management, OAuth2/OIDC integration
  • Infisical — secret management for multi-container deployments

Programming

  • Python — primary language for backend systems, ML tooling, and automation
  • C / C++ — performance-critical and systems-level programming
  • C# — application development (Unity, XR platforms)
  • Lua — production reverse proxy scripting (OpenResty) and rapid prototyping
  • SQL — schema design, normalisation, query optimisation

Experience

Researcher — AI Optimisation (Postdoctoral)

Kingston University, London | 2024 – Present

RAIDO — EU Horizon Europe (Grant No. 101135800)

  • Designed and deployed a synthetic data generation pipeline using StyleGAN2, Stable Diffusion 1.5 with LoRA, and TimeGAN; served models via FastAPI + Celery + Redis + Flower in a Docker Compose stack on GPU and CPU
  • Built a Unity-based parameterised image capture tool using 3D digital twins, with configurable camera angles, sensor types (depth, IR, night vision), and environmental conditions (rain, fog, lighting)
  • Generative AI data augmentation improved image classification accuracy by up to 22.5 percentage points; subsequent model distillation reduced energy consumption by up to 75.8% and parameter count by 66% while retaining performance gains
  • Time-series MAE reduced by 52% through TimeGAN-generated synthetic data; distilled model size reduced from 2.28 MB to 0.77 MB
  • Deployed and integrated partner model distillation pipelines (PatchTST, MobileNetV3→MobileNetV2) into the same containerised stack
  • Integrated continuous learning components for image and time-series domains; deployed on AWS EC2, Azure, and on-premise GPU infrastructure

Research Engineer — AI Systems & MLOps

Kingston University, London | 2021 – 2024

Designed, deployed, and maintained ML systems across three funded projects spanning edge inference, generative AI, model distillation, and XR integration. Configured and maintained on-premise Linux servers and GPU workstations. Published 13 peer-reviewed papers across IEEE, Image and Vision Computing, and Journal of Systems Architecture. Initial development of the RAIDO data generation and distillation pipeline (continued under postdoctoral appointment).

TALON — EU Horizon Europe (Grant No. 101070181)

  • Developed a VR operator training application in Unity targeting Meta Quest 3 as part of the KU team within a multi-partner EU project
  • Built an edge inference backend running YOLOv5 with lifelong learning mechanisms; reduced AR detection transmission latency from 1 s to 200 ms (5×) to a HoloLens 2 client for real-time AR maintenance
  • Integrated ML backend services with XR frontends across a distributed research team on constrained edge hardware

Leonardo — University Contract

  • Developed a pre-processing pipeline for drone-captured imagery using ESRGAN super resolution to improve input quality for downstream small object detection in distant scenes
  • Implemented hyperparameter search for fine-tuning Faster R-CNN, YOLOv3, and RetinaNet on upscaled imagery
  • Research contributed directly to PhD thesis work on scene analysis for smart devices and immersive technologies

Publications

  1. Is it worth the energy? An in-depth study on the energy efficiency of data augmentation strategies for finetuning-based low/few-shot object detection — Journal of Systems Architecture, 2025
  2. Enhancing 3D object detection in autonomous vehicles based on synthetic virtual environment analysis — Image and Vision Computing, 2025
  3. Enhancing manufacturing training through VR simulations — IEEE International Conference on Engineering, Technology, 2025
  4. A complete survey on contemporary methods, emerging paradigms and hybrid approaches for few-shot learning — arXiv preprint, 2024
  5. Toward green and human-like artificial intelligence: A complete survey on contemporary few-shot learning approaches — arXiv preprint, 2024
  6. Evaluating the energy efficiency of few-shot learning for object detection in industrial settings — IEEE Real-Time and Intelligent Edge Computing Workshop, 2024
  7. A closer look at data augmentation strategies for finetuning-based low/few-shot object detection — IEEE International Symposium on Industrial Embedded Systems, 2024
  8. Scene analysis for smart devices and immersive technologies — PhD Thesis, Kingston University, 2024
  9. A modular deep learning framework for scene understanding in augmented reality applications — IEEE Industry 4.0, AI & Communications, 2023
  10. Evaluation of environmental conditions on object detection using oriented bounding boxes for AR applications — IEEE DCOSS, 2023
  11. Super resolution for augmented reality applications — IEEE INFOCOM Workshops, 2022
  12. Object recognition for augmented reality applications — Azerbaijan Journal of High Performance Computing, 2021
  13. Growth-based 3D modelling using stem-voxels encoded in digital-DNA structures — SIGGRAPH Asia Posters, 2020

Projects

Self-Hosted Infrastructure Stack

  • Designed and operate a full self-hosted service stack: API (FastAPI), async workers (Celery + Redis), object storage (MinIO), PostgreSQL, Lua-scripted reverse proxy (OpenResty), identity management (Keycloak + OAuth2), secret management (Infisical), monitoring (Grafana), and remote access (Guacamole)
  • Automated TLS certificate provisioning and environment-driven configuration across 27 Docker Compose service definitions
  • Deployed GitHub Actions CI/CD pipeline for automated builds, testing, and GitHub Pages documentation

Unitree G1 Robotics

  • Developed Python-based motion control and scripting for the Unitree G1 platform
  • Integrated robot control pipelines with ML-driven inference backends

Education

PhD in Artificial Intelligence — Kingston University | Awarded 2024

MSc Games Development (Programming) — Kingston University | 2019 – 2020

BSc (Hons) Computer Science (Games Programming) — Kingston University | 2015 – 2019

Additional

  • VR/AR development: Unity, Unreal Engine, Microsoft HoloLens
  • Hardware: server and workstation build and configuration, GPU setup, structured cabling and network switches
  • 3D modelling: Blender (surface design, STL export for fabrication)