Alex Sim at Berkeley Lab

Main: Publications; Demos/Posters; Software packages; Projects
About me: Facebook

LBNL
SDD
UCB/OCF
Mine
More on Google

Alex Sim, SDM, SDD, Lawrence Berkeley National Laboratory

ASimlbl.gov 510-495-2290 http://www.lbl.gov/~asim

Alex Sim is a senior computing engineer in Scientific Data Management group under Computational Research division at Lawrence Berkeley National Laboratory. Over the last twenty five years, he has worked on R&D in data analysis and management fields for scientific and industrial disciplines such as climate change simulation, combustion modeling, fusion science, high energy physics, nuclear science, astronomy, genomics, power grid electricity and behavioral economics. His recent research area includes high-frequency streaming data analysis methods, dynamic resource management, I/O optimization issues for exascale HPC applications, statistical modeling and machine learning methods, and autonomic scientific data infrastructure and data strategies. He has contributed to paper publications and technical reports, standard specifications, a number of open source software packages, and multiple patented and patent pending technologies. He has extensive grant proposal writing experience and led projects from the U.S. Department of Energy (DOE) and National Science Foundation (NSF) as a lead Principal Investigator (PI) or Co-PI, He also has involved in technical program committees, steering and advisory committees for conferences, journal editorial boards, review panels in data, cloud computing, HPC, and networking areas. He is a senior member of IEEE.

Selected projects

Data Analysis and Machine Learning Efforts

Publications from student projects

Selected publications

B. Weinger, J. Kim, A. Sim, M. Nakashima, N. Moustafa, K. Wu, "Enhancing IoT Anomaly Detection Performance for Federated Learning", Digital Communications and Networks, Special Issue on Edge Computation and Intelligence, 2021. In-press
M. Nakashima, A. Sim, Y. Kim, J. Kim, J. Kim, "Automated Variable Selection for Network Anomaly Detection", ACM Transactions on Management Information Systems (TMIS), 2021. doi:10.1145/3446636.
S. Kim, A. Sim, K. Wu, S. Byna, Y. Son, H. Eom, "Towards HPC I/O performance prediction through large- scale log analysis", ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC 2020), 2020. doi:10.1145/3369583.3392678
Q. Kang, A. Sim, P. Nugent, S. Lee, W.K. Liao, A, Agrawal, A. Choudhary, K. Wu, "Predicting Resource Requirement in Intermediate Palomar Transient Factory Workflow", the 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2020), 2020.
J. Kim, A. Sim, "A new approach to multivariate network traffic analysis", Journal of Computer Science and Technology, 2019, 34(2):388-402, doi: 10.1007/s11390-019-1915-y.
A. Lazar, L. Jin, C. A. Spurlock, K. Wu, A. Sim, A. Todd, "Evaluating the Effects of Missing Values and Mixed Data Types on Social Sequence Clustering Using t-SNE Visualization", ACM Journal of Data and Information Quality, 2019, 11:7:1-7:22, doi: 10.1145/3301294.
J. Kim, A. Sim, B. Tierney, S. Suh, I. Kim, "Multivariate Network Traffic Analysis using Clustered Patterns", Journal of Computing, Springer, 2018. doi:10.1007/s00607-018-0619-4.
J. Wang, W. Yoo, A. Sim, P. Nugent, K. Wu, "Parallel Variable Selection for Effective Performance Prediction", the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid2017), doi:10.1109/CCGRID.2017.47, 2017
D. Lee, A. Sim, J. Choi, K. Wu, "Novel Data Reduction Based on Statistical Similarity", Proceedings of the 28th International Conference on Scientific and Statistical Database Management (SSDBM2016), 2016.
W. Yoo, A. Sim, "Time-series Forecast Modeling on High-Bandwidth Wide Area Network Measurements", Journal of Grid Computing, Vol. 14, Issue 3, pp 463-476, Sep. 2016.
T. Kim, D. Lee, J. Choi, A. Spurlock, A. Sim, A. Todd, K. Wu, "Extracting Baseline Electricity Usage Using Gradient Tree Boosting", International Conference on Big Data Intelligence and Computing (DataCom 2015), 2015, Best Paper Award
K. Hu, J. Choi, A. Sim, J. Jiang, "Best Predictive Generalized Linear Mixed Model with Predictive Lasso for High-Speed Network Data Analysis", International Journal of Statistics and Probability, vol. 4, no. 2, p132-148, 2015