I'm a Machine Learning Researcher passionate about building innovative AI solutions and pushing the boundaries of ML research.

🎯 Machine Learning Engineer & Researcher

• I specialize in Deep Learning, NLP, Generative Models, and Recommendation Systems.
• Currently focusing on Large Language Models (LLMs), Video Understanding, and Contrastive Learning.
• Love exploring the intersection of machine learning and real-world applications to create innovative solutions.
• Always curious about cutting-edge research in AI and building solutions that make an impact.

✅ Machine Learning Research – Deep learning, NLP, LLMs, SVD/PCA, and generative models.
✅ AI Applications – Building ML-based applications, automation, and scalable AI systems.
✅ Data Science – Statistical analysis, predictive modeling, and data-driven insights.
✅ Recommendation Systems – Developing personalized content and improving user engagement using ML.
✅ Computer Vision – Image and video analysis, StyleGAN, and video understanding with state-of-the-art models.

🌱 Deep Learning & Neural Networks – CNN, RNN, and Transformer-based architectures.
🌱 LLMs and NLP – Exploring prompt engineering, multimodal models, and language generation.
🌱 Contrastive Learning – Fine-tuning models for improved performance and better embeddings.
🌱 Recommendation Systems – Designing intelligent systems to improve user experience and decision-making.
🌱 Video Understanding – Working with VideoMamba and Video-MME for long-term video QA and recognition.

• Developed a generative model using StyleGAN and CLIP to create attractive human faces for a dating app.
• Collected user feedback through a Telegram bot for online learning and improving model performance.

• Built an LLM-based chatbot for Instagram to automate message responses and improve engagement.
• Integrated classification and sentiment analysis to categorize incoming messages.

• Performed sentiment analysis and topic modeling on 100+ essays to extract insights about motivation and decision-making.
• Used BERT embeddings and clustering to identify patterns and key motivational drivers.

• Tested VideoMamba on the Video-MME benchmark.
• Developed a custom training pipeline using contrastive loss and balanced sampling of video frames.

• Implemented PCA and SVD from scratch in Python.
• Applied them to dimensionality reduction and feature extraction in ML models.

• Python, TensorFlow, PyTorch, Keras, OpenCV, NumPy, Pandas, Scikit-learn

• Supervised and Unsupervised Learning, Deep Learning, Transfer Learning
• NLP, LLMs, Transformers, Attention Mechanisms

• Data Cleaning, Feature Engineering, Statistical Analysis
• Data Visualization (Matplotlib, Plotly, Seaborn)

• Docker, Kubernetes, AWS, Git, Selenium
• Jupyter Notebooks, Colab, FastAPI

💡 I'm always excited to collaborate on:
✅ Machine Learning Research
✅ AI and NLP-based Applications
✅ Open-Source ML Projects
✅ Automation and Data Analysis

• 💬 Telegram: @aysel_mirzoeva
• 📧 Email: [email protected]
• 🌐 LinkedIn:
• 🌍 Personal Website

I'm fascinated by the ability of machine learning to transform industries and solve complex problems.
My ultimate goal is to develop innovative AI solutions that positively impact people's lives.

⭐️ Feel free to connect with me or check out my work! 😎