Dr Mohan Sridharan
Dr Mohan Sridharan is a senior lecturer in the Department of Electrical and Computer Engineering at the University of Auckland. Prior to his current appointment, he was a faculty member at Texas Tech University (USA), where he is currently an Adjunct Associate Professor in the Department of Mathematics and Statistics. He received his PhD in Electrical and Computer Engineering from The University of Texas at Austin (USA) in 2007, and spent one year as a research fellow in the School of Computer Science at the University of Birmingham (UK).
Dr Sridharan's primary research interests include machine learning, knowledge representation and reasoning, computational vision, and cognitive science, as applied to autonomous robots and intelligent agents.
Research | Current
My research objective is to enable robots to learn from, and collaborate with, non-expert human participants in dynamic domains. Towards this objective, I design algorithms and develop architectures to:
a) represent and reason with qualitative and quantitative descriptions of knowledge and uncertainty.
b) learn from multimodal cues and minimal high-level human feedback.
I am also interested in developing stochastic learning and inference algorithms for non-robotics applications; recent examples includes agricultural irrigation management, yield mapping, and climate modeling.
Teaching | Current
COMPSYS 303 - Microcomputers and Embedded Systems
COMPSYS 725 - Computer Networks and Distributed Applications
Distinguished Paper Award, International Conference on Automated Planning and Scheduling (ICAPS), 2008
Paper of Excellence Award, International Conference on Development and Learning and Epigentic Robotics (ICDL-EpiRob), 2012
Best Paper Award, International Conference of the Florida AI Research Society (FLAIRS), 2014
Areas of expertise
- Autonomous robots
- Machine learning
- Knowledge representation and reasoning
- Computational vision
- Cognitive science
Institute of Electrical and Electronics Engineers (IEEE)
IEEE Robotics and Autonomous Systems (RAS) Society
Association for the Advancement of Artificial Intelligence (AAAI)
Selected publications and creative works (Research Outputs)
- Holman, D., Sridharan, M., Gowda, P., Porter, D., Marek, T., Howell, T., & Moorhead, J. (2014). Gaussian process models for reference ET estimation from alternative meteorological data sources. Journal of Hydrology, 517, 28-35. 10.1016/j.jhydrol.2014.05.001
- Sridharan, M., & Rainge, S. (2014). Integrating Reinforcement Learning and Declarative Programming to Learn Causal Laws in Dynamic Domains. Paper presented at 6th International Conference on Social Robotics (ICSR), Sydney, Australia. 27 October - 29 October 2014. Lecture Notes in Computer Science. 10.1007/978-3-319-11973-1_33
- Zhang, S., Sridharan, M., Gelfond, M., & Wyatt, J. (2014). Towards an Architecture for Knowledge Representation and Reasoning in Robotics. Paper presented at 6th International Conference on Social Robotics, Sydney, Australia. 27 October - 29 October 2014. Lecture Notes in Computer Science. (pp. 11). 10.1007/978-3-319-11973-1_41
- Featherston, E., Sridharan, M., Urban, S., & Urban, J. (2014). DOROTHY: Enhancing Bidirection Communication between a 3D Programming Interface and Mobile Robots. Paper presented at Fifth Symposium on Educational Advances in Artificial Intelligence (EAAI), Quebec City, Canada. 28 July - 29 July 2014. Proceedings of the Fifth Symposium on Educational Advances in Artificial Intelligence. Related URL.
- Salmani, K., & Sridharan, M. (2014). Multi-Instance Active Learning with Online Labeling for Object Recognition. Paper presented at 27th International Conference of the Florida AI Research Society (FLAIRS), Pensacola Beach, Florida. 21 May - 23 May 2014. Related URL.
- Zhang, S., Sridharan, M., & Washington, C. (2013). Active Visual Planning for Mobile Robot Teams Using Hierarchical POMDPs. IEEE Transactions on Robotics, 29 (4), 975-985. 10.1109/TRO.2013.2252252
- Sridharan, M. (2013). An Integrated Framework for Robust Human-Robot Interaction. In J. Garcia-Rodriguez, M. A. Cazorla Quevedo (Eds.) Robotic Vision: Technologies for Machine Learning and Vision Applications (pp. 281-301). Hershey, Pennsylvania: Information Science Reference. 10.4018/978-1-4666-2672-0.ch016
- Li, X., & Sridharan, M. (2013). Move and the Robot will Learn: Vision-based Autonomous Learning of Object Models. Paper presented at 16th International Conference on Advanced Robotics, Montevideo, Uruguay. 25 November - 29 November 2013. 10.1109/ICAR.2013.6766513