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Hongyi Wang is an Assistant Professor at Rutgers CS working on efficient machine learning systems and LLM infrastructure.
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Posts
Blog Post number 4
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Blog Post number 1
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portfolio
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publications
Recognizing Actions during Tactile Manipulations through Force Sensing
G. Subramani, D. Rakita, H. Wang, J. Black, M. Zinn, M. Gleicher, IROS 2017, [link]
Robotics Sensing
Details
Citation
G. Subramani, D. Rakita, H. Wang, J. Black, M. Zinn, M. Gleicher, IROS 2017, [link]
Draco: Robust Distributed Training against Adversaries
L. Chen, H. Wang, D. Papailiopoulos, SysML 2018, [link]
Distributed Training Robustness
Details
Citation
L. Chen, H. Wang, D. Papailiopoulos, SysML 2018, [link]
DRACO: Robust Distributed Training via Redundant Gradients
L. Chen, H. Wang, Z. Charles, D. Papailiopoulos, ICML 2018, [link]
Distributed Training Robustness
Details
Citation
L. Chen, H. Wang, Z. Charles, D. Papailiopoulos, ICML 2018, [link]
ATOMO: Communication-efficient Learning via Atomic Sparsification
H. Wang*, S. Sievert*, Z. Charles, S. Wright, D. Papailiopoulos, NeurIPS 2018, [link]
Distributed Training Optimization
Details
Citation
H. Wang*, S. Sievert*, Z. Charles, S. Wright, D. Papailiopoulos, NeurIPS 2018, [link]
The Effect of Network Width on the Performance of Large-batch Training
L. Chen, H. Wang, J. Zhao, D. Papailiopoulos, P. Koutris, NeurIPS 2018, [link]
Optimization Deep Learning
Details
Citation
L. Chen, H. Wang, J. Zhao, D. Papailiopoulos, P. Koutris, NeurIPS 2018, [link]
ErasureHead: Distributed Gradient Descent without Delays Using Approximate Gradient Coding
H. Wang, Z. Charles, D. Papailiopoulos [arXiv]
Distributed Training Gradient Coding
Details
Citation
H. Wang, Z. Charles, D. Papailiopoulos [arXiv]
Demonstration of Nimbus: Model-based Pricing for Machine Learning in a Data Marketplace
L. Chen, H. Wang, L. Chen, P. Koutris, A. Kumar, ACM SIGMOD 2019 demo track, [link]
ML Systems Data Markets
Details
Citation
L. Chen, H. Wang, L. Chen, P. Koutris, A. Kumar, ACM SIGMOD 2019 demo track, [link]
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation
S. Rajput*, H. Wang*, Z. Charles, D. Papailiopoulos, NeurIPS 2019, [link]
Distributed Training Robustness
Details
Citation
S. Rajput*, H. Wang*, Z. Charles, D. Papailiopoulos, NeurIPS 2019, [link]
Federated Learning with Matched Averaging
H. Wang, M. Yurochkin, Y. Sun, D. Papailiopoulos, Y. Khazaeni, ICLR 2020, ($\color{red}{\text{Oral}}$) [link][blog][talk]
Federated Learning Model Fusion
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
H. Wang, K. Sreenivasan, S. Rajput, H. Vishwakarma, S. Agarwal, J. Sohn, K. Lee, D. Papailiopoulos, NeurIPS 2020, [link]
Federated Learning Privacy & Security
Details
Citation
H. Wang, K. Sreenivasan, S. Rajput, H. Vishwakarma, S. Agarwal, J. Sohn, K. Lee, D. Papailiopoulos, NeurIPS 2020, [link]
FedML: A Research Library and Benchmark for Federated Machine Learning
C. He, S. Li, J. So, M. Zhang, H. Wang, X. Wang, P. Vepakomma, A. Singh, H. Qiu, L. Shen, P. Zhao, Y. Kang, Y. Liu, R. Raskar, Q. Yang, M. Annavaram, S. Avestimehr, NeurIPS 2020 SpicyFL workshop, ($\color{red}{\text{the Baidu Best Paper Award}}$) [arXiv]
