Computer Science PhD student/Research Fellow at MLSP 2016

Author: Santosh Tirunagari (PhD Student and Research Fellow)

The 26th IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2016) took place at Lloyd’s Baia Hotel in Salerno, Italy from 13 Sept to 16 Sept 2016. MLSP is organised annually by the IEEE Signal Processing Society with a primary focus on  bringing Information Technologies and Databases & Information Systems professionals on to a common platform where they can exchange ideas and opinions to advance knowledge for Signal Processing, Machine Learning, Information Processing, Data Analysis and Pattern Recognition.

The conference included oral and poster presentations on the most recent and exciting advances in machine learning for signal processing through keynote talks, tutorials, as well as special and regular single-track sessions. Attending this conference was a great opportunity for me, as I met several top researchers in my field of  work i.e., in machine learning and signal processing. The conference had special sessions on Bayesian Machine Learning for Neural Signal Processing, Advances in Gaussian Processes for Machine Learning and Signal Processing and Computational Methods for Audio Analysis. All these sessions are of interest to me.

My paper entitled “Automatic Classification Of Irregularly Sampled Time Series With Unequal Lengths: A Case Study On Estimated Glomerular Filtration Rate”, was accepted  at this conference and I was invited to present this work as a 15 minute  lecture presentation.  This paper was produced as part of MRC CKD project which essentially deals with modelling Chronic Kidney Disease (CKD, which  is considered as significant cause of morbidity and mortality across the  world. Patients with CKD have increased risk of death from cardiovascular disease and end stage kidney failure, leading to dialysis and kidney transplant. Indeed, according to an NHS Kidney Care report in 2012 , CKD was estimated to cost £1.45 billion in 2009-10; 1.8 million people were diagnosed with CKD in England; and, there were potentially 900,000 to 1.8 million people with undiagnosed CKD. Therefore, the importance and urgency of managing CKD cannot be over-emphasized. In this paper using the state-of-the-art machine learning techniques and methodologies we automatically screen patients’ data to identify those whose kidney function is at risk of deteriorating into more severe stages of CKD, thus enabling the clinicians to monitor their patients remotely.

This work was supported by the Medical Research Council under grant number MR/M023281/1.  The project details can be found at

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