Demo Data from EDBT 2026 / reviewers in the wild / expert
Haotian Zhang 0029
dblp:83/4184-29
· DBLP profile ↗
3ranked-venue papers
0as first author
3since 2021 · last 2026
0009-0000-1483-6261ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 3 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Learning and educational technologies · 100% | |
| Artificial intelligence
1 paper |
Language models and text generation · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Learning and educational technologies › educational assessment
automated grading |
0.9 | 1 | 2025 | MathMistake Checker: A Comprehensive Demonstration for Step-by-Step Math Problem Mistake Finding by Prompt-Guided LLMs · AAAI 2025 |
Natural language and speech › Language models and text generation › mathematical reasoning
mathematical problem solving |
0.3 | 1 | 2025 | MathMistake Checker: A Comprehensive Demonstration for Step-by-Step Math Problem Mistake Finding by Prompt-Guided LLMs · AAAI 2025 |
Methods — techniques the papers use, named apart from their topics
large language model · 1.7computer vision · 1.7chain-of-thought prompting · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Towards enterprise-specific question-answering for IT operations and maintenance based on retrieval-augmented generation mechanism
Zhuoxuan Jiang, Tianyang Zhang 0004, Shengguang Bai, Haotian Zhang 0029, Yinong Xun, Wen Si |
Expert Syst. Appl. | 5 |
| 2025 | MathMistake Checker: A Comprehensive Demonstration for Step-by-Step Math Problem Mistake Finding by Prompt-Guided LLMsabstractWe propose a novel system, MathMistake Checker, designed to automate step-by-step mistake finding in mathematical problems with lengthy answers through a two-stage process. The system aims to simplify grading, increase efficiency, and enhance learning experiences from a pedagogical perspective. It integrates advanced technologies, including computer vision and the chain-of-thought capabilities of the latest large language models (LLMs). Our system supports open-ended grading without reference answers and promotes personalized learning by providing targeted feedback. We demonstrate its effectiveness across various types of math problems, such as calculation and word problems. Tianyang Zhang 0004, Zhuoxuan Jiang, Haotian Zhang 0029 |
AAAI | 3 |
| 2025 | LLMA4ITOps: A Lightweight LLM-Based Multi-Agent Framework for IT Operations and Maintenance
Zhuoxuan Jiang, Tianyang Zhang 0004, Haotian Zhang 0029, Yinong Xun, Yang Liu 0218, Dehua Feng, Wen Si |
NLPCC (2) | 3 |