About Me

I am a Ph.D. student in Computer Science and Engineering at Pennsylvania State University, advised by Prof. Mahmut Kandemir and Prof. Kamesh Madduri. I previously recieved a B.S. in Biomechatronics and an M.S. in Electrical Engineering from National Taiwan University.

I have a strong interest in Machine Learning Systems, including AutoML, deep learning compilers, and heterogeneous computing. My work primarily involves LLM, GNN, and DLRM. Currently, I am focusing on optimizing parallelization strategies for LLM training and improving the training performance of heterogeneous GNNs and graph transformers.

Publications

Parallelization Strategies for DLRM Embedding Bag Operator on AMD CPUs
K. Nair et al., including Jun-Liang Lin
IEEE Micro, 2024  
[Paper]

Thorough Characterization and Analysis of Large Transformer Model Training At-Scale
Scott Cheng, Jun-Liang Lin, Murali Emani, Siddhisanket Raskar, Sam Foreman, Zhen Xie, Venkatram Vishwanath, Mahmut Taylan Kandemir
Proceedings of the ACM on Measurement and Analysis of Computing Systems (SIGMETRICS), 2024  
[Paper]

Quantization for Bayesian Deep Learning: Low-Precision Characterization and Robustness
Jun-Liang Lin, Ranganath Krishnan, Keyur Ruganathbhai Ranipa, Mahesh Subedar, Vrushabh Sanghavi, Meena Arunachalam, Omesh Tickoo, Ravishankar Iyer, Mahmut Taylan Kandemir
IEEE International Symposium on Workload Characterization (IISWC), 2023.  
[Paper] [Code]

ezGeno: an automatic model selection package for genomic data analysis
Jun-Liang Lin*, Tsung-Ting Hsieh*, Yi-An Tung*, Xuan-Jun Chen, Yu-Chun Hsiao, Chia-Lin Yang, Tyng-Luh Liu, Chien-Yu Chen
Bioinformatics, 2022  
[Paper] [Code]

The Maximum a Posteriori Estimation of DARTS
Jun-Liang Lin*, Yi-Lin Sung*, Cheng-Yao Hong*, Han-Hung Lee, and Tyng-Luh Liu
IEEE International Conference on Image Processing (ICIP), 2021  
[Paper]

Communication-Efficient Separable Neural Network for Distributed Inference on Edge Devices
Jun-Liang Lin, and Sheng-De Wang
arXiv preprint arXiv:2111.02489, 2021  
[Paper]

Experiences

Research Intern | Qualcomm
May 2024 - August 2024

Research Intern | Intel
May 2022 - April 2023

Research Assistant | Academia Sinica
September 2019 - August 2021

Awards

Student Travel Grant, SIGMETRICS 2024
Student Travel Grant, IISWC 2023
Graduate Research Fellowship, NTU, 2018
Professor Tomotake Takasaka Scholarship, NTU, 2016
Rong‑Zunn Wang Culture and Education Foundation Scholarship, NTU, 2015
Presidential Award, NTU, 2013, 2014, 2015

Services

Reviewer
International Conference on Learning Representations (ICLR)
Annual Conference on Neural Information Processing Systems (NeurIPS)
IEEE Transactions on Computers