// Computer Engineering Student · AI/ML Enthusiast
Building intelligent systems at the intersection of mathematics, data, and machine learning. On a mission to research and engineer the future of AI.
I'm Asim Arghaule, a Computer Engineering student with a deep passion for Artificial Intelligence, Machine Learning, and Data Science. I believe that the most powerful ideas live at the boundary between mathematics and code.
My goal is to not just use ML tools — but to understand them deeply, contribute to research, and build systems that solve real problems. I approach every project with mathematical rigor and engineering precision.
When I'm not training models or writing code, I'm studying the mathematical foundations that power modern AI — linear algebra, probability theory, calculus, and optimization.
Computer Engineering Student
Actively building & learningAI/ML Engineering & Research
Deep Learning · Statistics · OptimizationML Engineer / AI Researcher
Bridging theory and applicationasimarg369@gmail.com
Open to opportunitiesA classical ML classification pipeline using Support Vector Machines to identify iris flower species. Includes feature engineering, cross-validation, and model evaluation with detailed performance metrics.
Comprehensive EDA toolkit built with Pandas and visualization libraries. Automates pattern discovery, outlier detection, correlation analysis, and generates publication-ready plots.
Advanced SQL-based data analytics system featuring complex queries, window functions, CTEs, and stored procedures to derive actionable business insights from raw relational data.
Implemented a feedforward neural network using only NumPy — backpropagation, gradient descent, and activation functions built from first principles to solidify mathematical understanding of deep learning.
Exploring the geometric and algebraic structures underlying modern machine learning — manifold learning, kernel methods, and the mathematical theory of optimization landscapes.
Understanding why deep neural networks generalize, the dynamics of gradient descent in high-dimensional spaces, and the implicit biases learned during training.
Bayesian inference, probabilistic graphical models, and the theoretical connections between statistics and machine learning for principled uncertainty quantification.
Investigating how data quality, distribution, and labeling strategies affect model performance — building systems that are robust, fair, and interpretable.
Exploring transformer architectures, attention mechanisms, and the mathematical principles behind large language models and representation learning.
Study of gradient-based and gradient-free optimization methods — from classical convex theory to modern adaptive optimizers like Adam, RMSProp, and beyond.
Core studies in algorithms, data structures, computer architecture, and systems programming. Self-directed specialization in AI/ML, data science, and applied mathematics.
Hands-on study of machine learning algorithms, neural network architectures, and mathematical foundations. Implementing research papers and building projects from scratch.
Deep study of linear algebra, multivariate calculus, probability theory, and statistics as applied to machine learning and data science.
I'm open to internships, research collaborations, project partnerships, and any opportunity to learn and grow in AI/ML. If you're working on something interesting, I'd love to hear about it.