Publications
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
Project Page Paper Code Video Poster
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
Project Page Paper Code 10-min Video 1-min Video
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)
Project Page Paper Code
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)
Project Page Paper Supplementary Code Poster
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
Paper Code Poster
Work Experiences
Research Intern, Creative Intelligence Lab, Adobe Research
Research Assistant, Vision & Graphics Group, HKUST
Teaching Experiences
- Teaching Assistant, Winter 2021, Computer Graphics: Geometric Modeling/Processing (CS 348a), Stanford University
Selected Awards
- 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
Projects
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.
Underwater Object Detection
- Advised by Prof. Chi-Keung Tang
- Studied the performance of real-time object detection models, both using handcrafted features and deep learning networks, for underwater diver detection in robotics applications
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.