Federated Learning ML Systems
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Citation
C. He, S. Li, J. So, M. Zhang, H. Wang, X. Wang, P. Vepakomma, A. Singh, H. Qiu, L. Shen, P. Zhao, Y. Kang, Y. Liu, R. Raskar, Q. Yang, M. Annavaram, S. Avestimehr, NeurIPS 2020 SpicyFL workshop, ($\color{red}{\text{the Baidu Best Paper Award}}$) [arXiv]
Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification
Distributed Training Optimization
AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness
D. Li, H. Wang, E. P. Xing, H. Zhang, NeurIPS 2022 [arXiv]
Distributed Training Model Parallelism
Details
Citation
D. Li, H. Wang, E. P. Xing, H. Zhang, NeurIPS 2022 [arXiv]
Rare Gems: Finding Lottery Tickets at Initialization
K. Sreenivasan, J. Sohn, L. Yang, M. Grinde, A. Nagle, H. Wang, E. P. Xing, K. Lee, D. Papailiopoulos, NeurIPS 2022 [arXiv]
Optimization Deep Learning
Details
Citation
K. Sreenivasan, J. Sohn, L. Yang, M. Grinde, A. Nagle, H. Wang, E. P. Xing, K. Lee, D. Papailiopoulos, NeurIPS 2022 [arXiv]
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation
K. Zhang, Y. Wang, H. Wang, L. Huang, C. Yang, X. Chen, L. Sun, Findings of EMNLP 2022
Federated Learning Privacy & Security
Details
Citation
K. Zhang, Y. Wang, H. Wang, L. Huang, C. Yang, X. Chen, L. Sun, Findings of EMNLP 2022
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach
H. Guo, P. Greengard, H. Wang, A. Gelman, E. P. Xing, Y. Kim, ICLR 2023 [link]
Federated Learning Optimization
Details
Citation
H. Guo, P. Greengard, H. Wang, A. Gelman, E. P. Xing, Y. Kim, ICLR 2023 [link]
MPCFormer: fast, performant and private Transformer inference with MPC
D. Li*, R. Shao*, H. Wang*, H. Guo, E. P. Xing, H. Zhang, ICLR 2023, ($\color{red}{\text{Spotlight}}$) [link]
Privacy & Security Efficient Inference
Details
Citation
D. Li*, R. Shao*, H. Wang*, H. Guo, E. P. Xing, H. Zhang, ICLR 2023, ($\color{red}{\text{Spotlight}}$) [link]
Cuttlefish: Low-rank Model Training without All The Tuning
H. Wang, S. Agarwal, P. U-chupala, Y. Tanaka, E. P. Xing, D. Papailiopoulos, MLSys 2023 [link] [arXiv]
Efficient Training Optimization
FedNAR: Federated Optimization with Normalized Annealing Regularization
Federated Learning Optimization
Fusing Models with Complementary Expertise
Optimization Model Fusion
Does compressing activations help model parallel training?
S. Bian, D. Li, H. Wang, E. P. Xing, S. Venkataraman, MLSys 2024 [arXiv]
Distributed Training Model Compression
Details
Citation
S. Bian, D. Li, H. Wang, E. P. Xing, S. Venkataraman, MLSys 2024 [arXiv]
RedCoast: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs
B. Tan, Y. Zhu, L. Liu, H. Wang, Y. Zhuang, J. Chen, E. P. Xing, Z. Hu, NAACL Demo 2024 ($\color{red}{\text{the Best Demo Runner Up}}$) [link] [arXiv]
LLM Systems Distributed Training
Crystal: Illuminating LLM Abilities on Language and Code
T. Tao, J. Li, B. Tan, H. Wang, W. Marshall, B. M Kanakiya, J. Hestness, N. Vassilieva, Z. Shen, E. P. Xing, Z. Liu, COLM 2024 [arXiv]
LLM Systems Data & Evaluation
Details
Citation
T. Tao, J. Li, B. Tan, H. Wang, W. Marshall, B. M Kanakiya, J. Hestness, N. Vassilieva, Z. Shen, E. P. Xing, Z. Liu, COLM 2024 [arXiv]
LLM360: Towards Fully Transparent Open-Source LLMs
