One of data science’s pioneers is coming to Barcelona
Boris Polyak, a professor at the Moscow Institute of Physics and Technology and an early experimenter in data science, will be explaining Principal Component Analysis at the international data processing conference this October.
Boris Polyak was a data scientist before the field of data science existed. He’s been a visiting professor in Israel, the south of France and the midwest of the United States. He leads the laboratory at the Institute for Control Sciences at the Moscow Institute of Physics and Technology. In a few weeks, he will be traveling to Barcelona from his office at the Moscow Institute of Physics and Technology to give a talk on his research for the 11th International Conference on Intelligent Data Processing. The main trend of modern data analysis is to reduce huge data bases to their low-dimensional approximations.” — Boris Polyak, Dr.Sci, Professor at MIPT
Polyak’s area of research is in robust principal component analysis. Don’t know what that is? Allow him to explain: “The main trend of modern data analysis is to reduce huge data bases to their low-dimensional approximations,” Polyak said in an email. The tool to do that? Principal Component Analysis, or PCA, he said. However, normal PCA is sensitive to outliers and other deviations from standard assumptions, Polyak said. “Thus the goal is to construct robust PCA.” Robust PCA, and even regular PCA, has hundreds of uses in data mining, signal processing and image recognition, Polyak said, adding that he hopes students at the conference will become interested in this direction of research. He will be discussing some approaches to making robust PCA at the conference. His main interests include optimisation, control, statistics and information technology — and he’s hoping to hear some novel ideas within those topics. The 11th International Conference on Intelligent Data Processing is presented by the Russian Academy of Sciences, the Moscow Institute of Physics and Technology and Harbour.Space University. From Oct. 10-14, 2016, data scientists from all over the world will gather to share research and industry knowledge on topics in machine learning, big data analytics, deep learning, computer vision, optimisation and case studies. You can find more information on the conference, its speakers and registration here .