Tianlong Chen received the Ph.D. degree in Electrical and Computer Engineering from University of Texas at Austin, TX, USA, in 2023. He will start as an Assistant Professor of Computer Science at The University of North Carolina at Chapel Hill in Fall 2024. Before that, he will be a Postdoctoral Researcher at Massachusetts Institute of Technology (CSAIL@MIT), Harvard (BMI@Harvard), and Borad Institute of MIT & Harvard in 2023-2024.
His research focuses on building accurate, trustworthy, and efficient machine learning systems. He devotes his most recent passion to various (A) important machine learning problems - sparsity, robustness, learning to optimize, graph learning, and diffusion models; (B) interdisciplinary scientific challenges - bioengineering and quantum comptuing. He received IBM Ph.D. Fellowship, Adobe Ph.D. Fellowship, Graduate Dean's Prestigious Fellowship, and the Best Paper Award from the inaugural Learning on Graphs (LoG) Conference 2022.
I am looking for highly motivated students, in terms of RA/TA/externship/internship/visiting students. Interested candidates are strongly encouraged to contact me by email, together with resume and transcripts.
• Apr., 2024 [PAPER] One Bioinformatics accepted - scRNA Imputation via Bi-level Feature Propagation.
• Apr., 2024 [Tutorial] Our tutorial of "Mixture-of-Experts in the Era of LLMs: A New Odyssey" has been accepted by ICML'24. See you at Vienna!
• Mar., 2024 [PAPER] One NAACL'24 accepted - Multiple LLM Agents Gaming.
• Mar., 2024 [STATUS] UNITES welcomes five new Ph.D. students: Sukwon Yun (KAIST), Xinyu Zhao (Peking University), Pingzhi Li (USTC), Mufan Qiu (USTC), and Huanzhi Qu (USTC).
• Mar., 2024 [PAPER] Two CVPR'24 accepted - Diffusion Quantization + Molecular Data Programming.
• Feb., 2024 [PAPER] Two ICLR'24 accepted - SMoE Merging (Spotlight) + Multilingual SMoE. Congratulations to Pingzhi Li and Xinyu Zhao for their first ICLR papers.
• Jan., 2024 [Tutorial] Our tutorial of "Zeroth-Order Machine Learning: Fundamental Principles and Emerging Applications in Foundation Models" has been accepted by AAAI'24 and ICASSP'24.
• Jan., 2024 [PAPER] One EACL'24 accepted - LLM + Knowledge Graph.
• Dec., 2023 [PAPER] One AAAI'24 accepted - Metacognitive Intervention for LLMs via Sparsity.
• Nov., 2023 [HONOR] Congratulations to Pingzhi Li and our amazing collaborators for wining the 1st place at 2023 Quantum Computing for Drug Discovery Challenge at ICCAD.
• Sep., 2023 [PAPER] Two NeurIPS'23 accepted - Essential Sparsity in LLM + LLM Heavy-Hitter Oracle.
• Jul., 2023 [STATUS] I started my PostDoc at CSAIL@MIT and BMI@Harvard.
• Jul., 2023 [PAPER] Three ICCV'23 accepted - Adaptive Multi-Task Vision MoE + Robust MoE (Oral) + Generalizable NeRF w. MoE.
• Jul., 2023 [PAPER] One QCE'23 accepted - Sparse Circuit Design for Quantum Computing.
• Jun., 2023 [HONOR] I received the 2023 AdvML Rising Star Award . Many thanks for the acknowledgment.
• May, 2023 [PAPER] One ACL'23 accepted - Sparse LLM Tuning.
• May, 2023 [PAPER] Three ICML'23 accepted - Instant Soup (Oral) + Graph Ladling + L2O Game.
• May, 2023 [STATUS] I received the Ph.D. degree from ECE@UT Austin. I deeply appreciate all the support and help from my family, advisor (Prof. Atlas Wang) , collaborators, and friends!
University of North Carolina, AI Trustworthiness, Efficiency, and for Science (UNITES) Group will be an active research lab at UNC Chapel Hill. Our research interests span the area of artificial intelligence (AI), machine learning (ML), optimization, computer vision, natural language processing, and data science, with two major focuses on (A) establishing robust and efficient AI systems; (B) bridging the gap between AI and societal & scientific challenges. Students' information is presented below.
