_FULL CURRICULUM

Baptiste Mokas, _PhdDATA SCIENTIST

03/28/1994295 rue Leon Gambetta baptiste.mokas@gmail.com59000, LILLE +33 7 83 57 24 54

FRANCE https://emergence-lfe.org/https://wikimap.com/

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Summary:Student-researcher in theoretical machine learning, cognitive and mathematical bioscience dedicated to the modeling ofthe integration of information in complex adaptive and multiobjective systems. Student-researcher in theoretical machinelearning, cognitive and mathematical bioscience dedicated to the modeling of the integration of information in.

Contents

RESEARCH 3PUBLICATIONS (0) & ACADEMIC PRODUCTIONS (7) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3MACHINE LEARNING - KAAGLE & GIT (11) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

ARTICLES & BLOG (0) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

EDUCATION 5DOCTORAT IN MATHEMATICS: PROBABILISTIC GRAPHICAL MODELS FOR RISK AND INFORMATION INTEGRATION . . 5

SCIENTIFIC COMMUNITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

MASTERS : - MATHEMATICS AND COMPUTING FOR COGNITIVE SCIENCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6- NEUROCOGNITION & AFFECTIVE NEUROSCIENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

LICENCE : - BEHAVIORAL SCIENCE / EXPERIMENTAL PSYCHOLOGY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

BOOTCAMPS (1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7SUMMERS SCHOOLS (0) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7MOOCS (14) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7CONFERENCES - SEMINARS & EVENTS (9) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

BUSINESS 9EMPLOYMENT EXPERIENCES & POSITIONS (7) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9BUSINESS MANAGEMENT (2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

ART 9MUSIC PRODUCTION (3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11INFOGRAPHY & WEB DESIGN (3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

BIO 13

mailto:baptiste.mokas@gmail.com callto:+33783572454https://emergence-lfe.orghttps://wikimap.comhttps://www.linkedin.com/in/baptiste-mokas-8574ab108/https://www.researchgate.net/https://github.com/baptistemokas?tab=repositorieshttps://github.com/baptistemokas?tab=repositorieshttps://github.com/baptistemokas?tab=repositorieshttps://github.com/baptistemokas?tab=repositorieshttps://emergence-lfe.org/map/baptiste-mokas/

SHEET CHEAT COMPLEXITY SKILL SYNTHESIS Legend : � = actual skills / � = futur goals

I Computing 21 Programming / Datavizualization . . . . . . . . . . . . . . . . . . . . . . . . . 2

Windows . . . ����� | PHP . . . . . . . . ����� | Python . . . . . .����� 2MacOSx . . . . ����� | JAVA . . . . . . . . ����� | R . . . . . . . . . . . ����� 2Linux . . . . . . . ����� | HTML . . . . . . ����� | Matlab . . . . . .����� 2Docker . . . . . . ����� | CSS . . . . . . . . . ����� | Mapple . . . . . ����� 2GitHub . . . . . ����� | Javascript . . . ����� | SAGE . . . . . . . ����� 2Atom . . . . . . . ����� | JQuery . . . . . . ����� | Mathematica ����� 2Sublime text ����� | Ajax . . . . . . . . ����� | Excel . . . . . . . ����� 2RegEX . . . . . . ����� | JSON . . . . . . . ����� | SPSS . . . . . . . .����� 2C . . . . . . . . . . . ����� |Wordpress . . ����� | SAS . . . . . . . . .����� 2C++ . . . . . . . ����� | Filezilla . . . . . ����� | TABLEAU . . . ����� 2C# . . . . . . . . . . ����� | LaTeX . . . . . . . ����� | Gephy . . . . . . ����� 2SQL . . . . . . . . . ����� | R Shinny . . . . ����� | SCALA . . . . . .����� 2NoSQL . . . . . . ����� |WebScrapping����� | D3.js . . . . . . . .����� 2NewSQL . . . . ����� | TypeScript . . ����� | STATVIEW . . ����� 2Neo4J . . . . . . ����� | Swift . . . . . . . ����� | AMOS . . . . . . ����� 2Julia . . . . . . . . ����� | Ruby . . . . . . . . ����� | leaflet . . . . . . .����� 22 (Non linear) Dynamical analysis . . . . . . . . . . . . . . . . . . . . . . . . . ����� 23 Distributed computing & Big Data tools . . . . . . . . . . . . . . . . . . ����� 24 Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 25 Cybersecurity / Network Administration . . . . . . . . . . . . . . . . . ����� 26 Graphics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 2

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II Mathematics 57 Single Variable Calculus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 58 Optimisation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 69 Suites and Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 710 Numerical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 811 Multivariable Calculus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 912 Complex Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 1013 Affine Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 1114 Polynomials. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 1215 Primitive & Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 1316 Differential Calculus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 1417 Convolution, Fourier & Laplace Transform . . . . . . . . . . . . . . . ����� 1518 Spectral Theory. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 16

19 Algebra : Groups / Ring / Body & Fields . . . . . . . . . . . . . . . . . ����� 1720 Category Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 1821 Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 1922 Set Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 2023 Linear Algebra / Vector Spaces . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 2124 Multi-Linear Algebra / Tensors Calculus. . . . . . . . . . . . . . . . . . ����� 2225 Linear systems / Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 23

