Update pooria.bib

Author: PooriaNamyar

_bibliography/people/pooria.bib

@@ -17,18 +17,40 @@ @inproceedings{alcoz2025packs
 }
 
 @inproceedings{namyar2024learning,
-  title={End-to-End Performance Analysis of Learning-enabled Systems},
-  author={Namyar, Pooria and Schapira, Michael and Govindan, Ramesh and Segarra, Santiago and Beckett, Ryan and Kakarla, Siva Kesava Reddy and Arzani, Behnaz},
-  booktitle={Proceedings of the 23rd ACM Workshop on Hot Topics in Networks},
-  year={2024},
+  author = {Namyar, Pooria and Schapira, Michael and Govindan, Ramesh and Segarra, Santiago and Beckett, Ryan and Kakarla, Siva Kesava Reddy and Arzani, Behnaz},
+  title = {End-to-End Performance Analysis of Learning-enabled Systems},
+  year = {2024},
+  isbn = {9798400712722},
+  publisher = {Association for Computing Machinery},
+  address = {New York, NY, USA},
+  url = {https://doi.org/10.1145/3696348.3696875},
+  doi = {10.1145/3696348.3696875},
+  abstract = {We propose a performance analysis tool for learning-enabled systems that allows operators to uncover potential performance issues before deploying DNNs in their systems. The tools that exist for this purpose require operators to faithfully model all components (a white-box approach) or do inefficient black-box local search. We propose a gray-box alternative, which eliminates the need to precisely model all the system's components. Our approach is faster and finds substantially worse scenarios compared to prior work. We show that a state-of-the-art learning-enabled traffic engineering pipeline can underperform the optimal by 6\texttimes{} --- a much higher number compared to what the authors found.},
+  booktitle = {Proceedings of the 23rd ACM Workshop on Hot Topics in Networks},
+  pages = {86–94},
+  numpages = {9},
+  keywords = {Machine Learning for Systems, Performance Analysis},
+  location = {Irvine, CA, USA},
+  series = {HOTNETS '24},
   abbr={HotNets},
 }
 
 @inproceedings{karimi2024Xplain,
-  title={Towards Safer Heuristics With Xplain},
-  author={Karimi, Pantea and Pirelli, Solal and Kakarla, Siva Kesava Reddy and Beckett, Ryan and Segarra, Santiago and Li, Beibin and Namyar, Pooria and Arzani, Behnaz},
-  booktitle={Proceedings of the 23rd ACM Workshop on Hot Topics in Networks},
-  year={2024},
+  author = {Karimi, Pantea and Pirelli, Solal and Kakarla, Siva Kesava Reddy and Beckett, Ryan and Segarra, Santiago and Li, Beibin and Namyar, Pooria and Arzani, Behnaz},
+  title = {Towards Safer Heuristics With XPlain},
+  year = {2024},
+  isbn = {9798400712722},
+  publisher = {Association for Computing Machinery},
+  address = {New York, NY, USA},
+  url = {https://doi.org/10.1145/3696348.3696884},
+  doi = {10.1145/3696348.3696884},
+  abstract = {Many problems that cloud operators solve are computationally expensive, and operators often use heuristic algorithms (that are faster and scale better than optimal) to solve them more efficiently. Heuristic analyzers enable operators to find when and by how much their heuristics underperform. However, these tools do not provide enough detail for operators to mitigate the heuristic's impact in practice: they only discover a single input instance that causes the heuristic to underperform (and not the full set) and they do not explain why.We propose XPlain, a tool that extends these analyzers and helps operators understand when and why their heuristics underperform. We present promising initial results that show such an extension is viable.},
+  booktitle = {Proceedings of the 23rd ACM Workshop on Hot Topics in Networks},
+  pages = {68–76},
+  numpages = {9},
+  keywords = {Domain-Specific Language, Explainable Analysis, Heuristic Analysis},
+  location = {Irvine, CA, USA},
+  series = {HOTNETS '24},
   abbr={HotNets},
 }