Specialization

Reinforcement Learning, Artificial Intelligence, Multiagent Systems, Multi-robot/Multi-drone Systems

Email

armaan.garg@thapar.edu

Education
1.    PhD in Computer Science and Engineering from Indian Institute of Technology of Ropar (2019-2024).
o    Title – Learning Multi-UAV Policies Using Deep Reinforcement Learning for Flood Area Coverage and Object Tracking
2.    M. Tech. in Software Engineering from Delhi Technological University (2016-2018).
3.    B. Tech. in Computer Science and Engineering from Jaypee University of Information Technology (2012-2016).

Trainings
1.    Project delegate at HPAIR, Harvard University 
2.    Scholar attendee at the OxML program, University of Oxford
3.    Co-ordinator/ teaching assistant in the conduct of the AICTE ATAL Faculty Development Program (FDP) on Reinforcement Learning and Its Applications” at Indian Institute of Technology of Ropar.

Experience
1.    Assistant Professor, Computer Science and Engineering Department, Thapar University Patiala (Jan 2025 - present)
2.    Assistant Professor, Computer Science and Engineering Department, Punjab Engineering College (Jan 2024 – Dec 2024)
3.    Assistant Professor, Computer Science and Engineering Department, Chandigarh University (Jan 2019 – July 2019)
4.    Mobikwik, SDE 1 (July 2018 – Dec 2018)

Journal Publications
1.    A. Garg and S. S. Jha. Deep Reinforcement Learning based Multi-UAV Control for Moving Convoy Tracking. Engineering Applications of Artificial Intelligence, Volume 126, Part D, 2023, 107099, ISSN 0952-1976, [Impact Factor: 8]
2.    A. Garg and S. S. Jha. Learning continuous multi-UAV controls with directed explorations for flood area coverage. Robotics and Autonomous Systems, Volume 180, 104774, ISSN 0921-8890, [Impact Factor: 4.3]
3.    A. Garg and S. S. Jha. Multi-UAV Assisted Navigation of Waterborne Vehicles during Floods. ASME. J. Comput. Inf. Sci. Eng. Volume 24(10), 101003, ISSN 1530-9827. [Impact Factor: 3.1]
 

Conference Publications & Workshops
1.    A. Garg and S. S. Jha. Decentralized critical area coverage using multi-uav system with guided explorations during floods. IEEE 19th International Conference on Automation Science and Engineering (CASE), 2023, pp. 1–6. DOI: 10.1109/CASE56687.2023.10260489. [CASE is the flagship automation conference of the IEEE Robotics and Automation Society]
2.    A. Garg and S. S. Jha. Real-time serviceable path planning using uavs for waterborne vehicle navigation during floods. Advances In Robotics - 6th International Conference of The Robotics Society, 2023. DOI: 10.1145/3610419.3610433.
3.    A. Garg and S. S. Jha. Directed Explorations During Flood Disasters Using Multi-UAV System. IEEE 18th International Conference on Automation Science and Engineering (CASE), 2022, pp. 2154-2161, doi: 10.1109/CASE49997.2022.9926454.
4.    A. Garg. Addressing Data Intrinsic Characteristics for Augmentation for Breast Cancer Classification. In Proceedings of the 6th Joint International Conference on Data Science & Management of Data (10th ACM IKDD CODS and 28th COMAD) (CODS-COMAD), 2023.
5.    P. Kaushik, A. Garg, and S. S. Jha. On Learning Multi-UAV Policy for Multi-Object Tracking and Formation Control. IEEE 18th India Council International Conference (INDICON), 2021, pp. 1-6, doi: 10.1109/INDICON52576.2021.9691567.
6.    A. Kumar, A. Virdi, N. Sharma, A. Garg and S. S. Jha. AMAF-CD: A Game-Changing Approach for Optimizing Squad Movement in StarCraft. In Proceedings of the 8th Joint International Conference on Data Science & Management of Data (12th ACM IKDD CODS and 30th COMAD) (CODS-COMAD), 2025. [Accepted]
7.    A. Garg and S. S. Jha. Autonomous Flood Area Coverage using Decentralized Multi-UAV System with Directed Explorations. Adaptive and Learning Agents (ALA) Workshop at the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2023. [AAMAS is a core A* conference]
8.    A. Garg and S. S. Jha. Real-Time Flood Navigation: Multi-UAV Assisted Vehicle Guidance via Deep Reinforcement Learning. Learning Robot Super Autonomy Workshop at the 36th International Conference on Intelligent Robots and Systems (IROS), 2023. Accepted [IROS is a core A conference]

Book Chapter Publication
1. A. Garg, V. Aggarwal, N. Taneja. Classification of Imbalanced Data: Addressing Data Intrinsic Characteristics. Futuristic Trends in Networks and Computing Technologies (FTNCT), 2019. Communications in Computer and Information Science, vol 1206. Springer. https://doi.org/10.1007/978-981-15-4451-4_21.
2. A.Kumar, K.Verma, A.Garg, S.S. Jha (2025). Attentive A* for Visual Cue Based Path Planning in Complex Environments. In: Agents and Robots for reliable Engineered Autonomy. AREA ECAI, 2024. Communications in Computer and Information Science, vol 2230. Springer, Cham. https://doi.org/10.1007/978-3-031-73180-8_9.

Peer reviewer for Journals
●    Engineering Applications of Artificial Intelligence