Mingzhe Du
PhD Candidate @ NTU CCDS / Research Associate @ NUS IDS / Research Engineer @ CISCO NUS
Du Mingzhe is a third-year PhD Candidate at the College of Computing and Data Science (CCDS), Nanyang Technological University (NTU), supervised by Prof. Luu Anh Tuan. He is also a Research Associate at the Institute of Data Science (IDS), National University of Singapore (NUS), working closely with Prof. See-Kiong Ng. Mingzhe got his master’s degree at the University of Melbourne, under the guidance of Prof. Richard Sinnott. Before his PhD journey, Mingzhe developed Tiktok search engine at ByteDance.
Currently, Mingzhe’s research interests lie in Natural Language Processing (NLP) and its downstream tasks [Google Scholar]. By the way, his favorite drink is Iced Americano ☕️.
- 📮: 3 Research Link, Singapore 117602
- 📧: mingzhe [at] nus.edu.sg / mingzhe001 [at] ntu.edu.sg / mz [at] alumni.unimelb.edu.au
| Feb 01 2025 | I was nominated as a NeurIPS’25 reviewer. |
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| Jan 11 2025 | I developed a multiple language remote runtime Monolith. Please feel free to have a try 😀. |
| Jan 06 2025 | Our paper “Unraveling Online Mental Health Through the Lens of Early Maladaptive Schemas: An AI-Enabled Study of Online Mental Health Communities” has been accepted into Journal of Medical Internet Research (JMIR). |
| Dec 09 2024 | Our paper “Towards Verifiable Text Generation with Generative Agent” has been accepted into AAAI’25 🎉 |
| Dec 01 2024 | I was nominated as an ICML’25 reviewer. |
| Nov 30 2024 | Our paper “Curriculum Demonstration Selection for In-Context Learning” has been accepted into SAC’25. |
| Nov 29 2024 | I completed the 10KM run at SCSM with a time of 58:58 🏃! |
| Nov 21 2024 | I passed the PhD Qualification Examination (QE) at NTU 🎉. |
| Nov 21 2024 | We released the Venus code generation dataset. |
| Oct 26 2024 | Our team, Black Mesa participated in the Singapore AI CTF and achieved an 23rd place out of more than 500 teams! 🎉 |
| Oct 01 2024 | I was nominated as an AISTATS’25 reviewer. |
| Sep 26 2024 | Our paper “Mercury: A Code Efficiency Benchmark for Code Large Language Models” has been accepted to NeurIPS’24 Track Datasets and Benchmarks 🎉 |