Mohan Zhang (USTC, Spring 2024) [Remote]
Haiyang Xu (USTC, Spring 2024) [Remote]
Daiwei Li (UCSD, Summer 2023, Spring 2024) [Remote]
Jialin Wu (USTC, Summer 2023, Spring 2024) [Remote]
Xinnan Zhang (USTC, Summer 2023, Spring 2024) [Remote]
Tiancheng He (SJTU+UMich, Summer 2023, Spring 2024) [Remote]
Yaoyao Xu (Columbia, Summer 2023, Spring 2024) [Remote]
Can Jin (USTC, Summer 2023) [Remote]
Kaixin Zheng (Summer 2023) [Remote]
Silin Cai (Summer 2023) [Remote]
Full publications on Google Scholar.
‡ indicates authors with equal contribution. ☆ indicates my students or interns.
MARSBench: Evaluating Multi-Agent Multi-Turn Strategic Reasoning of Large Language Models and Beyond
Jinhao Duan, Shiqi Wang, James Diffenderfer, Lichao Sun, Tianlong Chen, Bhavya Kailkhura, Kaidi Xu
NAACL'24: The North American Chapter of the Association for Computational Linguistics
Molecular Data Programming: Towards Molecule Pseudo-labeling with Systematic Weak Supervision
Xin Juan, Kaixiong Zhou, Ninghao Liu, Tianlong Chen, Xin Wang
CVPR'24: Conference on Computer Vision and Pattern Recognition
TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion Models
Yushi Huang, Ruihao Gong, Jing Liu, Tianlong Chen, Xianglong Liu
CVPR'24: Conference on Computer Vision and Pattern Recognition
Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy
Pingzhi Li☆, Zhenyu Zhang, Prateek Yadav, Yi-Lin Sung, Yu Cheng, Mohit Bansal, Tianlong Chen
ICLR'24: International Conference on Learning Representations
Sparse Moe with Language Guided Routing for Multilingual Machine Translation
Xinyu Zhao☆, Xuxi Chen, Yu Cheng, Tianlong Chen
ICLR'24: International Conference on Learning Representations
Contextualization Distillation from Large Language Model for Knowledge Graph Completion
Dawei Li☆, Zhen Tan, Tianlong Chen, Huan Liu
EACL'24: European chapter of the Association for Computational Linguistics
Sparsity-Guided Holistic Explanation for LLMs with Interpretable Inference-Time Intervention
Zhen Tan, Tianlong Chen, Zhenyu Zhang, Huan Liu
AAAI'24: Association for the Advancement of Artificial Intelligence
The Emergence of Essential Sparsity in Large Pre-trained Models: The Weights that Matter
Ajay Jaiswal, Shiwei Liu, Tianlong Chen, Zhangyang Wang
NeurIPS'23: Advances in Neural Information Processing Systems
H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models
Zhenyu Zhang, Ying Sheng, Tianyi Zhou, Tianlong Chen, Lianmin Zheng, Ruisi Cai, Zhao Song, Yuandong Tian, Christopher Ré, Clark Barrett, Zhangyang Wang, Beidi Chen
NeurIPS'23: Advances in Neural Information Processing Systems
AdaMV-MoE: Adaptive Multi-Task Vision Mixture-of-Experts
Tianlong Chen‡, Xuxi Chen‡, Xianzhi Du, Abdullah Rashwan, Fan Yang, Huizhong Chen, Zhangyang Wang, Yeqing Li
ICCV'23: International Conference on Computer Vision
Robust Mixture-of-Expert Training for Convolutional Neural Networks
Yihua Zhang, Ruisi Cai, Tianlong Chen, Guanhua Zhang, Huan Zhang, Pin-Yu Chen, Shiyu Chang, Zhangyang Wang, Sijia Liu
ICCV'23 (Oral): International Conference on Computer Vision
GNT-MOVE: Generalizable NeRF Transformer with Mixture-of-View-Experts
Wenyan Cong, Hanxue Liang, Peihao Wang, Zhiwen Fan, Tianlong Chen, Mukund Varma, Yi Wang, Zhangyang Wang
ICCV'23: International Conference on Computer Vision
QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits
Tianlong Chen, Zhenyu Zhang, Hanrui Wang, Jiaqi Gu, Zirui Li, David Z. Pan, Frederic Chong, Song Han, Zhangyang Wang
QCE'23: International Conference on Quantum Computing and Engineering
DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models
Xuxi Chen, Tianlong Chen, Weizhu Chen, Ahmed Hassan Awadallah, Zhangyang Wang, Yu Cheng
ACL'23: Annual Meeting of the Association for Computational Linguistics
Instant Soup: Cheap Pruning Ensemble in A Single Pass Can Draw Lottery Tickets from Large Models
Ajay Jaiswal, Shiwei Liu, Tianlong Chen, Ying Ding, Zhangyang Wang
ICML'23: International Conference on Machine Learning
Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication
Ajay Jaiswal, Shiwei Liu, Tianlong Chen, Ying Ding, Zhangyang Wang
ICML'23: International Conference on Machine Learning
Learning to Optimize Differential Games
Xuxi Chen, Nelson Vadori, Tianlong Chen, Zhangyang Wang
ICML'23: International Conference on Machine Learning
The Lottery Ticket Hypothesis for Pre-trained BERT Networks
Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Zhangyang Wang, Michael Carbin
NeurIPS'20: Conference on Neural Information Processing Systems
Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together!
