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I am a Post-Doctoral researcher in CMU Cylab
hosted by Dr. Corina Pasareanu.
I graduated with a PhD from the School of Computer Science
at Georgia Tech in 2020.
[rmangal@andrew.cmu.edu]
[CV]
[Google Scholar]
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I am interested in developing formal methods for analyzing the correctness and safety of software systems. I currently focus on developing formal methods for Trustworthy Machine Learning , i.e., for safety, robustness and explainability analysis of machine learnt models as well as formal safety analysis of systems with such learning-enabled components.
I will be starting as an Assitant Professor in the Department of Computer Science at Colorado State University in Fall 2024. If you are interested in topics related to my research and would like to collaborate or work with me, feel free to reach out!
[Research Statement]
(* indicates equal contribution, (#) indicates alphabetical ordering)
- Concept-based Analysis of Neural Networks via Vision-Language Models
Ravi Mangal, Nina Narodytska, Divya Gopinath, Boyue Caroline Hu, Anirban Roy, Susmit Jha, Corina Pasareanu
Preprint, 2024
- Controller Synthesis for Autonomous Systems with Deep-Learning Perception Components
(#) Radu Calinescu, Calum Imrie, Ravi Mangal, Genaina Nunes Rodrigues, Corina Pasareanu, Misael Alpizar Santana, and Gricel Vazquez
IEEE Transactions on Software Engineering (TSE), 2024
- Transfer Attacks and Defenses for Large Language Models on Coding Tasks
Chi Zhang, Zifan Wang, Ravi Mangal, Matt Fredrikson, Limin Jia, Corina Pasareanu
Preprint, 2023
- Is Certifying Lp Robustness Still Worthwhile?
Ravi Mangal*, Klas Leino*, Zifan Wang*, Kai Hu*, Weicheng Yu, Corina Pasareanu, Matt Fredrikson, Anupam Datta
Preprint, 2023
- Assumption Generation for Learning-Enabled Autonomous Systems
Corina Pasareanu, Ravi Mangal, Divya Gopinath, and Huafeng Yu
International Conference on Runtime Verification (RV), 2023
- Closed-loop Analysis of Vision-based Autonomous Systems: A Case Study
Corina Pasareanu, Ravi Mangal, Divya Gopinath, Sinem Getir Yaman, Calum Imrie, Radu Calinescu, and Huafeng Yu
International Conference on Computer Aided Verification (CAV), 2023
- On the Perils of Cascading Robust Classifiers
Ravi Mangal*, Zifan Wang*, Chi Zhang*, Klas Leino, Corina Pasareanu, and Matt Fredrikson
International Conference on Learning Representations (ICLR), 2023
[code]
- Feature-Guided Analysis of Neural Networks
(#) Divya Gopinath, Luca Lungeanu, Ravi Mangal, Corina Pasareanu, Siqi Xie, and Huafeng Yu
Fundamental Approaches to Software Engineering (FASE), 2023
- Degradation Attacks on Certifiably Robust Neural Networks
Klas Leino*, Chi Zhang*, Ravi Mangal*, Matt Fredrikson, Bryan Parno, and Corina Pasareanu
Transactions on Machine Learning Reasearch (TMLR), 2022
[code]
- Self-Correcting Neural Networks For Safe Classification
Klas Leino, Aymeric Fromherz, Ravi Mangal, Matt Fredrikson, Bryan Parno, and Corina Pasareanu
Workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS), 2022
[code]
- A Cascade of Checkers for Run-time Certification of Local Robustness
Ravi Mangal and Corina Pasareanu
Workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS), 2022
[code]
- Reasoning About Programs in Statistically Modeled First-Order Environments
Ravi Mangal
Dissertation, 2020
- Observational Abstract Interpreters
Ravi Mangal
Preprint, 2020
- Probabilistic Lipschitz Analysis of Neural Networks
Ravi Mangal, Kartik Sarangmath, Aditya V. Nori, and Alex Orso
Static Analysis Symposium (SAS), 2020
[code]
- Robustness of Neural Networks: A Probabilistic and Practical Perspective
Ravi Mangal, Aditya V. Nori, and Alex Orso
NIER track of the 41st IEEE and ACM SIGSOFT International Conference on Software Engineering (ICSE-NIER), 2019
[slides]
- Checking Probabilistic Properties of Neural Networks via Symbolic Methods and Sampling
Ravi Mangal, Aditya V. Nori, and Alessandro Orso
First ICSE Workshop on Testing for Deep Learning and Deep Learning for Testing (DeepTest), 2019
[slides]
- On Optimally Combining Static and Dynamic Analyses For Intensional Program Properties
Ravi Mangal, David Devecsery, and Alessandro Orso
The Southeast Regional Programming Languages Seminar (SERPL), 2019
[slides]
- Accelerating Program Analyses by Cross-Program Training
Sulekha Kulkarni, Ravi Mangal, Xin Zhang, and Mayur Naik
ACM Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA), 2016
[slides]
- Scaling Relational Inference using Proofs and Refutations
Ravi Mangal, Xin Zhang, Aditya Kamath, Aditya V. Nori, and Mayur Naik
AAAI Conference on Artificial Intelligence (AAAI), 2016
[poster]
- Query-guided Maximum Satisfiability
Xin Zhang, Ravi Mangal, Aditya V. Nori, and Mayur Naik
Principles of Programming Languages (POPL), 2016
[slides]
- Volt: A Lazy Grounding Framework for Solving Very Large MaxSAT Instances
Ravi Mangal, Xin Zhang, Aditya V. Nori, and Mayur Naik
International Conference on Theory and Applications of Satisfiability Testing (SAT), 2015
[slides]
- A User-Guided Approach to Program Analysis
Ravi Mangal, Xin Zhang, Aditya V. Nori, and Mayur Naik
ACM Symposium on Foundations of Software Engineering (FSE), 2015
ACM SIGSOFT Distinguished Paper Award
[slides]
- Solving Weighted Constraints with Applications to Program Analysis
Ravi Mangal, Xin Zhang, Mayur Naik, and Aditya V. Nori
SCS Technical Report, GT-CS-15-03, Georgia Institute of Technology, February, 2015
- On Abstraction Refinement for Program Analyses in Datalog
Xin Zhang, Ravi Mangal, Radu Grigore, Mayur Naik, and Hongseok Yang
ACM Conference on Programming Language Design and Implementation (PLDI), 2014
Distinguished Paper Award
[long version] [slides]
- Hybrid Top-Down and Bottom-Up Interprocedural Analysis
Xin Zhang, Ravi Mangal, Mayur Naik, and Hongseok Yang
ACM Conference on Programming Language Design and Implementation (PLDI), 2014
[long version] [slides]
- A Correspondence between Two Approaches to Interprocedural Analysis in the Presence of Join
Ravi Mangal, Mayur Naik, and Hongseok Yang
European Symposium on Programming (ESOP), 2014
Best Paper Award Nominee
[long version] [slides]