Mikaela Angelina Uy

Mikaela Angelina Uy is a research scientist at NVIDIA (Learning and Perception Research) working on 3D vision, computer graphics and geometry processing. From Manila, Philippines, she represented the Philippines at the International Mathematical Olympiad in 2012 and 2013. She earned a BSc in Mathematics and Computer Science at HKUST (2013-2017), a Master of Computing at the National University of Singapore (2017-2018), and a PhD in Computer Science at Stanford University (2019-2024) advised by Leonidas Guibas. Her research spans point-cloud learning, 3D shape deformation and retrieval, and neural surface/NeRF reconstruction, with first-author papers at CVPR, ICCV, ECCV and NeurIPS, including the PointNetVLAD and ScanObjectNN works. In undergraduate summer 2015 she held a Trader Intern role at Jane Street Asia Limited in Hong Kong.

Notes6

• Quant employment verification: CONFIRMED. LinkedIn profile (via enrichment index) lists a 'Trader Intern' role at Jane Street Asia Limited, Hong Kong, dated 2015, with a description of building Excel-based data-analysis tools to identify trading opportunities in ETFs, commodities and ADRs. Jane Street is a global quantitative trading firm and market maker, so this is an actual internship at a quantitative trading firm by a supported identity. Identity support: the same profile cross-links to github.com/mikacuy and mikacuy.github.io and matches the CV, X profile and NVIDIA/Stanford/HKUST record. Caveat: the only evidence for the role is the person's own LinkedIn profile as surfaced through an enrichment index; no independent primary source (offer letter, press, second profile) corroborates it, and the index shows year granularity (2015) rather than the lead's claimed Jun-Aug months.
• Full-time role since 2024 is Research Scientist at NVIDIA (Learning and Perception Research), per her X bio 'Research Scientist @ Nvidia' and the NVIDIA research author page; this is a research role, not a quantitative-finance role.2026
• Researcher in 3D point-cloud deep learning: lead/first author on PointNetVLAD (CVPR 2018), Revisiting Point Cloud Classification / ScanObjectNN (ICCV 2019 oral), Deformation-Aware 3D Shape Embedding (ECCV 2020), Joint Learning of 3D Shape Retrieval and Deformation (CVPR 2021), Point2Cyl (CVPR 2022), SCADE NeRFs (CVPR 2023), NeRF Revisited (NeurIPS 2023).
• Education: PhD in Computer Science at Stanford University (2019-2024) advised by Prof. Leonidas Guibas, with 3D vision/graphics/ML focus.
• Prior degrees: Master of Computing (Computer Science) at NUS (2017-2018) on a full NUS Graduate Scholarship for ASEAN Nationals, and BSc in Mathematics and Computer Science at HKUST (2013-2017), First Class Honors.
• Research internships across industry labs: Google (2022-2023, Project Starline, NeRF reconstruction), Autodesk AI Lab (2021), Adobe Research (2020) - all computer-graphics/vision research, none in finance.
• From Manila, Philippines; represented the Philippines at the International Mathematical Olympiad in 2012 (bronze medalist per Philippines IMO team post) and 2013.
• Public contact email listed on her CV: [contact omitted].
• Undergraduate software roles before the Jane Street internship: HKUST Robotics Team software engineer (2014-2015, ROV control software), HKUST CSE lab helper (2014), and TeraSystem developer intern (2014, SQL).
• GitHub handle mikacuy hosts the reference implementations (scanobjectnn, point2cyl, pointnetvlad, deformation_aware_embedding) - code releases accompany each paper.