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Dimitri Lozeve 2018-11-13 20:00:01 +01:00
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*~
.stack-work/
_cache/
_site/

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---
title: Contact
---
- E-mail :: [[mailto:dimitri+web@lozeve.com][dimitri+web@lozeve.com]]
- GitHub :: [[https://github.com/dlozeve/][dlozeve]]
- LinkedIn :: [[https://www.linkedin.com/in/dimitrilozeve][Dimitri Lozeve]]
- StackOverflow :: [[https://stackoverflow.com/story/dimitrilozeve][dimitrilozeve]]
- Twitter :: [[https://twitter.com/DimitriLozeve][DimitriLozeve]]

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html {
font-size: 62.5%;
}
body {
font-size: 1.6rem;
color: #000;
}
header {
border-bottom: 0.2rem solid #000;
}
nav {
text-align: right;
}
nav a {
font-size: 1.8rem;
font-weight: bold;
color: black;
text-decoration: none;
text-transform: uppercase;
}
footer {
margin-top: 3rem;
padding: 1.2rem 0;
border-top: 0.2rem solid #000;
font-size: 1.2rem;
color: #555;
}
h1 {
font-size: 2.4rem;
}
h2 {
font-size: 2rem;
}
article .header {
font-size: 1.4rem;
font-style: italic;
color: #555;
}
.logo a {
font-weight: bold;
color: #000;
text-decoration: none;
}
@media (max-width: 319px) {
body {
width: 90%;
margin: 0;
padding: 0 5%;
}
header {
margin: 4.2rem 0;
}
nav {
margin: 0 auto 3rem;
text-align: center;
}
footer {
text-align: center;
}
.logo {
text-align: center;
margin: 1rem auto 3rem;
}
.logo a {
font-size: 2.4rem;
}
nav a {
display: block;
line-height: 1.6;
}
}
@media (min-width: 320px) {
body {
width: 90%;
margin: 0;
padding: 0 5%;
}
header {
margin: 4.2rem 0;
}
nav {
margin: 0 auto 3rem;
text-align: center;
}
footer {
text-align: center;
}
.logo {
text-align: center;
margin: 1rem auto 3rem;
}
.logo a {
font-size: 2.4rem;
}
nav a {
display: inline;
margin: 0 0.6rem;
}
}
@media (min-width: 640px) {
body {
width: 60rem;
margin: 0 auto;
padding: 0;
}
header {
margin: 0 0 3rem;
padding: 1.2rem 0;
}
nav {
margin: 0;
text-align: right;
}
nav a {
margin: 0 0 0 1.2rem;
display: inline;
}
footer {
text-align: right;
}
.logo {
margin: 0;
text-align: left;
}
.logo a {
float: left;
font-size: 1.8rem;
}
}

