Dongkuan (DK) Xu

Assistant Professor of Computer Science

C ONTACTI NFORMATION

Web: dongkuanx27.github.io/
E-mail: dxu27@ncsu.edu
Mobile: 814-699-0860

R ESEARCHI NTERESTSMy research is fundamentally grounded in advancing Artificial General Intelligence, withparticular emphasis on studying the autonomy of intelligent agents (task planning, external
tool use), decision/reasoning reliability (alignment, uncertainty, adaptability), and resource efficiency (parameter, data, computation) in generative AI systems (ChatGPT, GPT-X, diffusion
models). I’m leading the NCSU Generative Intelligent Computing Lab. My long-term research goal is to liberate AI productivity and democratize its application to serve a broader
range of populations and real-world applications, equally, sustainably, and responsibly.

W ORKING

Assistant Professor, North Carolina State University, NC, USA.
• Department of Computer Science• Microsoft Accelerating Foundation Models Research Award, 2024
• NCSU Carla Savage Award, 2024
• ICCCN Best Paper Award, 2023

E DUCATION

PhD, Penn State University, PA, USA.
• College of IST Award for Excellent Teaching [Top 2]

2022

MS, University of Chinese Academy of Sciences, Beijing, China• Chinese Academy of Sciences President’s Fellowship [Top 1]

2017

BE, Renmin University of China, Beijing, China

2014

P UBLICATION
S UMMARY

Google Scholar: [Link]
Twitter: https://twitter.com/DongkuanXu
Updated: Sep 9th, 2024

Aug 2022-Present

Published: 61 papers, 30 first/advising-authored papers, and 10 filed patents.
Impact: 3773 citations, h-index: 17, i10-index: 26 (as of Sep 9th, 2024). My publications canbe generally categorized as follows (with a representative paper under each category).
Published at NeurIPS, ICLR, AAAI, ACL, EMNLP, NAACL, CVPR, ECCV, ICCV, DAC, etc.
• Improving Parameter Efficiency in Foundation Models [17][21][35][38][40][43][44][49]— X. Liu, B. Lei, R. Zhang, D. Xu. Adaptive Draft-Verification for Efficient Large Language Model Decoding [C]. (ArXiv, Aug 2024) Project [Link], Demo [Link], Paper [Link]• Improving Computation Efficiency of Foundation Models [14][29][30][36][37][2]— S. Tang, Y. Wang, C. Ding, Y. Liang, Y. Li, D. Xu. AdaDiff: Accelerating Diffusion
Models through Step-Wise Adaptive Computation [C]. The 18th European Conference on
Computer Vision (ECCV 2024)• Improving Data Efficiency in Foundation Models [18][13][39][45][47][51][52][54]— L. Zhang, J. Zhang, B. Lei, S. Mukherjee, X. Pan, B. Zhao, C. Ding, Y. Li, D. Xu. Accelerating Dataset Distillation via Model Augmentation [C]. The 34th IEEE/CVF Conference
on Computer Vision and Pattern Recognition (CVPR 2023, Highlight Paper, 2.5%)

C URRENTP ROJECTS

Harnessing Links between Historical Business & Household Microdata and Street-View
Images to Assess Transit-Induced Neighborhood Changes at Small Spatial Scales• Source of Support: NSF• Project Start Date: 08/2024• Project End Date: 08/2027

1 of 13

• PI: Eleni Bardaka (NC State, Civil Engineering)• Co-PI: DK Xu (NC State, Computer Science)• Total Project Amount: $396,600
Collaborative Research: CyberTraining: Implementation: Medium: EcoTern: Pioneering a CI Workforce for Sustainable and Transdisciplinary Environmental Science Research• Source of Support: NSF• Project Start Date: 12/2024• Project End Date: 11/2027• NCSU Team: Xipeng Shen (PI, Systems), DK Xu (Co-PI, AI), Roy He (Co-PI, Ocean)• FIU Team: Wenqian Dong (PI, HPC), J. Obey (Co-PI, Climate), J. Liu (Co-PI, Simulation)• Total Project Amount: $979,901
Scalable and Adaptable Evaluation of LLMs’ Trustworthiness Through Generative Techniques• Source of Support: Microsoft Accelerating Foundation Models Research• Project Start Date: 01/2024• Project End Date: 06/2025• Single PI: DK Xu (NC State, Computer Science)• Total Project Amount: $50,000
C OMMUNITYE NGAGEMENT• Workshop on DL-Hardware Co-Design for Generative AI Acceleration @DAC’24, Chair• Workshop on Dataset Distillation for Computer Vision @CVPR’24, Co-Chair• 2nd Resource-efficient Learning for Knowledge Discovery Workshop @KDD’24, Co-Chair• 1st Workshop on DL-Hardware Co-Design for AI Acceleration @AAAI’23, Chair• 1st Resource-Efficient Learning for Knowledge Discovery Workshop @KDD’23, Co-Chair• ML & NLP Learning Community (Chinese), Founding Committee Member• ACM SIGAI Newsletter, Column Editor• NSF CAREER Panel Reviewer, 2023
• Area Chair, Session Chair, (Senior) Program Committee Member for ≥ 50 times

