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Profile photo of Amr Amer

Machine Learning Engineer | Computer Vision | Multimodal Generative Models

<p>
  Machine Learning Engineer with a <strong>Master’s in Visual Computing from Saarland University</strong> and
  research experience at <strong>DFKI (German Research Center for Artificial Intelligence)</strong>.
  I design and build <strong>end-to-end machine learning systems</strong> for real-world applications in
  <strong>computer vision</strong>, <strong>medical imaging</strong>, and
  <strong>multimodal generative models</strong>.
</p>

<p>
  Currently seeking roles in <strong>Machine Learning Engineering</strong> and
  <strong>Computer Vision</strong>.
</p>
<p>
  <a href="mailto:amribrahim.amer@gmail.com">Email</a> ·
  <a href="https://www.linkedin.com/in/amr-amer-2023-cs/">LinkedIn</a> ·
  <a href="https://github.com/amramer">GitHub</a> ·
  <a href="/assets/Amr_Amer_CV.pdf" download>Download CV</a>
  &nbsp;|&nbsp;

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    Open to work
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👨‍💻 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 Projects

🎓 Master’s Thesis

Personality-Aware Non-verbal Behavior Generation in Dyadic Interactions

Multimodal generative model for generating realistic listener avatars in dyadic conversations, conditioned on personality traits.

The model predicts facial expressions, head motion, and upper-body gestures of a listener from the speaker’s audio and motion signals.

Generated avatars

• • •

Problem

Human conversations rely heavily on non-verbal cues such as facial expressions, eye gaze, and body movement.
Most prior work does not model how these behaviors vary with personality traits.

• • •

Approach

Developed a multimodal generative architecture combining:

  • Transformer encoder for temporal modeling
  • VQ-VAE for quantized motion representation learning
  • SMPL-X body parameters for realistic face and upper-body avatar generation
  • Integration of audio, facial motion, and body gesture signals

The model generates personality-aware listener behavior sequences in response to a speaker.

• • •

Results

Quantitative evaluation:

  • Face: FID 6.15, P-FID 10.31
  • Upper Body: FID 43.16, P-FID 87.73

User study:

  • Participants correctly identified extroverted vs introverted avatars in 86% of cases
  • 71% preference for the personality-aware model over the personality-agnostic baseline

• • •

Tech Stack

PyTorch · Transformers · VQ-VAE · SMPL-X (PIXIE)
Librosa · OpenCV · CUDA · TensorBoard
SLURM · Enroot · Multi-GPU Training (A100)

Links

🌐 Website
Thesis Website

💻 Repository
GitHub Repository

📄 Thesis Document
Read the Full Master’s Thesis

🏸 Badminton-VisionAI — AI Match Analytics from Video

Badminton-VisionAI is an end-to-end computer vision pipeline that converts badminton match videos into structured performance analytics for players and coaches.

The system tracks both players and the shuttlecock, detects shot events, projects motion onto a mini-court representation, and generates a downloadable coach-style performance report.

• • •

🎥 System Demo

Tracking & On-Court Intelligence
Player tracking • Shuttle tracking • Shot detection • Court mapping

Badminton tracking: players, shuttle, shots, and court mapping


Analytics Dashboard (Streamlit)
Player statistics • Heatmaps • Shot analysis • Coach-ready report

Streamlit analytics dashboard for badminton tracking

• • •

Technical Challenges

Badminton tracking presents several challenges for computer vision systems:

  • Extremely small and fast shuttlecock
  • Frequent player occlusions
  • Rapid direction changes that break naive trackers

This project combines object detection, multi-object tracking, geometric projection, and temporal analytics to produce coach-readable match insights, not just raw detections.

• • •

Key Features

  • Stable multi-object player tracking
  • Shuttlecock trajectory tracking with speed estimation
  • Shot type classification with temporal stabilization
  • Mini-court projection using homography
  • Interactive analytics dashboard for match analysis
  • Automated coach report with movement and shot statistics

• • •

Tech Stack

Computer Vision: Python · OpenCV · YOLO · Multi-Object Tracking
Geometry & Tracking: Court Homography · Trajectory Projection
Event Analysis: Shot Detection · Temporal Label Stabilization
Visualization: Streamlit · Plotly
Reporting: ReportLab PDF Export

• • •

Demo & Links

• • •

This project reflects my interest in real-world computer vision systems that transform model outputs into actionable insights.

🧠 Other Projects

🚗 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

Autonomous driving segmentation results

Repository:
Github repo.

Tech Used: PyTorch · Fastai · Semantic Segmentation · Hyperparameter Optimization · Experiment Tracking

🩺 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

Brain tumor segmentation

Repository:
Github repo.

Tech Used: PyTorch · MONAI · 3D SegResNet · Multi-modal MRI
3D Medical Image Transforms · Sliding Window Inference · Experiment Tracking (W&B)

Vision–Language Models

Realtime Vision Captioning

Exploratory project focused on applying vision–language models in realtime webcam-based settings.

  • Realtime image captioning and visual question answering using BLIP via Hugging Face Transformers
  • Image classification using ResNet-50 pretrained on ImageNet
  • Realtime Gradio-based webcam application for live image captioning and classification with adjustable inference parameters

The project is implemented as a set of Jupyter notebooks, progressing from offline image understanding to a realtime vision application.

realtime vision capitioning

Repository:
Github repo.

Tech Used:
Python · PyTorch · Torchvision · Hugging Face Transformers · Gradio · PIL · NumPy

🎙️ Conversational AI — Voice Assistant (Ongoing)

AI Conversational Agent with Voice, Avatar, and Web Interface

Conversational AI system enabling real-time voice and text interaction through a custom web-based interface.

  • Speech-to-text and text-to-speech for natural dialogue

  • LLM-powered conversational reasoning

  • Human-like conversational avatar integrated into the UI

  • Modular frontend–backend design for extensibility

AI Conversational Agent Web-interface

Tech Used: Python · OpenAI GPT · Speech-to-Text · Text-to-Speech · HTML · CSS · JavaScript · Bootstrap · jQuery

Status: 🚧 Ongoing

🧠 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

🎥 Beyond Work

Outside of my professional work, I enjoy aerial photography and videography using drones. This section highlights selected visual projects created in my free time.

<iframe width="45%" src="https://player.vimeo.com/video/1152190588" frameborder="0" allowfullscreen></iframe> <iframe width="45%" src="https://player.vimeo.com/video/1152190476" frameborder="0" allowfullscreen></iframe> <iframe width="45%" src="https://player.vimeo.com/video/1152190673" frameborder="0" allowfullscreen></iframe>

View more Drone videos on Vimeo →

📬 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/