Sevvandi is a mathematician working on data science/analytics related topics such as anomaly detection, meta-learning and streaming data.
Sevvandi is a mathematician working on data science/analytics related topics such as anomaly detection, meta-learning and streaming data. She likes working on real world problems, especially ones that are motivated by industry.
She came to statistical learning from a pure mathematics background. Her PhD was in mean curvature flow, which lies in the intersection of partial differential equations and differential geometry. She likes to bring her geometric intuition to solve data science/analytics problems.
Sevvandi is interested in data science/analytics research projects broadly in the following areas:
- Anomaly/outlier detection
- Streaming data
- Event detection
- Spatio-temporal data
- Dimension reduction
- Bushfire mitigation
She is also interested in industry projects. From 2016 to 2019, she worked with an industry partner on intrusion detection.
- 2015 - Graduate Certificate in Data Mining and Applications, Stanford University
- 2011 - PhD in Mathematics, Monash University
- 2007 - MSc Preliminary in Mathematics, Monash University
- 2002 - BSc Eng. in Computer Science and Engineering, University of Moratuwa, Sri Lanka
- Kandanaarachchi, S.,Hyndman, R. (2021). Dimension reduction for outlier detection using DOBIN In: Journal of Computational and Graphical Statistics, 30, 204 - 219
- Kandanaarachchi, S.,Munoz, M.,Hyndman, R.,Smith-Miles, K. (2020). On normalization and algorithm selection for unsupervised outlier detection In: Data Mining and Knowledge Discovery, 34, 309 - 354
- Talagala, P.,Hyndman, R.,Smith-Miles, K.,Kandanaarachchi, S.,Munoz, M. (2020). Anomaly Detection in Streaming Nonstationary Temporal Data In: Journal of Computational and Graphical Statistics, 29, 13 - 27
- Athanassenas, M.,Kandanaarachchi, S. (2020). (In Press) Singularities of axially symmetric volume preserving mean curvature flow In: Communications in Analysis and Geometry, , 1 - 20
- Kandanaarachchi, S.,Hyndman, R.,Smith-Miles, K. (2020). Early classification of spatio-temporal events using partial information In: PLOS One, 15, 1 - 39
- Kandanaarachchi, S.,Anantharama, N.,Munoz, M. (2020). (In Press) Early detection of vegetation ignition due to powerline faults In: IEEE Transactions on Power Delivery, , 1 - 11
- Leigh, C.,Kandanaarachchi, S.,McGree, J.,Hyndman, R.,Alsibai, O.,Mengersen, K.,Peterson, E. (2019). Predicting sediment and nutrient concentrations from high-frequency water-quality data In: PLoS ONE, 14, 1 - 22
- Leigh, C.,Alsibai, O.,Hyndman, R.,Kandanaarachchi, S.,King, O.,McGree, J.,Neelamraju, C.,Strauss, J.,Talagala, P.,Turner, R.,Mengersen, K.,Peterson, E. (2019). A framework for automated anomaly detection in high frequency water-quality data from in situ sensors In: Science of the Total Environment, 664, 885 - 898
- Kandanaarachchi, S.,Munoz, M.,Smith-Miles, K. (2019). Instance Space Analysis for Unsupervised Outlier Detection In: Proceedings of the 1st Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning (EDML 2019), Calgary, Canada, 2-4 May 2019
- Ryan, S.,Thaler, S.,Kandanaarachchi, S. (2016). Machine learning methods for predicting the outcome of hypervelocity impact events In: Expert Systems with Applications, 45, 23 - 39
- Novel water-quality monitoring for healthy platypus habitat. Funded by: ARC Centre of Excellence Grant - Via Other University from (2021 to 2021)
- Revolutionising Monitoring of Waterway Health in Merri Creek. Funded by: City of Whittlesea - Contract from (2020 to 2021)