E DUCATIONO UTREACH• Grand Challenges Scholars Program (GCSP-REU) Summer 2024, Research Mentor• NCSU Educational Workshops (Integrating ChatGPT into K-12 Classrooms), Co-Chair• NSF REU Site (SRCA, Socially Relevant Computing and Analytics), UG Mentor• NCSU CSC 298 (Introduction to Computer Science Research Methods), Mentor (2 UGs)
• NCSU COE REU Program, UG Mentor ($3,000 Research Award awarded to my student)• NSF-funded IUSE project (ExplainIt), UG Class Instructor• NSF REU Site proposal (Topic: Algorithms and Theory), Mentor (2 UGs for 3-5 years)

O PEN - SOURCEP ROJECT

Gentopia.AI: A Collaborative Platform for Tool-Augmented LLMs
• Goal: Aim to specialize & share agents to overlay collective growth for greater intelligence• Teams: Researchers from NC State, George Mason, NYU, UMich, CMU• Web: https://github.com/Gentopia-AI, Demo [link], Quick Start [link]• Paper: Accepted to EMNLP’23 (System Demo) [link]

P EER - REVIEWED[1] H. Reichert, B. Tabarsi, Z. ZHang, C. Fennell, I. Bhandari, D. Robinson, M. Drayton, C.
Crofton, M. Lococo, D. Xu, T. Barnes. Empowering Secondary School Teachers: Creating, Executing, and Evaluating a Transformative Professional Development Course
on ChatGPT [C]. IEEE Frontiers in Education Conference 2024 (FIE’24)

CONFERENCE AND
JOURNAL PAPERS[2] S. Tang, Y. Wang, C. Ding, Y. Liang, Y. Li, and D. Xu. AdaDiff: Accelerating Diffusion
Models through Step-Wise Adaptive Computation [C]. The 18th European Conference
2 of 13

on Computer Vision (ECCV’24)[3] J. Liu, Z. Peng, D. Xu, Y. Liu. Revolutionizing Wireless Modeling and Simulation with
Network-Oriented LLMs [C]. The 43rd IEEE International Performance Computing
and Communications Conference (IPCCC’24)[4] Z. Zhang, Y. Liu, Z. Peng, M. Chen, D. Xu, and S. Cui. Digital Twin-Assisted DataDriven Optimization for Reliable Edge Caching in Wireless Networks [J]. IEEE Journal
on Selected Areas in Communications (IEEE JSAC, 2024, Impact Factor is 16.4)[5] P. Dong, J. Zhuang, Z. Yang, S. Ji, Y. Li, D. Xu, H. Huang, J. Hu, A. Jones, Y. Shi,
Y. Wang, P. Zhou. EQ-ViT: Algorithm-Hardware Co-Design for End-to-End Acceleration of Real-Time Vision Transformer Inference on Versal ACAP Architecture [C].
The International Conference on Hardware/Software Codesign and System Synthesis(CODES+ISSS’24)[6] X. Wang, S. Duan, X. Yi, J. Yao, S. Zhou, Z. Wei, P. Zhang, D. Xu, M. Sun, X. Xie. On the
Essence and Prospect: An Investigation of Alignment Approaches for Big Models [C].
International Joint Conference on Artificial Intelligence (Survey Track) (IJCAI’24)[7] Y. Wang, Q. Zhao, D. Xu, and X. Liu. Purpose Enhanced Reasoning through Iterative
Prompting: Uncover Latent Robustness of ChatGPT on Code Comprehension [C]. 2024
International Joint Conference on Artificial Intelligence (IJCAI’24)[8] X. Luo, Z. Li, Z. Peng, D. Xu, Y. Liu. RM-Gen: Conditional Diffusion Model-BasedRadio Map Generation for Wireless Networks [C]. International Federation for Information Processing Networking Conference (IFIP/IEEE Networking’24)[9] B. Lei, D. Xu, R. Zhang, and B.K Mallick. Embracing Unknown Step by Step: Towards
Reliable Sparse Training in Real World [J]. (Transactions on Machine Learning Research, 2024)[10] B. Lei, D. Xu, R. Zhang, S. He, B. K. Mallick. Balance is Essence: Accelerating Sparse
Training via Adaptive Gradient Correction [C]. The 2024 Conference on Parsimony andLearning (CPAL’24)[11] Z. Zhang*, Z. Dong*, Y. Shi, N. Matsuda, T. Price, D. Xu. Students’ Perceptions andPreferences of Generative Artificial Intelligence Feedback for Programming [C]. The
14th Symposium on Educational Advances in Artificial Intelligence (AAAI/EAAI’24)[12] Z. Wang, Q. Zhao, J. Cui, X. Liu, and D. Xu. AutoST: High-performance and Energyefficient Spiking Transformer Architecture Search [C]. The 2024 IEEE International
Conference on Acoustics, Speech, and Signal Processing (ICASSP’24)[13] L. Zhang, J. Zhang, B. Lei, S. Mukherjee, X. Pan, B. Zhao, C. Ding, Y. Li, and D. Xu.
Accelerating Dataset Distillation via Model Augmentation [C]. The 34th IEEE/CVF
Conference on Computer Vision and Pattern Recognition (CVPR’23, Highlight Paper)
Acceptance rate: 235/9155=2.5%[14] S. Tang, Y. Wang, Z. Kong, T. Zhang, Y. Li, C. Ding, Y. Wang, Y. Liang, and D. Xu.
You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision
Language Model [C]. The 34th IEEE/CVF Conference on Computer Vision and Pattern
Recognition (CVPR’23)[15] B. Xu, X. Liu, H. Shen, Z. Han, Y. Li, M. Yue, Z. Peng, Y. Liu, Z. Yao, and D. Xu. Gentopia: A Collaborative Platform for Tool-Augmented LLMs [C]. The 2023 Conferenceon Empirical Methods in Natural Language Processing. (EMNLP’23, System Track)

