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August 13, 2026
‘Working with AI Solves a Wide Range of Engineering Problems
Artificial intelligence is a working tool based on a balanced combination of algorithms and engineering. Experts and doctoral students from the HSE Moscow Institute of Electronics and Mathematics explain how AI technologies can improve an application, device, or system, and what engineering tasks are solved in the process.
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Whatever task Saraa Ali, Junior Research Fellow at the Laboratory of Methods for Big Data Analysis (LAMBDA) of the AI and Digital Science Institute (HSE Faculty of Computer Science), is working on, she thinks about how it can benefit people. She told the Young Scientists of HSE University project about her large family, diagnosing three-phase motors, and her dream of building a children’s home in her native country.
August 11, 2026
‘The Peak of Stupidity and ‘The Valley of Despair: HSE Economists Propose an Explanation for the Dunning–Kruger Effect
The Dunning–Kruger effect, which describes a sharp surge in self-confidence among beginners followed by an equally rapid decline as they gain experience, can be explained by the nature of the learning process and the acquisition of new knowledge. This conclusion was reached by Andrey Vorchik of the HSE Faculty of Economic Sciences together with independent researcher Murat Mamyshev. They developed a mathematical model of learning and demonstrated how subjective confidence is formed and changes as knowledge accumulates, as well as how teachers can reduce the ‘valley of despair’ experienced by learners.

 

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Proceedings 16th International Symposium, ISBRA 2020, Moscow, Russia, December 1–4, 2020. Lecture Notes in Computer Science

Vol. 12304. Springer Publishing Company, 2020.
Academic editor: Z. Cai, I. Mandoiu, G. Narasimhan, P. Skums, X. Guo

This book constitutes the proceedings of the 16th International Symposium on Bioinformatics Research and Applications, ISBRA 2020, held in Moscow, Russia, in December 2020.
The 23 full papers and 18 short papers presented in this book were carefully reviewed and selected from 131 submissions. They were organized in topical sections named: genome analysis; systems biology; computational proteomics; machine and deep learning; and data analysis and methodology.

Chapters
Cancer Breakpoint Hotspots Versus Individual Breakpoints Prediction by Machine Learning Models
Cheloshkina K., Bzhikhatlov I., Poptsova M., , in: Proceedings 16th International Symposium, ISBRA 2020, Moscow, Russia, December 1–4, 2020. Lecture Notes in Computer ScienceVol. 12304.: Springer Publishing Company, 2020. P. 217–228.
Genome rearrangement is a hallmark of all cancers. Cancer breakpoint prediction appeared to be a difficult task, and various machine learning models did not achieve high prediction power. We investigated the power of machine learning models to predict breakpoint hotspots selected with different density thresholds and also compared prediction of hotspots versus individual breakpoints. We ...
Added: November 3, 2020
Language: English
DOI
Keywords: machine learningclassificationcomputer scienceartificial intellegence
Proceedings 16th International Symposium, ISBRA 2020, Moscow, Russia, December 1–4, 2020. Lecture Notes in Computer Science
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