Jiayun (Peter) Wang
Postdoc, California Institute of Technology | peterw at caltech dot edu
I am a postdoctoral researcher in the Computing + Mathematical Sciences Department at California Institute of Technology, working with Anima Anandkumar . Prior to joining Caltech, I completed my Ph.D. in Vision Science and Berkeley AI Research (BAIR) at UC Berkeley, where I worked with Stella Yu on fundamental machine learning and computer vision models and Meng Lin on applying them to healthcare.
I am on the 2024-2025 job market looking for full-time positions. Please feel free to reach out!
Research Interest: My research lies at the intersection of machine learning, computer vision and AI for healthcare. My research highlights:
- Minimally Supervised ML. Self-supervised learning from unlabled data for recognition & detection (TPAMI’21) and for geometry (ECCV’24).
- Efficient AI Algorithms. AI models as duality to the data. Models can be made efficient if they are aware of data structures, such as orthogonality (CVPR’20) and recurrence (WACV’23).
- AI for Health. Minimally supervised ML for enhanced clinical effectiveness Applications include lung ultrasound imaging (arXiv’24) and assistive diagnosis (MICCAI’24).
news
Nov 25, 2024 | Unified Model for MRI is on arXiv. |
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Aug 01, 2024 | Pose-Aware Self-Supervised Learning accepted to ECCV 2024 as an oral presentation! 🎊 |
Apr 29, 2024 | INSIGHT accepted to MICCAI 2024! |
Dec 19, 2023 | Deep Multimodal Fusion won the best paper at ML4H 2023! 🏆 |