Machine Learning Engineer | Computer Vision | Multimodal Generative Models
I design and build end-to-end machine learning systems with a focus on
computer vision, medical imaging, and
multimodal generative models.
Currently seeking roles in Machine Learning Engineering and
Computer Vision.
👨💻 What I Do
- Design and train deep learning models for real-world computer vision problems
- Build end-to-end ML pipelines: data → training → evaluation → deployment & visualization
- Optimize models through systematic experimentation and hyperparameter tuning
- Develop interactive demos and dashboards for analysis and presentation
🔥 Featured Project — Master’s Thesis
Personality-Aware Non-verbal Behavior Generation in Dyadic Interactions
- Transformer + VQ-VAE generative architecture
- Generates listener avatars (face + upper body) conditioned on personality traits
- Evaluated using Fréchet Distance (FD), Paired FD (P-FD), and user studies
- User study achieved 86% personality trait recognition accuracy
Live Demo:
Thesis website
Repository:
Github repo.
Tech Used: Python · PyTorch · Transformers · VQ-VAE · SMPL-X (PIXIE)
OpenCV · CUDA · Librosa · SLURM / Enroot · Multi-GPU (A100) · TensorBoard
🩺 Medical Imaging — Computer Vision
3D Brain Tumor Segmentation (MRI)
Multi-label 3D semantic segmentation of glioma sub-regions from volumetric MRI scans.
- Dataset: Medical Decathlon / BraTS (multi-modal MRI)
- Model: 3D SegResNet (MONAI)
- Input Modalities: FLAIR · T1 · T1Gd · T2
- Target Structures: Whole Tumor (WT), Tumor Core (TC), Enhancing Tumor (ET)
- Training & Evaluation: Dice Loss and Mean Dice
- Results: Mean Dice = 0.78 on validation (WT: 0.90, TC: 0.82, ET: 0.59)
- Pipeline: preprocessing → training → inference → deployment & visualization
Repository:
Github repo.
Tech Used: PyTorch · MONAI · 3D SegResNet · Multi-modal MRI
3D Medical Image Transforms · Sliding Window Inference · Experiment Tracking (W&B)
🚗 Computer Vision — Autonomous Driving
Semantic Segmentation for Autonomous Vehicles
End-to-end semantic segmentation of urban street scenes for autonomous driving perception.
- Dataset: BDD100K
- Task: Multi-class semantic segmentation
- Classes: Road, Traffic Light, Traffic Sign, Vehicle, Person, Bicycle, Background
- Evaluation: mean Intersection over Union (mIoU)
- Results: Achieved mIoU ≈ 0.45, with strong performance on dominant classes
(Road: ~0.88, Vehicle: ~0.78) - Optimization: Systematic hyperparameter optimization via experiment sweeps
- Pipeline: data preparation → training → evaluation → deployment & visualization
Repository:
Github repo.
Tech Used: PyTorch · Fastai · Semantic Segmentation · Hyperparameter Optimization · Experiment Tracking
🏸 Sports Analytics — Computer Vision
Badminton-VisionAI
Real-time AI-driven badminton analytics system with player and shuttlecock tracking, mini-court projection, shot type and power analysis, and Streamlit-dashboard.
- Ongoing project focused on real-time sports analytics
- Multi-object tracking of players and shuttlecock
- Shot type classification using Roboflow + Supervision
- Emphasis on temporal consistency, tracking robustness, and visualization
- Interactive analytics dashboard for performance analysis
Tech Used: OpenCV · YOLO · Supervision · Streamlit
🧠 Technical Profile
| Area | Skills & Tools |
|---|---|
| Computer Vision Tasks | Object Detection · Segmentation · Tracking · Video Motion Analysis · Digital Avatar Generation |
| Models & Frameworks | PyTorch · Tensorflow · OpenCV · YOLO · SAM · CNNs · Vision Transformers (ViT) · TrackNet · Supervision |
| Training & Evaluation | Transfer Learning · Fine-tuning · Loss Design · Metric Selection · Hyperparameter Optimization · Multi-GPU Training (CUDA) |
| Data & Experimentation | Dataset Preparation · Data Augmentation · Efficient Data Loading · Weights & Biases · TensorBoard · Ablation Studies |
| Deployment & Inference | Docker · GPU Inference Pipelines · Batch & Real-time Inference · AWS (EC2 GPU) · Streamlit (Demos) |
| Compute & Infrastructure | SLURM (HPC) · GPU Job Scheduling · Multi-node Training · Large-scale GPU Experiments |
| Software Engineering Foundations | Python · C++. Object-Oriented Design · Version Control (Git) · Debugging · Logging · Unit Testing |
📬 Contact
If you’re hiring or would like to collaborate:
Email: amribrahim.amer@gmail.com
LinkedIn: https://www.linkedin.com/in/amr-amer-2023-cs/