The key theme in my research is to automate the process of finding and explaining bugs, vulnerabilities, and fairness issues in large-scale software and machine learning systems. In particular, I am interested in analyzing a differential class of properties that include confidentiality, privacy, and fairness. The findings help discover multiple performance bugs in popular ML libraries such as scikit-learn, fairness bugs in the state-of-the-art machine learning algorithms, and timing side-channel vulnerabilities in critical Java libraries such as OpenJDK and Apache ([1],[2],[3],[4],[5]). I lead the Responsible, Informative, and Secure Computing Lab (RISC) at UTEP.

I am looking for motivated students at any point in their education to join RISC lab

• Accountable and Fair Sociotechnological Software Systems [ICSE'23, ICSE-SEIS'23, ICSE'22]
• Performance Testing and Debugging with SE for ML [ISSTA'20 , AAAI'18 , TACAS'17]
• Automated Verification, Fuzz Testing, and Debugging of Security Vulnerabilities [TNSM'22,ISSTA'21, NDSS'20, CAV'19]
• Application of Deep Neural Network for Side-Channel Analysis [STTT'21,RV'19]

News

• Dec 2023. The preliminary version of our paper on Maintaining Tax Prep Software with Large Language Models has been accepted to the 2024 IRS-TPC Research Conference.
• Dec 2023. I invited to serve as a PC of ISSTA'24.
• Nov 2023. I invited to serve as a PC of ASE'24.
• Oct 2023. Our paper On the Potential and Limitations of Few-Shot In-Context Learning to Generate Metamorphic Specifications for Tax Preparation Software has been accepted to In the 5th Natural Legal Language Processing (NLLP 2023).
• Sep 2023. Our workshop InteNSE'24: International Workshop on Interpretability, Robustness, and Benchmarking in Neural Software Engineering (Second Edition: Large Language Models) has been accepted to ICSE 2024 Workshop Track.
• Aug 2023. I am extremely honored to receive an NSF DASS award, Assessing Accountability of Tax Preparation Software Systems in collaboration with Dr. Krystia Reed (UTEP, Pyschology) and Ashutosh Trivedi (CU Boulder, CS): $750k for UTEP+CU Boulder and $530,000 for UTEP. Thank You, NSF!
• July 2023. I invited to serve as a PC of FSE'24.
• June 2023. I presented a workshop on safe and ethical AI to 10 middle- and high- school teachers from various El Paso K-12 schools.
• May 2023. I received an outstanding award for securing external fuding from the OSRP office at UTEP.
• May 2023. I presented our ICSE'23 and ICSE-SEIS'23 papers in Melbourne, Austalia.
• May 2023. Together with Dr. Jabbarvand, we successfully held the first ICSE workshop on Interpretability and Robustness in Neural Software Engineering (InteNSE’23) in Melbourne, Austalia.
• May 2023. Congratulations to Normen Yu for a successful BS Thesis project: FairLay-ML: Intuitive Remedies for Unfairness in Data-Driven Social-Critical Algorithms.
• March 2023. I am thrilled to receive my first PI grant funding from NSF SaTC Program, Detecting and Localizing Non-Functional Vulnerabilities in Machine Learning Libraries in collaboration with Dr. Tan ($600k for UTEP+Penn State; $353,357 for UTEP). Thank You, NSF!
• Dec 2022. "Metamorphic Testing and Debugging of Tax Preparation Software" has been accepted to ICSE-SEIS'23!
• Dec 2022. "Information-Theoretic Testing and Debugging of Fairness Defects in Deep Neural Networks" lead by my PhD student (Verya Monjezi) has been accepted to ICSE 2023!
• Nov 2022. Our Workshop "InteNSE: Interpretability and Robustness in Neural Software Engineering" has been accepted to ICSE workshop track.
• Oct 2022. I invited to serve as a PC of Programming Languages and Law (ProLaLa'23).
• Oct 2022. "Detecting Unseen Anomalies in Network Systems by Leveraging Neural Networks" has been accepted to IEEE Transactions on Network and Service Management (IEEE TNSM).
• Dec 2021. "Fairness-aware Configuration of Machine Learning Libraries" has been accepted to ICSE 2022!
• Apr 2021. "QFuzz: Quantitative Fuzzing for Side Channels" has been accepted to ISSTA 2021!
• Apr 2020. "Detecting and Understanding Real-World Differential Performance Bugs in Machine Learning Libraries" has been accepted to ISSTA 2020!
• Feb 2020. "Data-driven debugging for functional side channels" has been accepted to NDSS 2020!
• Nov 2019. Our RV'19 paper is recognized as one of the top papers in RV'19 and invited for submitting to STTT Journal.
• July 2019. "Efficient Detection and Quantification of Timing Leaks with Neural Networks" has been accepted to RV 2019!
• Apr 2019. "Quantitative Mitigation of Timing Side Channels" has been accepted to CAV 2019!
• Apr 2018. I received "2018 Gold Research Award" from ECEE department of CU Boulder!
• Nov 2017. "Differential Performance Debugging with Discriminant Regression Tree" has been accepted to AAAI 2018!
• Dec 2016. "Discriminating Traces with Time" has been accepted to TACAS 2017!
• Apr 2016. I won the Second Prize in the First Microsoft Open Source Challenge by Microsoft Research.