I am a fourth-year PhD student at Columbia University advised by Carl Vondrick. The core of machine learning often involves humans instructing machines with existing knowledge. My focus is on advancing machine learning methods that can impart novel insights back to humans. My research interests span interpretability and knowledge discovery across multi-modal domains such as vision, muscle activity, and audio.
I previously graduated from the University of Pennsylvania advised by Jianbo Shi, with a BA in Computer Science, and minors in Math, Computational Neuroscience, and Systems Eng. In my free time I like to read in parks (my GoodReads), dance, play piano, and sometimes, scuba dive.
Research
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Evolving Interpretable Visual Classifiers with Large Language Models Mia Chiquier, Utkarsh Mall, Carl Vondrick
PaperProject Page
arXiv 2024
Multiparty Perception for Navigation Hui Lu, Mia Chiquier, Carl Vondrick
PaperProject Page
Neurips 2022
Real-Time Neural Voice Camouflage Mia Chiquier, Chengzhi Mao, Carl Vondrick
PaperProject PageScience.orgTalk (12 min)
ICLR 2022 (Oral,Top 1.6%)
The Boombox: Visual Reconstruction from Acoustic Vibrations Boyuan Chen, Mia Chiquier, Hod Lipson, Carl Vondrick
PaperProject Page
CoRL 2021
Adversarial Attacks are Reversible with Natural Supervision Chengzhi Mao, Mia Chiquier, Hao Wang, Junfeng Yang, Carl Vondrick
Paper
ICCV 2021Teaching/Service
Courses
Advanced Computer Vision (COMS 4731, Summer 2021)
Head Teaching AssistantColumbia UniversityDynamical Systems (ESE 210, Fall 2019)
Head Teaching AssistantUniversity of PennsylvaniaService
Organizer: Quo Vadis, Computer Vision? Workshop (ICCV 2023)
Workshop OrganizerICCV 2023Reviewer
ICCV 2021, CVPR 2022Columbia UniversityBEARS Mentorship (Spring 2021, Spring 2022)
Undergraduate MentorBarnard CollegeOrganizer: Learning from Unlabeled Video Workshop (CVPR 2021)
Workshop OrganizerCVPR 2023