Projects
- Rapid Detection of Rare Events from in situ X-ray Diffraction Data using Machine Learning
- Parallel Reinforcement Learning Framework for Graph Optimization Problems
- Jan. 2019 - Dec. 2022
- Design and implement an open-source, parallel AI environment to address large-scale graph problems.
- Achieve good scalability for both inference and training using up to 192 GPUs.
- Outperform the state-of-the-art distributed multi-agent reinforcement learning approach by up to 2.6 times.
- Two paper has been accepted by [ICCS’20] and [TOPC’23].
- Keywords: distributed reinforcement learning, graph optimization problems, distributed-GPU computing.
- Software Analytics Toolkit
- Jan. 2018 - Dec. 2018
- Design and implement a software analytics toolkit for understanding open source community-based scientific code.
- Applying graph algorithms to the software analysis.
- Two papers accepted by [ICCS’19] and [SE-HER’19].
- Keywords: application software analysis, static code analysis, high performance scientific application.
- High Performance Synchronization-reducing Clustering Library
- Jan. 2016 - Dec. 2018
- Design and implement a parallel asynchronous clustering algorithm.
- The new algorithm use the hybrid MPI/Pthreads computing model.
- Significantly faster than the parallel standard k-means with less communication and similar SSE costs.
- Two papers accepted by [MLHPC’19] and [IJPHCA’20].
- Keywords: massively parallel algorithm, synchronization-reducing algorithm, clustering algorithm, multi-thread programming.
- Scalable Universal QR Factorization Solver (suCAQR)
- Aug. 2014 - Dec. 2015
- Design and implement a simplified, tuning-less scalable universal QR factorization solver (suCAQR).
- Achieve 30 times and 30% faster performance than benchmarks ScaLAPACK and DPLASMA on 1024 cores.
- Two papers accepted by [ICPADS’16] and [PPL’18 ].
- Keywords: scientific computing, performance analysis and optimization, dataflow runtime system.
- Classifying Images of Bacteria
- Feb. 2015 - May 2015
- Given 1800+ images of bacteria, classifying them into four categories.
- Achieve over 95% classification accuracy and ranked second among 23 teams in “Unsupervised feature learning for classifying images of bacteria at the genus level” Kaggle competition.
- Keywords: unsupervised feature learning, representation learning, k-means, classification, image processing.
- The Indiana State Parks History Tour App
- June 2013 - July 2013
- Developed the mobile App “Centennial State Park Tour Explored Through Visitor Smart-phone” sponsored by Indiana Department
of Natural Resources.
- iOS application named “The Indiana State Parks History Tour” is available in iTunes store.
- Keywords: mobile App development.
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