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---
title: "Curriculum Vitæ"
---
* Work experience
*** Destygo: R&D Data Scientist
2018-present
*** Sysnav: Real-time geolocalization algorithm on an embedded device
March-August 2017
- mathematical modelling of the human walk
- step detection using artificial intelligence on inertial sensors data
- statistical methods for trajectory reconstruction
- /Award:/ Best Research Internship 2017, from École polytechnique
- [[./files/sysnav_internship.pdf][Outline of the confidential report (PDF, in French)]] and [[https://dlozeve.github.io/stage3a/][slides]]
*** Natixis London Branch, Global Infrastructure and Projects: Intern Analyst
June-August 2016
Origination and execution of project finance transactions: offshore windfarms, hydroelectric dams, and a biomass power plant now under construction in northeastern England.
*** French Engineering Corps: Army officer
2014-2015
Operation Sangaris: Military intervention in the Central African
Republic.
- Instructor for new recruits for 4 months (around 30 soldiers)
- Security Officer during the building of a Bailey bridge in Bangui
- Participation in an intelligence gathering mission in the Northeastern part of the country
- /French Republic Distinctions:/ Overseas Medal, National Defence Medal
* Education
*** University of Oxford: [[https://www.ox.ac.uk/admissions/graduate/courses/msc-statistical-science][MSc in Statistical Science]]
2017-2018
- Applied and computational statistics, statistical machine learning and statistical inference
*** [[https://www.polytechnique.edu/][École polytechnique]]
2014-2017
- Majors in Applied Mathematics and Data Science, Computer Science, and Physics
- /Distinctions:/ Outstanding Investment, Outstanding Leadership
*** Lycée Privé Sainte-Geneviève, Versailles: Preparatory classes to French *Grandes Écoles*
2012-2014
- Majors in Mathematics and Physics
* Scientific projects
*** Topological Data Analysis of time-dependent networks
2018
- Master's thesis, joint work with Oxford's Department of Statistics and Mathematical Institute
- Application of the recent advances in Topological Data Analysis (TDA) and Persistent Homology to periodicity detection in temporal networks
- [[./files/tdanetworks.pdf][Dissertation]]
*** Research work on Community Detection in Social Networks
2016-2017
- Comparison of the performance of state-of-the-art algorithms (theory and implementations)
- Application to large-scale, real-world social networks
- [[./files/communitydetection.pdf][Dissertation (PDF, in French)]] and [[https://dlozeve.github.io/reveal_CommunityDetection/][slides]]
*** SERB X-CubeSat II program: a nano-satellite dedicated to Sun-Earth relationship
2015-2016
- Solar Irradiance and Earth Radiation Budget: Payload preliminary design
- Co-authored [[http://dx.doi.org/10.1117/12.2222660][SPIE Proceedings article]] on the projects technical specifications
- [[./files/serb.pdf][Dissertation (PDF, in French)]]
*** Research work on Markov Chains and Queuing Theory
2013-2014
- Study on the convergence of queues through algebra and numerical simulations
- [[./files/filesdattente.pdf][Dissertation (PDF, in French)]]
* Languages and skills
*** Computer science
*Python:*
- Numerical computing: [[http://www.numpy.org/][Numpy]], [[https://www.scipy.org/][Scipy]]
- Data analysis: [[https://pandas.pydata.org/][Pandas]]
- Machine Learning: [[http://scikit-learn.org/][Scikit-Learn]]
- Deep Learning: [[https://www.tensorflow.org/][TensorFlow]], [[http://pytorch.org/][PyTorch]]
- Network analysis: [[https://networkx.github.io/][NetworkX]], [[https://graph-tool.skewed.de/][graph-tool]]
- Research projects and hackathons: [[https://github.com/dlozeve/community-detection][community detection]] and [[https://github.com/dlozeve/DocAssist][DocAssist]]
*R:*
- Statistical modelling and inference (generalised linear models, bayesian analysis)
- Data analysis: [[https://www.tidyverse.org/][Tidyverse]]
- Network analysis: [[http://igraph.org/][igraph]]
*C, Java:* Embedded and [[https://github.com/dlozeve/Satrap][network]] programming, [[https://github.com/dlozeve/topological-persistence][scientific computing]]
*SQL:* Basics
*Haskell, Lisp (Scheme):* Hobby projects ([[https://github.com/dlozeve/orbit][N-body simulation]], [[https://github.com/dlozeve/Civilisation-hs][SAT solver]], [[https://github.com/dlozeve/aoc2017][Advent of Code 2017]])
*Software:* Git, GNU/Linux, LaTeX, [[https://aws.amazon.com/][Amazon AWS]], [[https://www.mongodb.com/][MongoDB]], [[https://www.wolfram.com/mathematica/][Wolfram Mathematica]], Microsoft Office
See also [[./skills.html][a complete list of my skills in Statistics, Data Science and Machine Learning]].
*** Languages
- French
- English
- Basic German
*** Sports
- Fencing :: vice-president of the 2016 [[http://x-systra.com/][X-SYSTRA International Fencing Challenge]]; 29th in the 2016 sabre French Student Championships
- Scuba-diving :: CMAS * * *, 170+ dives

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---
title: Home
---
<h2>Welcome</h2>
<img src="/images/haskell-logo.png" style="float: right; margin: 10px;" />
<p>Hi! I am a Data Scientist
at <a href="https://www.destygo.com/">Destygo</a>, working on making
chatbots smarter through Machine Learning.</p>
<p>During my free time, I love tinkering with functional programming
languages for scientific simulations and with embedded development
and electronics.</p>
<a href="/cv.html">See my full CV.</a>
<h2>Recent Posts</h2>
$partial("templates/post-list.html")$
<p>Find more in the <a href="/archive.html">archives</a>.</p>

