Dr Mohan Sridharan PhD

Dr Mohan Sridharan

School of Computer Science
Senior Lecturer in Intelligent Systems

Contact details

Address
UG38, School of Computer Science
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Dr Mohan Sridharan is a Senior Lecturer in Intelligent Systems in the School of Computer Science at University of Birmingham. He designs and algorithms and architectures to address challenges in knowedge representation and reasoning, cognitive systems, machine learning, and computational vision, as applied to adaptive robots and agents collaborating with humans. In addition, he also designs and adapts algorithms to address estimation and prediction problems in non-robotics domains such as intelligent transportation, agricultural automation, and climate informatics. For more information, please visit Mohan's School webpage.

Qualifications

  • PhD in Electrical and Computer Engineering, The University of Texas at Austin (USA).
  • MS in Electrical and Computer Engineering, The University of Texas at Austin (USA).

Biography

Mohan obtained his Masters and PhD degrees in Electrical and Computer Engineering from The University of Texas at Austin (USA). Prior to his current appointment, he held academic positions in the US (Texas Tech University) and NZ (University of Auckland), where he currently holds honorary positions. For more details, please see his personal web site and his CV (pdf, 225KB).

Teaching

  • Intelligent Robotics + Intelligent Robotics Extended
  • Advanced Robotics

You can also view my teaching statement (pdf, 53KB).

Research

My primary research interests include knowledge representation and reasoning, cognitive systems, machine learning, and computational vision, as applied to adaptive robots and software agents. Specifically, I design algorithms and architectures to address the following research questions:

  • How best to enable robots to represent and reason reliably and efficiently with qualitative and quantitative descriptions of incomplete knowledge and uncertainty?
  • How best to enable robots to learn interactively and cumulatively from sensor inputs and limited human feedback?
  • How best to enable designers to understand the robot’s behavior and establish that it satisfies desirable properties?

I take an integrated cognitive systems approach to address these questions, i.e., my algorithms and architectures explicitly exploit the dependencies between (and jointly explore) the representation, reasoning, learning, and control problems.

In parallel to my research in human-robot collaboration, I develop and adapt algorithms to address estimation and prediction problems in domains such as intelligent transportation, agricultural irrigation management, climate informatics, and software project management. For more information, please see my personal web site and research statement (pdf, 125KB).

Publications

Recent publications

Article

Weerasekera, R, Sridharan, M & Ranjitkar, P 2020, 'Implications of Spatiotemporal Data Aggregation on Short-Term Traffic Prediction Using Machine Learning Algorithms', Journal of Advanced Transportation, vol. 2020, 7057519, pp. 1-21. https://doi.org/10.1155/2020/7057519

Gomez, R, Sridharan, M & Riley, H 2020, 'What do you really want to do? Towards a Theory of Intentions for Human-Robot Collaboration', Annals of Mathematics and Artificial Intelligence. https://doi.org/10.1007/s10472-019-09672-4

Riley, H & Sridharan, M 2019, 'Integrating Non-monotonic Logical Reasoning and Inductive Learning With Deep Learning for Explainable Visual Question Answering', Frontiers in Robotics and Artificial Intelligence, vol. 6, 125. https://doi.org/10.3389/frobt.2019.00125

Sridharan, M, Gelfond, M, Zhang, S & Wyatt, J 2019, 'REBA: a refinement-based architecture for knowledge representation and reasoning in robotics', Journal of Artificial Intelligence Research, vol. 65, pp. 87-180. https://doi.org/10.1613/jair.1.11524

Sridharan, M & Meadows, B 2018, 'Knowledge representation and interactive learning of domain knowledge for human-robot interaction', Advances in Cognitive Systems, vol. 7, pp. 69-88. <http://www.cogsys.org/papers/ACSvol7/papers/paper-7-6.pdf>

Conference contribution

Mota, T & Sridharan, M 2019, Commonsense reasoning and knowledge acquisition to guide deep learning on robots. in A Bicchi, H Kress-Gazit & S Hutchinson (eds), Robotics: Science and Systems XV., 77, Robotics: Science and Systems Proceedings, vol. 15, Robotics: Science and Systems, Robotics, Freiburg, Germany, 22/06/19. https://doi.org/10.15607/RSS.2019.XV.077

