Numerical modeling and data analysis lie at the heart of modern technology and our understanding of the world. But can you learn to manage data? Skoltech professors are positive that yes, you can!
Join our lecture to learn more about data management. Skoltech professor Vladimir Palyulin will talk about randomness and how it models the world around us. Professor Dmitry Dylov will explain how to recover corrupted images using fast computer vision.
And, most importantly, we will present Skoltech’s MSc and PhD programs, admission and selection procedures, learning process, and opportunities for recent graduates.
Join the lecture to find out:
- what is the mathematics of imaging and how it uses the Fourier frequency formalism,
- how advanced AI models help achieve super resolution and remove artifacts,
- how to recover corrupted images by making MRI 20 times faster,
- how effective models and numerical methods use the Monte Carlo approach, the generation of random variables and distributions, and probabilistic thinking,
- what educational programs teach all this at Skoltech and how to enroll in a tuition-free program.
About the speakers
Vladimir Palyulin, an assistant professor at Skoltech, earned his Ph.D. degree in Statistical and Polymer Physics from MSU. A winner of a German research grant, he studied anomalous random processes, non-equilibrium systems and target search optimization at the Technical University of Munich and the University of Potsdam (Germany). During his stay in Germany, he authored many publications, including two with over 100 citations per paper. He worked at the University of Cambridge (UK) where he published a series of papers on the mechanics of disordered systems (glass). Since joining Skoltech in 2019, Vladimir has been focusing on the applications of the theory of random processes in conjunction with machine learning, as well as statistical physics.
Dmitry Dylov, an associate professor at Skoltech, received his Ph.D. from Princeton University (USA). As a Lead Scientist, he managed medical image analysis, AI and bioimaging projects at GE Global Research (USA). Dmitry developed a new theoretical and computational paradigm for noise processing in imaging systems and published his findings in top journals, including Physical Review Letters and Nature Photonics. Dmitry has over 20 international patents and 110 peer-reviewed publications.