Mikaela Angelina Uy
I am a fourth year CS PhD candidate at Stanford University advised by Prof. Leonidas Guibas. I am generally interested in computer vision, graphics and machine learning. My recent research focuses on the representation and generation of objects/scenes for 3D content creation. Specifically, I have worked on mesh deformations, CAD models and neural implicit fields. My goal is to achieve high-quality photorealistic reconstruction and user-controllable 3D generation.
I received my Bachelor's degree double majoring in Mathematics and Computer Science from the Hong Kong University of Science and Technology (HKUST) in 2017, and my Master's in Computing from the National University of Singapore in 2018. I then returned to HKUST as a Research Assistant for a year and had the pleasure of working with Prof. Sai-Kit Yeung, Dr. Binh-Son Hua and Dr. Duc Thanh Nguyen.
Also check out a feature article for Women in Computer Vision from RSIP Vision here.
Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion Cylinders
Mikaela Angelina Uy*, Yen-yu Chang*, Minhyuk Sung, Purvi Goel, Joseph Lambourne, Tolga Birdal, Leonidas Guibas
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022
Joint Learning of 3D Shape Retrieval and Deformation
Mikaela Angelina Uy, Vladimir G. Kim, Minhyuk Sung, Noam Aigerman, Siddhartha Chaudhuri, Leonidas Guibas
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021
Deformation-Aware 3D Model Embedding and Retrieval
Mikaela Angelina Uy, Jingwei Huang, Minhyuk Sung, Tolga Birdal, Leonidas Guibas
European Conference on Computer Vision (ECCV), 2020
LCD: Learned Cross-Domain Descriptors for 2D-3D Matching
Quang-Hieu Pham, Mikaela Angelina Uy, Binh-Son Hua, Duc Thanh Nguyen, Gemma Roig, Sai-Kit Yeung
AAAI Conference on Artificial Intelligence (AAAI), 2020 (Oral)
Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data
Mikaela Angelina Uy, Quang-Hieu Pham, Binh-Son Hua, Duc Thanh Nguyen, Sai-Kit Yeung
IEEE International Conference on Computer Vision (ICCV), 2019 (Oral)
PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition
Mikaela Angelina Uy, Gim Hee Lee
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018
Research Intern, Project Starline, Google
Research Intern, Autodesk AI Lab
- VinAI Seminar Series, July 22, 2022, Learning to Vary 3D Models for Universally Accessible 3D Content Creation
- Brown Vision Computing Seminar, April 11, 2022, Learning to Vary 3D Models for Universally Accessible 3D Content Creation
- Stanford G-Cafe, March 10, 2022, Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion Cylinders
- Stanford CS 348n Guest Lecture, February 16, 2022, Neural Shape Variation and Generation
- Teaching Assistant, Winter 2021, Computer Graphics: Geometric Modeling/Processing (CS 348a), Stanford University
- School of Engineering Fellowship, Stanford University, 2019-2020
- HKSAR Government Targeted Scholarship (Full 4-year university scholarship)
- NUS Graduate Scholarship for ASEAN Nationals (Full masters scholarship)
- Google Women Techmakers Scholarship, 2016
- Epsilon Fund Award, HKUST Mathematics Department, 2017
- International Mathematical Olympiad (IMO) Bronze Medalist, 2012, 2013
- Philippine Mathematical Olympiad 1st runner-up, 2012, 2013
Interpretable & Actionable Models using Attribute & Uncertainty Information
- Deep-learning models can be difficult to understand and control intuitively due to the black-box nature of these models. However, such lack of interpretability and human actionability in the models’ decision processes make it difficult to trust these models in critical applications that affect the lives of people. We propose to alleviate these problems through the use of attribute and uncertainty models in deep networks.
HKUST Robotics Team, Remotely Operated Vehicle (ROV) Subteam
- Overall 3rd Place (Explorer Class) – 14th Annual MATE International Underwater Robotics Competition in St John’s, Newfoundland and Labrador, Canada
- Asia Champion in 2015 MATE Asia Regional Underwater Robotics Competition
- Built the main control software of the ROV, which operates with ROS and is controlled with an Xbox controller, and Qt GUI’s for the competition runs
- The team is composed of 15 engineers who built and designed the ROV from scratch.
Hobbies and Interests
For most of my pre-university life, I was into competitive mathematics, with geometry being a favorite topic. I competed in various math competitions both local and abroad representing the Philippine Team. During my spare time back at home, I now train elementary and high school students for international math competitions. I was part of the training team of the 2017-2020 PH IMO team, and I led the PH team to a number of elementary math competitions.
I also enjoy playing soccer, frisbee and scuba diving. I was part of the HKUST Women's Soccer Team back in my senior year.
Reviewer: CVPR, ICCV, ECCV, SIGGRAPH, SIGGRAPH Asia, BMVC, 3DV, AAAI, TVCG