Dmitry Laptev

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About & Contacts


Everyone knows that this is impossible. But ones comes an ignorant, to whom it is unknown - and he makes a discovery.

One of fake quotes by Albert Einstein

I am a PhD student in the Machine Learning Laboratory headed by Prof. Joachim M. Buhmann in the Department of Computer Science at ETH Zurich, Switzerland.

I recieved my diploma in Applied Math and Computer Science in 2011 from the Lomonosov Moscow State University, Russian Federation, in Bayesian Methods research group headed by Dmitry P. Vetrov.

Currently I am working on computer vision researches in brain imaging. Keywords for my area of interests are: structured learning, graphical models, optimisation, time-series analysis, applied statistics, and some related topics.

You can contact me via:

  • e-mail: dlaptev@inf.ethz.ch
  • phone: +41 44 632 83 60
  • foot: CAB F 61.2, Universitaetstrasse 6, 8092, Zurich

Research in inverse chronological order


Sinergia project (current)

The global goal of the project is to develop image processing and machine learning methods and algorithms to detect, classify and reconstruct neuroanatomical structures like neurons, dendrites and synapses from transmission electron microscopy images.

Currently I am focusing on the problems of anisotropic data segmentation. Trying to improve the segmentation using dense correspondence across sections.

The framework: based on the non-linear correspondings the algorithm evaluates the warped images (a). Then, feature vectors in the corresponding pixels are evaluated: (b). After that the method concatenates them and passes the concatenated feature vector to a RF (c). RF returns a probability map that is segmented by Graph Cut algorithm (d)

Time-series anomaly detection

The goal was to develop a system that would detect, analyse and classify anomal behavior in Internet market measures (ejections, changes of trend, etc.). The project was developed in and for Yandex.

Time-series algorithm involved independent analysis of trend, seasonal component, and characteristics of noise. Trend and a seasonal component were fitted using modified least squares regression. To model the noise we used Variance Gamma distribution with adoptive parameters.

Example of discovered anomalies in time-series

Short term solar activity forecast

We developed an automated algorithm for short-term solar flare activity forecast. The project was done in collaboration with Microsoft Research Cambridge.

Using images of the Sun taken in different spectrum, we analysed the presence of the Sun spots and its features and the dynamics of its changes. Basing on that features we trained a short-term predictor.

Magnetogram image and the region with the sun spot

Dual decomposition in Hidden Markov Models

The idea of the research was to add a term to HMM functional that concern global prior (i.e. total length of the signal being in each of the states).

Functional, that we finally obtained, was effectively optimized using dual decomposition approach.

Comparison of different signal segmentation techniques:
true segmentation (a), Viterbi segmentation (b), DD HMM segmentation (c)

ELM-algorithm for signals and images segmentation

We incorporated global prior information into EM-algorithm. For images, global prior can be equal to the total number of pixels belonging to a segment.

The task was solved using iterative process of variational approximation and transportation problem.

Satellite photo to be segmented (a), true segmentation (b),
the result of unconstrained segmentation (c) and of constrained segmentation (d)

Publications in inverse chronological order


My Google Scholar profile: link

Selected publications


2012

  • D. Laptev, A. Vezhnevets, S. Dwivedi, J.M. Buhmann. Anisotropic ssTEM Image Segmentation Using Dense Correspondence Across Sections. MICCAI, 2012, pdf, poster, link

2011

  • V. Chernyshov, D. Laptev, D. Vetrov. Short-term solar flare forecast. Graphicon 2011 (21th International Conference on Computer Graphics and Vision), 2011, pdf

2010

  • D. Kropotov, D. Laptev, A. Osokin, D. Vetrov. Signal Segmentation with Label Frequency Constraints using Dual Decomposition Approach for Hidden Markov Models 8th International Conference Intellectualization of Information Processing, pp. 403-406, 2010, pdf
  • D. Kropotov, D. Laptev, A. Osokin, D. Vetrov. Variational Segmentation Algorithms with Label Frequency Constraints. International Conference on Pattern Recognition and Image Analysis (PRIA), vol. 20, pp. 324-334, 2010, pdf, link

All publications


2013

  • K. Nekrasov, D. Laptev, D. Vetrov. Automatic Determination of Cell Division Rate Using Microscope Images. PRIA, 2012, pdf

2012

  • D. Laptev, A. Vezhnevets, S. Dwivedi, J.M. Buhmann. Anisotropic ssTEM Image Segmentation Using Dense Correspondence Across Sections. MICCAI, 2012, pdf, poster, link

2011

  • V. Chernyshov, D. Laptev, D. Vetrov. Short-term solar flare forecast. Graphicon 2011 (21th International Conference on Computer Graphics and Vision), 2011, pdf
  • D. Laptev. Searching for informative features in Magnetogram Solar images. Diploma thesis, Lomonosov Moscow State University, 2011, pdf
  • D. Laptev. Searching for active regions in the image. Lomonosov 2011 (International student, postgraduate and young scientist conference), 2011, pdf

2010

  • K. Nekrasov, D. Laptev, D. Vetrov. Automatic Detection of Cell Division Intensity in Budding Yeast. International Conference on Pattern Recognition and Image Analysis (PRIA), vol. 2, pp. 335-339, 2010, pdf
  • D. Kropotov, D. Laptev, A. Osokin, D. Vetrov. Signal Segmentation with Label Frequency Constraints using Dual Decomposition Approach for Hidden Markov Models 8th International Conference Intellectualization of Information Processing, pp. 403-406, 2010, pdf
  • D. Kropotov, D. Laptev, A. Osokin, D. Vetrov. Variational Segmentation Algorithms with Label Frequency Constraints. International Conference on Pattern Recognition and Image Analysis (PRIA), vol. 20, pp. 324-334, 2010, pdf, link

2009

  • D. Laptev. Automatic detection of the number of dividing cells based on the microscope images. Graphicon 2009 (19th International Conference on Computer Graphics and Vision), 2009 First prize in “Young scientists” nomination, pdf

Experience


Conferences & workshops (publication activity not included)


Industry jobs


Teaching at ETH Zurich


Code & Software


VarGamma package for python


A package for Python to work with Variance Gamma distribution. Contains probability and cumulative density functions, random point generator, and two methods for parameter fitting (method of moments and maximum likelihood method).

Required packages: numpy and scipy.

Other activities


I try to help other people and ask for help using StackExchange. Check my profiles in CrossValidated and in StackOverflow. And here is my flairtm badge:

I appreciate the idea of ResearchGate.net to openly share knowledge and information. So please...

Dmitry Laptev

One of my hobbies is mobile development as a part of Xorio.net Studio. We have developed: StereoCrack - a game for your imagination and brains.