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I am a computer science PhD student at University of California, Los Angeles (UCLA), and a member of UCLA Computer Graphics & Vision Laboratory . In my PhD journey, I am honored to be advised by Professor Demetri Terzopoulos. My research interests span across computer vision, deep learning and artificial intelligence. I am the recipient of the 2018 UCLA Henry Samueli School of Engineering and Applied Science (HSSEAS) Edward K. Rice Outstanding Masters Student Award. Email / Google Scholar / LinkedIn / GitHub |
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I'm interested in building truly intelligent systems by incorporating concepts from computer vision, machine learning, deep learning and Artificial Intelligence (AI). One of the main highlights of my work is medical image analysis in which my goal is to establish the next generation of smart clinical platforms by incorporating a mixture of model-based and function-based AI approaches. I have also worked in the area of Computer Aided-Design (CAD) in which my work has etablished the first deep learning-based CAD framework called the Boundary Learning Optimization Tool (BLOT). |
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This work presents the first end-to-end 3D deep learning-based lung lobe segmentation model which sets a new state-of-the-art record.The proposed model has accomplished overall Dice scores of 0.94 and 0.95 on LIDC and LTRC data-sets while running under 2 seconds on a single NVIDIA Titan XP GPU during inference. This paper has received the NVIDIA Best Paper Award at the MICCAI Workshop on Deep Learning for Medical Image Analysis . |
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This paper builds upon the concept of Boundary Learning Optimization Tool (BLOT) and introduces a new approach for automatic hyper-parameter initialization as well as a novel optimization algorithm for tracing the performance boundaries by leveraging Sequential Quadratic Programming(SQP) and Augmented Lagrangian Pattern Search(ALPS). In comparison to the original BLOT paper, it is more efficient and equipped to rigorously search conflicting boundary regions. |
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In this work, the first deep learning-based CAD framework called the Boundary Learning Optimization Tool (BLOT) has been introduced. BLOT helps designers to rapidly explore the design space of their synthesized topologies to identify the corresponding optimal design instantiations by creating physical simulations and leveraging the power of non-linear optimization and deep learning. This paper has received the Theoretical Contributions in Compliant Mechanisms Award at (ASME) Mechanisms and Robotics Conference in the International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE). |
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