Join HSE's English-taught Courses on Coursera
In July, HSE is launching two English-taught courses on Coursera. Enrollement for the courses is still open. The full list of online courses offered by HSE is available on HSE MOOCs page.
Addressing Large Hadron Collider Challenges by Machine Learning
The course is part of the Advanced Machine Learning specialization. It is intended for those who have already studied the methods of machine learning and would like to apply them to practical research problems, in particular, in high energy physics. Many of the experiments are carried out on a Large Hadron Collider and require processing of huge amounts of information. The course will help participants to understand how to do it. Background in physics is not required.
The course starts July 9 and applications are open till July 14 (the course is relaunched every two weeks). The course is taught in English.
Mathematics for Economists
The course is intended for bachelor’s and first-year master’s students who would like to gain skills in applying mathematical knowledge and expertise to solving economic problems. Participants will also learn how to present proofs. The authors of the course believe that mathematical concepts are not only beautiful theories in books; they can help to better understand the real life situations.
The course starts July 16. The course is taught in English.
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Analysing Genetic Information Can Help Prevent Complications after Myocardial Infarction
Researchers at HSE University have developed a machine learning (ML) model capable of predicting the risk of complications—major adverse cardiac events—in patients following a myocardial infarction. For the first time, the model incorporates genetic data, enabling a more accurate assessment of the risk of long-term complications. The study has been published in Frontiers in Medicine.
'Our Mathematics Is for Far-Reaching Minds'
Anna Presnova focuses on automatic control theory, a branch of science that addresses global problems. In this interview with the HSE Young Scientists project, she talks about her aspiration to achieve practical results, the energy she gains from teaching, and why she would be interested to meet with Alexandr Lyapunov, founder of stability theory.
'It Was a Great Learning Experience for the New Generation of Mathematicians'
From November 5 to 9, 2024, HSE University hosted a five-day conference on algebraic geometry and mathematical physics, organised jointly with the Beijing Institute of Mathematical Sciences and Applications. Over 30 mathematicians from the world's leading universities, including early-career scientists, participated in the event.
‘We Bring Together the Best Russian Scientists and AI Researchers at HSE University Site’
On October 25–26, 2024, HSE University’s AI and Digital Science Institute and the AI Research Centre hold the Fall into ML 2024 conference in Moscow. This year’s event will focus on the prospects in development of fundamental artificial intelligence, with SBER as its conference title partner.
HSE University Wins Two Medals at the International Mathematics Competition for University Students
Two students, one graduate and one undergraduate, from the HSE Faculty of Computer Science (FCS) programme in ‘Applied Mathematics and Information Science’ won prestigious awards at the International Mathematics Competition for University Students (IMC). Graduate Maksim Kazadaev and first-year student Daria Linichenko both represented HSE University at the IMC, winning gold and silver medals respectively.
Prepare for the INTO HSE Olympiad with HSE Students
Winners of the INTO HSE International Maths Olympiad receive scholarships and admission offers to 37 different programmes at HSE University. Angelina Yudina, a master's student and invited instructor of maths at the Faculty of Economic Sciences, offers a video lesson where she analyses the demo version of the maths problems from the Olympiad.
HSE Researchers Demonstrate Effectiveness of Machine Learning in Forecasting Inflation
Inflation is a key indicator of economic stability, and being able to accurately forecast its levels across regions is crucial for governments, businesses, and households. Tatiana Bukina and Dmitry Kashin at HSE Campus in Perm have found that machine learning techniques outperform traditional econometric models in long-term inflation forecasting. The results of the study focused on several regions in the Privolzhskiy Federal District have been published in HSE Economic Journal.
Immortal Cells and Mathematics Reveal Mechanism behind Coronavirus Infection
A mathematical model has helped describe the course of infection caused by two variants of coronavirus: Omicron and Delta, and explain the differences between them. It appears that the cell entry rate is lower for Omicron, allowing infected cells ample time to alert neighbouring cells of the threat and trigger the activation of their innate immune response. In the future, the developed model could be employed to investigate any other variant of COVID-19, potentially leading to effective strategies for combating new hazardous strains, such as Pirola and JN.1. The findings from the study conducted with the participation of HSE researchers have been published in PeerJ.
‘The Goal of the Spring into ML School Is to Unite Young Scientists Engaged in Mathematics of AI’
The AI and Digital Science Institute at the HSE Faculty of Computer Science and Innopolis University organised a week-long programme for students, doctoral students, and young scientists on the application of mathematics in machine learning and artificial intelligence. Fifty participants of Spring into ML attended 24 lectures on machine learning, took part in specific pitch sessions, and completed two mini-courses on diffusion models—a developing area of AI for data generation.
Mathematicians Reveal the Mechanism behind Neuron Synchronisation: Hyperchaos
Scientists of the International Laboratory of Dynamic Systems and Applications at HSE Campus in Nizhny Novgorod have described a rare case of synchronisation in a system of chemically coupled neuron models. The study findings enable a mathematical description of atypical brain functioning modes, including those associated with neurodegenerative diseases. The study has been published in Regular and Chaotic Dynamics.