| 2026 | ACL | SciText2Eq: Assessing LLMs for Explainable Equation Generation for Scientific Creativity. | Yifan Mo, Xiao Fu, Yue Su, Qingyu Meng, Koen V. Hindriks, Qingzhi Liu, Jiahuan Pei |
| 2025 | CIKM | Trustworthy AI Psychotherapy: Multi-Agent LLM Workflow for Counseling and Explainable Mental Disorder Diagnosis. | Mithat Can Ozgun, Jiahuan Pei, Koen V. Hindriks, Lucia Donatelli, Qingzhi Liu, Junxiao Wang |
| 2024 | EuroPar | Accelerating Scientific Computing Kernels by Fusing the Polyhedral and Tensor Compilers. | Qingzhi Liu, Changbo Chen, Hanwen Dai |
| 2024 | ICPADS | MARS: Multi-Agent Deep Reinforcement Learning for Real-Time Workflow Scheduling in Hybrid Clouds with Privacy Protection. | Long Cheng, Haoyang He, Yan Gu, Qingzhi Liu, Zhiming Zhao, Fang Fang |
| 2022 | IPCCC | Performance Evaluation of Resource Management Schemes for Cloud Native Platforms with Computing Containers. | Yuqi Fu, Naseem Machlovi, Ying Mao, Jiayin Wang, Long Cheng, Qingzhi Liu |
| 2020 | PSB | Network-Based Matching of Patients and Targeted Therapies for Precision Oncology. | Qingzhi Liu, Min Jin Ha, Rupam Bhattacharyya, Lana X. Garmire, Veerabhadran Baladandayuthapani |
| 2019 | CCGRID | Deep Reinforcement Learning for IoT Network Dynamic Clustering in Edge Computing. | Qingzhi Liu, Long Cheng, Tanir Ozcelebi, John Murphy, Johan Lukkien |
| 2019 | ISNCC | Performance Evaluation of Thread Protocol based Wireless Mesh Networks for Lighting Systems. | Srikanth Sistu, Qingzhi Liu, Tanir Ozcelebi, Esko Dijk, Teresa Zotti |
| 2019 | WCNC | CluFlow: Cluster-based Flow Management in Software-Defined Wireless Sensor Networks. | Qingzhi Liu, Tanir Ozcelebi, Long Cheng, Fernando A. Kuipers, Johan Lukkien |
| 2019 | SERVICES | Learning Process Models in IoT Edge. | Long Cheng, Cong Liu, Qingzhi Liu, Yucong Duan, John Murphy |
| 2018 | EuroPar | Minimizing Network Traffic for Distributed Joins Using Lightweight Locality-Aware Scheduling. | Long Cheng, John Murphy, Qingzhi Liu, Chunliang Hao, Georgios Theodoropoulos |
| 2011 | MSWIM | GDE: a distributed gradient-based algorithm for distance estimation in large-scale networks. | Qingzhi Liu, Andrei Pruteanu, Stefan Dulman |