Verification of OSPF vulnerabilities by colored Petri net. - Saeid Tizpaz-Niari; Amir Hossein Jahangir (SIN, 2013)

Discriminating Traces with Time. - Saeid Tizpaz-Niari; Pavol Cerný; Bor-Yuh Evan Chang; Sriram Sankaranarayanan 001; Ashutosh Trivedi 001 (TACAS (2), 2017)

Differential Performance Debugging With Discriminant Regression Trees. - Saeid Tizpaz-Niari; Pavol Cerný; Bor-Yuh Evan Chang; Ashutosh Trivedi 001 (AAAI, 2018)

Quantitative Mitigation of Timing Side Channels. - Saeid Tizpaz-Niari; Pavol Cerný; Ashutosh Trivedi 001 (CAV (1), 2019)

Efficient Detection and Quantification of Timing Leaks with Neural Networks. - Saeid Tizpaz-Niari; Pavol Cerný; Sriram Sankaranarayanan 001; Ashutosh Trivedi 001 (RV, 2019)

Detecting and understanding real-world differential performance bugs in machine learning libraries. - Saeid Tizpaz-Niari; Pavol Cerný; Ashutosh Trivedi 001 (ISSTA, 2020)

Data-Driven Debugging for Functional Side Channels. - Saeid Tizpaz-Niari; Pavol Cerný; Ashutosh Trivedi 001 (NDSS, 2020)

QFuzz: quantitative fuzzing for side channels. - Yannic Noller; Saeid Tizpaz-Niari (ISSTA, 2021)

Quantitative estimation of side-channel leaks with neural networks. - Saeid Tizpaz-Niari; Pavol Cerný; Sriram Sankaranarayanan 001; Ashutosh Trivedi 001 (Int. J. Softw. Tools Technol. Transf., 2021)

Fairness-aware Configuration of Machine Learning Libraries. - Saeid Tizpaz-Niari; Ashish Kumar; Gang Tan; Ashutosh Trivedi 001 (ICSE, 2022)

Metamorphic Testing and Debugging of Tax Preparation Software. - Saeid Tizpaz-Niari; Morgan Wagner; Shiva Darian; Krystia Reed; Ashutosh Trivedi 001 (CoRR, 2022)

What Is a Natural Probability Distribution on the Class of All Continuous Functions: Maximum Entropy Approach Leads to Wiener Measure. - Vladik Kreinovich; Saeid Tizpaz-Niari (Uncertainty, Constraints, and Decision Making, 2023)

Over-Measurement Paradox: Suspension of Thermonuclear Research Center and Need to Update Standards. - Hector A. Reyes; Saeid Tizpaz-Niari; Vladik Kreinovich (Uncertainty, Constraints, and Decision Making, 2023)

How to Detect (and Analyze) Independent Subsystems of a Black-Box (or Grey-Box) System. - Saeid Tizpaz-Niari; Olga Kosheleva; Vladik Kreinovich (Uncertainty, Constraints, and Decision Making, 2023)

Information-Theoretic Testing and Debugging of Fairness Defects in Deep Neural Networks. - Verya Monjezi; Ashutosh Trivedi 001; Gang Tan; Saeid Tizpaz-Niari (ICSE, 2023)

Metamorphic Testing and Debugging of Tax Preparation Software. - Saeid Tizpaz-Niari; Verya Monjezi; Morgan Wagner; Shiva Darian; Krystia Reed; Ashutosh Trivedi 001 (ICSE (SEIS), 2023)

Fast - Asymptotically Optimal - Methods for Determining the Optimal Number of Features. - Saeid Tizpaz-Niari; Luc Longpré; Olga Kosheleva; Vladik Kreinovich (IUKM (1), 2023)

FairLay-ML: Intuitive Remedies for Unfairness in Data-Driven Social-Critical Algorithms. - Normen Yu; Gang Tan; Saeid Tizpaz-Niari (CoRR, 2023)

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 001; Maria Leonor Pacheco (CoRR, 2023)

Detecting Unseen Anomalies in Network Systems by Leveraging Neural Networks. - Mohammad J. Hashemi; Eric Keller; Saeid Tizpaz-Niari (IEEE Trans. Netw. Serv. Manag., 2023)

Worst-Case Convergence Time of ML Algorithms via Extreme Value Theory. - Saeid Tizpaz-Niari; Sriram Sankaranarayanan 001 (CAIN, 2024)

Causal Graph Fuzzing for Fair ML Sofware Development. - Verya Monjezi; Ashish Kumar; Gang Tan; Ashutosh Trivedi 001; Saeid Tizpaz-Niari (ICSE Companion, 2024)

NeuFair: Neural Network Fairness Repair with Dropout. - Vishnu Asutosh Dasu; Ashish Kumar; Saeid Tizpaz-Niari; Gang Tan (ISSTA, 2024)

Predicting Fairness of ML Software Configurations. - Salvador Robles Herrera; Verya Monjezi; Vladik Kreinovich; Ashutosh Trivedi 001; Saeid Tizpaz-Niari (PROMISE, 2024)

Data Science in a Mathematics Classroom: Lessons on AI Fairness. - Berenice Sotelo; Kirsten Wieseman; Saeid Tizpaz-Niari (SIGITE, 2024)

Predicting Fairness of ML Software Configuration. - Salvador Robles Herrera; Verya Monjezi; Vladik Kreinovich; Ashutosh Trivedi 001; Saeid Tizpaz-Niari (CoRR, 2024)

Metamorphic Debugging for Accountable Software. - Saeid Tizpaz-Niari; Shiva Darian; Ashutosh Trivedi 001 (CoRR, 2024)

Probabilistic Guarantees for Practical LIA Loop Invariant Automation. - Ashish Kumar; Jilaun Zhang; Saeid Tizpaz-Niari; Gang Tan (CoRR, 2024)

Summary of the 1st Interpretability and Robustness in Neural Software Engineering (InteNSE 2023). - Reyhaneh Jabbarvand; Saeid Tizpaz-Niari; Earl T. Barr; Satish Chandra 001 (ACM SIGSOFT Softw. Eng. Notes, 2024)

Timing Side-Channel Mitigation via Automated Program Repair. - Haifeng Ruan; Yannic Noller; Saeid Tizpaz-Niari; Sudipta Chattopadhyay 001; Abhik Roychoudhury (ACM Trans. Softw. Eng. Methodol., 2024)

Fairness Testing Through Extreme Value Theory. - Verya Monjezi; Ashutosh Trivedi 001; Vladik Kreinovich; Saeid Tizpaz-Niari (ICSE, 2025)

FairLay-ML: Intuitive Debugging of Fairness in Data-Driven Social-Critical Software. - Normen Yu; Luciana Carreon; Gang Tan; Saeid Tizpaz-Niari (ICSE Companion, 2025)

Attention Pruning: Automated Fairness Repair of Language Models via Surrogate Simulated Annealing. - Vishnu Asutosh Dasu; Md. Rafi Ur Rashid; Vipul Gupta; Saeid Tizpaz-Niari; Gang Tan (CoRR, 2025)

Technical Challenges in Maintaining Tax Prep Software with Large Language Models. - Sina Gogani-Khiabani; Varsha Dewangan; Nina Olson; Ashutosh Trivedi 001; Saeid Tizpaz-Niari (CoRR, 2025)