Zahir Alsulaimawi — Data Scientist / ML Engineer
Ph.D. researcher and engineer specializing in privacy-preserving machine learning, federated learning, and SWARM intelligence.
Background
Zahir Alsulaimawi is a dedicated researcher and engineer with a strong background in electrical and computer engineering. He holds a Ph.D. from Oregon State University, where he specialized in developing innovative frameworks for privacy preservation and fairness in machine learning. In his role, Zahir harnesses the power of data to drive value for his company and clients — managing the complete lifecycle of machine learning initiatives from data collection and cleaning to preprocessing, training sophisticated models, and deployment into production.
In his day-to-day work, Zahir collaborates closely with Business, Product, and Data Analysis teams to uncover narratives hidden within the data and provide insights that inform strategic decisions and enhance product offerings. He is deeply involved in designing and developing cutting-edge algorithms based on widely recognized industry frameworks — pivotal for mapping, strategic planning, localization, free space estimation, object detection and classification, and sensor calibration.
Zahir also spearheads software engineering initiatives for SWARM technology, including swarm intelligence algorithms like particle swarm optimization, ant colony optimization, and genetic algorithms — solving complex and dynamic problems. His passion lies at the intersection of deep learning, machine learning, and privacy-preserving technologies. Zahir's work spans various domains, including federated learning, signal processing, and software-defined radio, with a proven track record of academic excellence.
Focus areas
- Neural Networks
- Transfer Learning
- Natural Language Processing (NLP)
- Computer Vision
- Time Series Analysis
- Ensemble Learning
- Ethical, innovation-focused technology.
- Federated Learning
- Privacy-Preserving Algorithms
- Model Aggregation
- Client-Server Communication
- Distributed Deep Learning
- Data Partitioning
- Algorithm Optimization
- Fairness and Bias Mitigation
- Privacy-Preserving Technologies
- Transparency and Explainability
- Algorithmic Accountability
- Data Ethics
- User-Centered Design
- Python
- MATLAB
- C / C++
- PyTorch
- Keras
- Signal Processing
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