I am a researcher, data scientist, and machine learning engineer with a strong foundation in mathematics, statistics, and optimization theory. My work focuses on developing principled computational models and intelligent systems, with particular expertise in applying these methods to understand complex systems and solve real-world problems. I combine theoretical insights with practical implementation skills to bridge the gap between mathematical foundations and practical applications.
I hold both my bachelor's and master's degrees in Electrical Engineering from the University of Tehran, where I developed a strong foundation in mathematical modeling, signal processing, and machine learning. During my studies, I engaged in various research projects and teaching assistantships that helped shape my analytical and problem-solving skills.
- 🔭 Research Focus: Mathematical modeling, statistical inference, optimization algorithms, and their applications in machine learning and complex systems.
- Research collaborations in machine learning and computational neuroscience
- Industry opportunities in AI/ML engineering and data science
- Open source contributions and community engagement
- Mentorship opportunities in ML and data science
| Publication Title | Code Repository | Venue / Link |
|---|---|---|
| Microsaccade Selectivity for Object Decoding | ms-selectivity-feature - Implementation of microsaccade selectivity analysis for object decoding | iScience (2024) |
| EEG Phase Amplitude Coupling for Motor Vigor | pacnet-neuro-vigor - Phase Amplitude Coupling analysis for motor vigor estimation | medRxiv (2024) |
| TranCIT: Transient Causal Interaction Toolbox | trancit - Causal analysis in multivariate time series data | arXiv (2025) |
- Direction-of-Arrival - MATLAB implementations for direction of arrival estimation
- Telecommunication-Projects - Collection of telecommunication research projects including agent prediction, authentication, digital communication, and resource allocation
- Advanced-Deep-Learning - Comprehensive implementations of advanced deep learning concepts including transformers, GANs, and self-supervised learning
- Pattern-Recognition - Implementations of classical and modern pattern recognition algorithms including clustering, classification and dimensionality reduction
- Statistical-Inference - Projects covering Bayesian inference, hypothesis testing, maximum likelihood estimation and probabilistic modeling
- Reinforcement-Learning - Implementation of key RL algorithms including Q-Learning, Policy Gradients, and Actor-Critic methods with practical applications
- Optimization - Collection of optimization techniques including gradient descent variants, evolutionary algorithms and constrained optimization
- Deep-Learning-Applications - Real-world applications of deep learning in computer vision, NLP, and time series analysis with detailed implementations
- Data-Analytics - End-to-end data analysis projects covering exploratory analysis, statistical modeling, and predictive analytics
- Advanced-Programming - Advanced C++ programming concepts including design patterns, multithreading, and memory management with practical examples
- sa-nouri - GitHub profile repository
- Architect and deploy enterprise-scale MLOps pipelines for distributed data processing
- Develop production-ready machine learning services with high availability and fault tolerance
- Lead comprehensive statistical analysis initiatives and experimental design
- Design interactive data visualization dashboards for stakeholder insights
- Implement systematic A/B testing frameworks for product validation
- Build scalable recommendation systems for personalized user experiences
- Conduct advanced feature engineering and selection for model optimization
- Deploy end-to-end machine learning solutions from prototype to production
- Optimize data processing pipelines for enhanced performance and efficiency