Zaven Badalyan verfügbar

Zaven Badalyan

Professional Data Scientist Machine Learning Experte AI KI Neuronale Netze Mathematiker Berlin

verfügbar
Profilbild von Zaven Badalyan Professional Data Scientist Machine Learning Experte AI KI Neuronale Netze Mathematiker Berlin aus Berlin
  • 13055 Berlin Freelancer in
  • Abschluss: Mathematik Bachelor und Master mit bester Note 1.0 an der Humboldt-Universität zu Berlin, Bachelor Physik an der Humboldt-Universität zu Berlin
  • Stunden-/Tagessatz:
  • Sprachkenntnisse: deutsch (Muttersprache) | englisch (verhandlungssicher)
  • Letztes Update: 26.11.2020
SCHLAGWORTE
PROFILBILD
Profilbild von Zaven Badalyan Professional Data Scientist Machine Learning Experte AI KI Neuronale Netze Mathematiker Berlin aus Berlin
DATEIANLAGEN
CV

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SKILLS
For more information please visit my own personal homepage: badalyan.it

Programming Skills:
Data Science
• Python, Go (GoLang), Java, C++
• NumPy,Tensorflow, Keras, PyTorch
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Long Short-Term Memory Networks (LSTM) 
• Scikit-learn
BigData
• Scala
• Databricks, Spark, PySpark, Hadoop, Kafka
• Python Pandas
• SQL, NoSQL, MySQL, MongoDB
• Docker, Kubernetes, Git, Jira, Confluence, Bash
Matlab
• Statistics and Machine Learning Toolbox 
• Optimization Toolbox
• Curve Fitting Toolbox
• Partial Differential Equation Toolbox
Cloud Computing
• AWS
• Azure
• Google Cloud
• Cuda, Cudnn
Frontend Development
• HTML & CSS
• JavaScript
• VueJS
• ReactJS
• Angular
Backend Development
• NodeJS
• RESTful API
Other skills:
Latex
• Beamer
• PGF/TikZ
MS Office
• Word
• Excel
• Powerpoint
Vim, Nano
Linux, Mac OS, Windows
Trello, Jira, Bitbucket

Programming Way: more than 10 years of Coding Experience
  • 2009: Choosing Compute Science school classes, building my first applications with the programming language "Delphi"
  • 2010: Programming small HTML Website for the School
  • 2011: Choosing CAS Mathematics, solving mathematical algorithms on the "Derive" Computer Algebra System, writing own solvers for square root problems
  • 2012:WritingownregressionApptofitthemeasurements at my physics experimental lectures
  • 2013: Starting to learn Python for the University courses, writing practical Apps with my university college to make easy to solve our home tasks
  • 2014: Developing a website with HTML, CSS and JavaScript for my Professor
  • 2015: Writing own Finite-Element realistic Simulation software in Matlab, which requires a very strong mathematical intuition and knowledge
  • 2016: Writing FEM Simulation Codes in Python, utilizing very modern Matrix-Vector Linear Algebra Computational C++ Modules
  • 2017: Utilizing Jupyter Notebook, Latex and Python to write my Bachelor’s thesis in Mathematics, which was 70% coding, found numerical bad behavior in the theory of the mathematics paper used for my Bachelor’ thesis, for which I was rewarded from my professor
  • 2017:AccomplishingMachineLearningIattheTechnische Universität Berlin
  • 2018: Accomplishing Machine Learning II at the Technische Universität Berlin
  • 2018: Accomplishing Deep Learning Courses at the Freie Universität Berlin
  • 2018: Accomplishing Python Programming for Deep Learing Courses at the Technische Universität Berlin
  • 2019:AccomplishingDeepLearningattheTechnischeUniversität Berlin
  • 2019: 11 months permanent writing Python, Matlab, Java Codes for my Master’s Thesis in Mathematics, more than 50% of the work was the programming part, gained with my numerical experiments new results in the area of Neural Networks and Differential Equations
PROJEKTHISTORIE
  • 05/2020 - bis jetzt

