SRIKANTH THUDUMU
Also known as: S Thudumu
Institute of Applied Artificial Intelligence and Robotics, Deakin University, Swinburne University of Technology, University of South Australia
0000-0002-7848-9008
About
Dr. Srikanth Thudumu is currently the Director of AI at the Institute of Applied Artificial Intelligence and Robotics (IAAIR), United States, where he leads strategic research initiatives, industry collaborations, and the application of AI to solve real-world problems. His expertise lies in applied AI, particularly in AI/ML optimisation, AI model reduction, intelligent automation, and scalable AI solutions, with a strong focus on bridging the gap between fundamental AI research and practical implementation.
He holds a PhD in Artificial Intelligence and has an extensive background in applied AI research and development. Prior to his current role, he was a Senior Research Fellow at the Applied Artificial Intelligence Institute (A²I²), Deakin University, where he led high-impact research projects in collaboration with CSIRO and the Australian Department of Defence.
Dr. Thudumu actively contributes to the AI research community and serves as an Associate Editor, Review Editor, and Reviewer for several leading academic journals.
Employment
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Institute of Applied Artificial Intelligence and Robotics Director2025 - Present
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Deakin University Senior Research Fellow2023 - Present
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Deakin University Research Fellow2021 - 2023
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University of South Australia Research Associate2021 - 2021
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Swinburne University of Technology Tutor/Research Assistant2016 - 2020
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University of Tasmania Tutor2015 - 2016
Education
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Swinburne University of Technology PhD? - 2021
Projects & Funding
Projects & funding information is unavailable.
Publications (44)
- Goal-Oriented Multi-Agent Reinforcement Learning for Decentralized Agent Teams Save
- Device-Robust Spectral Grading and Origin Detection from UV-Vis-NIR Images: Towards Practical Gemstone Quality Assessment Save
- Topology Matters: Evaluating Multi-Agent Organizations for Resilient Flood Detection Save
- NQ-SVM: Scalable Quantum Kernel Learning via Nyström-Based Support Vector Machines Save
- A comprehensive survey on deep reinforcement learning in object tracking Save
- The M-factor: A Novel Metric for Evaluating Neural Architecture Search in Resource-Constrained Environments Save
- Supervised quantum machine learning: A future outlook from qubits to enterprise applications Save
- Overcoming Semantic Dilution in Transformer-Based Next Frame Prediction Save
- Optimizing Deep Reinforcement Learning Configurations for Single Object Tracking Save
- OpenAg: Democratizing Agricultural Intelligence Save