Supervised Belief Propagation: Scalable Supervised Inference on Attributed Networks. - Jaemin Yoo; Saehan Jo; U Kang (ICDM, 2017)

Fast and Scalable Distributed Loopy Belief Propagation on Real-World Graphs. - Saehan Jo; Jaemin Yoo; U Kang (WSDM, 2018)

The FactChecker: Verifying Text Summaries of Relational Data Sets. - Saehan Jo; Immanuel Trummer; Weicheng Yu; Daniel Liu; Niyati Mehta (CoRR, 2018)

SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning. - Immanuel Trummer; Samuel Moseley; Deepak Maram; Saehan Jo; Joseph Antonakakis (Proc. VLDB Endow., 2018)

Verifying Text Summaries of Relational Data Sets. - Saehan Jo; Immanuel Trummer; Weicheng Yu; Xuezhi Wang 002; Cong Yu 001; Daniel Liu; Niyati Mehta (SIGMOD Conference, 2019)

SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning. - Immanuel Trummer; Junxiong Wang; Deepak Maram; Samuel Moseley; Saehan Jo; Joseph Antonakakis (SIGMOD Conference, 2019)

AggChecker: A Fact-Checking System for Text Summaries of Relational Data Sets. - Saehan Jo; Immanuel Trummer; Weicheng Yu; Xuezhi Wang 002; Cong Yu 001; Daniel Liu; Niyati Mehta (Proc. VLDB Endow., 2019)

Mining an "Anti-Knowledge Base" from Wikipedia Updates with Applications to Fact Checking and Beyond. - Georgios Karagiannis; Immanuel Trummer; Saehan Jo; Shubham Khandelwal; Xuezhi Wang 002; Cong Yu 001 (Proc. VLDB Endow., 2019)

BitGourmet: Deterministic Approximation via Optimized Bit Selection. - Saehan Jo; Immanuel Trummer (CIDR, 2020)

Demonstration of BitGourmet: Data Analysis via Deterministic Approximation. - Saehan Jo; Immanuel Trummer (SIGMOD Conference, 2020)

Demonstration of ScroogeDB: Getting More Bang For the Buck with Deterministic Approximation in the Cloud. - Saehan Jo; Jialing Pei; Immanuel Trummer (Proc. VLDB Endow., 2020)

SkinnerDB: Regret-bounded Query Evaluation via Reinforcement Learning. - Immanuel Trummer; Junxiong Wang; Ziyun Wei; Deepak Maram; Samuel Moseley; Saehan Jo; Joseph Antonakakis; Ankush Rayabhari (ACM Trans. Database Syst., 2021)

ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning. - Tarique Siddiqui; Saehan Jo; Wentao Wu 001; Chi Wang 001; Vivek R. Narasayya; Surajit Chaudhuri (SIGMOD Conference, 2022)

Demonstration of ThalamusDB: Answering Complex SQL Queries with Natural Language Predicates on Multi-Modal Data. - Saehan Jo; Immanuel Trummer (SIGMOD Conference Companion, 2023)

SMART: Automatically Scaling Down Language Models with Accuracy Guarantees for Reduced Processing Fees. - Saehan Jo; Immanuel Trummer (CoRR, 2024)

ThalamusDB: Approximate Query Processing on Multi-Modal Data. - Saehan Jo; Immanuel Trummer (Proc. ACM Manag. Data, 2024)

SpareLLM: Automatically Selecting Task-Specific Minimum-Cost Large Language Models under Equivalence Constraint. - Saehan Jo; Immanuel Trummer (Proc. ACM Manag. Data, 2025)