CNShanghai Jiao Tong UniversitySchool of Integrated Circuits (Information and Electronic Engineering)
Hao He
副教授
Works on information extraction and generation in NLP, logical reasoning for big data and knowledge-graph-based decision inference.
Hao He's lab at SJTU works on knowledge, QA & retrieval, reasoning & agents and efficient training & inference. 19 papers since 2023, 11 at top venues, mostly ICASSP and AAAI. Applications close .
At a glance
- papers, 2023–2026
- 5/ year
- at T1 venues
- 58%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom DBLP, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 5 papers2025 · T1 4
- 3 papers2024 · T1 2
- 11 papers2023 · T1 5
T1 venuesOtherVenues: ICASSP, AAAI, EMNLP, Data Mining and Knowledge Discovery, Expert Systems with Applications
Research focuspaper titles that use each topic's words
- Reasoning & agents5 papers
- Knowledge, QA & retrieval1 paper
- Efficient training & inference0 papers
- logical reasoning0 papers
- logical0 papers
Paper list
From DBLP.
20255 papers
- Data Mining and Knowledge DiscoveryA multi-scale time series forecasting framework with temporal hierarchical information fusion and reconciliation (opens in a new tab)
- ICASSP (T1)Faithful Self-Refinement in Mathematical Reasoning via Progressive Back-Translation (opens in a new tab)
- NAACL (T1)Forest for the Trees: Overarching Prompting Evokes High-Level Reasoning in Large Language Models (opens in a new tab)
- ICASSP (T1)Look Before You Leap: Problem Elaboration Prompting Improves Mathematical Reasoning in Large Language Models (opens in a new tab)
- ICASSP (T1)TopoRefine: Iterative Refinement with Reasoning Topology as High-Level Feedback (opens in a new tab)
20243 papers
- AAAI (T1)Can Large Language Models Serve as Rational Players in Game Theory? A Systematic Analysis (opens in a new tab)
- ICASSP (T1)Comparable Demonstrations Are Important In In-Context Learning: A Novel Perspective On Demonstration Selection (opens in a new tab)
- Expert Systems with ApplicationsUnlock the Potential of Counterfactually-Augmented Data in Out-Of-Distribution Generalization (opens in a new tab)
202311 papers
- Appl. Intell.Accurate use of label dependency in multi-label text classification through the lens of causality (opens in a new tab)
- EMNLP (T1)Chain-of-Thought Tuning: Masked Language Models can also Think Step By Step in Natural Language Understanding (opens in a new tab)
- Appl. Intell.Contrast with major classifier vectors for federated medical relation extraction with heterogeneous label distribution (opens in a new tab)
- ICASSP (T1)Improving the out-of-Distribution Generalization Capability of Language Models: Counterfactually-Augmented Data is not Enough (opens in a new tab)
- AAAI (T1)Latent Constraints on Unsupervised Text-Graph Alignment with Information Asymmetry (opens in a new tab)
- CSAILDSeq: Latent Diffusion Models for Sequence to Sequence Text Generation (opens in a new tab)
- Findings of ACLMTR: A Dataset Fusing Inductive, Deductive, and Defeasible Reasoning (opens in a new tab)
- CSAIOntology-Semantic Alignment On Contrastive Video-Language Model for Multimodel Video Retrieval Task (opens in a new tab)
- AAAI (T1)Preference-Controlled Multi-Objective Reinforcement Learning for Conditional Text Generation (opens in a new tab)
- EMNLP (T1)Task-Level Thinking Steps Help Large Language Models for Challenging Classification Task (opens in a new tab)
- NLPCCTwo-Stage Topic Sentence Extraction for Chinese Student Essays (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to SJTUFall 2027
Computer Science PhD (international applicants)
- Round
- Self-funded
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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Data
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