Hello, I'm Asiye

I am a Machine Learning Scientist
& AI Engineer. I build intelligent systems
that make an impact.

Asiye Baghbani
About

Passionate about AI, GenAI, and Data Science.

I am Asiye Baghbani, a Machine Learning Scientist and AI Engineer with a passion for building intelligent systems that drive innovation and solve complex problems. With expertise in deep learning, natural language processing (NLP), and data-driven AI solutions, I specialize in transforming raw data into actionable insights and cutting-edge technologies.

My journey in AI is fueled by a strong academic background, hands-on research, and real-world applications in machine learning, computer vision, and large-scale data processing. I thrive at the intersection of theory and practice, bridging the gap between innovative AI research and impactful solutions that make a difference.

I am always excited about collaborations, research, and AI-driven projects that push the boundaries of technology. If you’re looking for an expert to work on challenging AI problems, let’s connect and build the future together.

Expertise

My key areas of expertise.

I specialize in developing intelligent systems that drive innovation and efficiency. My expertise spans multiple domains, from AI-driven solutions to data-driven decision-making and research.

Machine Learning & Deep Learning

I design and implement advanced ML and deep learning models, specializing in GNNs, Seq2Seq, and attention mechanisms. My expertise includes model optimization, reinforcement learning, and deploying scalable AI solutions for real-world applications.

AI Engineering & Cloud Deployment

I build and deploy AI systems using Docker, Azure, and MLOps best practices. With strong Python, SQL, and big data skills, I ensure AI models are production-ready, scalable, and seamlessly integrated into real-time environments.

Large Language Models & NLP

I work with transformer models like BERT, GPT, and T5 for NLP applications, including semantic search, conversational AI, and intelligent automation. My focus is on fine-tuning LLMs for high-accuracy, real-world language processing.

Research & Development

Leading AI-driven research projects, mentoring students, and publishing scientific papers in top Applied AI journals and conferences. I develop data-driven solutions backed by statistical modeling and machine learning insights.

Technologies & Tools I Use

I leverage industry-leading tools and frameworks to build scalable, efficient, and production-ready AI solutions.

Building intelligent, scalable, and future-ready AI systems requires the right combination of expertise and tools. I specialize in machine learning, deep learning, and MLOps, leveraging cutting-edge frameworks and cloud infrastructure to turn ideas into impactful AI solutions.

From designing robust models to deploying production-grade AI, I ensure that my work is not only technically sound but also efficient, interpretable, and adaptable. My approach blends scientific research, software engineering, and strategic thinking, allowing me to create AI systems that are both innovative and practical.

Recent Articles

My Research & Publications

Published in IEEE Transactions ON Intelligent Transportation Systems, 2023.
In this survey, we provide an overview of GNN studies in the general domain of ITS. We explore how GNN frameworks have evolved for different ITS applications, including traffic forecasting, demand prediction, autonomous vehicles, intersection management, parking management, urban planning, and transportation safety. Read More

Published in 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC).
In this study, we propose Multi-Component Spatial Temporal Graph Attention Convolution Neural Network (Multi-STGAC), a graph deep learning model to predict short-term bus passenger flow. It incorporates a Graph Attention Network and a Graph Convolution Neural Network to consider spatial correlations in bus network data. Read More

Published in Transportation Research Record: Journal of the Transportation Research Board.
This paper develops a bus network graph convolutional long short-term memory (BNG-ConvLSTM) neural network model to forecast short-term passenger flows in bus networks. Read More