26 Probability Theory & Integration . . . . . . . . . . . . . . . . . . . . . . . . . ����� 2427 Asymptotic Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 2528 Information Theory / Entropy & Signal Processing . . . . . . ����� 2629 Combinatorics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 2730 Time Series, Forecasting & Survival Analysis . . . . . . . . . . . . . ����� 2831 Martingales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 2932 Dynamical systems / Control Theory . . . . . . . . . . . . . . . . . . . . . ����� 30

33 Graphs & (Multiplex) Networks theory . . . . . . . . . . . . . . . . . . . ����� 3134 Markov Chains. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 3135 Percolation theory & Path analysis . . . . . . . . . . . . . . . . . . . . . . . ����� 32

36 Cryptography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 3337 Game Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 3438 Wavelets / Solitons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 3539 Random Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 36

40 Topology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 3741 Algebric Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 3842 Differential Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 3943 Information Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 4044 Hyperbolic / Lobachevskian Geometry . . . . . . . . . . . . . . . . . . . ����� 4145 Sympleptic Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 42

III Statistics 4346 Experimental Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 4347 Correlation / Covariance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 4448 Maximum Likelihood Estimation . . . . . . . . . . . . . . . . . . . . . . . . . ����� 45

49 General Linear Model / Regression methods . . . . . . . . . . . . . ����� 4650 Penalized Ridge, Lasso & Elastic Net Regressions . . . . . . . . ����� 4851 Frequentist Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 5152 Non Parametric Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 52

53 Bayesian Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 5354 Bayesian Regression models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 5555 EM algorithm with Latent (hidden) variables . . . . . . . . . . . . ����� 5756 Variational Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 5857 Latent Dirichlet Allocations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 5958 Variational Autoencoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 6059 Markov Chain Monte Carlo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 61

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IV Machine Learning 6260 Learning theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 6261 Divergence & Loss Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 6362 Hyperparameter tuning & Feature selection. . . . . . . . . . . . . . ����� 6463 Bayesian Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 6564 (Features) Sensitivity Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 6665 Data Cleaning, Outliers / KNN & Missingness . . . . . . . . . . . . ����� 67

66 Structural Equation Modeling & Factors analysis . . . . . . . . ����� 6867 Bayesian Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 6968 Dynamic Bayesian Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 6969 Markov Random Fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 6970 Hidden Markov Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 69

71 Convolution Neural Networks / Deep Learning . . . . . . . . . . ����� 7072 Neural Networks Renormalization group. . . . . . . . . . . . . . . . . ����� 7173 Generative Adversials models . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 7274 Attention Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 7375 Geometric / Manifold based Deep Learning . . . . . . . . . . . . . . ����� 7476 Boltzmann Machine / Hopfield network . . . . . . . . . . . . . . . . . . . ����� 7577 Energy Based Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 76

78 Exploratory Analysis / PCA & Dimension reduction . . . . . . ����� 7779 Spatial Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 7880 Manifold Learning/ Self-organizing map . . . . . . . . . . . . . . . . ����� 79

81 (Deep) Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 8082 Association rule Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 8183 Genetic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 8284 Multiagent Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 83

85 Continual Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 8486 Transfer Learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 85

87 Naive Bayes Classifier / Discriminant Analysis . . . . . . . . . . . ����� 8688 SVM classifier / SVR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 8789 K-Nearest Neighbors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 8890 Decision Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 8991 Kernel Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 9092 Mining / Unsupervised Clustering Methods . . . . . . . . . . . . . . ����� 9193 Gaussian Mixture Models / Dirichlet process. . . . . . . . . . . . . ����� 92

94 Bagging, Boostrap aggregating & Random Forest . . . . . . . . ����� 9395 Boosting / Ensemble Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 9496 Bootstrapping / Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 95

97 Computer Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 9698 Natural Langage Processing / Topic modeling . . . . . . . . . . . ����� 97

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V Theoretical Dynamics 9899 Waves, Oscillation & Vibrations . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 98100Statistical Field Theory / Path Integration. . . . . . . . . . . . . . . . ����� 99101Thermodynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 100102Classical / Analytical / Statistical Mechanics . . . . . . . . . . . . . ����� 101103Hamiltonian Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 102

RESEARCH

SCIENTIFIC PUBLICATIONS & POSTERS:

Being Processed..—

ACADEMIC PRODUCTIONS

• Bayesian Estimation in Probabilistic Graphical Models for Cybersecurity risk modeling RiskNtic, La defense, ParisApplication of bayesian modeling and PGMs for information integration in Risk assessment in cybersecurity context Now

For this project we used probabilistic graphical model such as bayesian networks for modeling risk, with the aim to help companies to assesstheir risk in the context of cybersecurity. I discovered a lot of interesting concepts and applications. The project was a really good way toadapt the formal academic view to the real worl. For the construction of the graph we used an elicitation process. Locally we used a bayesianMCMC based method to infer parameter.

