Sami Melhem Portfolio

Core Concepts I'm Proficient In:

• Production-Focused Neural Networks: Extensive use of torch.nn for building ML models to optimize chemical processes, product production, and staff allocation with emphasis on practical deployment

• Physics-Informed Neural Networks (PINNs): Advanced implementation of PINNs incorporating Physics loss and Mass Balance loss for chemical process optimization and metal precipitation retention prediction

• Pragmatic Model Development Philosophy: Strategic three-phase approach: build simplest working model → make it right for the situation → make it fast and production ready

• Chemical Process Optimization: Specialized application of PyTorch for optimizing chemical processes to increase product yield and improve process evaluation for staff allocation efficiency

• Computer Vision Integration: Implementation of CNNs for object detection in autonomous vehicle applications, seamlessly integrating with broader system architecture

• Optimal Training Workflows: Strategic dataset splitting (60% training, 20% evaluation, 20% testing) for maximum model validation effectiveness

• Research & Development Integration: Using R&D methodologies to create production-ready PINNs for real-world chemical process optimization

Advanced Development Patterns:

• Constraint-Based Neural Networks: Sophisticated implementation of physics constraints in neural network architectures, ensuring models respect fundamental physical laws while optimizing for practical outcomes

• Multi-Loss Function Architecture: Advanced loss function design combining Physics loss and Mass Balance loss to create models that understand and respect chemical process constraints

• Production-First Framework Choice: Strategic selection of PyTorch over TensorFlow for application-focused development, leveraging PyTorch's superior production deployment capabilities

• Simplicity-Driven Model Design: Intentional focus on building simple, effective models rather than complex architectures, recognizing that most industrial problems require straightforward ML solutions

• Pre-Built Model Optimization: Strategic use of PyTorch's pre-built models and workable layers to eliminate guesswork and accelerate development cycles

• Industry-Specific Adaptation: Tailoring neural network architectures specifically for chemical engineering applications and process optimization challenges

Complex Problem-Solving Examples:

Chemical Process Optimization with PINNs: Developed sophisticated Physics-Informed Neural Networks at Momentum Technologies that optimize chemical processes for maximum product yield and efficiency. The challenge involved incorporating fundamental physics constraints (Physics loss) and chemical engineering principles (Mass Balance loss) into neural network architectures to predict metal precipitation retention percentages. Successfully implemented models that respect chemical process limitations while optimizing for practical industrial outcomes, demonstrating deep understanding of both machine learning and chemical engineering principles.

Production-Ready Model Development Workflow: Established a systematic three-phase development process that consistently delivers production-ready models: first building the simplest working solution, then adapting it to specific situational requirements, and finally optimizing for speed and production deployment. This methodology has proven effective across multiple chemical process optimization projects, ensuring rapid iteration while maintaining industrial-grade reliability and performance standards.

Staff Allocation and Process Evaluation Optimization: Created ML models that optimize staff allocation and reduce work time while maintaining accuracy in chemical process evaluation. This involved understanding complex workforce dynamics, process timing constraints, and quality requirements to develop models that improve both efficiency and effectiveness in industrial chemical production environments.

Autonomous Vehicle Computer Vision Integration: Successfully implemented CNN architectures for object detection in autonomous vehicle simulation projects, demonstrating ability to apply PyTorch across diverse domains beyond chemical engineering. The models integrated seamlessly with broader autonomous vehicle systems while maintaining real-time performance requirements.

Areas for Continued Growth:

• GPU Optimization Mastery: Learning advanced GPU optimization techniques and distributed training strategies for handling large-scale datasets in chemical process modeling

• Distributed Training Architecture: Developing expertise in PyTorch's distributed training capabilities for scaling machine learning models across multiple processing units

• Advanced Model Optimization: Exploring cutting-edge optimization techniques to maximize model performance in resource-intensive chemical process applications

• Scalable Production Deployment: Mastering enterprise-scale deployment strategies for PyTorch models in industrial chemical production environments

• Advanced Physics Integration: Deepening expertise in incorporating complex physics constraints and chemical engineering principles into neural network architectures

PyTorch

My Experience

Internships

Technical Deep Dive

Projects Using PyTorch

Autonomous Vehicle Simulation