Ebrahim Bagheri
Professor
Studies information retrieval, social network analytics and responsible AI.
Ebrahim Bagheri's lab at Toronto works on knowledge, QA & retrieval, language & society and social computing & responsible AI. 92 papers since 2023, 37 at top venues, mostly CIKM and ECIR. 4 PhD students and 9 master's students. 81 alumni. Applications close .
At a glance
- papers, 2023–2026
- 24/ year
- at T1 venues
- 40%
- PhD students
- 4
- MS students
- 9
- alumni listed
- 81
Papers by yearfrom the lab homepage, 2023–2026
- 37 papers2026 · T1 22 · collecting
- 27 papers2025 · T1 10
- 14 papers2024 · T1 2
- 14 papers2023 · T1 3
T1 venuesOtherVenues: CIKM, ECIR, SIGIR, TIST, WWW
Research focuspaper titles that use each topic's words
- Knowledge, QA & retrieval20 papers
- Social computing & responsible AI6 papers
- Language & society3 papers
- faculty0 papers
- social network0 papers
Paper list
From the lab homepage.
202637 papers
- CIKM (T1)A Benchmark & Dataset for Detecting AI-Manipulated Visual Evidence in the Court System (opens in a new tab)
- SIGIR (T1)A Large-Scale Dataset for Gender-Fair Query Reformulations (opens in a new tab)
- Machine Learning JournalA Regularization Framework for Gender Bias Mitigation in Dense Neural Rankers (opens in a new tab)
- SIGIR (T1)A Reproducibility Study of LLM-Based Query Reformulation (opens in a new tab)
- ACM Transactions on Social ComputingA Social Media Lens on the Needs and Concerns of Information Workers (opens in a new tab)
- ECIRAdversarial Edge Perturbation Framework in Graph-based Retrieval (opens in a new tab)
- Information Processing and ManagementAI ethics education: A scoping review of pedagogy, curriculum, and assessment (opens in a new tab)
- WSDM (T1)Can LLMs Uphold Research Integrity? Evaluating the Role of LLMs in Peer Review Quality (opens in a new tab)
- WWW (T1)Decentralized in Name Only: The Centralization of DAO Labor (opens in a new tab)
- Information Processing and ManagementDiffusion-Based Generative Modeling for Expert Team Formation (opens in a new tab)
- TOISEMPRA: Embedding Perturbation Rank Attack against Neural Ranking Models (opens in a new tab)
- CIKM (T1)EviQE: Evidence Selection for LLM-Based Query Expansion (opens in a new tab)
202527 papers
- Machine Learning JournalA Contrastive Neural Disentanglement Approach for Query Performance Prediction (opens in a new tab)
- SIGIR-APAdversarial Attacks against Neural Ranking Models via In-Context Learning (opens in a new tab)
- ECIRBenchmarking Prompt Sensitivity in Large Language Models (opens in a new tab)
- SIGIR (T1)Bias-Aware Curriculum Sampling For Fair Ranking (opens in a new tab)
- CIKM (T1)Building Trustworthy Peer Review Quality Assessment Systems (opens in a new tab)
- CIKM (T1)Datasets for Supervised Adversarial Attacks on Neural Rankers (opens in a new tab)
- ECIRexHarmony: Authorship and Citations for Benchmarking the Reviewer Assignment Problem (opens in a new tab)
- Research Synthesis MethodsExploring the methodological quality and risk of bias in 200 systematic reviews: A comparative study of ROBIS and AMSTAR-2 tools, (opens in a new tab)
- International ACM SIGIR Conference on Information Retrieval in the Asia PacificFew-Shot Adversarial Attacks against Neural Ranking Models (opens in a new tab)
- Machine Learning JournalJointly Learning Content-Network Representations for Collaborative Expert Discovery (opens in a new tab)
- TISTLearning Context-aware Term Importance for Query Performance Prediction (opens in a new tab)
- Knowledge and Information SystemsLearning to Rank Under Uncertainty: A Robust Neural Approach (opens in a new tab)