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• Psychophysiology of Emotionnal response to Music: Biological Time Series analysis Plaine Image, TourcoingTime series, Multimodal and behavioral analysis of musical activities, Eye tracking, Motion Capture Fev. 2016 - Jun. 2016During this project, we explored psychophysiological responses to music. By recording signals taken from an eye tracker, a system of motioncapture, an ECG and electrodermal sensors, we were looking for understanding how our body and physiological variability would explainmental and affective states during playing music. The project was conducted by the Cristal Laboratory with the INRIA partenrship .(For more detail on the projet )

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• Riemann Geometry based Machine Learning & Embodied social interaction (with Yann Coello) Plaine Image, TourcoingComputationnal modeling of humain embodied social interactions Sep. 2017 – Jun. 2018Social Interaction are embodied in the flesh. Gesture, mouvement, facial expression are some example of a flourishing complexity present onthe physical world. They may represent some psychological affective caracteristics of the human mind. In this experiment, we were recording3D cinematics of participants in an interaction with an avatar. Based on a riemman manifold, we analysed the cimatics to understand socialfacilitation of information present in our mouvement that we use to share our intention. The project was supervised by Yann Coello thedirector of the master degree of Neuropsychology and Affective Neuroscience of Lille. The project was situated on the IRDIVE technologicalplatform.

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• Multi-Agents Modeling and Sensitivity Analysis (with David Chavalarias) Institut des systemes complexes, ParisMultiagent modeling and complex model analysis with Netlogo Sep. 2016 – Fev. 2017During this internship in the Complex-Systems-Institute of Paris, I worked on multi-agent modeling and computational simulation applied tosocial science. We used statistical model analysis like probability calibration, sensitivity analysis, and differents technics for the analysis ofthese models. Differents scale, chaos..etc.. I dicovered technics to analyse complex dynamics, chaos, and so on. I was directed by DavidChavalarias ( ) the director of the complex system institute of paris (ISCPIF)

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• Brain-Computer Interfaces / Neurofeedback (with Olivier Oullier) Aix-Marseille UniversityElectroencephalogram, Filters, Times Series analysis Fev. 2016 - Jun. 2016During the research project, we explored the possibility to apply neurotechnology for music production and sound design. We focused ontechnical aspects of brain imaging, comparing tools (IRMf, EEG, MEG, etc.), studied data analysis technics (time series, filtering, denoising),methodology, and recording. Behind this, we focused on fundamentals questions, on how people understand the brain and various aspects ofconsciousness, thought, and higher order activities of the mind.

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• Communication in Social Networks Classe35, MarseilleSetting up professional social networks for a large company Fev. 2016 - Jun. 2016For this internship in the Classe35 communication company, I had to implement an intern social network for companies. I discovered differentpossibility to improve intern communication for big companies. To be shure of what they needed, I created online survey, wich helped me toundersand their needs.

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https://emergence-lfe.org/research/publications/https://www.cristal.univ-lille.fr/https://www.inria.fr/https://www.olitocin.com/toscade/https://pro.univ-lille.fr/yann-coello/http://www.irdive.fr/https://hal.archives-ouvertes.fr/hal-01692111v2https://iscpif.fr/chavalarias/https://iscpif.fr/http://www.classe35.com/

• Philosophy of Mind (with Thierry Ripoll) Aix-Marseille UniversityComplexity of modeling in sociale science / Mind body problem Fev. 2016 - Jun. 2016What is the nature of the mind? Can it be reduced to our brain, and its physical and chemical properties? In this project, we discussed andresearched on the philosophy of mind. I discovered the most famous scientists, theories, and philosophical way to understand the nature ofthoughts, emotions, feelings, and others properties of the human being with different approaches (reductionism, functionalism, dualism,eliminativism). I proposed a physical way to explain the emergence of the feeling of being conscious. In particular, I explored the theory ofdissipative systems of I. Prigogine and the concept autopoiesis of F. Varela.

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BOOKS BLOG & ARTICLES:

Being Processed..—

MACHINE LEARNING PROJECTS - KAAGLE & GIT:

• Online application for collaboration and research in complexity and cognitive science (beta) New York City, USAJavascript, Wordpress & Adobe Illustrator SVG based application Sep 2019 - now—

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•Toscade project

Lille - 2019 git. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

•Matlab biological data analysis

Lille - 2019 git. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

•Javascript Ajax web page

Online svg based application with the ability toclick and dynamically interactLille - 2019 git. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

•Rmarkdown

Lille - 2019 git. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

•Web scrapping with Selenium

For this project, I code a script to open andclose webpages to scrap data from the web–Lille - 2019 git. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

•Google scholar scrap Shinny app

Small app able to show how to scrap googlescholar paper and make analysis.–New York - 2020 git. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

•Bayesian Multinomial Regression

Markov-Chain-Monte-Carlo bayesianestimation for a multinomial logistic regression–Paris - 2019 git. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

•Sensitivity analysis in Bayesian Network

Application of Morris method to quantify theinfluence of parameter inside a bayesiannetworkParis - 2019 git. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

•Kaagle project - Advanced regression

Predicting houses prices with differents labels.New York - 2020 git. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

•Capstone project NYCDSA

New York - 2019 git. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

•ReGex process Bibtex conversion

New York - 2019 git. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

https://emergence-lfe.org/research/publications/ Personal GitHub

https://emergence-lfe.org/welcomedev/mapbeta.htmlhttps://github.com/baptistemokas/MLhttps://github.com/baptistemokas/MLhttps://github.com/baptistemokas/Ajax_Js_Svg_Apphttps://github.com/baptistemokas/MLhttps://github.com/baptistemokas/Web_scrapping_Project/blob/master/Selenium_python_Code.pyhttps://baptistemokas.shinyapps.io/shiny_app/https://github.com/baptistemokas/Bayesian_Multinomial_Logistic_Regressionhttps://github.com/baptistemokas/Sensitivity_Analysis_Morris_Methodhttps://github.com/baptistemokas/MLhttps://github.com/baptistemokas/MLhttps://github.com/baptistemokas/MLhttps://emergence-lfe.org/research/publications/https://github.com/baptistemokas