Shiwei Liu‡, Tianlong Chen‡, Zhenyu Zhang, Xuxi Chen, Tianjin Huang, Ajay Kumar Jaiswal, Zhangyang Wang
ICLR'23: International Conference on Learning Representations
Learning to Optimize: A Primer and A Benchmark
(α-β) Tianlong Chen, Xiaohan Chen, Wuyang Chen, Howard Heaton, Jialin Liu, Zhangyang Wang, Wotao Yin
JMLR'22: Journal of Machine Learning Research
Scalable Learning to Optimize: A Learned Optimizer Can Train Big Models
Xuxi Chen‡, Tianlong Chen‡, Yu Cheng, Weizhu Chen, Ahmed Awadallah, Zhangyang Wang
ECCV'22: European Conference on Computer Vision
Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning
Tianlong Chen, Sijia Liu, Shiyu Chang, Yu Cheng, Lisa Amini, Zhangyang Wang
CVPR'20: Conference on Computer Vision and Pattern Recognition
Linearity Grafting: How Neuron Pruning Helps Certifiable Robustness
Tianlong Chen‡, Huan Zhang‡, Zhenyu Zhang, Shiyu Chang, Sijia Liu, Pin-Yu Chen, Zhangyang Wang
ICML'22: International Conference on Machine Learning
Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study
Tianlong Chen‡, Kaixiong Zhou‡, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang
TPAMI'22: IEEE Transactions on Pattern Analysis and Machine Intelligence
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets
Tianjin Huang, Tianlong Chen, Meng Fang, Vlado Menkovski, Jiaxu Zhao, Lu Yin, Yulong Pei, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy, Shiwei Liu
(Best Paper Award) LOG'22: Learning on Graphs Conference
Graph Contrastive Learning with Augmentations
Yuning You‡, Tianlong Chen‡, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen
NeurIPS'20: Conference on Neural Information Processing Systems
HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing
Tianlong Chen‡, Chengyue Gong‡, Daniel Jesus Diaz, Xuxi Chen, Jordan Tyler Wells, qiang liu, Zhangyang Wang, Andrew Ellington, Alex Dimakis, Adam Klivans
ICLR'23: International Conference on Learning Representations
QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits
Tianlong Chen, Zhenyu Zhang, Hanrui Wang, Jiaqi Gu, Zirui Li, David Z. Pan, Frederic Chong, Song Han, Zhangyang Wang
QCE'23: International Conference on Quantum Computing and Engineering
Full Resume in PDF.
• [Student Recuriting] ADS in Chinese, ADS in English
• [Teaching] TBD
• [Job Application Package] Research Statement, Job Talk Slides, Resume
• [Tool] Learning to Optimize
• [UNC] Department's Why-UNC Page, UNC CS Ranking
• I am a big fan of Pokémon. Playing Pokémon Go is one of my daily activities. Join me, catch the Pokémon in North Carolina, and be a good Pokémon trainer!
• I also enjoy Hip-Hop and Country music. Air (艾热) is one of my favorite Chinese Hip-Hop stars.