About Me

I am a Ph.D. student in Computer Science at Rutgers University, advised by Prof. Zheng (Eddy) Z. Zhang.

My research lies at the intersection of quantum computing systems, and LLM reasoning. On the quantum computing side, I work on Hamiltonian simulation, fermionic simulation, quantum circuit compilation, and hardware-aware optimization. On the LLM reasoning, I am interested in improving the reasoning capabilities of large language models through inference-time steering, scalable oversight, and weak-to-strong learning.

Before joining Rutgers, I received my B.S. in Computer Science from Lanzhou University, where I worked with Prof. Yonggang Lu on graph machine learning and community detection. I also worked with Prof. Fajie Yuan at Westlake University on deep learning for biological applications.

Research Interests

• Hamiltonian and fermionic simulation, particularly on emerging and hardware-constrained quantum architectures.
• Large language model reasoning, including inference-time steering, scalable oversight, and weak-to-strong learning.

News

• Jul. 2026: Released an updated version of Latent Reward Steering (LRS).
• Jun. 2026: Released new preprints on weak-critic scalable oversight and hardware-aware fermionic simulation.
• Jun. 2025: Our paper Genesis was presented at ISCA 2025 in Tokyo, Japan.
• May 2025: Received an ISCA Student Travel Award.
• Jul. 2024: Our survey on community detection was published in Neurocomputing.

Selected Publications

^† Equal contribution.
^* Corresponding author.

Large Language Model Reasoning & Scalable Oversight

Latent Reward Steering: An Adaptive Inference-Time Framework that Implicitly Promotes Cognitive Behaviors in Reasoning LLMs

Jiakang Li^†, Guanyu Zhu^†, Can Jin^†, Chenxi Huang, Dexu Yu, Ronghao Chen, Yang Zhou, Hongwu Peng, Xuanqi Lan, Dimitris N. Metaxas^*, and Youhua Li^*

EMNLP, 2026.

[Paper] [Code]

Weak Critics Make Strong Learners: On-Policy Critique Distillation for Scalable Oversight

Can Jin^†, Jiakang Li^†, Rui Wu, Eddy Zhang, and Dimitris N. Metaxas

International Conference on Machine Learning (ICML) @AI4math, 2026.

[Paper] [arXiv]

Quantum Computing Systems, Compilation & Simulation

Linear Complexity Fermionic Simulation on Quantum Devices with Hardware Connectivity Constraints

Xiangyu Gao^†, Winston Li^†, Jiakang Li, Zirui Li, Yipeng Huang, Costin Iancu, and Eddy Z. Zhang

arXiv preprint, 2026.

[Paper]

Genesis: A Compiler for Hamiltonian Simulation on Hybrid CV-DV Quantum Computers

Zihan Chen^†, Jiakang Li^†, Minghao Guo^†, Henry Chen, Zirui Li, Joel Bierman, Yipeng Huang, Huiyang Zhou, Yuan Liu, and Eddy Z. Zhang

Proceedings of the 52nd Annual International Symposium on Computer Architecture (ISCA 2025), pp. 1583–1597.

[Paper] [arXiv] [Code]

Leveraging Phase Polynomials for Quantum Circuits Optimization

Zihan Chen, Henry Chen, Xiangyu Gao, Yuwei Jin, Minghao Guo, Enhyeok Jang, Jiakang Li, Caitlin Chan, Won Woo Ro, and Eddy Z. Zhang

Proceedings of the 6th IEEE International Conference on Quantum Computing and Engineering (QCE 2025, poster), Vol. 2, pp. 638–639.

[Paper] [Poster] [Code]

Graph Learning & Community Detection

A Comprehensive Review of Community Detection in Graphs

Jiakang Li^†, Songning Lai^†, Zhihao Shuai, Yuan Tan, Yifan Jia, Mianyang Yu, Zichen Song, Xiaokang Peng, Ziyang Xu, Yongxin Ni, Haifeng Qiu, Jiayu Yang, Yutong Liu, and Yonggang Lu^*

Neurocomputing, Volume 600, Article 128169, 2024.

[Paper] [arXiv]

Community Detection Using Revised Medoid-Shift Based on KNN

Jiakang Li, Xiaokang Peng, Jie Hou, Wei Ke, and Yonggang Lu^*

International Conference on Intelligent Computing (ICIC 2023, oral presentation).

[Paper] [arXiv] [Code]

Education

Rutgers University

Ph.D. in Computer Science
Sep. 2024–Present

Research areas: quantum computing systems, compiler optimization, and large language model reasoning.

Lanzhou University

B.S. in Computer Science
Sep. 2019–Jun. 2023

• GPA ranking: Top 13%.
• Affiliated with the Center for Computer Software and Theory.

Additional Study

• University of California, Berkeley — Exchange Student, Jan.–Jun. 2021.
• Massachusetts Institute of Technology — Winter Course in Vision Science, Jan.–Feb. 2021.

Teaching Experience

• Teaching Assistant, CS 314: Principles of Programming Languages, Rutgers University, Spring 2026.
• Teaching Assistant, CS 461: Machine Learning Principles, Rutgers University, Fall 2025.
• Teaching Assistant, CS 415: Compilers, Rutgers University, Spring 2025.
• Teaching Assistant, CS 206: Discrete Structures, Rutgers University, Fall 2024.

Industry Experience

Trip.com Group

Machine Learning Algorithm Intern
Jun. 2022–Oct. 2022 · Shanghai, China

• Developed machine learning models for airfare price prediction.
• Worked on risk-control models for detecting potentially malicious users.
• Trained a detection model that achieved approximately 90% precision in the target evaluation setting.

Academic Service

• Reviewer, IEEE International Conference on Multimedia and Expo (ICME 2025).
• Reviewer, International Joint Conference on Neural Networks (IJCNN 2025).

Honors and Awards

• ISCA Student Travel Award, 2025.
• Lanzhou University Third-Class Scholarship, 2022.
• Lanzhou University Second-Class Scholarship, 2021.
• Lanzhou University Second-Class Scholarship, 2020.
• University-Level Academic Excellence Award, 2020, 2021, and 2022.

Beyond Research

Outside research, I enjoy competitive MOBA games, singing, and community building. I also create content about Arena of Valor and other MOBA games.

I am the founder of the Lanzhou University Flyer organization, a student-led initiative that connects current students with alumni and shares information about graduate study, career development, and international opportunities.

I enjoy meeting people with different backgrounds and exploring new research ideas. Feel free to reach out if you would like to discuss quantum computing, large language models, AI for science, academic collaboration, or shared interests.