PUBLICATIONS

Journal Publications

• Detecting Unseen Anomalies in Network Systems by Leveraging Neural Networks , Mohammad J. Hashemi*, Eric Keller, and Saeid Tizpaz-Niari, In IEEE Transactions on Network and Service Management (IEEE TNSM), 2023.
• Quantitative Estimation of Side Channel Leaks with Neural Networks , Saeid Tizpaz-Niari, Pavol Černý, Sriram Sankaranarayanan, and Ashutosh Trivedi, In International Journal on Software Tools for Technology Transfer (STTT), 2021.

Selective Conference Publications

• Worst-Case Convergence Time of ML Algorithms via Extreme Value Theory, Saeid Tizpaz-Niari and Sriram Sankaranarayanan, In 3rd International Conference on AI Engineering – Software Engineering for AI (CAIN'24).
• Information-Theoretic Testing and Debugging of Fairness Defects in Deep Neural Networks , Verya Monjezi*, Ashutosh Trivedi, Gang Tan, Saeid Tizpaz-Niari, In IEEE/ACM 45th International Conference on Software Engineering (ICSE'23, acceptance rate 26.1%), [Slides, Artifact].
• Metamorphic Testing and Debugging of Tax Preparation Software , Saeid Tizpaz-Niari, Verya Monjezi*, Morgan Wagner*, Shiva Darian*, Krystia Reed, and Ashutosh Trivedi, In IEEE/ACM 45th International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS'23, acceptance rate 25%), [Presentations, Slides, Artifact].
• Fairness-aware Configuration of Machine Learning Libraries , Saeid Tizpaz-Niari, Ashish Kumar*, Gang Tan, and Ashutosh Trivedi, In IEEE/ACM 44th International Conference on Software Engineering (ICSE'22, acceptance rate 26%), [5 mins presentation, 20 mins presentation, artifact].
• QFuzz: Quantitative Fuzzing for Side Channels , Yannic Noller and Saeid Tizpaz-Niari, In 30th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA'21, acceptance rate 21.8%), [Presentation, artifact].
• Detecting and Understanding Real-World Differential Performance Bugs in Machine Learning Libraries, Saeid Tizpaz-Niari, Pavol Cerny, and Ashutosh Trivedi, In the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA'20, acceptance rate 26%), [Presentation, artifact].
• Data-driven Debugging for Functional Side Channels, Saeid Tizpaz-Niari, Pavol Cerny, and Ashutosh Trivedi, In 2020 ISOC Network and Distributed System Security Symposium (NDSS'20, acceptance rate 17.4%), [Presentation].
• Efficient Detection and Quantification of Timing Leaks with Neural Networks , Saeid Tizpaz-Niari, Pavol Černý, Sriram Sankaranarayanan, and Ashutosh Trivedi, In Runtime Verification (RV'19). (acceptance rate: 45%)
• Quantitative Mitigation of Timing Side Channels , Saeid Tizpaz-Niari, Pavol Černý, and Ashutosh Trivedi, In Computer-Aid Verification (CAV'19). (acceptance rate: 26%), source code.
• Differential Performance Debugging with Discriminant Regression Trees , Saeid Tizpaz-Niari, Pavol Černý, Bor-Yuh Evan Chang, and Ashutosh Trivedi, In the AAAI Conference on Artificial Intelligence (AAAI'18). (acceptance rate: 24%), source code.
• Discriminating traces with Time , Saeid Tizpaz-Niari, Pavol Černý, Bor-Yuh Evan Chang, Sriram Sankaranarayanan, and Ashutosh Trivedi, In Tools and Algorithms for the Construction and Analysis of Systems (TACAS'17). (acceptance rate: 29%), source code.

Other Conference and Workshop Publications

• Maintaining Tax Preparation Software with Large Language Models , Varsha Dewangan*, Sina Khiabani*, Nina Olson, Ashutosh Trivedi, and Saeid Tizpaz-Niari, In 2024 IRS-TPC Research Conference, Washington, DC, 2024 (Abstract Version).
• Summary of the 1st Interpretability and Robustness in Neural Software Engineering (InteNSE 2023), Reyhaneh Jabbarvand, Saeid Tizpaz-Niari, Earl T. Barr, Satish Chandra, In ACM SIGSOFT Software Engineering Notes, Volume 49, Issue 1, Jan 2024.
• How to Best Retrain a Neural Network If We Added One More Input Variable , Saeid Tizpaz-Niari and Vladik Kreinovich, Proceedings of the 5th International Conference on Artificial Intelligence and Computational Intelligence (AICI 2024).
• On the Potential and Limitations of Few-Shot In-Context Learning to Generate Metamorphic Specifications for Tax Preparation Software , Dananjay Srinivas*, Rohan Das*, Saeid Tizpaz-Niari, Ashutosh Trivedi, and Maria Leonor Pacheco, In the 5th Natural Legal Language Processing (NLLP 2023), [Presentation].
• Fast -- Asymptotically Optimal -- Methods for Determining the Optimal Number of Features , Saeid Tizpaz-Niari, Luc Longpre, Olga Kosheleva, and Vladik Kreinovich, Proceedings of the 10th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2023).
• How to Detect (and Analyze) Independent Subsystems of a Black-Box (or Grey-Box) System , Saeid Tizpaz-Niari, Olga Kosheleva, and Vladik Kreinovich. Uncertainty, Constraints, and Decision Making, Springer, 2023.
• Over-Measurement Paradox: Suspension of Thermonuclear Research Center and Need to Update Standards , Hector Reyes*, Saeid Tizpaz-Niari, and Vladik Kreinovich. Uncertainty, Constraints, and Decision Making, Springer, 2023.
• What Is a Natural Probability Distribution on the Class of All Continuous Functions: Maximum Entropy Approach Leads to Wiener Measure , Vladik Kreinovich and Saeid Tizpaz-Niari. Uncertainty, Constraints, and Decision Making, Springer, 2023.
• Hyper-trace debugging for performance and security , Saeid Tizpaz-Niari, Pavol Černý, and Ashutosh Trivedi, In Workshop on Machine Learning for Software Engineering (ML4SE'19).
• CONfidentiality CERTifier: a Modeling and Verification Framework for Program Confidentiality , Saeid Tizpaz Niari, Winner of Second Prize in the First Microsoft Open Source Challenges, Microsoft Research, April 2016.
• Verification of OSPF Vulnerabilities by Colored Petri Net , Saeid Tizpaz Niari, Amir Hossein Jahangir, In Security of Information and Network SIN'13, pp. 102-109. (acceptance rate: 27%)