General Lab Information

Yihui (Ray) Ren

Research Staff 5 Computational, Comput. Sci. Mach. Learning, Computational Science Initiative

Yihui (Ray) Ren

Brookhaven National Laboratory

Computational Science Initiative
Bldg. 725, Room 2-205
P.O. Box 5000
Upton, NY 11973-5000

(631) 344-4638
yren@bnl.gov

Preferred Gender Pronouns (PGPs): "he, him, his"

Yihui, a.k.a. "Ray", works in the general area of Artificial Intelligence (AI), its applications in science and its interaction with novel hardware. Ray's current research topics include unpaired image translation to bridge the gap between simulation and experiments, neural network optimization and deployment for real-time systems, novel hardware exploration and benchmarking, privacy-preserving AI, and bringing advanced AI methods to scientific domains.

Expertise | Education | Appointments | Publications | Highlights


Expertise

  • Deep learning
  • High-performance computing
  • Performance analysis
  • Network dynamics and modeling

Education

  • Ph.D., Physics, University of Notre Dame, 2015
  • Ph.D., Physics, Hunan University, China, 2009

Professional Appointments

  • 2021-present, Associate Research Scientist, Brookhaven National Laboratory
  • 2019-2021, Assistant Research Scientist, Brookhaven National Laboratory
  • 2018-2019, Postdoctoral Research Associate, Brookhaven National Laboratory
  • 2015-2018, Postdoctoral Research Associate, Virginia Tech

Selected Publications

  • Huang Y, Ren Y, Yoo S, Huang J (2023) Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data. Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis. https://doi.org/10.1145/3624062.3625127
  • Torbunov D, Huang Y, Yu H, et al (2023) UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). https://doi.org/10.1109/wacv56688.2023.00077
  • Luo X, Nadiga BT, Park JH, et al (2022) A Bayesian Deep Learning Approach to Near-Term Climate Prediction. Journal of Advances in Modeling Earth Systems 14:. https://doi.org/10.1029/2022ms003058
  • Miryala S, Mittal S, Ren Y, et al (2022) Waveform processing using neural network algorithms on the front-end electronics. Journal of Instrumentation 17:C01039. https://doi.org/10.1088/1748-0221/17/01/c01039
  • Miryala S, Zaman MA, Mittal S, et al (2022) Peak Prediction Using Multi Layer Perceptron (MLP) for Edge Computing ASICs Targeting Scientific Applications. 2022 23rd International Symposium on Quality Electronic Design (ISQED). https://doi.org/10.1109/isqed54688.2022.9806285
  • Huang Y, Ren Y, Yoo S, Huang J (2021) Efficient Data Compression for 3D Sparse TPC via Bicephalous Convolutional Autoencoder. 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). https://doi.org/10.1109/icmla52953.2021.00179
  • Han S, Zhang Y, Ren Y, et al (2020) 3D distributed deep learning framework for prediction of human intelligence from brain MRI. Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging. https://doi.org/10.1117/12.2549758
  • Ren Y, Yoo S, Hoisie A (2019) Performance Analysis of Deep Learning Workloads on Leading-edge Systems. 2019 IEEE/ACM Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS). https://doi.org/10.1109/pmbs49563.2019.00017
  • Ren Y, Cedeno-Mieles V, Hu Z, et al (2018) Generative Modeling of Human Behavior and Social Interactions Using Abductive Analysis. 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). https://doi.org/10.1109/asonam.2018.8508282
  • Cedeno-Mieles V, Ren Y, Ekanayake S, et al (2018) PIPELINES AND THEIR COMPOSITIONS FOR MODELING AND ANALYSIS OF CONTROLLED ONLINE NETWORKED SOCIAL SCIENCE EXPERIMENTS. 2018 Winter Simulation Conference (WSC). https://doi.org/10.1109/wsc.2018.8632478
  • Nath M, Ren Y, Eubank S (2018) An Approach to Structural Analysis Using Moore-Shannon Network Reliability. Complex Networks and Their Applications VII 537–549. https://doi.org/10.1007/978-3-030-05411-3_44
  • Kuhlman CJ, Ren Y, Lewis B, Schlitt J (2017) Hybrid Agent-based modeling of Zika in the united states. 2017 Winter Simulation Conference (WSC). https://doi.org/10.1109/wsc.2017.8247857
  • Ren Y, Eubank S, Nath M (2016) From network reliability to the Ising model: A parallel scheme for estimating the joint density of states. Physical Review E 94:. https://doi.org/10.1103/physreve.94.042125
  • Ren Y, Ercsey-Ravasz M, Wang P, et al (2014) Predicting commuter flows in spatial networks using a radiation model based on temporal ranges. Nature Communications 5:. https://doi.org/10.1038/ncomms6347

Research Highlights

Google Scholar Page

Yihui (Ray) Ren

Brookhaven National Laboratory

Computational Science Initiative
Bldg. 725, Room 2-205
P.O. Box 5000
Upton, NY 11973-5000

(631) 344-4638
yren@bnl.gov

Yihui (Ray)'s Links