3 of 13

[16] J. Li, Q. Lei, W. Cheng, and D. Xu. Towards Robust Pruning: An Adaptive KnowledgeRetention Pruning Strategy for Language Models [C]. The 2023 Conference on Empirical Methods in Natural Language Processing. (EMNLP’23)[17] J. Li, W. Gao, Q. Lei, and D. Xu. Breaking through Deterministic Barriers: Randomized Pruning Mask Generation and Selection [C]. The 2023 Conference on Empirical
Methods in Natural Language Processing. (EMNLP’23, Findings)[18] J. Gu, Z. Nan, Z. Peng, X. Shen, and D. Xu. Co-evolving Data-driven and NLU-driven
Synthesizers for Generating Code in Domain Growth and Data Scarcity [C]. The 2023
Conference on Empirical Methods in Natural Language Processing. (EMNLP’23, PanDL Workshop)[19] D. Zhu, B. Lei, J. Zhang, Y. Fang, Y. Xie, R. Zhang, and D. Xu. Rethinking Data Distillation: Do Not Overlook Calibration [C]. International Conference on Computer Vision(ICCV’23)[20] J. Wang, X. Yang, S. Cui, L. Che, L. Lyu, D. Xu, and F. Ma. Towards Personalized
Federated Learning via Heterogeneous Model Reassembly [C]. The 37th Conference
on Neural Information Processing Systems (NeurIPS’23)[21] S. Li, H. Mei, J. Li, H. Wei, and D. Xu. Toward Efficient Traffic Signal Control: Smaller
Network Can Do More [C]. 62nd IEEE Conference on Decision and Control (CDC’23)[22] B. Lei, R. Zhang, D. Xu, and B. K Mallick. Calibrating the Rigged Lottery: Making All
Tickets Reliable [C]. The 11th International Conference on Learning Representations(ICLR’23)[23] Q. Zhang, S. Chen, D. Xu, Q. Cao, X. Chen, T. Cohn, and M. Fang. A Survey for EfficientOpen Domain Question Answering [C]. The 61th Annual Meeting of the Association
for Computational Linguistics (ACL’23)[24] L. Wu, B. Lei, D. Xu, and D. Zhou. Towards Reliable Rare Category Analysis on Graphs
via Individual Calibration [C]. The 29th ACM SIGKDD Conference on Knowledge
Discovery and Data Mining (KDD’23)[25] C. Liu, D. Doshi, M. Bhargava, R. Shang, J. Cui, D. Xu, and E. Gehringer. Labels Are
Not Necessary: Assessing Peer-Review Helpfulness Using Domain Adaptation Basedon Self-Training [C]. The 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA’23)[26] Z. Dong, D. Xu. Exploring the Augmented Large Language Model with Mathematical
tools in Personalized and Efficient Education [C]. The 6th International Conference on
Artificial Intelligence and Big Data (ICAIBD’23)[27] Y. Liu, M. Chen, D. Xu, Z. Yang, and S. Zhao. E-App: An Environment-Aware Access Point Planning Framework for mmWave Wireless LANs [C]. The 32nd International Conference on Computer Communications and Networks (ICCCN’23, Best Paper Award)[28] Y. X., D. Zhu, B. Lei, D. Xu, and R. Zhang. Efficient Informed Proposals for Discrete Distributions via Newton’s Series Approximation [C]. The 26th International Conferenceon Artificial Intelligence and Statistics (AISTATS’23)[29] S. Huang, H. Fang, K. Mahmood, B. Lei, N. Xu, B. Lei, Y. Sun, D. Xu, Wu. Wen, and C.
Ding. Neurogenesis Dynamics-inspired Spiking Neural Network Training Acceleration[C]. The 60th Design Automation Conference (DAC’23)

