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My research aims to further our understanding of neural-network-powered intelligent systems and how they could be more efficiently and reliably built in the resource-constrained world.
Concretely, I’m interested in a wide range of topics in computing, including (but not limited to; percentage in parenthesis indicates my perceived depth of exploration in the literature):
Some more generic ones:
If you have any questions regarding our work or would like to collaborate, please send all correspondences to peiyuan [at] praxispioneering [dot] com.
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My research aims to further our understanding of neural-network-powered intelligent systems and how they could be more efficiently and reliably built in the resource-constrained world.
- To empower real-world use cases, we need to better understand neural network properties to align them to our intuitions.
- To build intelligent systems more efficiently, we need to deeply understand system and hardware instantiations of different types of neural networks and utilize various forms of tapped and untapped computational resources.
- To make intelligent systems more reliable, we need to elucidate the role of data, factual or fictitious, in the end-to-end lifecycle of algorithmic development, benchmarking, delivery, and maintenance.
Concretely, I’m interested in a wide range of topics in computing, including (but not limited to; percentage in parenthesis indicates my perceived depth of exploration in the literature):
- (?%) foundation models in entertainment
- (60%) a communication-optimized scheduler enabling fine-grained chunking of neural network workloads
- (5%) an incentive structure motivating a decentralized mesh of consumer compute
- (30%) an information-theoretic framework to identify adversarial/passive gradients in said structure
- (25%) proof of neural network computation (forward & backward) in privacy-preserving & succinct scenarios
- (80%) datasets that train better neural networks and better benchmarks for neural networks
- (?%) curation & watermarking of data artifacts that are distinctively non-generated
Some more generic ones:
- make neural networks run faster, sometimes on weird hardware
- tradeoff space of many things we care about in machine learning (accuracy, "privacy," etc.)
- using neural networks to super-charge resource-limited fields, sometimes non-STEM
If you have any questions regarding our work or would like to collaborate, please send all correspondences to peiyuan [at] praxispioneering [dot] com.
selected publications
services