EDUCATION:

DOCTORAT IN APPLIED MATHEMATICS

• Cognitive Morphodynamics LE HAVRE, FRANCEInformation Integration, Statistical field theory, Energy based models, Multiobjective Optimisation Now

How is information integrated ? How to determine the phase-space and the dynamic of multi-objective optimization systems ? How to understandlocal functions sets interactions; how to integrate their locals dynamics to understand their globals dynamics ?—We propose a general geometric framework, to explain the integration of information present behind the adaptive optimization process in distributedcomplex adaptive organic or artificial multi-objective systems in the context of heterogeneous and dynamical environment, for modeling and simulatecomplex and non-linear dynamics that occurs in such multi-scale structures. We propose the building of a theoretical framework to mathematizethe evolutive algorithmic multi-objective optimization. In this perspective, we seek to understand how a co-evolving and co-determined systemwith an heterogeneous and dynamical environment, composed of several instances, applies to synchronize its different component - its differentfunctions sets - in a coerant, useful, synergistic, syntonic network of interaction, allowing the optimization of the fitness. We propose to geometrizethe information endogeneisation - the perception / action cycle - and make a mathematical sens of reinforcement feed-backed process, with theperspective to understand the emergence of cognitive complexity, its recursiveness, autopoietic and meta-dynamical characteristics.—The theory is mainly based on structural realism, bayesianism, functionalism, morphogenesis and gestalt theory. It fuse the complex and dynamicalsystem theory with the point of view taken from cognitive and computer science, mathematical bioscience, machine learning theory, statisticalphysics and ecology and is calling the application of the statistical field theory, dynamical bayesian analysis, coevolution modeling and the percolationtheory in the context of network theory and information geometry.—The framework consist to geometrize dynamics of multi-objective adaptive behaviors, defining it as a decentralized, and non-stationary perceptionof a fragmented network of probable anticipated causes extracted, a probabilistic graphical model – and whose space phases / attractor basin /energy function - would be defined on continuum mechanics properties present in the environnement. The definition of cognition takes a geometricalmeaning in the parallelization of differents functional regimes and in the heterogeneity, however integrated, of informational endogeneised fields- global latent function, on which they focus. The cognition would be reduced dynamically to the cohabitation / interaction of endogeneisedinformational fields, each one converging towards a multidimentional cristalised function determined by the effect that it bears on all others in thenetwork, defining a local optimization, a gradient descent - and which collectively maintain by interactions, the global attractor - the operationalclosure. As fields are hierarchized and because of the economy principle they cannot be treated all together at the same time, the system thenoscillate in a multi-stable and evolutive phase space, that we may explain by a field percolation process.

—For a graphical version of the theory : Theoretical_roadmap

To read the project : download / see LaTeX document

SCIENTIFIC COMMUNITY & NETWORKING:Please consult the page "https://emergence-lfe.org/community/" here

https://emergence-lfe.org/welcometest_/mapbeta.htmlhttps://fr.overleaf.com/project/5e1b4e793813510001274355https://emergence-lfe.org/contributor/

LICENCE & MASTER:

• MASTER OF MATHEMATICS AND COMPUTING FOR COGNITIVE SCIENCE Lille University, FRANCEMathematical modeling and computer science applied for cognitive science 2017 – 2019

Transversal Units— Data Science with Python: Machine Learning— Probability— Statistical Linear Models & Regression— English

M1— Web— Computing for Neurocognitive Science— Digital development for Neuropsychology— Philosophy of Mind— Ergonomy & Product design— R programming

— Non Parametric Statistics— SAS for datascience— E-marketing— Technology for Psychological Research

M2— Ethics & deontology— Functionnal Neuroscience— Emotionnal Process & Affective neuroscience— Neurocognition— Artificial Neural Networks— Programming for Experimental research— UX design / Product and Experience optimization

• MASTER OF NEUROCOGNITION & AFFECTIVE NEUROSCIENCES Lille University, FRANCESpecialization in research on Emotions, Neuropsychology & Affective Neurosciences 2016 – 2017

— Exploratory data analysis / Statistics— Cognitve Psychology & Neurocognition— Neuropsychological Evaluation— Neural & Affective processing— Neuropsychology— Affectives Neuroscience

— Cognitve Psychology— Data-Analysis & Computing— Cognitive rehabilitation— Neuropsychology of Action & Language— English

• BEHAVIORAL SCIENCE / EXPERIMENTAL PSYCHOLOGY DEGREE Aix Marseille University, FRANCEExperimental scientific methodology / Theories of human and intelligent behavior 2012 - 2015

Transversal Units— Clinical Psychology— Cognitive Psychology— Developpemental Psychology— Social Psychology— Differential Psychology— English— Statistics / Probability

L1— Scientific Research Methodology— Fundamentals of Neurals networks— Behavioral Neurosciences— Multiscale Psychological Analysis— Cellular Biology— Genetics— Psychometry & Evaluation methodology

— Memory and Learning theoryL2

— Neurobiology— History and Concept of Psychology— Experimental Methodology

L3— Psychophysiology— Emotionnal / Affective Developpement— Psychology of Emotions— Cognitive Neuropsychology— Experiment Methodology— Social groups methodology— Methods and technique of brain imaging— Eating Behavior— Psychophysiology of sleep

https://www.coursera.org/learn/linear-algebra-machine-learninghttps://ppnsa.univ-lille3.fr/https://allsh.univ-amu.fr/licence-psychologie