4 of 13

[30] S. Huang, B. Lei, D. Xu, H. Peng, Y. Sun, M. Xie, and C. Ding. Dynamic Sparse Training
via Balancing the Exploration-Exploitation Trade-off [C]. The 60th Design Automation
Conference (DAC’23)[31] J. Li, T. Zhang, E. Yan, and D. Xu. FP8-BERT: Post-Training Quantization for Transformer [C]. The 1st Workshop on DL-Hardware Co-Design for AI Acceleration (DCAA’23)[32] Y. Xie, Z. Li, H. Bao, X. Jia, D. Xu, X. Zhou, and S. Skakun. Auto-CAM: Label-Free
Earth Observation Imagery Composition and Masking Using Spatio-Temporal Dynamics [C]. The 37th AAAI International Conference on Artificial Intelligence (AAAI’23)[33] D. Luo, W. Cheng, Y. Wang, D. Xu, J. Ni, W. Yu, X. Zhang, Y. Liu, Y. Chen, H. Chen, and
X. Zhang. Time Series Contrastive Learning with Information-Aware Augmentations[C]. The 37th AAAI International Conference on Artificial Intelligence (AAAI’23)[34] Y. Tian, Weizhi Gao, Qin Zhang, Pu Sun, and D. Xu. Improving long-tailed classification
by disentangled variance transfer [J]. Internet of Things (2023): 100687.
[35] D. Xu, S. Mukherjee, X. Liu, D. Dey, W. Wang, X. Zhang, A. H. Awadallah, and J.
Gao. Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models [C]. The 36th Conference on Neural Information Processing Systems(NeurIPS’22)[36] I. Yen, Z. Xiao, and D. Xu. S4: a High-sparsity, High-performance AI Accelerator [C].
Sparsity in Neural Networks 2022 Workshop (SNN’22)[37] S. Huang, N. Liu, Y. Liang, H. Peng, H. Li, D. Xu, M. Xie, and C. Ding. An Automaticand Efficient BERT Pruning for Edge AI Systems [C]. The 23rd IEEE International
Society for Quality Electronic Design (ISQED’22)[38] S. Huang∗ , D. Xu∗ , I. Yen, S. Chang, B. Li, S. Chen, M. Xie, H. Liu, and C. Ding. Sparse
Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm[C]. The 60th Annual Meeting of the Association for Computational Linguistics (ACL’22)
Acceptance rate: 714/3350=21.3%[39] D. Xu, W. Cheng, D. Luo, H. Chen, and X. Zhang. InfoGCL: Information-Aware Graph
Contrastive Learning [C]. The 35th Conference on Neural Information Processing Systems (NeurIPS’21)
Acceptance rate: 2372/9122=26.0%[40] D. Xu, I. Yen, J. Zhao, and Z. Xiao. Rethinking Network Pruning - under the Pre-train
and Fine-tune Paradigm [C]. 2021 Annual Conference of the North American Chapterof the Association for Computational Linguistics (NAACL-HLT’21)
Acceptance rate: 477/1797=26.5%[41] X. Dong, Y. Zhu, Z. Fu, D. Xu, and G. de Melo. Data Augmentation with Adversarial
Training for Cross-Lingual NLI [C]. The 59th Annual Meeting of the Association for
Computational Linguistics (ACL’21)
Acceptance rate: 714/3350=21.3%[42] D. Xu, W. Cheng, J. Ni, D. Luo, Masanao Natsumeda, D. Song, B. Zong, H. Chen, and X.
Zhang. Deep Multi-Instance Contrastive Learning with Dual Attention for Anomaly
Precursor Detection [C]. The 21th SIAM International Conference on Data Mining(SDM’21)
Acceptance rate: 85/400=21.3%