202414 papers
- ECIRContext-Aware Query Term Difficulty Estimation for Performance Prediction (opens in a new tab)
- CIKM (T1)Enhanced Retrieval Effectiveness through Selective Query Generation (opens in a new tab)
- ECIREstimating Query Performance Through Rich Contextualized Query Representations (opens in a new tab)
- International ACM SIGIR Conference on Information Retrieval in the Asia PacificEvaluating Relative Retrieval Effectiveness with Normalized Residual Gain (opens in a new tab)
- BMJ OpenExploring decision-makers’ challenges and strategies when selecting multiple systematic reviews: insights for AI decision support tools in healthcare (opens in a new tab)
- Machine Learning JournalGender Disentangled Representation Learning in Neural Rankers (opens in a new tab)
- International ACM SIGIR Conference on Information Retrieval in the Asia PacificIt Takes a Team to Triumph: Collaborative Expert Finding in Community QA Networks (opens in a new tab)
- ECIRLaQuE: Enabling Entity Search at Scale (opens in a new tab)
- ECIRLearning to Jointly Transform and Rank Difficult Queries (opens in a new tab)
- Information Processing and ManagementPredicting Users’ Future Interests on Social Networks: A Reference Framework (opens in a new tab)
- The European Conference on Information Retrieval (ECIR )Query Performance Prediction: From Fundamentals to Advanced Techniques (opens in a new tab)
- International ACM SIGIR Conference on Information Retrieval in the Asia Pacific (SIGIR AP )Query Performance Prediction: Techniques and Applications in Modern Information Retrieval (opens in a new tab)
202314 papers
- Journal of Biomedical InformaticsA Self-supervised Language Model Selection Strategy for Biomedical Question Answering (opens in a new tab)
- TOISA Variational Neural Architecture for Skill-based Team Formation (opens in a new tab)
- ECIRDe-Biasing Relevance Judgements for Fair Ranking (opens in a new tab)
- ECIRDon't Raise Your Voice, Improve Your Argument: Learning to Retrieve Convincing Arguments (opens in a new tab)
- Information SciencesDyHNet: Learning Dynamic Heterogeneous Network Representations (opens in a new tab)
- Information Retrieval JournalLearning Heterogeneous Subgraph Representations for Team Discovery (opens in a new tab)
- ECIRLearning Query-Space Document Representations for High-Recall Retrieval (opens in a new tab)
- Information SciencesLearning to Rank and Predict: Multi-Task Learning for Ad hoc Retrieval and Query Performance Prediction (opens in a new tab)
- ECIRNeural Ad hoc Retrieval Meets Information Extraction (opens in a new tab)
- ECIRNeural Ad-Hoc Retrieval Meets Open Information Extraction (opens in a new tab)
- CIKM (T1)Neural Disentanglement of Query Difficulty and Semantics (opens in a new tab)
- CIKM (T1)Noisy Perturbations for Estimating Query Difficulty in Dense Retrievers (opens in a new tab)
Where alumni went81 listed
Apply to TorontoFall 2027
Computer Science MSc/PhD
- Round
- Single round (Dec 1)
- Opens
- —
- GRE
- optional
- English
- TOEFL/IELTS if applicable
- Fee
- CAD 135
Confirmed on the program page.
Similar labsnearest by research summary
- USUCLA
Saadia Gabriel
- Language & society
- SGNTU
Jordan Boyd-Graber
- Large language models
- USCMU
Carolyn P. Rosé
- Human-AI collaboration & trust
- USGeorgia Tech
Alexander Lerch
- Music, audio & sound
- USCMU
Maarten Sap
- Language & society
- GBEdinburgh
Bjorn Ross
- Language & society
Your notes
Save this lab to keep notes.
Data
Checked against the lab homepage.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.
Torontocloses