I build production-grade intelligent systems that combine numerical optimization with domain physics, developing models that are both data-efficient and physically consistent. additionally My work spans performance optimization, robust modeling, inference acceleration, and deploying AI solutions in resource-constrained environments.

📍 Cairo, Egypt | 📧 hadywaliedkamel@gmail.com

The Problem: Large transformer models are prohibitively expensive for deployment in production environments with latency and resource constraints.

The Solution: End-to-end compression pipeline combining knowledge distillation and quantization. Implemented custom training loop with PyTorch Lightning, integrated W&B for experiment tracking, and built production-ready inference engine.

Results: 75% model size reduction • 3x faster inference • <2% accuracy loss • Deployed with Qt-based demo application

Stack: PyTorch Lightning, Transformers, W&B, Quantization, Model Distillation

The Problem: Traditional EDA optimization approaches ignore underlying physical constraints, leading to unrealistic solutions and poor generalization.

The Solution: Hybrid optimization system combining gm/ID methodology-based methods with physics-informed constraints. Built custom computational engine leveraging NumPy/Numba/Sympy for performance-critical operations and integrated semiconductor-specific physical models.

Impact: multiple folds reduction in solution iteration time • Deployed to production serving industrial applications

Stack: Python, C++, NumPy, Numba, Optimization Algorithms, AWS

The Challenge: Understanding automatic differentiation and backpropagation at the implementation level.

The Solution: Lightweight autograd engine and neural network library built in modern C++17—no framework dependencies. Implements core ML primitives: computational graphs, reverse-mode autodiff, gradient descent optimizers, and basic neural network layers.

Purpose: Deep dive into ML fundamentals, performance-critical C++ design, and educational resource for learning autodiff mechanics.

Stack: Modern C++17, CMake, Template Metaprogramming

• Building a Simple Modern RAG Application with Asyncio and Chainlit
• From Documents to Dialogue: A step-by-step RAG Journey

ML/AI Core: PyTorch (Lightning) • TensorFlow • scikit-learn • Model Compression • Knowledge Distillation
Performance Engineering: C++ • Rust • Python optimization • Numba • CUDA basics • Memory profiling
MLOps & Deployment: Docker • AWS (EC2, S3, CI/CD) • Model serving • Experiment tracking (W&B) • MLFlow • ETL (Pandas)
Scientific Computing: NumPy • SciPy • Statistical modeling • Optimization algorithms • DSP

• Physics-Informed Neural Networks (PINNs) for solving differential equations and inverse problems
• Model optimization techniques: Pruning, quantization, distillation for edge deployment
• High-performance ML inference: Exploring Rust and C++ for production ML systems
• Hybrid approaches: Combining classical optimization with deep learning

B.S.E. Electronics & Electrical Communications Engineering • Cairo University • 2021
Relevant Coursework: Linear Algebra, Calculus (ODE/PDE), Classical & Deep ML, DSP, Statistical Methods

Professional Certifications:

• Deep Learning Specialization (Coursera - deeplearning.ai)
• NLP Specialization (Coursera - deeplearning.ai)
• Google Certified Associate Android Developer (2020-2023)

I'm actively seeking roles in:

• ML Engineering: Model development, optimization, and production deployment
• Applied AI Research: Physics-Informed ML, model compression, efficient inference
• ML Systems Engineering: High-performance inference engines, C++/Python integration
• Research Scientist positions: PIML, hybrid physics-ML approaches, scientific ML

Best way to reach me: LinkedIn or email