5 of 13

[43] D. Xu, W. Cheng, X. Dong, B. Zong, W. Yu, J. Ni, D. Song, X. Zhang, H. Cheng, and
X. Zhang. Multi-Task Recurrent Modular Networks [C]. The 35th AAAI International
Conference on Artificial Intelligence (AAAI’21)
Acceptance rate: 1692/7911=21.4%[44] D. Xu, J. Liang, W. Cheng, H. Wei, H. Cheng, and X. Zhang. Transformer Style Relational
Reasoning with Dynamic Memory Updating for Temporal Network Modeling [C]. The
35th AAAI International Conference on Artificial Intelligence (AAAI’21)
Acceptance rate: 1692/7911=21.4%[45] H. Wei, D. Xu, J. Liang, and Z. Li. How Do We Move: Modeling Human Movement with
System Dynamics [C]. The 35th AAAI International Conference on Artificial Intelligence (AAAI’21)
Acceptance rate: 1692/7911=21.4%[46] J. Liang, Y. Wu, D. Xu, and V. Honavar. Longitudinal Deep Kernel Gaussian Process
Regression [C]. The 35th AAAI International Conference on Artificial Intelligence(AAAI’21)
Acceptance rate: 1692/7911=21.4%[47] D. Luo, W. Cheng, D. Xu, W. Yu, B. Zong, H. Chen, and X. Zhang. Parameterized
Explainer for Graph Neural Network [C]. The 34th Conference on Neural Information
Processing Systems (NeurIPS’20)
Acceptance rate: 1900/9454=20.1%[48] X. Dong, Y. Zhu, Y. Zhang, Z. Fu, D. Xu, S. Yang, and G. de Melo. Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification [C]. The 43rd
International ACM SIGIR Conference on Research and Development in Information
Retrieval (SIGIR’20)
Acceptance rate: 300/1062=28.2%[49] D. Xu, W. Cheng, B. Zong, D. Song, J. Ni, W. Yu, Y. Liu, H. Chen, and X. Zhang.
Tensorized LSTM with Adaptive Shared Memory for Learning Trends in Multivariate
Time Series [C]. The 34th AAAI International Conference on Artificial Intelligence(AAAI’20)
Acceptance rate: 1591/7737=20.6%[50] J. Liang, D. Xu, Y. Sun, and V. Honavar. Longitudinal Multi-Level Factorization Machines[C]. The 34th AAAI International Conference on Artificial Intelligence (AAAI’20)
Acceptance rate: 1591/7737=20.6%[51] D. Xu, W. Cheng, D. Luo, X. Liu, and X. Zhang. Spatio-Temporal Attentive RNN for Node Classification in Temporal Attributed Graphs [C]. The 28th
International Joint Conference on Artificial Intelligence (IJCAI’19)
Acceptance rate: 850/4752=17.9%[52] D. Xu, W. Cheng, D. Luo, Yameng Gu, X. Liu, J. Ni, B. Zong, H. Chen, and X. Zhang.
Adaptive Neural Network for Node Classification in Dynamic Networks [C]. The 19th
IEEE International Conference on Data Mining (ICDM’19)
Acceptance rate: 183/930=19.7%[53] D. Xu, W. Cheng, B. Zong, J. Ni, D. Song, W. Yu, Y. Chen, H. Chen, and X. Zhang. Deep
Co-Clustering [C]. The 19th SIAM International Conference on Data Mining (SDM’19)
Acceptance rate: 90/397=22.7%[54] J. Ni, S. Chang, X. Liu, W. Cheng, H. Chen, D. Xu, and X. Zhang. Co-Regularized Deep
Multi-Network Embedding [C]. The 27th International Conference on World Wide Web(WWW’18)
Acceptance rate: 170/1175=14.5%
6 of 13

[55] Y. Tian, D. Xu, and C. Zhang. A Review of Multi-Instance Learning Research [J]. Operations Research Transactions, 2018, 02: 1-17
[56] D. Xu, J. Wu, D. Li, Y. Tian, X. Zhu, and X. Wu. SALE: Self-Adaptive LSH Encodingfor Multi-Instance Learning [J]. Pattern Recognition, 2017 (7.74 impact factor)[57] D. Li, D. Xu, J. Tang, and Y. Tian. Metric Learning for Multi-Instance Classification with
Collapsed Bags [C]. The 30th IEEE International Joint Conference on Neural Networks(IJCNN’17)[58] D. Li, W. Zhang, D. Xu, and Y. Tian. Multi-Metrics Classification Machine [C]. International Conference on Information Technology and Quantitative Management (ITQM’16)[59] D. Xu, and Y. Tian. A Comprehensive Survey of Clustering Algorithms [J]. Annals of
Data Science, 2015, 2(2): 165-193[60] D. Xu, T. Chen, and W. Xu. A Support Vector Machine-Based Ensemble Prediction for
Crude Oil Price with VECM and STEPMRS [J]. International Journal of Global Energy
Issues, 2015[61] D. Xu, Y. Zhang, C. Cheng, W. Xu, and L. Zhang. A Neural Network-Based Ensemble
Prediction Using PMRS and ECM [C]. The 47th IEEE Hawaii International Conference
on System Sciences (HICSS’14)
I NDUSTRYE XPERIENCE

ACADEMIAE XPERIENCE

T EACHING
E XPERIENCE

Microsoft Research (MSR), Redmond, WA• Research Intern, Mentors: Subho Mukherjee, X. Liu,
D. Dey, A. H. Awadallah, J. Gao• Project: Task-agnostic Auto-Transformer Search [NeurIPS 2022]

2021

Moffett.AI, Los Altos, CA• Research Intern, Mentor: I. Yen, Co-founder• Project: Data-free Model Compression [NAACL 2021 & a U.S. patent]

