Babak Salimi
Assistant Professor
Combines data management, causal inference and machine learning for responsible data science and causal relational learning.
Babak Salimi's lab at UCSD works on probabilistic & causal inference, data management & big data and efficient training & inference. 23 papers since 2023, 18 at top venues, mostly SIGMOD and VLDB. 4 PhD students. 16 alumni. Applications close .
At a glance
- papers, 2023–2026
- 6/ year
- at T1 venues
- 78%
- PhD students
- 4
- MS students
- 0
- alumni listed
- 16
Papers by yearfrom the lab homepage, 2023–2026
- 3 papers2026 · T1 3 · collecting
- 9 papers2025 · T1 8
- 5 papers2024 · T1 2
- 6 papers2023 · T1 5
T1 venuesOtherVenues: SIGMOD, VLDB, NeurIPS, AISTATS, ICDE
Research focuspaper titles that use each topic's words
- Data management & big data11 papers
- Probabilistic & causal inference10 papers
- causal10 papers
- Efficient training & inference1 paper
- relational0 papers
Paper list
From the lab homepage.
20263 papers
- VLDB (T1)Matryoshka: Uncovering Relevant Features in Data Lakes to Enhance Machine Learning Applications (opens in a new tab)
- SIGMOD (T1)Stress-Testing Causal Claims via Cardinality Repairs (opens in a new tab)
- VLDB (T1)SubCure: A System for Stress-Testing Causal Conclusions Using Cardinality Repairs (opens in a new tab)
20259 papers
- VLDB (T1)Causal DAG Summarization (opens in a new tab)
- SIGMOD (T1)CausaLens: A System for Summarizing Causal DAGs (opens in a new tab)
- AISTATS (T1)Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects (opens in a new tab)
- NeurIPS (T1)KAIROS: Scalable Model-Agnostic Data Valuation (opens in a new tab)
- SIGMOD (T1)Navigating Data Errors in Machine Learning Pipelines: Identify, Debug, and Learn (opens in a new tab)
- AISTATS (T1)Scalable Out-of-distribution Robustness in the Presence of Unobserved Confounders (opens in a new tab)
- PVLDB ; VLDBStress-Testing ML Pipelines with Adversarial Data Corruption (opens in a new tab)
- NeurIPS (T1)TokenSwap: A Lightweight Method to Disrupt Memorized Sequences in LLMs (opens in a new tab)
- SIGMOD (T1)Zorro: Quantifying Uncertainty in Models & Predictions Arising from Dirty Data (opens in a new tab)
20245 papers
- ISSN @ VLDBDEMA: Enhancing Causal Analysis through Data Enrichment and Discovery in Data Lakes
- IEEE/CVF @ CVPREnforcing Conditional Independence for Fair Representation Learning and Causal Image Generation
- NeurIPS (T1)Learning from Uncertain Data: From Possible Worlds to Possible Models (opens in a new tab)
- SIGMOD (T1)OTClean: Data Cleaning for Conditional Independence Violations using Optimal Transport (opens in a new tab)
- IEEE Data Engineering BulletinOvercoming Data Biases: Towards Enhanced Accuracy and Reliability in Machine Learning (opens in a new tab)
20236 papers
- VLDB (T1)Causal Data Integration (opens in a new tab)
- Reasoning WebCausal Inference in Data Analysis with Applications to Fairness and Explanations (opens in a new tab)
- ICDE (T1)Causal What-If and How-To Analysis Using Hyper
- VLDB (T1)Consistent Range Approximation for Fair Predictive Modeling (opens in a new tab)
- SIGMOD (T1)NEXUS: On Explaining Confounding Bias (opens in a new tab)
- ICDE (T1)On Explaining Confounding Bias (opens in a new tab)
Where alumni went16 listed
Apply to UCSDFall 2027
UCSD CSE Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- See program page
- English
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- Fee
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Not yet confirmed for this intake.
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