Akshay Mambakam

Dr. Akshay Mambakam is an Assistant Professor of Computer Science and Engineering at Mahindra University. He holds a Ph.D. in Computer Science from Université Grenoble Alpes, France, and B.Tech and M.Tech degrees in Computer Science and Engineering from IIT Kharagpur, India. His expertise spans formal methods, artificial intelligence, machine learning, runtime verification, and cyber-physical systems.
Before joining academia, Dr. Mambakam worked in the industry, contributing to the development and deployment of enterprise AI solutions involving large language models (LLMs), vision-language models (VLMs), agentic AI workflows, retrieval-augmented generation (RAG), and cloud-native AI applications on Azure and AWS. His earlier experience also includes deep learning, computer vision, inference optimization, and AI software development for embedded systems.
Dr. Mambakam’s research focuses on parametric timed formalisms, specification mining, explainable AI, runtime verification, anomaly detection, and learning interpretable specifications for cyber-physical systems. His work has been published in leading journals and conferences in formal methods and hybrid systems.

• From September 2019 To July 2023: Ph.D. (Computer Science), Université Grenoble Alpes, France.
  Thesis Title: Parametric Timed Formalisms for Specification and Monitoring.
  Supervisors: Dr. Thao Dang, Prof. Nicolas Basset
• From July 2012 To May 2018: B.Tech and M.Tech Dual Degree (CSE), IIT Kharagpur, India.
  Thesis Title: Application of Machine Learning for Feature-Based Coverage Analysis.
  Supervisor: Prof. Pallab Dasgupta

• Akshay Mambakam, José Ignacio Requeno Jarabo, Alexey Bakhirkin, Nicolas Basset, and Thao Dang. Mining of extended signal temporal logic specifications with paretolib 2.0. Formal Methods Syst. Des., 62(1):260–284, 2024.
• Akshay Mambakam, Eugene Asarin, Nicolas Basset, and Thao Dang. Pattern matching and parameter identification for parametric timed regular expressions. In Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control, HSCC 2023, San Antonio, TX, USA, May 9-12, 2023, pages 14:1–14:13. ACM, 2023.
• Nicolas Basset, Thao Dang, Akshay Mambakam, and José Ignacio Requeno Jarabo. Learning specifications for labelled patterns. In Formal Modeling and Analysis of Timed Systems, pages 76–93, Cham, 2020. Springer International Publishing.
• Ain, A. Mambakam, and P. Dasgupta. “Feature Based Coverage Analysis of AMS Circuits”. In 2018 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), pages 423–428, July 2018.
• Ain, A. Mambakam, P. Dasgupta, and S. Mukhopadhyay. “Feature Based Identification of Transmission Line Faults by Synchronous Monitoring of PMUs”. In 2017 30th International Conference on VLSI Design and 2017 16th International Conference on Embedded Systems (VLSID), pages 245–250, Jan 2017.

• From September 2024 To November 2025: AI Engineer, Qylis, Hyderabad.
• From July 2018 To August 2019: Software Engineer, Texas Instruments, Bangalore.

His research interests include Cyber-Physical Systems, Timed languages and Automata, Formal Methods, and Artificial Intelligence.