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I enjoy rapidly prototyping functional program artifacts and weakly scaling them to bigger problem sizes and resources.
Over the past few years, I have found myself driven more by curiosity and the ability to expand my understanding than sticking to particular topics, but there are some recurring themes:
Models that can saturate exa-scale compute.
Unsupervised training methods to side-step expensive human supervisions, perhaps driven by curiosity.
Procedural environments to provide feedbacks so that they can improve in emergent ways.
Massively multimodal in their inputs and outputs, that goes way beyond what is familiar to us, such as text, image, audio or video.
Having witnessed, just like many of us, the possibility of misuse in deploying advanced technologies, I particularly value the role of ethics and taste in research endeavors:
The need for small, bootstrapped, personalized, and efficient models and algorithms to train them.
New methods to detect and understand automated behavior on the Internet, to understand how automation and synthetic content is disrupting and changing professional work and popular culture.
Re-imagined operating systems, mobile and embedded systems, hardware form factors, display and interactive primitives to faciliate a cooperative relationship between humans and AI.
Privacy-enhancing technologies that uphold fundamental human privacy and rights against the growing, unchecked corporate powers.
Services
- Artifact Evaluation: MLSys, CGO
- Journal Review: IEEE TPAMI, IEEE TKDE
- Conference Review: NeurIPS, LoG, AISTATS, ICML, ICLR, ACL
A small caveat
- I publish under both Liao Peiyuan and Peiyuan Liao, though I prefer the former since (Peiyuan, 2024) is much more informative than (Liao, 2024).