Brett Mullins, Ryan McKenna, and Gerome Miklau’s paper “Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data” is accepted to AISTATS 2024. Authors: Miguel Fuentes, Brett Mullins, Ryan McKenna, Gerome Miklau, Daniel Sheldon

The following DREAM Lab papers were accepted to the 2023 Winter Simulation Conference: Cen Wang and Peter Haas “Efficient Hybrid Simulation Optimization Via Graph Neural Network Metamodeling” Pracheta Amaranath, Peter Haas, David Jensen, and Sam Witty “Causal Dynamic Bayesian Networks for Simulation Metamodeling”

Aman Malali, Chenghao Lyu, and Peter Haas presented their work at the BigFastData Workshop. Aman Malali presented “Predictive ML model maintenance”. Peter Haas presented “In-Database Decision Support: Opportunities and Challenges”. Chenghao Lyu presented “An Adaptive, Multi-Resolution, and Multi-Objective Parameter Tuning Approach for Spark SQL”.

Brett Mullins, Gerome Miklau, and Dan Sheldon’s paper “Quantifying Uncertainty of Unsupported Linear Queries for Private Query Release” is accepted to TPDP 2023.

Juelin Liu, Sandeep Polisetty, and Marco Serafini’s paper “Minigraph: Accelerating Subgraph Enumeration Using Auxiliary Graphs” is accepted to PACT 2023. Authors: Juelin Liu, Sandeep Polisetty, Hui Guan, Marco Serafini

Cen Wang and Peter Haas‘ paper “NIM: Generative Neural Networks for Automated Modeling and Generation of Simulation Inputs” was published in the ACM Transactions on Modeling and Computer Simulation.

Anna Fariha (PhD 2022) is an incoming Assistant Professor at the University of Utah. She was previously a researcher at Microsoft. Anna completed her dissertation under Alexandra Meliou.

Cen Wang received the 2021-2022 Outstanding Synthesis Project Award from the Manning College of Information and Computer Science. This project is summarized in NIM: Modeling and Generation of Simulation Inputs via Generative Neural Networks from the 2020 Winter Simulation Conference.

Ryan McKenna received the 2021-2022 Outstanding Dissertation Award from the Manning College of Information and Computer Science for his thesis “Practical Methods for High-Dimensional Data Publication with Differential Privacy“.

Brett Mullins’ paper “The Shape of Explanations: A Topological Account of Rule-Based Explanations in Machine Learning” is accepted at R2HCAI Workshop at AAAI 2023.