Dr Chandrasekhar Kambhampati

Dr Chandrasekhar Kambhampati

Reader in Computer Science

Faculty and Department

  • Faculty of Science and Engineering
  • Department of Computer Science and Technology

Undergraduate

Teach on

08131 - Computer Systems

08336 - NEAT

08018 - Trustworthy Computing

08325 - Tutor and PR for year in Industry

Recent outputs

View more outputs

Conference Proceeding

A comparative study of missing value imputation with multiclass classification for clinical heart failure data

Zhang, Y., Kambhampati, C., Davis, D. N., Goode, K., & Cleland, J. G. F. (2012). A comparative study of missing value imputation with multiclass classification for clinical heart failure data. In Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on, (2840-2844). https://doi.org/10.1109/fskd.2012.6233805

Journal Article

Ionic Imbalances and Coupling in Synchronization of Responses in Neurons

Sadegh-Zadeh, S., Kambhampati, C., & Davis, D. N. (2019). Ionic Imbalances and Coupling in Synchronization of Responses in Neurons. J, 2(1), 17-40. https://doi.org/10.3390/j2010003

Use of a propositional rule learner for prognosis of mortality rates in heart failure patients

Bohacik, J., Kambhampati, C., Davis, D. N., & Cleland, J. G. F. (2014). Use of a propositional rule learner for prognosis of mortality rates in heart failure patients. Journal of information technologies, 7(1),

Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease

Alabed, A., Kambhampati, C., & Gordon, N. (in press). Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease. Advances in Intelligent Systems and Computing,

Issues in the mining of heart failure datasets

Poolsawad, N., Moore, L., Kambhampati, C., & Cleland, J. G. (2014). Issues in the mining of heart failure datasets. International Journal of Automation and Computing, 11(2), 162-179. https://doi.org/10.1007/s11633-014-0778-5

Research interests

Over the years I have worked on problems dealing with optimisation, optimal control, learning algorithms, neural networks, and networked control systems as applied to a variety of different applications including Robots, chemical processes and bio systems. I have also worked on problems dealing with walking of robots. More recently I have been working on two problems,one dealing with risk prediction in the clinical domain using live clinical data and the other neurological disorders and computation models from a neurons perspective. AT the same time recently been developing sensors based on photonics for controlling robots