BOOTCAMPS: 4/6

)

• NYC DATA SCIENCE ACADEMY New York, USAMachine Learning with Python / Big Data with Amazon Cloud, Hadoop/Spark and Docker Dec. 2020 – Apr. 2020

— Deep Learning / Convolutionnetworks

— Statistical models— Hadoop— Spark— AWS— Datavizualizatiuon— Linux system

— Advanced SQL— NoSQL— Web Scraping— Time Series Analysis— Reinforcement Learning— Computer Vision / Image processing— Generalized Linear Models— Tree Methods

— Support Vector Machines— Natural Language Processing— Code Optimization— Advanced Phyton— Advanced R— Advanded Regression methods

SUMMERS SCHOOLS:——

Being Processed..

• Summer School: Courses in Complexity in MIT Boston, USAComplexity science: modeling and networks, and data analytics Jun. 2019 – Jul. 2019—

• Harvard Summer School in Mathematics Boston, USAMultivariable Calculus / Differential Equation / Dynamical Systems / Linear Algebra Jul. 2020 – Aug. 2020—

• Summer school on Deep Learning and Bayesian Methods Moscow, RussiaBayesian Data Analysis Jun. 2020 – Aug. 2020—

• Machine Learning Summer School Moscow, RussiaBayesian Data Analysis Jun. 2020 – Aug. 2020—

• Complex systems summer schools (CSSS) / Santa Fe 2021 Santa Fe Institute (SFI), USAComplex behavior in mathematical, physical, living, and social systems Jun. 2021 – Aug. 2021—

MOOC & ONLINE COURSES:– Mathematics / Machine Learning –

• Mathematics for Machine Learning: Multivariate Calculus Imperial College LondonJacobian Matrix, Hessian, Gradient Descent, Partial differentiation, Power series Mar. 2019 - Mar. 2019—

• Probabilistic Graphical Models 1: Representation Stanford UniversityBayesian Network, Directed Models, Template model, Markov Networks, Decision making Jan. 2019 – Apr. 2019—

• Probabilistic Graphical Models 2: Inference Stanford UniversityMCMC, Gibbs Sampling, Belief Propagation, MAP Algorithms Jan. 2019 – Apr. 2019—

• Probabilistic Graphical Models 3: Learning Stanford UniversityParameter Estimation in Bayesian Networks, Learning with Incomplete Data, EM algorithm, Latent Variables Jan. 2019 – Apr. 2019—

• Bayesian Statistics : From Concept to Data Analysis Santa Cruz University, CaliforniaPriors / Posteriors, Inference, Models for Continuous Data, PDF, MLE, Bayesian Regression, Conjugate Jan. 2019 – Aug. 2019—

• Bayesian Statistics : Techniques and Models Santa Cruz University, CaliforniaMonte Carlo estimation, Markov chain Monte Carlo (MCMC), hierarchical modeling, Gibbs sampling Jan. 2019 – Aug. 2019—

• Neural Network and Deep Learning deeplearning.aiPython SQL, analyze visualize data, build machine learning models Jan. 2017 – Mar. 2017—

• Mathematics for Machine Learning: Linear Algebra Imperial College, London, EnglandApplication to machine learning : Vectors, Matrice transformation, Eigenvalues Jan. 2017 – Mar. 2017—

• Fondamentaux pour le Big Data Institut Mines Telecom, Paris, FRANCEProbability, Algebra, Perceptron, Python, Database, Analysis Aug. 2018 – Sep. 2018—

• Deep Reinforcement Learning Udacity.comTaylor Series, Limits, Asymptotic Mar. 2019 ——

• Calculus: Single Variable Part 1 - Functions University of PennsylvaniaTaylor Series, Limits, Asymptotic Mar. 2019 ——

https://nycdatascience.com/https://necsi.edu/summer-school##overview-summerhttps://cssociety.org/homehttp://deepbayes.ru/http://deepbayes.ru/https://www.santafe.edu/engage/learn/schools/sfi-complex-systems-summer-schoolhttps://www.coursera.org/learn/multivariate-calculus-machine-learninghttps://www.coursera.org/learn/deep-neural-network/home/welcomehttps://www.coursera.org/learn/deep-neural-network/home/welcomehttps://www.coursera.org/learn/deep-neural-network/home/welcomehttps://www.coursera.org/learn/bayesian-statisticshttps://www.coursera.org/learn/bayesian-statisticshttps://www.coursera.org/learn/neural-networks-deep-learning/home/welcomehttps://www.coursera.org/learn/linear-algebra-machine-learninghttps://www.fun-mooc.fr/courses/course-v1:MinesTelecom+04006+session09/abouthttps://eu.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893https://www.coursera.org/learn/single-variable-calculus

• Calculus: Single Variable Part 2 - Differentiation University of PennsylvaniaDifferentials and Operators, Linearization Mar. 2019 ——

• Calculus: Single Variable Part 3 - Integration University of PennsylvaniaOrdinary Differential Equations, Integration By Parts, Improper Integral, Integration By Substitution March. 2019 ——

• Calculus: Single Variable Part 4 - Applications University of PennsylvaniaComputing Areas and Volumes, Geometry March. 2019 ——