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name: newblog
version: 0.1.0.0
build-type: Simple
cabal-version: >= 1.10
executable site
main-is: site.hs
build-depends: base == 4.*
, pandoc == 2.*
, hakyll == 4.12.*
ghc-options: -threaded
default-language: Haskell2010

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--------------------------------------------------------------------------------
{-# LANGUAGE OverloadedStrings #-}
import Data.Monoid (mappend)
import Text.Pandoc.Options
import Text.Pandoc.Highlighting
import Hakyll
--------------------------------------------------------------------------------
main :: IO ()
main = hakyll $ do
match "images/*" $ do
route idRoute
compile copyFileCompiler
match "files/*" $ do
route idRoute
compile copyFileCompiler
match "css/*" $ do
route idRoute
compile compressCssCompiler
match (fromList ["contact.org", "cv.org", "skills.org", "projects.org"]) $ do
route $ setExtension "html"
compile $ customPandocCompiler
>>= loadAndApplyTemplate "templates/default.html" defaultContext
>>= relativizeUrls
match "posts/*" $ do
route $ setExtension "html"
compile $ customPandocCompiler
>>= loadAndApplyTemplate "templates/post.html" postCtx
>>= loadAndApplyTemplate "templates/default.html" postCtx
>>= relativizeUrls
create ["archive.html"] $ do
route idRoute
compile $ do
posts <- recentFirst =<< loadAll "posts/*"
let archiveCtx =
listField "posts" postCtx (return posts) `mappend`
constField "title" "Archives" `mappend`
defaultContext
makeItem ""
>>= loadAndApplyTemplate "templates/archive.html" archiveCtx
>>= loadAndApplyTemplate "templates/default.html" archiveCtx
>>= relativizeUrls
match "index.html" $ do
route idRoute
compile $ do
posts <- recentFirst =<< loadAll "posts/*"
let indexCtx =
listField "posts" postCtx (return posts) `mappend`
defaultContext
getResourceBody
>>= applyAsTemplate indexCtx
>>= loadAndApplyTemplate "templates/default.html" indexCtx
>>= relativizeUrls
match "templates/*" $ compile templateBodyCompiler
--------------------------------------------------------------------------------
postCtx :: Context String
postCtx =
dateField "date" "%B %e, %Y" `mappend`
defaultContext
customPandocCompiler :: Compiler (Item String)
customPandocCompiler =
let customExtensions = extensionsFromList [Ext_latex_macros]
defaultExtensions = writerExtensions defaultHakyllWriterOptions
newExtensions = defaultExtensions `mappend` customExtensions
writerOptions = defaultHakyllWriterOptions
{ writerExtensions = newExtensions
, writerHTMLMathMethod = MathML
}
in pandocCompilerWith defaultHakyllReaderOptions writerOptions

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---
title: "Skills in Statistics, Data Science and Machine Learning"
---
* Statistics
- Knowledge of Linear Models and Generalised Linear Models
(including logistic regression), both in theory and in
applications
- Classical Statistical inference (maximum likelihood estimation,
method of moments, minimal variance unbiased estimators) and
testing (including goodness of fit)
- Nonparametric statistics
- Bootstrap methods, hidden Markov models
- Knowledge of Bayesian Analysis techniques for inference and
testing: Markov Chain Monte Carlo, Approximate Bayesian
Computation, Reversible Jump MCMC
- Good knowledge of R for statistical modelling and plotting
* Data Analysis
- Experience with large datasets, for classification and regression
- Descriptive statistics, plotting (with dimensionality reduction)
- Data cleaning and formatting
- Experience with unstructured data coming directly from embedded
sensors to a microcontroller
- Experience with large graph and network data
- Experience with live data from APIs
- Data analysis with Pandas, xarray (Python) and the tidyverse (R)
- Basic knowledge of SQL
* Graph and Network Analysis
- Research project on community detection and graph clustering
(theory and implementation)
- Research project on Topological Data Analysis for time-dependent
networks
- Random graph models
- Estimation in networks (Stein's method for Normal and Poisson
estimation)
- Network Analysis with NetworkX, graph-tool (Python) and igraph (R
and Python)
* Time Series Analysis
- experience in analysing inertial sensors data (accelerometer,
gyroscope, magnetometer), both in real-time and in post-processing
- use of statistical method for step detection, gait detection, and
trajectory reconstruction
- Kalman filtering, Fourier and wavelet analysis
- Machine Learning methods applied to time series (decision trees,
SVMs and Recurrent Neural Networks in particular)
- Experience with signal processing functions in Numpy and Scipy
(Python)
* Machine Learning
- Experience in Dimensionality Reduction (PCA, MDS, Kernel PCA,
Isomap, spectral clustering)
- Experience with the most common methods and techniques
- Random forests, SVMs, Neural Networks (including CNNs and RNNs),
both theoretical knowledge and practical experience
- Bagging and boosting estimators
- Cross-validation
- Kernel methods, reproducing kernel Hilbert spaces, collaborative
filtering, variational Bayes, Gaussian processes
- Machine Learning libraries: Scikit-Learn, PyTorch, TensorFlow,
Keras
* Simulation
- Inversion, Transformation, Rejection, and Importance sampling
- Gibbs sampling
- Metropolis-Hastings
- Reversible jump MCMC
- Hidden Markov Models and Sequential Monte Carlo Methods

