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<dt>E-mail</dt>
<dd><a href="mailto:dimitri+web@lozeve.com">dimitri+web@lozeve.com</a>
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<dt>GitHub</dt>
<dd><a href="https://github.com/dlozeve/">dlozeve</a>
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<dt>LinkedIn</dt>
<dd><a href="https://www.linkedin.com/in/dimitrilozeve">Dimitri Lozeve</a>
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<dd><a href="https://stackoverflow.com/story/dimitrilozeve">dimitrilozeve</a>
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<h1>Curriculum Vitæ</h1>
<h1 id="work-experience">Work experience</h1>
<h3 id="destygo-rd-data-scientist">Destygo: R&amp;D Data Scientist</h3>
<p>2018-present</p>
<h3 id="sysnav-real-time-geolocalization-algorithm-on-an-embedded-device">Sysnav: Real-time geolocalization algorithm on an embedded device</h3>
<p>March-August 2017</p>
<ul>
<li>mathematical modelling of the human walk</li>
<li>step detection using artificial intelligence on inertial sensors data</li>
<li>statistical methods for trajectory reconstruction</li>
<li><em>Award:</em> Best Research Internship 2017, from École polytechnique</li>
<li><a href="./files/sysnav_internship.pdf">Outline of the confidential report (PDF, in French)</a> and <a href="https://dlozeve.github.io/stage3a/">slides</a></li>
</ul>
<h3 id="natixis-london-branch-global-infrastructure-and-projects-intern-analyst">Natixis London Branch, Global Infrastructure and Projects: Intern Analyst</h3>
<p>June-August 2016</p>
<p>Origination and execution of project finance transactions: offshore windfarms, hydroelectric dams, and a biomass power plant now under construction in northeastern England.</p>
<h3 id="french-engineering-corps-army-officer">French Engineering Corps: Army officer</h3>
<p>2014-2015</p>
<p>Operation Sangaris: Military intervention in the Central African Republic.</p>
<ul>
<li>Instructor for new recruits for 4 months (around 30 soldiers)</li>
<li>Security Officer during the building of a Bailey bridge in Bangui</li>
<li>Participation in an intelligence gathering mission in the Northeastern part of the country</li>
<li><em>French Republic Distinctions:</em> Overseas Medal, National Defence Medal</li>
</ul>
<h1 id="education">Education</h1>
<h3 id="university-of-oxford-msc-in-statistical-science">University of Oxford: <a href="https://www.ox.ac.uk/admissions/graduate/courses/msc-statistical-science">MSc in Statistical Science</a></h3>
<p>2017-2018</p>
<ul>
<li>Applied and computational statistics, statistical machine learning and statistical inference</li>
</ul>
<h3 id="école-polytechnique"><a href="https://www.polytechnique.edu/">École polytechnique</a></h3>
<p>2014-2017</p>
<ul>
<li>Majors in Applied Mathematics and Data Science, Computer Science, and Physics</li>
<li><em>Distinctions:</em> Outstanding Investment, Outstanding Leadership</li>
</ul>
<h3 id="lycée-privé-sainte-geneviève-versailles-preparatory-classes-to-french-grandes-écoles">Lycée Privé Sainte-Geneviève, Versailles: Preparatory classes to French <strong>Grandes Écoles</strong></h3>
<p>2012-2014</p>
<ul>
<li>Majors in Mathematics and Physics</li>
</ul>
<h1 id="scientific-projects">Scientific projects</h1>
<h3 id="topological-data-analysis-of-time-dependent-networks">Topological Data Analysis of time-dependent networks</h3>
<p>2018</p>
<ul>
<li>Master’s thesis, joint work with Oxford’s Department of Statistics and Mathematical Institute</li>
<li>Application of the recent advances in Topological Data Analysis (TDA) and Persistent Homology to periodicity detection in temporal networks</li>