Mota, T & Sridharan, M 2018, Incrementally Grounding Expressions for Spatial Relations between Objects. in J Lang (ed.), Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18). International Joint Conferences on Artificial Intelligence, International Joint Conference on Artificial Intelligence 2018, Stockholm, Sweden, 13/07/18. <https://www.ijcai.org/proceedings/2018/0266.pdf>

Sridharan, M & Meadows, B 2018, Knowledge Representation and Interactive Learning of Domain Knowledge for Human-Robot Interaction. in T Vaquero, M Roberts, S Bernardini, T Niemueller & S Fratini (eds), Proceedings of the 2nd Workshop on Integrated Planning, Acting, and Execution (IntEx 2018). International Conference on Automated Planning and Scheduling, pp. 60-68, Workshop on Integrated Planning, Acting and Execution at ICAPS 2018, Delft, Netherlands, 25/06/18. <http://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/workshops/workshop09/docs/proceedings.pdf>

Gomez, R, Sridharan, M & Riley, H 2018, Representing and Reasoning with Intentional Actions on a Robot. in A Finzi, E Karpas, G Nejat, A Orlandini & S Srivastava (eds), Proceedings of the 6th Workshop on Planning and Robotics (PlanRob 2018) . International Conference on Automated Planning and Scheduling, pp. 133-142, Workshop on Planning and Robotics (PlanRob) at ICAPS 2018, Delft, Netherlands, 26/06/18. <http://wpage.unina.it/alberto.finzi/public_html/proceedings.pdf>

Sridharan, M & Meadows, B 2017, An Architecture for Discovering Affordances, Causal Laws, and Executability Conditions. in Proceedings of the Fifth Annual Conference on Advances in Cognitive Systems (2017). vol. 5, 5, Advances in Cognitive Systems, vol. 5, Cognitive Systems Foundation, Fifth Annual Conference on Advances in Cognitive Systems, Troy, New York, United States, 12/05/17. <http://www.cogsys.org/papers/ACS2017/ACS_2017_paper_21_Sridharan.pdf>

Langley, P, Meadows, B & Sridharan, M 2017, Explainable Agency for Intelligent Autonomous Systems. in Proceedings of the Twenty-Ninth AAAI Conference on Innovative Applications (IAAI-17). Association for the Advancement of Artificial Intelligence, The Twenty-Ninth AAAI Conference on Innovative Applications (IAAI-17), Sam Francisco, United States, 6/02/17. <https://aaai.org/ocs/index.php/IAAI/IAAI17/paper/view/15046/13734>

Jones, KS, Cherry, B, Harris, DJ & Sridharan, M 2017, Formative Analysis of Aging in Place: Implications for the Design of Caregiver Robots. in Proceedings of the Human Factors and Ergonomics Society Annual Meeting: : 2017. 1 edn, vol. 61, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, no. 1, vol. 61, SAGE Publications, pp. 1145-1145, Human Factors and Ergonomics Society Annual Meeting 2017, Austin, TX, United States, 9/10/17. https://doi.org/10.1177/1541931213601770

Sridharan, M & Meadows, B 2017, Towards an architecture for discovering domain dynamics: affordances, causal laws, and executability conditions. in International Workshop on Planning and Robotics (PlanRob) at the International Conference on Automated Planning and Scheduling (ICAPS 2017)). AAAI Press, International Workshop on Planning and Robotics (PlanRob) at ICAPS 2017, 20/06/17.

Kunze, L, Sridharan, M, Dimitrakakis, C & Wyatt, J 2017, View planning with time constraints: an adaptive sampling approach. in Workshop on AI Planning and Robotics: Challenges and Methods (AIPlanRob): at the International Conference on Robotics and Automation (ICRA 2017). 2017 IEEE International Conference on Robotics and Automation (ICRA 2017), Singapore, 29/05/17. <http://homepages.engineering.auckland.ac.nz/~smohan/Papers/aiplanrob17_viewPlanning.pdf>

Paper

Mota, T & Sridharan, M 2018, 'Learning the grounding of expressions for spatial relations between objects', Paper presented at Workshop on Perception, Inference and Learning for Joint Semantic, Geometric and Physical Understanding at ICRA 2018, Brisbane, Australia, 21/05/18 - 21/05/18. <https://natanaso.github.io/rcw-icra18/assets/ref/ICRA-MRP18_paper_24.pdf>

View all publications in research portal