2020

NEC Labs America, Princeton, NJ• Research Intern, Mentor: W. Cheng• Project: Knowledge Transfer in Multi-Task Learning [AAAI 2021]• Project: Trend Learning in Multivariate Time Series [AAAI 2020]

2019

NEC Labs America, Princeton, NJ• Research Intern, Mentor: W. Cheng, Senior Researcher• Project: Contrastive Anomaly Detection [SDM 2021]

2018

Penn State University• Graduate Research Assistant, Adviser: X. Zhang• Thesis: Resource-efficient Deep Learning: Democratizing AI at Scale

2017-2022

Chinese Academy of Sciences, Beijing, China• Graduate Research Assistant, Adviser: Y. Tian
• Thesis: Efficient Multi-instance Learning

2014-2017

Renmin University of China, Beijing, China• Undergraduate Research Assistant, Adviser: W. Xu• Thesis: Ensemble Forecasting Model for Time Series Data

2012-2014

Instructor at NC State• CSC 422: Automated Learning and Data Analysis
Course Materials: Introduction to Data Mining (Second Edition)

7 of 13

Spring’23, Fall’24• CSC 791&591: Advanced Topics in Efficient Deep Learning
Course Materials: Dive into Deep Learning

Fall’22, Fall’23, Spring’24

Teaching Assistant at Penn State• SRA 268, Visual Analytics
Instructor: Prof. Mahir Akgun
Course Materials: Visual Analytics with Tableau(Responsible for teaching lab classes of 46 students)

Fall 2021• SRA 450, Cybercrime and Cyberwar
Instructor: Prof. John Hodgson
Course Materials: Cybersecurity: What Everyone Needs to Know

Fall 2021• DS/CMPSC 410, Programming Models for Big Data
Instructor: Prof. John Yen
Course Materials: Learning Spark

Spring 2021

• SRA 365, Statistics for Security and Risk Analysis
Instructor: Dr. James Farrugia
Course Materials: Discovering Statistics Using R

Fall 2020• DS 402, Introduction to Social Media Mining
Instructor: Prof. Suhang Wang
Course Materials: Social Media Mining: An Introduction

Spring 2020

• SRA 365, Statistics for Security and Risk Analysis
Instructor: Dr. Katherine Hamilton
Course Materials: Foundations and Practice of Intermediate Statistics

Spring 2019

• IST 210, Organization of Data
Instructor: Prof. X. Zhang
Course Materials: Database Systems Concepts(The Award for Excellence in Teaching Support)

Fall 2018

Guest Lecturer

M ENTORINGE XPERIENCE• COSI 133A, Graph Mining
Brandeis University, Slides [Link]

Fall 2021• COSI 165B, Deep Learning
Brandeis University, Slides: [Link]

Spring 2021

Ph.D. Students• Li-Chia (Jerry) Chang, Ph.D. at NC State University
Topic: Agentic LLM-Powered Agents• Muhammad Alahmadi, Ph.D. at NC State University
Topic: Data-Centric Reliability in LLMs
• Kaushik Pillalamarri, Ph.D. at NC State University
Topic: Multi-Modal Foundation Models
Master Students• Teddy Chen, Master at NC State University
Topic: Implicit Bias in Large Language Models• Homak Patel, Master at NC State University
Topic: Reliable Retrieval-Augmented Generation for Education

8 of 13Undergraduate Researchers• Noah (Precious) Donkor, Undergraduate at NC State University
Topic: Reliable LLM-powered Agents• Aditya Basarkar, Undergraduate at NC State University
Topic: LLM-powered Math Reasoning
Intern Researchers• Xukun Liu, Master at Northwestern University
Topic: Accelerating LLM Decoding• Berwin Chen, Master at University of Birmingham
Topic: Retrieval-Augmented Generation for Scientific Discovery• Shanlin Liu, Master at University of Shanghai for Science and Technology
Topic: Retrieval-Augmented Generation for Scientific Discovery• Huanhuan Ma, Master at Chinese Academy of Sciences
Topic: Trustworthiness Evaluation of LLMs
• Zihao Mao, Undergraduate at Wuhan University
Topic: Accelerating Diffusion Models• Feifei Qiao, Master at Tongji University
Topic: Robust Inference in Agentic LLMs
Alumni• Zhiyuan Peng, Postdoc, 2023-2024, now Meta
Topic: Tool-Augmented Large Language Model• Shengkun Tang, PhD, 2023-2023, now MBZUAI
Topic: Multi-Modal Foundation Models• Chengyuan Liu, PhD, 2022-2024
Topic: Data-Efficient Large Language Models for Education• Bowen Lei, Intern, 2022-2024, now Apple
Topic: Theoretical Foundations of Efficient Learning• Zhengdong Zhang, Intern, 2023-2024, now Amazon
Topic: Reliable Large Language Models for Education• Zihan (Z) Dong, Undergraduate, 2023-2024, now Georgia Tech
Topic: Reliable Large Language Models for Education• Binfeng Xu, Intern, 2023-2023, now Samsung
Topic: Tool-Augmented Large Language Model• Cong Zeng, Intern, 2024-2024, now MBZUAI
Topic: LLM-Generated Content Detection• John Zhu, Master, 2024, now NC State
Topic: Trustworthy Evaluation of LLMs
P ROFESSIONALS ERVICE

