Sotabase

Career

· Computer Scientist, Lawrence Livermore National Laboratory2023–
· ETH Postdoctoral Fellow, ETH Zürich2019–2022
· Computation Intern, Lawrence Livermore National Laboratory2016–
· Intern, Raytheon Centers of Innovation2015–
· PhD Computer Science, University of Illinois at Urbana-Champaign2014–2019
· Research Assistant, University of Illinois at Urbana-Champaign2014–2019
· High Energy Density Physics Intern, Lawrence Livermore National Laboratory2012–
· SPIN Fellow, National Center for Supercomputing Applications2012–2014
· BS Computer Science, University of Illinois at Urbana-Champaign2010–2014
· LLNL

Publications (40)

Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks
Journal of machine learning research · 2021
887
cited
Communication Quantization for Data-Parallel Training of Deep Neural Networks
Workshop on Machine Learning in High Performance Computing Environments · 2016
212
cited
Deep learning for post-processing ensemble weather forecasts
Philosophical Transactions of the Royal Society A · 2020
189
cited
Data Movement Is All You Need: A Case Study on Optimizing Transformers
Conference on Machine Learning and Systems · 2020
169
cited
Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics
ACM-SIGPLAN Symposium on Programming Language Design and Implementation · 2018
139
cited
Clairvoyant Prefetching for Distributed Machine Learning I/O
International Conference for High Performance Computing, Networking, Storage and Analysis · 2021
69
cited
Channel and Filter Parallelism for Large-Scale CNN Training
International Conference for High Performance Computing, Networking, Storage and Analysis · 2019
51
cited
Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism
IEEE International Parallel and Distributed Processing Symposium · 2019
51
cited
Motif Prediction with Graph Neural Networks
Knowledge Discovery and Data Mining · 2021
44
cited
Aluminum: An Asynchronous, GPU-Aware Communication Library Optimized for Large-Scale Training of Deep Neural Networks on HPC Systems
Workshop on Machine Learning in High Performance Computing Environments · 2018
40
cited
The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs With Hybrid Parallelism
IEEE Transactions on Parallel and Distributed Systems · 2020
40
cited
A Lightweight Communication Runtime for Distributed Graph Analytics
IEEE International Parallel and Distributed Processing Symposium · 2018
23
cited
ENS-10: A Dataset For Post-Processing Ensemble Weather Forecast
Neural Information Processing Systems · 2022
22
cited
A data-centric optimization framework for machine learning
International Conference on Supercomputing · 2021
19
cited
Neural Parameter Allocation Search
International Conference on Learning Representations · 2020
18
cited
Learning Combinatorial Node Labeling Algorithms
arXiv.org · 2021
17
cited
Co-design Center for Exascale Machine Learning Technologies (ExaLearn)
The international journal of high performance computing applications · 2021
16
cited
Breaking (Global) Barriers in Parallel Stochastic Optimization With Wait-Avoiding Group Averaging
IEEE Transactions on Parallel and Distributed Systems · 2020
15
cited
Spatial Mixture-of-Experts
Neural Information Processing Systems · 2022
15
cited
Neural Network Based Silent Error Detector
IEEE International Conference on Cluster Computing · 2018
13
cited
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Nikoli Dryden | Researcher Profile | Sotabase | Sotabase