Z. Liu, A. Qiao, W. Neiswanger, H. Wang, B. Tan, T. Tao, J. Li, Y. Wang, S. Sun, O. Pangarkar, R. Fan, Y. Gu, V. Miller, Y. Zhuang, G. He, H. Li, F. Koto, L. Tang, N. Ranjan, Z. Shen, R. Iriondo, C. Mu, Z. Hu, M. Schulze, P. Nakov, T. Baldwin, E. P. Xing, COLM 2024 [arXiv]
LLM Systems Open Models
Details
Citation
Z. Liu, A. Qiao, W. Neiswanger, H. Wang, B. Tan, T. Tao, J. Li, Y. Wang, S. Sun, O. Pangarkar, R. Fan, Y. Gu, V. Miller, Y. Zhuang, G. He, H. Li, F. Koto, L. Tang, N. Ranjan, Z. Shen, R. Iriondo, C. Mu, Z. Hu, M. Schulze, P. Nakov, T. Baldwin, E. P. Xing, COLM 2024 [arXiv]
Maestro: Uncovering Low-Rank Structures via Trainable Decomposition
Optimization Model Compression
TrustLLM: Trustworthiness in Large Language Models
LLM Systems Data & Evaluation
FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations
Z. Wang, Z. Shen, Y. He, G. Sun, H. Wang, L. Lyu, A. Li, NeurIPS 2024 [arXiv]
LLM Systems Federated Learning
Details
Citation
Z. Wang, Z. Shen, Y. He, G. Sun, H. Wang, L. Lyu, A. Li, NeurIPS 2024 [arXiv]
Model-GLUE: Democratized LLM Scaling for A Large Model Zoo in the Wild
X. Zhao, G. Sun, R. Cai, Y. Zhou, P. Li, P. Wang, B. Tan, Y. He, L. Chen, Y. Liang, B. Chen, B. Yuan, H. Wang, A. Li, Z. Wang, T. Chen, NeurIPS 2024 Datasets and Benchmarks [link]
LLM Systems Open Models
Details
Citation
X. Zhao, G. Sun, R. Cai, Y. Zhou, P. Li, P. Wang, B. Tan, Y. He, L. Chen, Y. Liang, B. Chen, B. Yuan, H. Wang, A. Li, Z. Wang, T. Chen, NeurIPS 2024 Datasets and Benchmarks [link]
SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning
Y. He, Z. Wang, Z. Shen, G. Sun, Y. Dai, Y. Wu, H. Wang, A. Li, NeurIPS 2024 [arXiv]
LLM Systems Data & Evaluation
Details
Citation
Y. He, Z. Wang, Z. Shen, G. Sun, Y. Dai, Y. Wu, H. Wang, A. Li, NeurIPS 2024 [arXiv]
From EduVisBench to EduVisAgent: A Benchmark and Multi-Agent Framework for Reasoning-Driven Pedagogical Visualization
H. Ji, S. Qiu, S. Xin, S. Han, Z. Chen, D. Zhang, H. Wang, H. Yao, ICLR 2026 [OpenReview]
AI Agents Data & Evaluation Education Visualization
Details
Citation
H. Ji, S. Qiu, S. Xin, S. Han, Z. Chen, D. Zhang, H. Wang, H. Yao, ICLR 2026 [OpenReview]
BibTeX
@inproceedings{ji2026from,
title={From EduVisBench to EduVisAgent: A Benchmark and Multi-Agent Framework for Reasoning-Driven Pedagogical Visualization},
author={Haonian Ji and Shi Qiu and Siyang Xin and Siwei Han and Zhaorun Chen and Dake Zhang and Hongyi Wang and Huaxiu Yao},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=FVCpV04ZRe}
}
talks
teaching
Teaching experience 1
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Teaching experience 2
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technical_reports
Scaling Dense Representations for Single Cell with Transcriptome-Scale Context
N. Ho, C. N. Ellington, J. Hou, et al., bioRxiv, 2024.
A Large-Scale Foundation Model for RNA Function and Structure Prediction
S. Zou, T. Tao, S. Mahbub, et al., bioRxiv, 2024.
Mixture of Experts Enable Efficient and Effective Protein Understanding and Design
N. Sun, S. Zou, T. Tao, et al., bioRxiv, 2024.
Accurate and General DNA Representations Emerge from Genome Foundation Models at Scale
C. N. Ellington, N. Sun, N. Ho, et al., bioRxiv, 2024.
LLM360 K2: Building a 65B 360-Open-Source Large Language Model from Scratch
Z. Liu, B. Tan, H. Wang, et al., arXiv technical report, 2025.
K2-V2: A 360-Open, Reasoning-Enhanced LLM
K2 Team, arXiv technical report, 2025.