Dr. Dávid Natingga
A mathematician, data scientist, software engineer; interested in research, algorithms and AI
Education
2019
University of Leeds, UK, Department of Pure Mathematics – Computability Theory (PhD)
Subjects taken: Set Theory, Proof Theory, Computability Theory, Category Theory
2014
Imperial College London, UK – Computing (MEng Artificial Intelligence, 2:1)
Subjects taken: Algorithms, Machine Learning, Statistics, Logic, Algebraic Topology, Smooth Manifolds
2010
Coventry University, UK – Mathematics and Computing (CertHE, distinction)
Industrial experience
Apr. 2019 – present
Data Science Freelancer, self-employeed serving international clients:
Toptal, Alteso, Inflowmatix, Focus Facilitation
May 2017 – March 2018
Software Engineer Contractor, TomTom, Amsterdam, Netherlands
June 2014 – Sep. 2014
Data Science Intern, Pact Coffee, London, UK
Apr. 2013 – Sep. 2013
Forward Deployed Engineer Intern, Palantir Technologies, Palo Alto, USA
June 2012 – Sep. 2012
Forward Deployed Engineer Intern, Palantir Technologies, Palo Alto, USA
July 2011 – Sep. 2011
Instep Research Intern, Infosys Labs, Bangalore, India
Machine learning
Concepts
bias-variance trade-off, recall & precision, supervised & unsupervised learning,
regularization, bagging and boosting
Deep learning
ANN, CNN (Dropout, Pool, ConvnD), architectures (VGG, AlexNet, ResNet)
Computer vision
object detection and recognition (e.g. Viola-Jones algorithm)
Statistics
least mean squares, Bayesian algorithms, regression, expectation maximization,
Markov chain, Monte Carlo method, A/B testing, hypothesis testing
Algebra
singular value decomposition
Clustering
k-means, DBSCAN, elbow method, Silhouette coefficient
Classification and regression
decision trees, random forests, gradient boosting, support vector machine,
k-nearest neighbours
Time series analysis
forecasting, trend and seasonality, burn-in, autoregressive evaluation,
dynamic time warping, anomaly detection, drift and offset detection
Online algorithms
recursive filters (e.g. Kalman filter), moving average and variance (e.g. EMA),
Welford’s algorithm
Dimensionality reduction
principal component analysis, t-Distributed Stochastic Neighbor Embedding
Bioinformatics
DNA analysis, oriC search, sequencing
Others
inductive inference, PageRank, genetic algorithms
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Visualization
Notebooks
matplotlib, seaborn, plotly
Jupyter, Google Colab, GCP AI platform
Libraries
NumPy, pandas, scikit-learn, TensorFlow, Keras, dlib, XGBoost
Software engineering
Algorithms and data structures
Best practices
computational and memory complexity; ADTs
architecture, specification, code style, design patterns, documentation, review
Concepts
Agile, TDD, BDD, Scrum, MVP, CI
Version control
Git, SVN
Major languages
C / C++, Java, Python
Minor languages
Assembly, Awk, Bash, Basic, Go, Haskell, Julia, Matlab, Pascal,
Java
code style: Checkstyle
Perl, Prolog, R, Ruby
testing: JUnit, code coverage reports (JaCoCo)
frameworks: Immutables, Lombok, Spring (components, SpringBoot)
web development: Java Server Faces, Prime Faces, Hybernate ORM
Web development
CSS, HTML, JavaScript, PHP, AJAX, jQuery, CGI, sockets
Mobile development
Android
Databases
MySQL, PostgreSQL, Timescale, Berkeley DB, NoSQL, MongoDB
Networking
Apache, CISCO, Linux
Cloud computing
Amazon Web Services: EC2, S3, Batch, Lightsail
Google Cloud Platform: Storage, AI platform
Testing
unit, validation and integration testing; mock objects
Build tools
Gradle, Maven
Management software
Jenkins (DSL scripting), CircleCI, Confluence, Gerrit, Github, Zube
IDEs
Eclipse, IntelliJ IDEA, Android Studio
Additional skills
Docker containers, Nvidia CUDA, Flex, Bison, ElasticSearch, OpenAPI (Swagger)
Teaching experience
Apr. 2016 – Sep. 2016
Teaching Assistant for Mathematical Logic, JAIST, Japan
Sep. 2014 – Jan. 2016
June 2011
Teaching Assistant for Mathematics, University of Leeds, UK – taught 7 courses
Teaching Assistant for Mathematics, Elthorne Park High School, London
2006 - 2009
Private tutor in mathematics, Slovakia
Publications
[1] Dávid Natingga. Data Science Algorithms in a Week: Top 7 algorithms for scientific computing, data analysis,
and machine learning. Packt Publishing Ltd, 2018.
[2] Dávid Natingga. Embedding Theorem for the automorphism group of the α-enumeration degrees. PhD thesis,
University of Leeds, 2019. http://etheses.whiterose.ac.uk/25517/.
[3] Sujatha R Upadhyaya and Dávid Tóth. An experiment with asymmetric algorithm: Cpu vs. gpu. In International
Conference on Database Systems for Advanced Applications, pages 272–281. Springer, 2012.
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Personal information
Birth date:
2nd January, 1990
Birth name:
Dávid Tóth
Marital status:
married
Children:
2 (born 5th October 2017 and 15th February 2020)
Citizenship:
Slovak (European Union)
Contact information
Address:
Popradská 2946/3, 010 08, Žilina, Slovakia
Email:
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Phone:
-
Skype:
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Blog:
davidnatingga.wordpress.com
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