Ibrahim Al-Ibrahim, an Electrical Engineer from Columbia University, excels in EEG analysis, economic indicator aggregation, ML and AI technologies, and X-ray crystallography. His extensive research experience and diverse skills make him a valuable asset for innovative solutions and analytical insights.
Domains of Expertise
Ibrahim's expertise spans several domains, including programming, front-end web development, EEG analysis, and vocal music extraction. He has a strong foundation in data science and extensive experience as a graduate researcher at Columbia University, making him a valuable asset for teams seeking innovative solutions and analytical insights.
Recent Work
Since April 2024, Ibrahim has been conducting comprehensive literature reviews to understand existing methodologies for aggregating economic indicators into a Financial Stability Index. He has implemented established theorems and methodologies from academic papers to address the multidimensionality challenge in capturing the collective impact of these indicators. Furthermore, Ibrahim has applied normalization techniques to standardize measurement units of individual indicators prior to aggregation.
Previous Experience
Previously, Ibrahim worked as a Quality Control Engineer specializing in Generative AI Models in New York. He employed cutting-edge technologies such as Machine Learning (ML) and Artificial Intelligence (AI), particularly Stable Diffusion, for tasks ranging from image generation to vocal music extraction processing. By leveraging advanced neural network (NN) models, he has significantly advanced data processing capabilities, enabling multifaceted applications across diverse domains.
Skills
Ibrahim is also adept at curating and categorizing datasets, ensuring their relevance and coherence for specific tasks. He evaluates and ranks the effectiveness of these datasets, optimizing the performance and accuracy of ML and AI algorithms. His front-end development skills go beyond HTML, CSS, and JavaScript proficiency, encompassing a deep understanding of User Interface (UI) and User Experience (UX) principles to create seamless and impactful digital journeys for users. He leverages responsive design techniques to ensure immersive experiences across various devices and screen sizes. Additionally, his adept DNS configuration and domain management skills enhance online navigation and reliability.
Research at Columbia
In his research at Columbia, Ibrahim specializes in utilizing EEG signals to train ML and AI models for detecting action intent. He is proficient in handling multiple epochs of EEG data, employing advanced techniques to preprocess and extract meaningful features for model training. Ibrahim optimizes model performance through rigorous experimentation and validation and implements outlier detection methods to ensure data integrity and model robustness. He adheres to efficient workflows, including version control with Git, to facilitate seamless collaboration and ensure project success.
Expertise in X-ray Crystallography
Ibrahim is also proficient in X-ray crystallography, adept at handling large image databases exceeding 45 GB efficiently. He utilizes advanced data science techniques to train machine learning and neural network models for class detection and specializes in outlier removal for signed integer images, particularly in X-ray applications. His disciplined Git workflow ensures seamless collaboration and version control across projects, driving impactful results in image analysis and data-driven decision-making.
Awards & recognition
• MOHE Distinguished Graduate Award - 2024
• Full Ride Masters Scholarship - 2022
• First Class Honors by Portsmouth University - 2021
• MOHE Merit Award - 2021
• Full Ride Bachelor's Scholarship - 2018
Github
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