— Programming —

• Hyperparameter tuning with Python DatacampGrid search / Random Search / Bayesian & Genetic hyperparameter tuning algorithms Feb. 2020 – Feb. 2020—

• SQL for Datascience University of California, DavisFull SQL Database Management Lesson Jan. 2020 – Jan. 2020—

• Introduction to Python / Numpy for Data Science Datacamp.comBasics, List, packages, function, Numpy Mar. 2017 – May. 2017—

• Introduction to AJAX University of MichiganMaking Asynchronous Requests with jQuery Sep. 2019 – Dec. 2019—

• JavaScript, jQuery, and JSON UDACITYPhp, JavaScript, JSON .... 2020 – .... 2020—

• Google Cloud UDACITYCloud Computing .... 2020 – .... 2020—

• Nanodegree Program Become a C++ Developer Udacity.comBasics, List, packages, function, Numpy Sep. 2019 – Dec. 2019—

— — Physics — —

• Thermodynamics : Basics Polytechnique Federale de Lausanne, SWISSEntropy, Enthalpy, Gibbs Free Energy, Phase transition, 1rst and 2nd principle Jul. 2019 - Jul. 2019—

• Lagrangian Mechanics Polytechnique Federale de Lausanne, SWISSLagrange Formalism, Pendulum, Oscillation, Waves Oct. 2019—

• Statistical Thermodynamics: Molecules to Machines Carnegie-Mellon UniversityIsing model, Mean Field Theory, Classical Thermodynamics Apr. 2019—

• Statistical Mecanics : Algorithms and computations Ecole Normal Superieur, ParisMonte Carlo algorithms, Entropic interactions, phase transitions, Sampling, Path integrals Apr. 2019

CONFERENCES - SEMINARS & EVENTS:

• European Cyber Week: Artificial Intelligence for Cybersecurity Renne, FRANCECybersecurity - Machine Learning - Defense - Cyberthreat Nov 2019—

• Dynamiques post-structurelles : devenir hétérogéne, intensif, singulier CAMS, ParisPhilosophy of shapes / Morphodynamics / Structuralism / Heterogenesis / Differential Geometry / Neurocognition Nov 2019 – Jun 2019—

• Annual Conference on Complex Systems - CSS2018 Thessaloniki, GreeceMultiplex Networks, Complex Dynamical Systems, Physics, Complexity in cognitive science 07 Nov 2017 - 09 Nov 2017—

• FIC 2019 : International Cybersecurity Forum Grand Palais, LILLECybersecurity conference Jan 2019—

• R&T Days : Institut Cognition a la Cite des Sciences et de l industrie Paris, FranceCognitive technology, Artificial Intelligence, Digital 07 Nov 2017 - 09 Nov 2017—

• 3rd Conference on Geometric Science of Information - GSI Mines ParisTech, Paris, FRANCEStatistics on non-linear data / Statistical Manifold / Hessian Information Geometry / Machine Learning 07 Nov 2017 - 09 Nov 2017—

• FIC 2018 : International Cybersecurity Forum Grand Palais, LILLECybersecurity conference Jan 2018—

• ODSC Europe 2018 Data Science Conference London, FRANCEDeep Learning / Machine Learning / Natural Language Processing / Data Visualization / Artificial Intelligence 14 Nov 2018 – 16 Nov 2018—

• Entropy 2018: From Physics to Information Sciences and Geometry Barcelona, SpainStatistical physics and Bayesian computation / Geometrical science of information, topology and metrics 14 May 2018 – 16 may 2018—

https://www.coursera.org/learn/differentiation-calculushttps://www.coursera.org/learn/integration-calculushttps://www.coursera.org/learn/integration-calculushttps://www.datacamp.com/courses/hyperparameter-tuning-in-pythonhttps://www.coursera.org/learn/sql-for-data-science/home/welcomehttps://www.datacamp.com/https://www.udacity.com/course/intro-to-ajax--ud110https://www.coursera.org/learn/javascript-jquery-jsonhttps://www.coursera.org/professional-certificates/cloud-engineering-gcp##courseshttps://eu.udacity.com/course/c-plus-plus-nanodegree--nd213https://www.coursera.org/learn/thermo-base/home/welcomehttps://www.coursera.org/learn/mecanique-lagrangiennehttps://www.coursera.org/learn/statistical-thermodynamics-cmhttps://www.coursera.org/learn/statistical-mechanicshttps://www.european-cyber-week.eu/https://enseignements-2018.ehess.fr/2018/ue/2691/https://cssociety.org/ccshttps://www.forum-fic.com/accueil.htmhttps://www.see.asso.fr/gsi2017https://www.forum-fic.com/accueil.htmhttps://odsc.com/londonhttps://entropy2018-1.sciforum.net/

BUSINESS:

POSITION & PROFESSIONAL ORGANIZATIONS:

• Euratechnology permanent member LILLE, FranceCommunity of tech companies May. 2019 – now

• New England Complex Systems Institute Member NECSI, MIT, BostonCommunity of researcher on complex systems May. 2019 – now

• Complex Systems Society Member Complex System SocietyCommunity of researcher on complex systems Aug. 2018 – now

• Association Member CASC AssociationStudent Association of cognitive science Mar. 2018 – now

• French Mathematical Society Member Société mathématique de FranceAssociation of Mathematicians Mar. 2018 – now

