diff --git a/source/_data/SymbioticLab.bib b/source/_data/SymbioticLab.bib
index e185870a..5cf6730b 100644
--- a/source/_data/SymbioticLab.bib
+++ b/source/_data/SymbioticLab.bib
@@ -1903,3 +1903,18 @@ @InProceedings{dpack:eurosys25
   publist_abstract = {
 Machine learning (ML) models can leak information about users, and differential privacy (DP) provides a rigorous way to bound that leakage under a given budget. This DP budget can be regarded as a new type of compute resource in workloads of multiple ML models training on user data. Once it is used, the DP budget is forever consumed. Therefore, it is crucial to allocate it most efficiently to train as many models as possible. This paper presents a scheduler for privacy that optimizes for efficiency. We formulate privacy scheduling as a new type of multidimensional knapsack problem, called privacy knapsack, which maximizes DP budget efficiency. We show that privacy knapsack is NP-hard, hence practical algorithms are necessarily approximate. We develop an approximation algorithm for privacy knapsack, DPack, and evaluate it on microbenchmarks and on a new, synthetic private-ML workload we developed from the Alibaba ML cluster trace. We show that DPack: (1) often approaches the efficiency-optimal schedule, (2) consistently schedules more tasks compared to a state-of-the-art privacy scheduling algorithm that focused on fairness (1.3–1.7X in Alibaba, 1.0–2.6X in microbenchmarks), but (3) sacrifices some level of fairness for efficiency. Therefore, using DPack, DP ML operators should be able to train more models on the same amount of user data while offering the same privacy guarantee to their users.  }
 } 
+
+@InProceedings{openinfra:hotinfra24,
+  author = {Jiaheng Lu and Yunming Xiao and Shmeelok Chakraborty and Silvery Fu and Yoon Sung Ji and Ang Chen and Mosharaf Chowdhury and Nalini Rao and Sylvia Ratnasamy and Xinyu Wang},
+  title         = {{OpenInfra}: A Co-simulation Framework for the Infrastructure Nexus},
+  booktitle = {HotInfra},
+  year          = {2024},
+  month         = {November},
+  publist_confkey = {HotInfra'24},
+  publist_link = {paper || openinfra-hotinfra24.pdf},
+  publist_link = {code || https://github.com/JhengLu/OpenInfra},
+  publist_topic = {Energy-Efficient Systems},
+  publist_abstract = {
+    Critical infrastructures like datacenters, power grids, and water systems are interdependent, forming complex "infrastructure nexuses" that require co-optimization for efficiency, resilience, and sustainability. We present OpenInfra, a co-simulation framework designed to model these interdependencies by integrating domain-specific simulators for datacenters, power grids, and cooling systems but focusing on stitching them together for end-to-end experimentation. OpenInfra enables seamless integration of diverse simulators and flexible configuration of infrastructure interactions. Our evaluation demonstrates its ability to simulate large-scale infrastructure dynamics, including 7,392 servers over 100+ hours.
+  }
+}
\ No newline at end of file
diff --git a/source/publications/files/openinfra:hotinfra24/openinfra-hotinfra24.pdf b/source/publications/files/openinfra:hotinfra24/openinfra-hotinfra24.pdf
new file mode 100644
index 00000000..0a310d0c
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diff --git a/source/publications/index.md b/source/publications/index.md
index bddbb452..cd052a37 100644
--- a/source/publications/index.md
+++ b/source/publications/index.md
@@ -265,6 +265,10 @@ venues:
   HotInfra:
     category: Workshops
     occurrences:
+    - key: HotInfra'24
+      name: Workshop on Hot Topics in System Infrastructure
+      date: 2024-11-03
+      url: https://hotinfra24.github.io/
     - key: HotInfra'23
       name: Workshop on Hot Topics in System Infrastructure
       date: 2023-06-18