Daniel Kang

Peter Bailis’s research group

Projects

LOA/Fixy
Learned Observation Assertions is an abstraction designed to find errors in ML deployment pipelines with as little manual specification of error types as possible. Fixy is a system for finding errors in perception data, implementing the LOA abstraction.
NoScope
A system for querying videos at scale using neural networks and for accelerating neural network inference by over 1000× by exploiting model specialization and dynamic cascades.
DAWNBench
A benchmark suite for end-to-end deep learning training and inference.

Kang, D., Arechiga, N., Pillai, S., Bailis, P., & Zaharia, M. (2022). Finding Label and Model Errors in Perception Data With Learned Observation Assertions. SIGMOD ’22: Proceedings of the 2022 International Conference on Management of Data, 496–505. https://doi.org/10.1145/3514221.3517907
Coleman, C., Kang, D., Narayanan, D., Nardi, L., Zhao, T., Zhang, J., Bailis, P., Olukotun, K., Ré, C., & Zaharia, M. (2019). Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark. ACM SIGOPS Operating Systems Review, 53(1), 14–25. https://doi.org/10.1145/3352020.3352024
Kang, D., Emmons, J., Abuzaid, F., Bailis, P., & Zaharia, M. (2017). NoScope: optimizing neural network queries over video at scale. Proceedings of the VLDB Endowment, 10(11), 1586–1597. https://doi.org/10.14778/3137628.3137664
Coleman, C., Narayanan, D., Kang, D., Zhao, T., Zhang, J., Nardi, L., Bailis, P., Olukotun, K., Ré, C., & Zaharia, M. (2017). DAWNBench: An End-to-End Deep Learning Benchmark and Competition. 31st Conference on Neural Information Processing Systems (NIPS 2017).