I am a PhD candidate in Machine Learning at Blekinge Institute of Technology, in Sweden. I am working under the project Scalable resource- efficient systems for big data analytics funded by the Knowledge Foundation, advised by Niklas Lavesson and Håkan Grahn.
The main focus of my thesis is on making machine learning algorithms more energy efficient. In particular, I have studied the energy consumption patterns of streaming algorithms, and then proposed new algorithm extensions that reduce their energy consumption. Some of my research interests are the following: Energy Efficiency in Machine Learning, Green AI, High Performance Computing, Big Data and Streaming Data, Green Computing.
We are currently organizing the second edition of the Green Data Mining workshop: Second International Workshop on Energy Efficient Scalable Data Mining and Machine Learning, held in conjunction with ECML-PKDD 2019, in September in Würzburg, Germany. Green Data Mining
I organized the 1st International Workshop on Energy Efficient Data Mining and Knowledge Discovery, held in conjunction with ECML-PKDD 2018, in September in Dublin, Ireland. Green Data Mining
I was helping at organizing the WiML dinner held at ICML, in Stockholm, July 2018.
I have given talks and/or presentations at the following events:
Hoeffding Trees with nmin adaptation • The 5th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2018), October 2018, Turin, Italy.
How to Measure Energy Consumption in Machine Learning algorithms • Green Data Mining workshop held at ECML-PKDD 2018, September 2018, Dublin, Ireland.
Energy-Efficient Data Stream Mining • HiPEAC 2018, European Network on High Performance and Embedded Architecture and Compilation, May 2018, Gothenburg, Sweden.
Extraction and Energy Efficient Processing of Streaming Data • Licentiate Presentation at Blekinge Institute of Technology, December 2017, Karlskrona, Sweden.
Adaptive Very Fast Decision Tree, preliminary results • 5th Swedish Workshop in Data Science (SweDS 2017), December 2017, Gothenburg, Sweden.
Adaptive Very Fast Decision Tree, preliminary results • 12th Women in Machine Learning Workshop (WiML 2017), in conjunction with NIPS 2017, December 2017, Long Beach, USA. Poster presentation.
Is it ethical to avoid error analysis? • Fairness, Accountability, and Transparency in Machine Learning Workshop (FAT/ML), held in conjunction with KDD 2017, August 2017, Halifax, Canada. Poster presentation.
Identification of Energy Hotspots: A Case Study of the Very Fast Decision Tree • Green, Pervasive, and Cloud Computing, May 2017, Cetara, Italy.
Energy Efficiency in Machine Learning: A position paper • 30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2017. May 2017, Karlskrona, Sweden.
Energy Efficiency in Machine Learning • 4th Swedish Workshop in Data Science (SweDS 2016), November 2016, Skövde, Sweden.