Dr. Mikhail Langovoy is a Research Scientist at the Max Planck Institute for Intelligent Systems and at the Max Planck Institute for Developmental Biology in Tuebingen. He received his Master of Science degree in algebra and number theory from St Petersburg State University, Russia, in 2001, and a Ph. D. in statistics from the University of Goettingen, Germany, in 2007. After completing his thesis on data-driven tests of statistical hypothesis, he has been doing research on probability theory and stochastic analysis at the University of Bonn in Germany, and research on spatial statistics and image analisis at the European research institute EURANDOM in the Netherlands. At present, he is working in both stochastics and machine learning.

RESEARCH INTERESTS

• Spatial statistics and statistical image analysis

• Machine learning theory and support vector machines

• Statistical inverse problems and their applications

• Statistics for stochastic processes, nonparametric statistics

• Randomized algorithms

• Stochastic analysis

• Econometrics and financial statistics

• Heavy-tailed distributions, strong dependencies and extremes

• Randomly growing networks and the Internet

18 results
(BibTeX)

**Statistical estimation for optimization problems on graphs**
In pages: 1-6, NIPS Workshop on Discrete Optimization in Machine Learning (DISCML): Uncertainty, Generalization and Feedback , December 2011 (inproceedings)

**Adaptive nonparametric detection in cryo-electron microscopy**
In *Proceedings of the 58th World Statistics Congress*, pages: 4456-4461, ISI, August 2011 (inproceedings)

**Statistical Image Analysis and Percolation Theory **
2011 Joint Statistical Meetings (JSM), August 2011 (talk)

**Statistical estimation for optimization problems on graphs**
Empirical Inference Symposium, 2011 (poster)

**Spatial statistics, image analysis and percolation theory**
In pages: 11, American Statistical Association, Alexandria, VA, USA, 2011 Joint Statistical Meetings (JSM), August 2011 (inproceedings)

**Algebraic polynomials and moments of stochastic integrals**
*Statistics & Probability Letters*, 81(6):627-631, June 2011 (article)

**Multiple testing, uncertainty and realistic pictures**
(2011-004), EURANDOM, Technische Universiteit Eindhoven, January 2011 (techreport)

**Statistical image analysis and percolation theory**
73rd Annual Meeting of the Institute of Mathematical Statistics (IMS), August 2010 (talk)

**Computationally efficient algorithms for statistical image processing: Implementation in R**
(2010-053), EURANDOM, Technische Universiteit Eindhoven, December 2010 (techreport)

**Statistical image analysis and percolation theory**
28th European Meeting of Statisticians (EMS), August 2010 (talk)

**Robust nonparametric detection of objects in noisy images**
(2010-049), EURANDOM, Technische Universiteit Eindhoven, September 2010 (techreport)

**Model selection, large deviations and consistency of data-driven tests**
(2009-007), EURANDOM, Technische Universiteit Eindhoven, March 2009 (techreport)

**Algebraic polynomials and moments of stochastic integrals**
(2009-031), EURANDOM, Technische Universiteit Eindhoven, October 2009 (techreport)

**Randomized algorithms for statistical image analysis based on percolation theory**
27th European Meeting of Statisticians (EMS), July 2009 (talk)

**Detection of objects in noisy images and site percolation on square lattices**
(2009-035), EURANDOM, Technische Universiteit Eindhoven, November 2009 (techreport)

**Data-driven goodness-of-fit tests**
2008 Barcelona Conference on Asymptotic Statistics (BAS), September 2008 (talk)

**Data-driven efficient score tests for deconvolution hypotheses**
*Inverse Problems*, 24(2):1-17, April 2008 (article)

**Efficient tests for the deconvolution hypothesis**
*Workshop on Statistical Inverse Problems*, March 2006 (poster)

Dr. Mikhail Langovoy is a Research Scientist at the Max Planck Institute for Intelligent Systems and at the Max Planck Institute for Developmental Biology in Tuebingen. He received his Master of Science degree in algebra and number theory from St Petersburg State University, Russia, in 2001, and a Ph. D. in statistics from the University of Goettingen, Germany, in 2007. After completing his thesis on data-driven tests of statistical hypothesis, he has been doing research on probability theory and stochastic analysis at the University of Bonn in Germany, and research on spatial statistics and image analisis at the European research institute EURANDOM in the Netherlands. At present, he is working in both stochastics and machine learning.

RESEARCH INTERESTS

• Spatial statistics and statistical image analysis

• Machine learning theory and support vector machines

• Statistical inverse problems and their applications

• Statistics for stochastic processes, nonparametric statistics

• Randomized algorithms

• Stochastic analysis

• Econometrics and financial statistics

• Heavy-tailed distributions, strong dependencies and extremes

• Randomly growing networks and the Internet