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# This file was automatically generated by 'stack init'
#
# Some commonly used options have been documented as comments in this file.
# For advanced use and comprehensive documentation of the format, please see:
# https://docs.haskellstack.org/en/stable/yaml_configuration/
# Resolver to choose a 'specific' stackage snapshot or a compiler version.
# A snapshot resolver dictates the compiler version and the set of packages
# to be used for project dependencies. For example:
#
# resolver: lts-3.5
# resolver: nightly-2015-09-21
# resolver: ghc-7.10.2
#
# The location of a snapshot can be provided as a file or url. Stack assumes
# a snapshot provided as a file might change, whereas a url resource does not.
#
# resolver: ./custom-snapshot.yaml
# resolver: https://example.com/snapshots/2018-01-01.yaml
resolver: lts-12.18
# User packages to be built.
# Various formats can be used as shown in the example below.
#
# packages:
# - some-directory
# - https://example.com/foo/bar/baz-0.0.2.tar.gz
# - location:
# git: https://github.com/commercialhaskell/stack.git
# commit: e7b331f14bcffb8367cd58fbfc8b40ec7642100a
# - location: https://github.com/commercialhaskell/stack/commit/e7b331f14bcffb8367cd58fbfc8b40ec7642100a
# subdirs:
# - auto-update
# - wai
packages:
- .
# Dependency packages to be pulled from upstream that are not in the resolver
# using the same syntax as the packages field.
# (e.g., acme-missiles-0.3)
# extra-deps: []
# Override default flag values for local packages and extra-deps
# flags: {}
# Extra package databases containing global packages
# extra-package-dbs: []
# Control whether we use the GHC we find on the path
# system-ghc: true
#
# Require a specific version of stack, using version ranges
# require-stack-version: -any # Default
# require-stack-version: ">=1.9"
#
# Override the architecture used by stack, especially useful on Windows
# arch: i386
# arch: x86_64
#
# Extra directories used by stack for building
# extra-include-dirs: [/path/to/dir]
# extra-lib-dirs: [/path/to/dir]
#
# Allow a newer minor version of GHC than the snapshot specifies
# compiler-check: newer-minor

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Here you can find all my previous posts:
$partial("templates/post-list.html")$

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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta http-equiv="x-ua-compatible" content="ie=edge">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Dimitri Lozeve - $title$</title>
<link rel="stylesheet" href="/css/default.css" />
</head>
<body>
<header>
<div class="logo">
<a href="/">Dimitri Lozeve</a>
</div>
<nav>
<a href="/">Home</a>
<a href="/contact.html">Contact</a>
<a href="/archive.html">Archive</a>
</nav>
</header>
<main role="main">
<h1>$title$</h1>
$body$
</main>
<footer>
Site proudly generated by
<a href="http://jaspervdj.be/hakyll">Hakyll</a>
</footer>
</body>
</html>

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<ul>
$for(posts)$
<li>
<a href="$url$">$title$</a> - $date$
</li>
$endfor$
</ul>

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<article>
<section class="header">
Posted on $date$
$if(author)$
by $author$
$endif$
</section>
<section>
$body$
</section>
</article>