    • Franz Habisreutinger GmbH & Co. KG
    • 250-500 Mitarbeiter
    • Industrie und Maschinenbau
  • Senior Data Scientist and Data Analyst
  • Developing an OCR for Invoice recognition software which checks for errors in the Invoice
    • Combining classical Image Recognition techniques with modern Neural Networks approaches
    • Data Mining, Data Tracking
    • Utilizing mathematical Statistics for Data Preparation and Data Cleansing
    • Writing an efficient SQL-Command search function to make the work easier
    • Utilizing Image recognition
    • Utilizing Deep Learning Techniques for Text Recognition on the invoice
    • Building Deep Auto-encoders for the dimensionality reduction and denoising of the Data
    • Developing the Neural Network for the Supervised Learning with custom losses and custom layers
    • Using Reinforcement Learning Techniques which suggests the worker what to do in the next step
    • Utilizing Convolutional Neural Networks (CNNs) for better Logo detection
    • Testing the software in the real use case
    • Transporting the code to the webserver,setting up an efficient SSH Connection and local Cloud Computing infrastructure 

    Qualifications: Neural Networks (CNNs, Recurrent Nets, RNNs), Machine Learning, Supervised Learning, Reinforcement Learning, Natural Language Processing (NLP), Statistics, Python, Tensorflow, Numpy BigData, Tesser- act, Pyesseract PySpark, Kafka, Git, Bitbucket, Docker, Bash, AWS, Azure, Cloud Computing, DataClustering, Databricks, Tinc VPN Jira, Confluence, Trello, Jupyter Lab, Jupyter Notebook, Spider, Visual Code

  • 02/2020 - 05/2020

    • yuvedo GmbH
    • 10-50 Mitarbeiter
    • Internet und Informationstechnologie
  • Senior Data Scientist and AI-Developer
  • Project details:
    Developing AI for a medicine application (Parkinson disease) which suggests the optimal behavior for the patient to reach and boost their healthcare goals.

    • Coupling Supervised Learning with Reinforcement Learning to enable online learning of patients needs and make instant suggestions to the user
    • DataMining,DataTracking
    • Utilizing mathematical Statistics for Data Preparation and Data Cleansing
    • Creation o fsynthetic data to boost the AI and findt he principal architecture of the AI which learns our original data more precise
    • Check with the synthetic data what is needed to couple the
    • Supervised Learning AI and the Reinforcement Learning AI
    • Building Deep Auto-encoders for the dimensionality reduction and denoising of the Data
    • Developing the Neural Network for the Supervised Learning
    • with custom losses and custom layers utilizing self-invented and more efficient custom Neural Networks architectures in Tensorflow
    • Developing the Neural Network for the Reinforcement Learning with custom losses and custom layer
    • Utilizing Recurrent Networks Architecture (RNNs) for time series combined with Convolutional Neural Networks (CNNs)
    • Connecting the Supervised Learning and the Reinforcement Learning Networks to each other to simulate the patient and doctor relations, which helps the AI to make realistic and useful suggestions to the Patient
    • Transporting the AI to the Webserver, setting up a local Cloud Computing infrastructure with advanced GPUs and efficient SSH Connection
    • Building BigData Pipelines with Hadoop and Spark to allow parallel and cluster computing which helps to find the better appropriate Neural Network models.
    Qualifications:
    • Neural Networks (CNNs, Recurrent Nets, RNNs),
      Machine Learning,
      Supervised Learning,
      Reinforcement Learning,
      Statistics,
      Python,
      R,
      Tensorflow,
      Numpy BigData,
      Spark,
      Scala,
      Hadoop,
      PySpark,
      Kafka,
      Git,
      Bitbucket,
      Docker,
      Bash,
      AWS,
      Azure,
      Cloud Computing,
      Data-Clustering,
      Databricks,
      Tinc VPN,
      Jira,
      Confluence,
      Trello,
      Jupyter Notebook,
      Spider,
      Visual Code