Panel Reviewer• NSF CORE Program, 2024
• NSF CAREER Program, 2023
Column Editor• ACM SIGAI Newsletter
Workshop Chair

9 of 13• Workshop on DeepLearning-Hardware Co-Design for Generative AI Acceleration @DAC2024• The First Workshop on Dataset Distillation for Computer Vision @CVPR2024• The 2nd Workshop on Resource-Efficient Learning for Knowledge Discovery @KDD2024• The 1st Workshop on DL-Hardware Co-Design for AI Acceleration @AAAI2023• The 1st Workshop on Resource-Efficient Learning for Knowledge Discovery @KDD2023
Session Chair• Scalable, Distributed Systems & Trustable AI @KDD2022• Deep Learning: New Architectures and Models @KDD2022
Academic Committee Member• Machine Learning & Natural Language Processing Community (MLNLP)
Area Chair• The Joint International Conference on Computational Linguistics, Language Resources and
Evaluation (LREC-COLING) 2024
Senior Program Committee• AAAI Conference on Artificial Intelligence (AAAI) 2024, 2025
• International Joint Conferences on Artificial Intelligence (IJCAI) 2021
Program Committee• Neural Information Processing Systems (NeurIPS) 2020, 2021, 2022, 2023
• International Conference on Learning Representations (ICLR) 2021, 2022, 2023, 2024
• International Conference on Machine Learning (ICML) 2021, 2022, 2023
• AAAI Conference on Artificial Intelligence (AAAI) 2020, 2021, 2022, 2023
• SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2020-2023• Association for Computational Linguistics (ACL) Rolling Review 2022
• North American Chapter of the Association for Computational Linguistics (NAACL) 2021
• Conference on Empirical Methods in Natural Language Processing (EMNLP) 2020, 2021
• International Conference on Computational Linguistics (COLING) 2022
• Learning on Graphs Conference (LoG) 2022
• International Joint Conferences on Artificial Intelligence (IJCAI) 2020, 2022
• ACM International Conference on Web Search and Data Mining (WSDM) 2022
• SIAM International Conference on Data Mining (SDM) 2022
• European Chapter of the Association for Computational Linguistics (EACL) 2021
• Conference on Information and Knowledge Management (CIKM) 2020, 2021, 2022
• Asia-Pacific Chapter of the Association for Computational Linguistics & International Joint
Conference on Natural Language Processing (AACL-IJCNLP) 2020
• International Joint Conference on Neural Networks (IJCNN) 2018, 2019, 2020, 2021
Journal Reviewer• IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)• IEEE Transactions on Neural Networks and Learning Systems (TNNLS)• IEEE Transactions on Knowledge and Data Engineering (TKDE)• IEEE Transactions on Cybernetics• Information Fusion• ACM Transactions on Knowledge Discovery from Data (TKDD)• Pattern Recognition• Neural Networks• ACM Transactions on Asian and Low-Resource Language Information Processing• IEEE Access• Neural Computation• Complexity• Soft Computing• Journal of Sports Engineering and Technology• Complex & Intelligent Systems

10 of 13• Multimedia Tools and Applications• Big Data
External Conference Reviewer• AAAI’18-20, ACM CIKM’18-19, Big Data’18, ICDM’18-19, IJCNN’16-17, ITQM’16-17,
KDD’18-21, SDM’18-22, TheWebConf (WWW)’20-22, WSDM’20-21
Conference Volunteer• The Annual Conference of NAACL-HLT, 2021
• Backuping SDM Session Chairs, 2021
• The 35th AAAI Conference on Artificial Intelligence, 2021
• The 26th SIGKDD Conference on Knowledge Discovery and Data Mining, 2020
PATENTS• Tensorized LSTM with Adaptive Shared Memory for Learning Trends
W. Cheng, H. Chen, J. Ni, D. Xu, and W. Yu
U.S. Patent. 11,783,181. Oct. 2023
• Spatio Temporal Gated Recurrent Unit
W. Cheng, H. Chen, and D. Xu
U.S. Patent. 11,461,619. Oct. 2022
• System and Method for Knowledge-Preserving Neural Network Pruning
E. Yan, D. Xu, and Z. Xiao
U.S. Patent. 11,200,497. Dec. 2021

