About me
I am an Sr. Machine Learning Engineer at Tesla (previously Applied Scientist at Amazon Last Mile Map Science Team), specializing in Computer Vision and Multi-Modal Machine Learning.
Currently, I am developing computer vision multi-modal large language models (VLLMs) for remote-sensing applications to improve Tesla destination behaviors.
Education Background
Ph.D. in Mechanical Engineeringat Carnegie Mellon University (Jan 2025)
Computational Bio-Modeling Lab (CBML) & Biohybrid and Organic Robotics Group (B.O.R.G.)
Advisors: Prof. Yongjie Jessica Zhang and Prof. Victoria Webster-Wood
Dissertation: ML/AI-driven Computational Mechanics | Awards: 4 Fellowships
M.S. Research in Mechanical Engineering at Carnegie Mellon University (May 2020)
Computational Bio-Modeling Lab (CBML) & Morphing Matter Lab at HCII
Advisors: Prof. Yongjie Jessica Zhang and Prof. Lining Yao
B.S. in Mechanical Engineering from the University of California, San Diego (Jun. 2018)
News Letters
Selected Research
Large Polygon Language Model for Efficient High Quality Feature Extraction
Patent-pending VLM with novel coordinate tokens and redesigned vocabulary encoding spatial correlation, customized SFT and reinforcement learning.
Innovative dual-purpose inference prompting, serving as “grammarly” for database.
K. Qian, Y. He, M. Moustafa. Rethinking language models for building outline extraction from remote sensing imagery. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2026.
GeoSpatial AI: Direct Polygon Generation with LLM
Transformer-based Feature Extraction with Next Token Blurring
Confidence boosting token blurring technique to enhance LLM prediction quality.
K. Qian, M. Moustafa. Edge Length Loss for Improved Building Polygon Extraction. The 4th ACM SIGSPATIAL International Workshop on Spatial Big Data and AI for Industrial Applications, 2025. Best Paper Award.
Novel High-order Algorithm on 3D Spline for Modeling Biological Neuron Growth
Custom C++ high performance algorithm for solving high-order nonlinear biophysics process on high-fidelity locally refined THB-splines
K. Qian, Y. J. Zhang. 3D neuron growth model using truncated hierarchical B-splines with multi-level local refinements.Computer Methods in Applied Mechanics and Engineering, 442, 118003, 2025.
ML Future Frame Prediction (Video) using MetaFormer Attention for Biological Cultures
Future video prediction architecture for biological neurite outgrowth and deterioration.
K. Qian, G. O. Suarez, T. Nambara, T. Kanekiyo, Y. J. Zhang. High-throughput machine learning framework for predicting neurite deterioration using MetaFormer attention.Computer Methods in Applied Mechanics and Engineering 442, 118003, 2025.
Computational Modeling of Alzheimer's disease with Real Patients’ Neuron Cell
K. Qian, G. O. Suarez, T. Nambara, T. Kanekiyo, A. S. Liao, V. A. Webster-Wood, Y. J. Zhang. Neurodevelopmental Disorders Modeling using Isogeometric Analysis, Dynamic Domain Expansion and Local Refinement. Computer Methods in Applied Mechanics and Engineering, 433: 117534, 2025.
Pioneering Reinforcement Learning in Mesh Generation
H. Tong, K. Qian, E. Halilaj, Y. J. Zhang. SRL-Assisted AFM: Generating Planar Unstructured Quadrilateral Meshes with Supervised and Reinforcement Learning-Assisted Advancing Front Method. Journal of Computational Science, 72:102109, 2023.
AI-Driven Shape Memory Material Design for 3D Printing
Y. Yu, K. Qian, et al. Hybrid IGA-FEA of Fiber Reinforced Thermoplastic Composites for Forward Design of AI-Enabled 4D Printing. Journal of Materials Processing Technology, Special Issue on Artificial Intelligence in Advanced Manufacturing Processes, 302:117497, 2022.
H. Yang, K. Qian, et al. SimuLearn: Fast and Accurate Simulator to Support Morphing Materials Design and Workflows. Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology (UIST), 71-84, 2020.
Design and 3D Printing of Suspended Edible Material
H. Yang, D. Luo, K. Qian, L. Yao. Freeform Fabrication of Fluidic Edible Materials. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1-10, 2021.