  • 01/2020 - 03/2020

    • Sopra Steria Group SA
    • 5000-10.000 Mitarbeiter
    • Internet und Informationstechnologie
  • Data Science Consultant, AI-developer
  • Project details:

    Consulting for an SAP Addon which is predicting the right quota of the bills making the contingation process easier for the employee, the Addon customizedly learns the way of working of the employee and makes more and more better predictions which bills belong to which contingent.
    • Developing one simple and one more complicated prototypes
    • Coupling Reinforcement Learning and Supervised Learning techniques to each other making past data and current data more efficient for the on fly learning
    • Data Preparation, Data Clustering using EM, ICA, PCA techniques
    • Utilizing mathematical Statistics for unsupervised learning and Clustering methods
    • Dimensionality Reduction of the Data with Deep Autoencoders, using denoising techniques in the Autoencoder by programming own custom Tensorflow Blocks
    • Writing Python and JavaScript APIs to enable the transfer between the SAP Addon and the Python Script with the AI
    • Dockerizing the Anaconda environment, setting up a virtual environment in the server
    • Utilizing BigData Technologies like Spark to build fast and efficient cluster computing environment
    • Utilizing Kafka Stream APIs to enable fast and efficient learning between the Reinforcement and the Supervised Learning AIs form the one side and the data transfer from user to AI from the other side
    • Utilizing Cloud Computing platforms like AWS to boost the training procedure of AIs
    Qualifications:
    Mathematical Statistics,
    Artificial Intelligence,
    Neural Networks,
    GANs,
    RNNs,
    CNNs,
    Machine Learning,
    Supervised Learning,
    Unsupervised Learning,
    Reinforcement Learning,
    Statistics,
    Python,
    R,
    Tensorflow,
    Numpy
    BigData,
    Spark,
    Kafka,
    Git,
    Bitbucket,
    Docker,
    Bash,
    AWS,
    Cloud Computing,
    DataClustering,
    Databricks,
    Trello

  • 12/2019 - 01/2020

    • self
    • < 10 Mitarbeiter
    • Internet und Informationstechnologie
  • Building my own personal homepage badalyan.it
  • Project details:   
    • Used top modern non-relational webdeveloping tools VueJS and NodeJS
    • Using HTML, CSS and JavaScript
    • Using non-relational Databases MongoDB
    • Using NodeJS for serverside programming and for data processing of users, writing Email and CV APIs with the help of NodeJS
    • Configuring a Strato Virtual Server as a Ubuntu Server to be able to use the efficient Linus environment

  • 05/2019 - 12/2019

    • IT-company psaichology.org from Berlin
    • 10-50 Mitarbeiter
    • Internet und Informationstechnologie
  • Data Scientist and AI-Developer
  • Project details:

    Adapting psychological Models to the AI, AI learns e.g. to make customized suggestion to the consumer based on its behavior.

    Creating synthetic data to build real and fake personalities
    • Using GANs to generate more synthetic data,making unrealistic data that the Neural Net later can distinguish between the original and the fake one making the Nets more robust on adversarial examples
    • Data Mining, DataTracking, DataAnalysis, Data Preparation, Data Optimization
    • Using Deep Autoencoders to denoise the Data
    • Data clustering/dimensionality reduction with statistical tools, self written code+python packages Pandas, using R
    • Design and creation of appropriated Neural Nets, Hyperparameter tuning
    • Heavily used my mathematical thinking and knowledge to choose the right optimization techniques, using very innovative ideas on Neural Networks which I developed in may master’s thesis
    • Usage of modern automated Tools like AutoML to run the Hyperparameter tuning more autonomous, using grid techniques for the searching processes also for the activation functions
    • Usage of cloud computing platforms: AWS, Azure
    • Write, Run and Debug self written custom Neural Network codes, using different ML Libraries: TensorFlow, PyTorch
    • Successful validation of self written NeuralNets, new result in the theoretical psychology

