Full correlation matrix analysis of fMRI data on Intel® Xeon Phi™ coprocessors. - Yida Wang 003; Michael J. Anderson; Jonathan D. Cohen 003; Alexander Heinecke; Kai Li 001; Nadathur Satish; Narayanan Sundaram; Nicholas B. Turk-Browne; Theodore L. Willke (SC, 2015)

Enabling factor analysis on thousand-subject neuroimaging datasets. - Michael J. Anderson; Mihai Capota; Javier S. Turek; Xia Zhu; Theodore L. Willke; Yida Wang 003; Po-Hsuan Chen; Jeremy R. Manning; Peter J. Ramadge; Kenneth A. Norman (IEEE BigData, 2016)

Real-time full correlation matrix analysis of fMRI data. - Yida Wang 003; Bryn Keller; Mihai Capota; Michael J. Anderson; Narayanan Sundaram; Jonathan D. Cohen 003; Kai Li 001; Nicholas B. Turk-Browne; Theodore L. Willke (IEEE BigData, 2016)

Large-scale analyses of functional interactions in the human brain - Yida Wang 003 (2016)

High-Performance Incremental SVM Learning on Intel® Xeon Phi™ Processors. - Dipanjan Sengupta; Yida Wang 003; Narayanan Sundaram; Theodore L. Willke (ISC, 2017)

BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. - Krzysztof J. Gorgolewski; Fidel Alfaro-Almagro; Tibor Auer; Pierre Bellec; Mihai Capota; M. Mallar Chakravarty; Nathan William Churchill; Alexander Li Cohen; R. Cameron Craddock; Gabriel A. Devenyi; Anders Eklund 002; Oscar Esteban; et al. (PLoS Comput. Biol., 2017)

Scheduling Computation Graphs of Deep Learning Models on Manycore CPUs. - Linpeng Tang; Yida Wang 003; Theodore L. Willke; Kai Li 001 (CoRR, 2018)

A Unified Optimization Approach for CNN Model Inference on Integrated GPUs. - Leyuan Wang; Zhi Chen 030; Yizhi Liu; Yao Wang; Lianmin Zheng; Mu Li 003; Yida Wang 003 (ICPP, 2019)

Optimizing CNN Model Inference on CPUs. - Yizhi Liu; Yao Wang; Ruofei Yu; Mu Li 003; Vin Sharma; Yida Wang 003 (USENIX ATC, 2019)

Ansor: Generating High-Performance Tensor Programs for Deep Learning. - Lianmin Zheng; Chengfan Jia; Minmin Sun; Zhao Wu; Cody Hao Yu; Ameer Haj-Ali; Yida Wang 003; Jun Yang; Danyang Zhuo; Koushik Sen; Joseph E. Gonzalez; Ion Stoica (OSDI, 2020)

FeatGraph: a flexible and efficient backend for graph neural network systems. - Yuwei Hu; Zihao Ye 001; Minjie Wang; Jiali Yu; Da Zheng; Mu Li 003; Zheng Zhang 001; Zhiru Zhang; Yida Wang 003 (SC, 2020)

Is Network the Bottleneck of Distributed Training? - Zhen Zhang 063; Chaokun Chang; Haibin Lin; Yida Wang 003; Raman Arora; Xin Jin 008 (NetAI@SIGCOMM, 2020)

Efficient Execution of Quantized Deep Learning Models: A Compiler Approach. - Animesh Jain; Shoubhik Bhattacharya; Masahiro Masuda; Vin Sharma; Yida Wang 003 (CoRR, 2020)

UNIT: Unifying Tensorized Instruction Compilation. - Jian Weng 002; Animesh Jain; Jie Wang 022; Leyuan Wang; Yida Wang 003; Tony Nowatzki (CGO, 2021)

Lorien: Efficient Deep Learning Workloads Delivery. - Cody Hao Yu; Xingjian Shi; Haichen Shen; Zhi Chen 030; Mu Li 003; Yida Wang 003 (SoCC, 2021)

LoWino: Towards Efficient Low-Precision Winograd Convolutions on Modern CPUs. - Guangli Li; Zhen Jia 001; Xiaobing Feng 002; Yida Wang 003 (ICPP, 2021)

Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference. - Haichen Shen; Jared Roesch; Zhi Chen 030; Wei Chen; Yong Wu; Mu Li 003; Vin Sharma; Zachary Tatlock; Yida Wang 003 (MLSys, 2021)

Bring Your Own Codegen to Deep Learning Compiler. - Zhi Chen 030; Cody Hao Yu; Trevor Morris; Jorn Tuyls; Yi-Hsiang Lai; Jared Roesch; Elliott Delaye; Vin Sharma; Yida Wang 003 (CoRR, 2021)

DietCode: Automatic Optimization for Dynamic Tensor Programs. - Bojian Zheng; Ziheng Jiang; Cody Hao Yu; Haichen Shen; Joshua Fromm; Yizhi Liu; Yida Wang 003; Luis Ceze; Tianqi Chen 001; Gennady Pekhimenko (MLSys, 2022)

Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning. - Lianmin Zheng; Zhuohan Li 001; Hao Zhang 025; Yonghao Zhuang 001; Zhifeng Chen; Yanping Huang; Yida Wang 003; Yuanzhong Xu; Danyang Zhuo; Eric P. Xing; Joseph E. Gonzalez; Ion Stoica (OSDI, 2022)

Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning. - Lianmin Zheng; Zhuohan Li 001; Hao Zhang 025; Yonghao Zhuang 001; Zhifeng Chen; Yanping Huang; Yida Wang 003; Yuanzhong Xu; Danyang Zhuo; Joseph E. Gonzalez; Ion Stoica (CoRR, 2022)

MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud. - Zhen Zhang 063; Shuai Zheng 004; Yida Wang 003; Justin Chiu; George Karypis; Trishul Chilimbi; Mu Li 003; Xin Jin 008 (Proc. VLDB Endow., 2022)

Hidet: Task-Mapping Programming Paradigm for Deep Learning Tensor Programs. - Yaoyao Ding; Cody Hao Yu; Bojian Zheng; Yizhi Liu; Yida Wang 003; Gennady Pekhimenko (ASPLOS (2), 2023)

Training Large-scale Foundation Models on Emerging AI Chips. - Aashiq Muhamed; Christian Bock; Rahul Solanki; Youngsuk Park; Yida Wang 003; Jun Huan (KDD, 2023)

Grape: Practical and Efficient Graphed Execution for Dynamic Deep Neural Networks on GPUs. - Bojian Zheng; Cody Hao Yu; Jie Wang 022; Yaoyao Ding; Yizhi Liu; Yida Wang 003; Gennady Pekhimenko (MICRO, 2023)

GEMINI: Fast Failure Recovery in Distributed Training with In-Memory Checkpoints. - Zhuang Wang; Zhen Jia 001; Shuai Zheng 004; Zhen Zhang 063; Xinwei Fu; T. S. Eugene Ng; Yida Wang 003 (SOSP, 2023)

Decoupled Model Schedule for Deep Learning Training. - Hongzheng Chen; Cody Hao Yu; Shuai Zheng 004; Zhen Zhang 063; Zhiru Zhang; Yida Wang 003 (CoRR, 2023)

RAF: Holistic Compilation for Deep Learning Model Training. - Cody Hao Yu; Haozheng Fan; Guangtai Huang; Zhen Jia 001; Yizhi Liu; Jie Wang 022; Zach Zheng; Yuan Zhou; Haichen Shen; Junru Shao; Mu Li 003; Yida Wang 003 (CoRR, 2023)

Perception and memory retrieval states are reflected in distributed patterns of background functional connectivity. - Y. Peeta Li; Yida Wang 003; Nicholas B. Turk-Browne; Brice A. Kuhl; J. Benjamin Hutchinson (NeuroImage, 2023)

Slapo: A Schedule Language for Progressive Optimization of Large Deep Learning Model Training. - Hongzheng Chen; Cody Hao Yu; Shuai Zheng 004; Zhen Zhang 063; Zhiru Zhang; Yida Wang 003 (ASPLOS (2), 2024)

HLAT: High-quality Large Language Model Pre-trained on AWS Trainium. - Haozheng Fan; Hao Zhou 036; Guangtai Huang; Parameswaran Raman; Xinwei Fu; Gaurav Gupta; Dhananjay Ram; Yida Wang 003; Jun Huan (IEEE Big Data, 2024)

Distributed Training of Large Language Models on AWS Trainium. - Xinwei Fu; Zhen Zhang 063; Haozheng Fan; Guangtai Huang; Mohammad El-Shabani; Randy Huang; Rahul Solanki; Fei Wu; Ron Diamant; Yida Wang 003 (SoCC, 2024)

DynaPipe: Optimizing Multi-task Training through Dynamic Pipelines. - Chenyu Jiang 002; Zhen Jia 001; Shuai Zheng 004; Yida Wang 003; Chuan Wu 001 (EuroSys, 2024)

Inference Optimization of Foundation Models on AI Accelerators. - Youngsuk Park; Kailash Budhathoki; Liangfu Chen; Jonas M. Kübler; Jiaji Huang; Matthäus Kleindessner; Jun Huan; Volkan Cevher; Yida Wang 003; George Karypis (KDD, 2024)

Lancet: Accelerating Mixture-of-Experts Training via Whole Graph Computation-Communication Overlapping. - Chenyu Jiang 002; Ye Tian; Zhen Jia 001; Shuai Zheng 004; Chuan Wu 001; Yida Wang 003 (MLSys, 2024)

DiffusionPipe: Training Large Diffusion Models with Efficient Pipelines. - Ye Tian; Zhen Jia 001; Ziyue Luo; Yida Wang 003; Chuan Wu 001 (MLSys, 2024)

DISTMM: Accelerating Distributed Multimodal Model Training. - Jun Huang; Zhen Zhang 063; Shuai Zheng 004; Feng Qin; Yida Wang 003 (NSDI, 2024)

Fast Convolution Meets Low Precision: Exploring Efficient Quantized Winograd Convolution on Modern CPUs. - Xueying Wang 003; Guangli Li; Zhen Jia 001; Xiaobing Feng 002; Yida Wang 003 (ACM Trans. Archit. Code Optim., 2024)

Tilus: A Virtual Machine for Arbitrary Low-Precision GPGPU Computation in LLM Serving. - Yaoyao Ding; Bohan Hou; Xiao Zhang; Allan Lin; Tianqi Chen 001; Cody Yu Hao; Yida Wang 003; Gennady Pekhimenko (CoRR, 2025)