Zahir Alsulaimawi

Data Scientist - Machine Learning Engineer

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. This involves meticulous data collection, data cleaning, and preprocessing, training sophisticated data models, and culminating in deployment into production environments.

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. These algorithms are 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, to solve 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. He has a proven track record of academic excellence, having earned multiple honors and awards for his outstanding achievements. Zahir Alsulaimawi’s expertise and contributions in privacy-preserving and fairness-enhancing machine learning make him a valuable asset to the technology community.

Deep Learning and Machine Learning

Neural Networks
Transfer Learning
Natural Language Processing (NLP)
Computer Vision
Time Series Analysis
Ensemble Learning

Innovation Solution

Pushing the boundaries of technology to create ethical innovative solution focused technologies.
Federated Learning
Privacy-Preserving Algorithms
Model Aggregation
Client-Server Communication
Distributed Deep Learning
Data Partitioning
Algorithm Optimization

Ethical Technologies and Regulations

Fairness and Bias Mitigation
Privacy-Preserving Technologies
Transparency and Explainability
Algorithmic Accountability
Data Ethics
User-Centered Design

Programming and Coding

Signal Processing