PATENTA PPLICATIONS• Information-aware Graph Contrastive Learning
W. Cheng, D. Xu, and H. Chen
U.S. Patent App. 17/728,071. Dec. 2022
• Neural Network Pruning Method and System via Layerwise Analysis
E. Yan, D. Xu, and J. Liu
U.S. Patent App. 17/107,046. Jun. 2022
• Bank-balanced-sparse Activation Feature Maps for Neural Network Models
E. Yan, D. Xu, and J. Liu
U.S. Patent App. 17/038,557. Mar. 2022
• Modular Networks with Dynamic Routing for Multi-task Recurrent Modules
W. Cheng, H. Chen, J. Ni, and D. Xu
U.S. Patent App. 17/158,483. July. 2021
• Unsupervised Multivariate Time Series Trend Detection for Group Behavior Analysis
W. Cheng, H. Chen, J. Ni, D. Xu, and W. Yu
U.S. Patent App. 16/987,734. Mar. 2021
• Adaptive Neural Networks for Node Classification in Dynamic Networks
W. Cheng, H. Chen, W. Yu, and D. Xu
U.S. Patent App. 16/872,546. Nov. 2020
• Automated Anomaly Precursor Detection
W. Cheng, D. Xu, H. Chen, and M. Natsumeda
U.S. Patent App. 16/520,632. Feb. 2020

P ROFESSIONAL
TALKS• The Impact of AI on Our Lives and Beyond
Fo Guang Shan Buddhist Temple, North Carolina, March 2024

11 of 13

• Leveraging Foundation Models for Enhanced Geospatial Analytics and Conservation
Forest Carbon Solutions Initiative (FCSI), NC State, March 2024
• How LLMs Work and Cutting-Edge Research on Generative AI
STARS AI Scholars Program, Dec 2023 [remote]• Sculpting the Future of Collective Growth in Collaborative AI
Microsoft Research Asia, Beijing, China, Sep 2023 [remote]• ChatGPT in Corporate Real Estate - Unlocking the Potential [link]
CoreNet Global, Raleigh, NC, USA, Aug 2023
• Testing Accuracy is Not All You Need: Less Training Cost & More Testing Reliability
Rutgers University, New Brunswick, USA, Feb 2023
• Resource-efficient Deep Learning: Democratizing AI at Scale
Pinterest, San Francisco, USA, Aug 2022
• Resource-efficient Deep Learning: Democratizing AI at Scale
Amazon Search (A9), USA, May 2022
• Resource-efficient Deep Learning: Democratizing AI at Scale
Vanderbilt University, Nashville, USA, April 2023
• Resource-efficient Deep Learning: Democratizing AI at Scale
University of Connecticut, Stamford, USA, April 2023
• Parameter Efficiency: Democratizing AI at Scale [Slides]
Brandeis University, Waltham, USA, Dec 2021
• Chasing Efficiency of Pre-trained Language Models
Microsoft Research Lab, Redmond, Washington, USA, Jun 2021
• BERT Pruning: Structural vs. Sparse [Slides]
Brandeis University, Waltham, USA, Apr 2021
• BERT, Compression, and Applications [Slides]
Xpeng Motors, Mountain View, USA, Apr 2021
• BERT Architecture and Computation Analysis
Moffett.AI, Los Altos, USA, May 2020.
• Anomaly Precursor Detection via Multi-Instance Contrastive Learning
NEC Laboratories America, Princeton, USA, May 2019
• Efficient Multiple Instance Learning [Slides]
NEC Laboratories America, Princeton, USA, May 2018
H ONORS AND
AWARDS

North Carolina State University• Microsoft Accelerating Foundation Models Research Award• NCSU Carla Savage Award• ICCCN Best Paper Award
The Pennsylvania State University• College of IST Award for Excellence in Teaching Support (top 2)• Third Place Winner (Eng.) in The 37rd Annual PSU Graduate Exhibition• NAACL Scholarship• SIAM Student Travel Award• KDD Student Registration Award• AAAI Student Scholarship• IST Travel Award

12 of 13

2024
2024
2023
2019
2022
2021
2021
2020
2020
2019-2021

University of Chinese Academy of Sciences• Chinese Academy of Sciences President’s Fellowship (the most prestigious award)
2016• National Graduate Scholarship, China (2% in university)
2016• Graduate Student Academic Scholarship2015-2017
Renmin University of China• First-class Scholarship of Sashixuan Elite Fund, China (5% in university)• Kwang-hua Scholarship of RUC, China• Second-class Scholarship of Excellent Student Cadre• Meritorious Winner in Mathematical Contest in Modeling, USA• First-class Scholarship of Social Work and Volunteer Service of RUC
E XTRACURRICULAR • IEEE Membership
ACTIVITIES• ACM Membership• ACL Membership• AAAI Student Membership• Volunteer of Beijing Volunteer Service Federation• President of Youth Volunteers Association of School of Information• Leader of National Undergraduate Training Programs

13 of 13

2014
2014
2014
2013
2013
2023-Present
2021-Present
2021-Present
2019-2021
2012-20142012-20132011-2012