I am a Master’s student in Computer Science & Engineering at UNIST and a researcher at the Interactive Multimodal ML Lab. My passion lies in exploring the frontiers of research and develop cutting-edge algorithms in the area of multimodal learning, particularly in the integration of vision and language. I am also interested in advancing the understanding and transparency of AI models. By bridging the gap between AI and human understanding, I aim to build more trustworthy and ethical AI systems that can be effectively utilized across various domains.

Besides my research interests, I have a broad interest in fintech, data science and the design / deployment of ML systems. I find great fascination in the practical application of machine learning techniques and their potential to solve real-world problems.

I always seek opportunities to expand my knowledge, stay updated with the latest advancements, and participate in meaningful projects that have a positive impact on society. I am enthusiastic about connecting and collaborating with fellow researchers, professionals, and organizations who share a similar vision.

• Programming Languages: Python, C++, JavaScript, R
• Frameworks & Tools: PyTorch, Hugging Face, DeepSpeed, Scikit-learn, OpenCV, NumPy, Pandas, Matplotlib, Git, LaTeX
• AI/ML Expertise:
  • LLMs: LoRA/QLoRA, RLHF/DPO Fine-tuning, Retrieval-Augmented Generation (RAG) Integration, Quantization, Multimodal LLM Adaptation, Synthetic Data Creation, Prompt Engineering
  • Traditional ML: Linear/Logistic Regression, Support Vector Machines (SVM), XGBoost, Principal Component Analysis (PCA), K-means Clustering, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN/LSTM)

• RingFormer: Constructed a parameter-efficient and recurrent version of transformer models, named RingFormer, which maintains strong performance with significantly fewer parameters compared to the original transformer models across translation and image-classification tasks
• Uzbek LLMs: Enhanced open-source LLMs for the low-resource Uzbek language through language-specific continual pretraining and instruction-tuning. The models are hosted on Hugging Face for broad accessibility.
• Guessing Game with Robot Arm: Designed a robot arm system and user interface to play a guessing game by integrating vision-language models like InternVL and YOLO-World. The robot can interpret user clues, identify objects, and pick the object that corresponds to the clue, providing its response through text and voice for an interactive experience.
• 4D Instruct-GS2GS: Extended semantic editing to dynamic 3D scenes using an iterative dataset update and efficient 4D-GS rendering method for consistent editing of Gaussian splatting scenes via text instructions.
• Forecasting Transaction Fees on the Ethereum Blockchain Network: Utilized advanced time series models such as SARIMA and LSTM to predict hourly gas fees on the Ethereum network.
• Loan Targeting Optimization Using Deep Learning and Time Series Modeling: As a team, ranked in the top 15 out of nearly 40 participating teams in a data science competition hosted by Hana Bank in Korea
• Portfolio Allocation Stability with CorrGAN: Investigated the stability of traditional and machine learning-powered portfolio allocation approaches using GAN-based models.
• General ML Applications: Developed various ML applications, including house price prediction using Lasso and GradientBoost methods, income classification with Logistic Regression, K-Nearest Neighbor and Random Forest, and healthcare expenditure modeling using regression analysis to examine the statistical significance of factors such as obesity influencing medical costs among the elderly.
• Data Structures for All Tastes: Implemented a variety of data structures from scratch in C++, ranging from simple ones like Linked Lists to more advanced types such as Balanced Binary Search Trees.

• Lotte Scholarship for International Master's Students: Awarded by the Lotte Scholarship Foundation for students with exceptional research potential in Korea, providing KRW 12,000,000 per year for the duration of graduate studies.
• Korean Government Scholarship for Graduate Studies: Comprehensive scholarship including a tuition fee waiver and a stipend of KRW 9,600,000 per year throughout graduate studies at UNIST.
• UNIST Global Dream Scholarship: Covered tuition fees, health insurance, and living expenses during undergraduate studies at UNIST.

• Machine Learning Engineering for Production Specialization – DeepLearning.AI specialization covering scalable production pipelines, ML model deployment, and MLOps best practices.
• Deep Learning Specialization – Taught by Andrew Ng, focusing on neural networks, convolutional networks, and sequence models for real-world AI tasks.
• Applied Data Science with Python Specialization – University of Michigan course series focusing on Python-based data analysis, including Pandas, Matplotlib, and machine learning fundamentals.
• Statistical Learning – Stanford Online course covering supervised and unsupervised learning, linear regression, and classification methods.
• Machine Learning – Classic Coursera course by Andrew Ng, covering algorithms, supervised/unsupervised learning, and neural networks.
• Mathematics for Machine Learning Specialization – Imperial College London specialization focusing on linear algebra, calculus, and statistics for machine learning foundations.

I am native in 🇺🇿 Uzbek, fluent in 🇷🇺 Russian and 🇬🇧 English, intermediate in 🇰🇷 Korean!