EMPLOYMENT EXPERIENCES:

• Datascientist in cybersecurity PARIS, FRANCECompany management in the contexte of catering / professional chain restauration Jan. 2012 – Now

• Team management in commercial entreprise Lille, FRANCECompany management in the contexte of catering / professional chain restauration Jan. 2012 – Now

BUSINESS & MANAGEMENT:

• Hubhouse Entrepreneurship Training Lille, FRANCEManagement, company creation, marketing, commmunication, social networks, brand image Sep. 2017 – Mar. 2018

• Formateur MAO / Harmony teaching Lille, FRANCEManagement, company creation, marketing, commmunication, social networks, brand image Sep. 2017 – Mar. 2018

• Wikimap company (23000 users monthly) Lille, FRANCEManagement, company creation, marketing, commmunication, social networks, brand image Sep. 2017 – Mar. 2018

• Mastering / Scoring for film and vidéo Games / STUDIO MOKAS) Lille, FRANCEManagement, company creation, marketing, commmunication, social networks, brand image Sep. 2017 – Mar. 2018

https://www.euratechnologies.com/https://necsi.edu/https://cssociety.org/homehttp://www.podcasc.com/https://smf.emath.fr/

SHEET CHEAT COMPLEXITY SKILL SYNTHESIS Legend : � = actual skills / � = futur goals

I Softwares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Infography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

����� . . . . . . . . . . . . . . . . . . . 22 Architecture / Design 3D / Motion Capture. . . . . . . . . . . . . . . . . . . . .

����� . . . . . . . . . . . . . . . . . . . 2

3 Music Production Sound desing / Acoustics . . . . . . . . . . . . . . . . . . . .����� . . . . . . . . . . . . . . . . . . . 2

Vidéo & Film Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ����� 2

ART:

MUSIC, MASTERING, SOUND DESIGN PROJECTS & REWARDS:

• Music Production STUDIO MOKAS, FRANCEScoring, Film industry music production Sep 2019 - now—

– Project Cloudmeister – P2 – P3

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

• STUDIO MOKAS publication on the Musictech Journal London, ENGLANDPresentation of the studio in an international journal for musical performances Mai. 2018

• Teaching Harmony, Composition, Orchestration & Mastering Lille, FRANCETeaching scales, harmony theory, pianistic technics, arpegias, circle of fifths, classics, transposition, jazz chords Mar. 2016 - Nov. 2019

• Programming WEB-music application Lille, FRANCEDeveloppement of of a standalone, music webpage for presenting musical projects Dec. 2018

• Music Label Member Magnetik music label, FRANCEMember of an Acoustics music production Label Mai. 2018

• Fiverr profileMember of an Acoustics music production Label Mai. 2018

INFOGRAPHY & WEB DESIGN:

• Creating a Website with Wordpress / Javascript / CSS & AJAX https://emergence-lfe.org/Front end Web design for my personal website Jan. 2017 – Now

https://emergence-lfe.org/welcomedev/mapbeta.htmlhttps://www.youtube.com/watch?v=WwS-37q2C6ghttp://emergence-lfe.org/sound-engineering/production/https://www.facebook.com/magnetiklab/https://www.facebook.com/magnetiklab/https://emergence-lfe.org/research/welcome/

LANGUAGES:

• English : B2/C1 New York, UsaIELTS score : Listening: 6.0 / Reading: 7.0 / Writing: 5.0 / Speaking: 6.5 Mar. 2020—

• French : Native ———— —

• Greek : Fluent ———— —

Baptiste Mokas, is a student-researcher in cognitive and mathematical bioscience dedicated to the modeling of the integration of information incomplex adaptive and multiobjective systems. He is also a young music producer for the film industry.

Centered on affective and behavioral sciences, data science and epistemology, Baptiste aspires to develop new cognitive models and mathematicalformalisms for understanding human behavior. Focused on various levels of observation, he seeks to theorize the integration of information within

complex organic and artificial adaptive systems.For several years, Baptiste has persevered in his research project to understand the cognitive processes underlying emotions, conscious perception

and the relation to “others” (social and emotional cognition). He seeks to understand the nature of thought and the dynamics of behavior.Well-versed in the classical paradigms of cognitive psychology and computational neurosciences, Baptiste crystallizes his knowledge on the theory

of complex and dynamical systems [*]; a scientific paradigm adapted to address non-linear interactions and complex dynamics that occurfrequently in such systems. These behaviors, with specific properties such as multi-stationarity, chaos, bifurcations, self-organization, emergenceand feedback loops, are defined within different levels of organization and are difficult to reduce to lower levels. The core of the approach, whichdefines the enactivity of cognition, makes it possible to approach systems within an appropriate, realistic and ecological scientific framework. The

omnipresence of multifactorial chaotic determinism (anything acting on everything) makes this approach particularly fruitful for theappropriation, understanding and exploitation of current and future scientific, technological and societal issues.

