Kiarash Naghavi Khanghah

I am a Ph.D. candidate in Mechanical Engineering at the University of Connecticut, focusing on AI-driven manufacturing and computational design. My research integrates deep learning, generative modeling, and process optimization to accelerate advanced manufacturing discovery.

• Multimodal retrieval and reasoning in large language models for design rule interpretation.
• Generative and physics-informed modeling of additive manufacturing and plasma-assisted processes.
• Representation learning for material–process–performance correlation discovery.

• Reconstruction and Generation of Porous Metamaterial Units via Variational Graph Autoencoder and Large Language Model, ASME IDETC/CIE 2024.
• ALFED: Automated LLM Framework for Equation Discovery in Manufacturing, in review.
• Large Language Models for Process-Structure-Property Reasoning, ongoing research (2025).

• Pratt & Whitney Advanced Systems Engineering Fellowship
• ASME DFMLC Best Paper Award
• ASME Hackathon 1st Place Winner
• John Lof Leadership Academy Fellow (2024–2026)

Deep Learning (PyTorch, TensorFlow), Large Language Models (GPT, Qwen, ColPali), Generative Design, Additive Manufacturing, Symbolic Regression, and Data Visualization.

President of the Mechanical Engineering Graduate Student Association (MEGSA), organizing workshops, professional development events, and academic writing series to support graduate students.

Feel free to reach out regarding research collaboration, mentoring, or speaking engagements.