About Me

🎓 Academic Pursuits

Hanzhi Zhang is currently a PhD Candidate in Computer Science, directed by Dr. Yunhe Feng in the Department of Computer Science and Engineering (CSE) at the University of North Texas (UNT), Denton, TX, with expected graduation in May 2027. She is a member of the Responsible AI Lab and the LLaVi Lab at UNT. Her academic journey is driven by a profound interest in exploring the efficiency and reliability of Artificial Intelligence (AI). Her research primarily focuses on ⚡ Efficient AI Systems, delving into hardware-aware LLM inference, sparse and mixed-precision computation, post-training quantization, and GPU optimization and HPC. She also explores 🤖 Reliable and Responsible AI, including 🌍 multilingual fact verification, 🧠 AI Hallucination, and 👁️ safety-critical perception. Her work aims to forge a path towards more accountable, transparent, and efficient AI systems.

📚 Professional Background

Hanzhi completed her 📊 MS in Data Science with 🏆 Distinction from the University of Birmingham (2021-2022) and her 💻 BS in Computer Science and Technology (Honours) from Xiamen University (2017-2021). She previously worked as a 💾 Storage Backend Intern at OPPO, Inc. (2020-2021), focusing on file systems development.

📰 News

🏆 October 2026: Recognized as a Top Reviewer at NeurIPS 2026
🎉 2026: Towards Visual Query Segmentation in the Wild paper accepted to NeurIPS 2026 (co-first author)
🌍 2026: Poly-FEVER paper accepted to EMNLP Findings 2026
🚀 2026: Contributed to Amazon Research Awards TileRoute-Trainium as lead student researcher and proposal co-writer
🎓 Fall 2026: Designed and delivered three 3-hour lectures on CNNs, NLP, and Transformers for CSCE 4201
🤝 Summer 2026: Served as Academic Coordinator and Research Mentor for nine undergraduates in UNT's NSF REU program
🎤 Summer 2026: Gave guest lecture on Long-Context Evaluation of Large Language Models at UNT's NSF REU program
📢 May 2026: Gave invited talk on Responsible AI at the Presidential AI Challenge
🔬 May 2026: Selected for ORNL's Summer 2026 research internship; continued as a remote research collaborator
🔬 2026: Started research collaboration with ORNL's AAIMS Group on 3D PDE operator learning
⚡ 2026: Started collaboration with Amazon scientist Qian Qu on mixed-precision LLM inference using AWS Trainium
📝 2026: Harmful Factuality paper published in EACL Findings 2026
🎬 2026: Co-developed VQS-4K benchmark with 4,111 videos and over 1.3 million frames
📚 2026: Serving as reviewer for ICLR 2027, NeurIPS 2026, ICML 2026, and ACL Rolling Review; external reviewer for ACM SEC 2026
💬 2024–2026: Presented research at UNT's LLaVi Lab AI Seminar
📢 Oct 2025: Gave invited talk on Responsible AI at AIxHEART
🎤 July 2025: Presented DAM at ACL Findings 2025
🎉 May 2025: DAM paper accepted to ACL Findings 2025
🚗 May 2025: Presented D2SO at IEEE MOST 2025
🏆 April 2025: Won IEEE MOST Student Travel Grant Award
🤝 April 2025: Participated in Women-in-Computing session at IEEE MOST 2025
📝 March 2025: Poly-FEVER benchmark published on arXiv
🚗 March 2025: D2SO paper accepted to IEEE MOST 2025
✅ February 2025: Passed PhD Qualifying Exam
🏥 February 2025: HALO paper accepted to IEEE/ACM CHASE 2025
🎓 Spring 2025: Served as TA for CSCE 5300 (64 graduate students); received an Excellent teaching evaluation
🛠️ March 2025: Shared TACC Lonestar6 HPC tutorial
👩‍💻 2025–present: Joined UNT's Women in Data Science (WinDS) chapter
📚 2025: Served as reviewer for CIKM 2025 and external reviewer for ICDM 2025
🖼️ 2024 & 2025: Presented at UNT University Research Day
🤝 December 2024: Volunteered at NSF CISE MSI Aspiring PI Workshop at UNT
🎤 November 2024: Presented at NSF eCAT 2024 Annual Fall Meeting
🎓 Fall 2024: Served as TA for CSCE 5300 (79 graduate students); received an Excellent teaching evaluation
💻 Summer 2024: Attended Argonne Training Program on Extreme-Scale Computing (ATPESC-24)
🔬 June 2024: Selected for Argonne Training Program on Extreme-Scale Computing (ATPESC-24)
🤝 May 2024: Volunteered at IEEE MOST 2024
🏆 March 2024: Won First Place at UNT Engineering Showcase Poster Presentation
📚 2024: ByteScience paper published at IEEE ICDMW 2024
🖥️ 2024–present: Serving as PI for ALCF Polaris computing allocations (5,000 node-hours per allocation)
📚 2024: Served as reviewer for ICDM 2024; external reviewer for WWW, AAAI, WACV, and ICNC 2024
💬 2023–2024: Presented at UNT Data Science and AI Talk Series
🖼️ 2023–2026: Presented at UNT CSE Open House
🎓 August 2023: Started PhD in Computer Science at UNT and joined Responsible AI Lab
🌱 2023–present: Mentoring 8 graduate students, 2 undergraduates, and 1 TAMS student in Responsible AI Lab, including Poly-FEVER and D2SO projects
🛠️ October 2023: Shared Docker setup tutorial
📚 2023: Served as reviewer for IEEE ICPADS 2023 and external reviewer for EISA and Inscrypt 2023
🎓 December 2022: Completed MS in Data Science with Distinction at University of Birmingham
🎓 August 2021: Completed BS in Computer Science and Technology (Honours) at Xiamen University
💾 2020–2021: Worked as Storage Backend Intern at OPPO

🌟 Human Elements

Hanzhi Zhang’s personality, as an 🎭 ENTP-A, is defined by her insatiable curiosity and robust intellectual flexibility. She revels in intellectual challenges and is the quintessential devil’s advocate, skillfully deconstructing arguments and occasionally challenging her own beliefs by exploring alternative viewpoints.

Hanzhi pursues an intriguing blend of interests. She delves into the mystical realms of 🔮 tarot and astrology, using them as unique tools to influence decisions in programming and research. She also enjoys unraveling the latest gossip and embarks on quests to locate lost items.

Her cultural appetites are satiated by frequent visits to 🎭 musicals, dramas, 🏛️ museums, galleries, and exhibitions, which enrich her understanding of diverse perspectives.

📸 Photography is another passion of hers, which she humorously describes as a sport. This hobby sees her traversing 🏔️ mountains, laden with heavy gear, tirelessly capturing moments through day and night—an endurance exercise that combines her love for nature and artistry.

Next goal: convince my advisor that consulting tarot cards for hyperparameter tuning counts as “novel optimization techniques.” Stay tuned.