In the interest of approaching complexity, Baptiste offers good analytical skills as well as a rigorous and efficient methodological, mathematicaland algorithmic foundation. He continually strengthens his skills in Data mining, Machine-Learning as well as in several programming languages(Python, R, Matlab, SQL, NoSQL). In the future, he wants to master distributed data analysis tools for large amount of data (using Hadoop,Hive and Spark). He has also had an exposure to semantic mining, which he discovered while at the Institut des Systèmes Complexes of Paris

(ISCPIF).In the long term, he would like to provide a theoretical framework common to the various fields of scientific research on human behavior, on alllevels of observation – from neuroscience to sociology. He rejects the heterogeneity of the current scientific field, and aspires to converge all

information into a singular area of representation, calling for a coherent, integrated and dynamic coexistence of theories, and functions currentlyput forward independently of one another. The reality pursued is more integrated, less compartmentalized, and therefore more predictable. Thisapproach calls for dynamic bayesian analysis, network theory, Markov chains and percolation theory in the context of information geometry, anapplication of differential geometry and topology to the theory of probabilities. The goal is to geometrize the dynamics of multiobjective adaptive

behaviors – defining it as the decentralized, parallel, logical and dynamic integration of a fragmented network of probable anticipated causes,allowing the optimization of fitness – and whose space phases, the attractor basin, would be defined on the topology of this same network. Thedefinition of cognition takes on a geometrical meaning in the parallelization of dynamics and in the heterogeneity, however integrated, of fields in

which they take place.Based on an epistemic structural realism, and only in the context of an algorithmic optimization of the behavior to its original function of

adaptation, the cognition would be reduced dynamically to the cohabitation of “fields” – each one converging towards a function determined bythe effect that it bears on all others, and which (collectively) maintain the global attractor. This approach could be inspired by statistical physics

(e.g. Multinomial Statistical Field Theory).Currently working on his Master of Applied Mathematics – Cognitive Science for Business, Baptiste has deepened his technical and

methodological knowledge of cognitive research. Taking place in a deep multidisciplinary scientific framework on cognitive and affective processes,on the relations between action and perception, this area of study allows for an understanding of how human behavior is constructed. This area

of study allows the appreciation of a rigorous theoretical approach based on the scientific argumentation, and is linked with appliedmethodological questions. It also allows an epistemological relfection on the question of experimental biases, the problem of overinterpretation(e.g the bias/variance dilemma) of the statistical inference necessary for any objective and reliable conclusion on the cognitive, emotional andmotor functioning during interactions, and in an ecological environment. Experimental design and bibliographic research capabilities provideskills in project management and protocol organization. Competence in managing constraints and creating compromises is a key component of

the training gained through field experience in the course of conducting research projects.Engineering and technological innovation is also a key component in Baptiste’s training. It allows the appropriation of the means of technicalanalysis for the study of cognition. Equipex and laboratory excellence (Plaine Image, IrDive, etc.) were available for the use with the latesttechnologies (BMCI, H-MI, Bio Neurofeedback, Psychophysiology, Eye tracking, Motion capture, EEG, etc.). Finally, Baptiste has developed

skills in product ergonomics, marketing, web design, and computer graphics. He is also familiar with encryption basics (AES-256), cybersecurityand has the basic skills for the management of a server/NAS in business.

In addition to his research, Baptiste offers a music composition service for movies and video games. Inspired by the greatest films, he enjoyscombining the human element with the digital, and joining nuanced and basiuc harmonies with the power of the digital and modern textures,accessible today through technology. He offers tailor-made virtual orchestrations for the creation of narrative atmospheres. He has also taughtpiano harmony classes [*] since 2016. Using the best virtual instruments, plug-ins and production techniques, the studio brings a high level of

flexibility to to further efforts in highlighting audio-visual productions in meaningful ways.The studio has a reliable and secure system (alarm, cloning disc, etc). Mastering is done on Genelec 8351 high-end audio monitors, for a neutraland precise mixing. Ergonomics meet the objectives for a fast and efficient design, for wich the studio won an international award in May of 2018

from MusicTech magazine. In the future, Baptiste’s desire would be to push this technique to its limits in order to develop new ways ofcomposing. Sound signal processing (spectral analysis, wavelet, etc.), the study of neural dynamics within a musical context and the applicationof machine learning could allow for higher levels of creativity, composing with an electroencephalogram, thus furthering an understanding of the

musical world.Exposed from an early age to entrepreneurialism via his family’s catering business, Baptiste also learned, through practical experience, the

diplomacy and pragmatism necessary for managing all aspects of a business. He has also had university training in management and marketing(Hubhouse de Lille) and in project management (Gantt charts, Agile Method). He is also interested in modified state of consciousness,

meditation and self-hypnosis, and offers introductory group sessions on these practices

RESEARCHPUBLICATIONS (0) & ACADEMIC PRODUCTIONS (7)MACHINE LEARNING - KAAGLE & GIT (11)ARTICLES & BLOG (0)EDUCATIONDOCTORAT IN MATHEMATICS: PROBABILISTIC GRAPHICAL MODELS FOR RISK AND INFORMATION INTEGRATIONSCIENTIFIC COMMUNITYMASTERS : - MATHEMATICS AND COMPUTING FOR COGNITIVE SCIENCE - NEUROCOGNITION & AFFECTIVE NEUROSCIENCESLICENCE : - BEHAVIORAL SCIENCE / EXPERIMENTAL PSYCHOLOGY BOOTCAMPS (1)SUMMERS SCHOOLS (0)MOOCS (14)CONFERENCES - SEMINARS & EVENTS (9)BUSINESSEMPLOYMENT EXPERIENCES & POSITIONS (7)BUSINESS MANAGEMENT (2)ARTMUSIC PRODUCTION (3)INFOGRAPHY & WEB DESIGN (3)

BIO