Welcome to my GitHub profile! I'm a passionate AI/Machine Learning researcher with expertise in designing and deploying ML/deep learning models and data science solutions for large-scale data analysis and real-time processing in production environments. Adept at building AI/ML solutions and end-to-end data pipelines on cloud platforms, specializing in predictive analytics, real-time anomaly detection, and optimization. Proficient in Transformer models, GANs, VAEs, Reinforcement Learning, and Federated Learning, with hands-on experience in network optimization, healthcare, marketing analytics, and supply chain management. Experienced in enhancing LLM capabilities through implementing Retrieval Augmented Generation (RAG) pipelines for context-aware solutions. Highly skilled in CI/CD integration, Python, TensorFlow, PyTorch, and cloud platforms (AWS, GCP, Azure), with a track record of improving model accuracy and business outcomes through innovative data-driven solutions.

• Current Role: Research Assistant - AI/ML Solutions at Mississippi State University
• Experience: Over 5 years of experience in machine learning, deep learning, and network optimization
• Specialties: Real-time data processing, ML model deployment, and generative AI applications

• AWS Certified Machine Learning – Specialty (MLS C01) - Exp. 2027
• AWS Certified Solutions Architect - Associate (SAA C03) - Exp. 2027
• OCI Generative AI Certified Professional(2025)-Oracle University-Exp. 2027
• OCI Certified Data Science Professional(2024)-Oracle University-Exp. 2027

• Programming Languages: Python, MATLAB, R
• Machine Learning/Deep Learning: DNNs (CNN, RNN, LSTM), Regression Models, Reinforcement Learning
• Generative AI/LLM: GANs, VAEs, Transformer Models (BERT, GPT)
• System Software & Containerization: Linux (Unix Shell), Kubernetes, Docker, CI/CD (GitHub Actions, GitLab CI)
• Data Processing & Big Data: NumPy, SciPy, Pandas, Spark, Kafka
• NLP Tools: NLTK, spaCy
• Cloud Platforms: AWS, Google Cloud Platform
• Database Systems: SQL (MySQL, PostgreSQL), NoSQL (InfluxDB, Redis, MongoDB)
• Visualization Tools: Tableau, Plotly Dash, Grafana, Matplotlib
• Optimization Algorithms: Bayesian Optimization, Genetic Algorithms, Simulated Annealing, Tabu Search, Hill Climbing

• Developed and deployed a scalable ML testbed for DRL and Federated RL models as containerized applications on Kubernetes.
• Built a time-series Intrusion Detection System using customized Transformer architectures and RNN Autoencoders.
• Designed and implemented a Retrieval Augmented Generation (RAG) pipeline using vector databases and LLMs for context-grounded natural language responses, focusing on medical applications.
• Hands-on experience on Fine-tuning and optimized LLM models through prompt engineering.
• Developed a very lightversion of multimodal AI-powered chatbot using LLaMA models for natural language interaction and CSV data analysis, integrating Hugging Face models to combine conversational AI with structured data processing.
• Utilized CUDA/cuDNN for GPU-accelerated computing, reducing ML model training time by 30%.
• Designed and deployed real-time ML inference dashboards using PyQt & Plotly, enhancing data-driven decision-making by 40%.

• Led large-scale network optimization projects, reducing monthly operational costs by 10-15%.
• Developed performance analytics dashboards in Tableau, improving decision-making processes by 20%.
• Managed the end-to-end deployment of BH/BB solutions for large-scale B2B networks, improving network efficiency by 15%. -->

• Email: mrkouchakii@gmail.com
• LinkedIn: linkedin.com/in/mohammadreza-kouchaki