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List of the proposed projects

  • CASF: Comparative assessment of scoring functions for docking problem

Pharmacological research is concentrated on constructing a molecule, a ligand, which docks a given protein. This research is expensive since it goes in-vitro. We have to propose a deep neural net to forecast the probability of docking, see details.

  • 3D-image reconstruction for the lens-free microscopy

One has to reconstruct an image of a small biological object (of hundreds micrometers, like a eucaryotic cell). Its video comes from a device of a brand-new technology: the free lens microscopy. This project combines modelling of interference images and its Fourier transform and GANs, the generative and discriminative deep learning models see details.

  • Atom-resolution synchrotron image reconstruction

For a given synchrotron image one has to reconstruct an object of nano-meter size. There given a reciprocal lattice image. We have to train a neural net to reconstruct the real and the complex part of a sample object, see details.

  • Long-live health monitoring with wearable devices

For daily accelerometer and gyroscope time series we have to reconstruct typical hand movements and represent these movements as clusters in the phase space.

  • Time series segmentation in low-dimensional space

One has to mark-up zero-phase segments of various hand movements using electronic watches. Neural nets on spherical harmonics seems to be a good tool.

  • Invariants and compositions of physical activities

We have to create a model to describe simple hand movements and compositions of movements. The composition of physical models must fit the composition of machine learning models.

  • Generating WANN: weighted agnostic neural networks

We have to generate agnostic nets and ensembles of agnostic nets to solve one of the well-known reinforcement learning problems: controlling toy cars on the road, steps, ans pendulums.

  • GAN creates WANN: generative adversarial network generates agnostic networks

We have to develop a variational autoencoder, VAE, to generate simple agnostic networks, which are evaluated by some discriminative model.