<li><a href="./files/tdanetworks.pdf">Dissertation</a></li>
</ul>
<h3 id="research-work-on-community-detection-in-social-networks">Research work on Community Detection in Social Networks</h3>
<p>2016-2017</p>
<ul>
<li>Comparison of the performance of state-of-the-art algorithms (theory and implementations)</li>
<li>Application to large-scale, real-world social networks</li>
<li><a href="./files/communitydetection.pdf">Dissertation (PDF, in French)</a> and <a href="https://dlozeve.github.io/reveal_CommunityDetection/">slides</a></li>
</ul>
<h3 id="serb-x-cubesat-ii-program-a-nano-satellite-dedicated-to-sun-earth-relationship">SERB X-CubeSat II program: a nano-satellite dedicated to Sun-Earth relationship</h3>
<p>2015-2016</p>
<ul>
<li>Solar Irradiance and Earth Radiation Budget: Payload preliminary design</li>
<li>Co-authored <a href="http://dx.doi.org/10.1117/12.2222660">SPIE Proceedings article</a> on the project’s technical specifications</li>
<li><a href="./files/serb.pdf">Dissertation (PDF, in French)</a></li>
</ul>
<h3 id="research-work-on-markov-chains-and-queuing-theory">Research work on Markov Chains and Queuing Theory</h3>
<p>2013-2014</p>
<ul>
<li>Study on the convergence of queues through algebra and numerical simulations</li>
<li><a href="./files/filesdattente.pdf">Dissertation (PDF, in French)</a></li>
</ul>
<h1 id="languages-and-skills">Languages and skills</h1>
<h3 id="computer-science">Computer science</h3>
<p><strong>Python:</strong></p>
<ul>
<li>Numerical computing: <a href="http://www.numpy.org/">Numpy</a>, <a href="https://www.scipy.org/">Scipy</a></li>
<li>Data analysis: <a href="https://pandas.pydata.org/">Pandas</a></li>
<li>Machine Learning: <a href="http://scikit-learn.org/">Scikit-Learn</a></li>
<li>Deep Learning: <a href="https://www.tensorflow.org/">TensorFlow</a>, <a href="http://pytorch.org/">PyTorch</a></li>
<li>Network analysis: <a href="https://networkx.github.io/">NetworkX</a>, <a href="https://graph-tool.skewed.de/">graph-tool</a></li>
<li>Research projects and hackathons: <a href="https://github.com/dlozeve/community-detection">community detection</a> and <a href="https://github.com/dlozeve/DocAssist">DocAssist</a></li>
</ul>
<p><strong>R:</strong></p>
<ul>
<li>Statistical modelling and inference (generalised linear models, bayesian analysis)</li>
<li>Data analysis: <a href="https://www.tidyverse.org/">Tidyverse</a></li>
<li>Network analysis: <a href="http://igraph.org/">igraph</a></li>
</ul>
<p><strong>C, Java:</strong> Embedded and <a href="https://github.com/dlozeve/Satrap">network</a> programming, <a href="https://github.com/dlozeve/topological-persistence">scientific computing</a></p>
<p><strong>SQL:</strong> Basics</p>
<p><strong>Haskell, Lisp (Scheme):</strong> Hobby projects (<a href="https://github.com/dlozeve/orbit">N-body simulation</a>, <a href="https://github.com/dlozeve/Civilisation-hs">SAT solver</a>, <a href="https://github.com/dlozeve/aoc2017">Advent of Code 2017</a>)</p>
<p><strong>Software:</strong> Git, GNU/Linux, LaTeX, <a href="https://aws.amazon.com/">Amazon AWS</a>, <a href="https://www.mongodb.com/">MongoDB</a>, <a href="https://www.wolfram.com/mathematica/">Wolfram Mathematica</a>, Microsoft Office</p>
<p>See also <a href="./skills.html">a complete list of my skills in Statistics, Data Science and Machine Learning</a>.</p>
<h3 id="languages">Languages</h3>
<ul>
<li>French</li>
<li>English</li>
<li>Basic German</li>
</ul>
<h3 id="sports">Sports</h3>
<dl>
<dt>Fencing</dt>
<dd>vice-president of the 2016 <a href="http://x-systra.com/">X-SYSTRA International Fencing Challenge</a>; 29th in the 2016 sabre French Student Championships