    Qualifications: Data Science, Big Data, Predictive Analytics, Machine Learning, Statistics, Neural Nets, Python, Pandas, CUDA, Tensorflow, PyTorch, Numpy, Spark, Hadoop, Kafka, Go, C++, Java, SQL, AWS, Azure


  • 05/2019 - 10/2019

    • justplan aktiv GmbH
    • 10-50 Mitarbeiter
    • Internet und Informationstechnologie
  • Big Data and AI-Developer, Data Scientist
  • Writing Python APIs, writing Data Pipeline scripts, configuring servers, installing Docker containers and Anaconda environments in the server to enable Data Science APPs and Projects to run on it more efficiently
    • Helping with the data mining procedure with the team of the justplan aktiv
    • Utilizing my advanced mathematical knowledge in Big Data analysis, preparation and optimization of the training data for Neural Networks
    • Data denoising/clustering/dimensionality reduction with statistical tools, self written code+python packages Pandas, using R
    • Gaining first good results with my self developed innovative Neural Networks, solving a 2 years lasted problem with unifying of several Neural Networks in one single Network
    • Design and creation of appropriated Neural Nets,Hyperparameter tuning
    • Usage of modern automated Tools : AutoML
    • Usageofcloudcomputingplatforms:AWS,Azure
    • Write, Run and Debug self written custom Neural Network codes, using different ML Libraries: TensorFlow, PyTorch
    • My team gained the better results on the same data than the other team, the code is also faster than the one of the other team Qualifications: Data Science, Big Data, Predictive Analytics, Machine Learning, Neural Nets, Python, Pandas, CUDA, Tensor- flow, PyTorch, Numpy, Azure, AWS, Java, Go, JavaScript, Spark, Hadoop, Kafka, C++, SQL

  • 08/2018 - 02/2019

    • Zalando
    • 5000-10.000 Mitarbeiter
    • Konsumgüter und Handel
  • Statistician and Data Scientist
  • Taking the role of a lead statistician, helping the team to develop statistical models, helping to develop data science and AI products
    • Establishingofstatisticalhypothesiswhichwerevalidatedlater
    • Developingadataminingprocedurefordynamicalgenerated prices
    • UtilizingadvancedmathematicalknowledgeinBigDataanalysis
    • Datadenoising/clustering/dimensionalityreductionwithsta- tistical tools, self written code+Python packages Pandas, using R
    • Validationofthedata,checkingformissingvalues
    • TimeSeriesforecasting
    • UsingSVMModels,Hyperparametertuning
    • UsingNeuralNetworks,Hyperparametertuning
    • UtilizingvariousstatisticalprocessingmethodslikeEM,tNC, PCA, ICA algorithms
    • AddingnewresultsintheSQLdatabases Qualifications: Data Science, Big Data, Predictive Analytics, Machine Learning, Neural Networks, Python, R, Pandas, Ten- sorflow, Numpy, Spark, SQL


  • 02/2017 - 07/2018

    • Siemens AG
    • >10.000 Mitarbeiter
    • Internet und Informationstechnologie
  • Junior Data Scientist in the Department AI and Machine Learning Cluster of Siemens AG
  • Project details:
    Helping the Team of the AI department to extend the Machine Learning cluster, to collect and prepare the vast amount of data, consulting about the utilization of the information in the collected data, building various small APIs to enable the working between the teams of the different IT- departments of Siemens
    • Utilize my mathematics knowledge to build Statistical Python Scripts for Data Preparation
    • Building Hadoop pipelines for the large available data of Siemens
    • Utilizing various statistical processing methods like Autoen- coders, EM, tNC, PCA, ICA algorithms
    • Doing Scientific research,reading,understanding and showing relevant Papers to the team
    Qualifications:
    Mathematics
    Differential Equations
    Statistics
    Machine Learning
    Neural Networks
    Python
    Tensorflow
    PyTorch
    Numpy
    EM
    ICA
    PCA
    Databricks
    Spark
    PySpark
    Kafka
    Hadoop
    Pandas

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