</dd>
<dt>Scuba-diving</dt>
<dd>CMAS * * *, 170+ dives
</dd>
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<h1>Home</h1>
<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>
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<p>Find more in the <a href="./archive.html">archives</a>.</p>
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<h1>Skills in Statistics, Data Science and Machine Learning</h1>
<h1 id="statistics">Statistics</h1>
<ul>
<li>Knowledge of Linear Models and Generalised Linear Models (including logistic regression), both in theory and in applications</li>
<li>Classical Statistical inference (maximum likelihood estimation, method of moments, minimal variance unbiased estimators) and testing (including goodness of fit)</li>
<li>Nonparametric statistics</li>
<li>Bootstrap methods, hidden Markov models</li>
<li>Knowledge of Bayesian Analysis techniques for inference and testing: Markov Chain Monte Carlo, Approximate Bayesian Computation, Reversible Jump MCMC</li>
<li>Good knowledge of R for statistical modelling and plotting</li>
</ul>
<h1 id="data-analysis">Data Analysis</h1>
<ul>
<li>Experience with large datasets, for classification and regression</li>
<li>Descriptive statistics, plotting (with dimensionality reduction)</li>
<li>Data cleaning and formatting</li>
<li>Experience with unstructured data coming directly from embedded sensors to a microcontroller</li>
<li>Experience with large graph and network data</li>
<li>Experience with live data from APIs</li>
<li>Data analysis with Pandas, xarray (Python) and the tidyverse (R)</li>
<li>Basic knowledge of SQL</li>
</ul>
<h1 id="graph-and-network-analysis">Graph and Network Analysis</h1>
<ul>
<li>Research project on community detection and graph clustering (theory and implementation)</li>
<li>Research project on Topological Data Analysis for time-dependent networks</li>
<li>Random graph models</li>
<li>Estimation in networks (Stein’s method for Normal and Poisson estimation)</li>
<li>Network Analysis with NetworkX, graph-tool (Python) and igraph (R and Python)</li>
</ul>
<h1 id="time-series-analysis">Time Series Analysis</h1>
<ul>
<li>experience in analysing inertial sensors data (accelerometer, gyroscope, magnetometer), both in real-time and in post-processing</li>
<li>use of statistical method for step detection, gait detection, and trajectory reconstruction</li>
<li>Kalman filtering, Fourier and wavelet analysis</li>
<li>Machine Learning methods applied to time series (decision trees, SVMs and Recurrent Neural Networks in particular)</li>
<li>Experience with signal processing functions in Numpy and Scipy (Python)</li>
</ul>
<h1 id="machine-learning">Machine Learning</h1>
<ul>
<li>Experience in Dimensionality Reduction (PCA, MDS, Kernel PCA, Isomap, spectral clustering)</li>
<li>Experience with the most common methods and techniques</li>
<li>Random forests, SVMs, Neural Networks (including CNNs and RNNs), both theoretical knowledge and practical experience</li>
<li>Bagging and boosting estimators</li>
<li>Cross-validation</li>
<li>Kernel methods, reproducing kernel Hilbert spaces, collaborative filtering, variational Bayes, Gaussian processes</li>
<li>Machine Learning libraries: Scikit-Learn, PyTorch, TensorFlow, Keras</li>
</ul>
<h1 id="simulation">Simulation</h1>
<ul>
<li>Inversion, Transformation, Rejection, and Importance sampling</li>
<li>Gibbs sampling</li>
<li>Metropolis-Hastings</li>
<li>Reversible jump MCMC</li>
<li>Hidden Markov Models and Sequential Monte Carlo Methods</li>
</ul>
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