This talk is about data science and statistics applied to flight safety in commercial aviation worldwide.
In the introductory part, we will stress the importance of monitoring your flight data and show you some real records coming from flight data recorders (aircraft “black boxes”). We will then explain how the data is recorded, downloaded, analysed, converted to safety events and finally, validated by experts in the field - flight data analysts. Data aggregation across many flights will result with statistical images of safety risks in airlines’ operations. However, this valuable tool can turn into a deadly weapon if used negligently – we’ll support this claim with examples. We are convinced the audience will know about some of these traps, regardless of the industry they are coming from, but hopefully there will be something valuable to take home, too.
In the second part, we are saying goodbye to the data analyst and the statistician – the two dominant guys from the first part of the presentation. However, a data scientist will stem from valuable experiences and domain knowledges of the two. This guy will walk the audience through three simple, but working examples. The first one is about how we can improve the accuracy of automated analysis by using historic data and a probabilistic, Bayesian approach. The second example is about finding novel safety risks in airlines’ operations by using simple principal component analysis. Lastly, we’ll use a Markov model to detect aircraft which have changed behaviour with respect to frequency of data downloads, so we collect as many flight data as possible.
We will try to make this chat as interesting and as interactive as we can and are looking forward to meeting you at this fun and interesting conference!
Marko Vasiljevski bio:
Marko is a physicist leading the data science department at Flight data services. His everyday work is about exploration of all available sources of aviation data, algorithm development, statistics, chatting with people and research. For data exploration and algorithm development he mainly uses open source tools like R, Python and PostgreSQL. The chats are mainly with fellow data scientists, pilots, developers and airlines’ flight safety officers, whilst the research covers reading the material on forgotten and novel approaches in machine learning, statistics and programming. Having fun is a very important aspect of his life and work, but he takes aviation safety extremely seriously and is keen to help airlines with interpretation and making use of their data. At flight safety and data science seminars and conferences, Marko is a speaker and promoter of safe flying, data sharing and data-driven decision making.
Raffaele Rainone bio:
Raffaele joined Flight data services as a data scientist having previously worked in a location analytics company as RF/DSP developer where, among other R&D tasks, he dealt with BigData and contributed to developing and improving a WiFi localisation algorithm by combining his knowledge of Python and PostgreSQL with probabilistic tools such as Markov chain Monte Carlo methods. Raff mainly focuses on analysing flight data with a goal to develop machine learning algorithms aimed at improving flight safety. He also spends a great deal of time on exploration of job parallelisation and distribution using Apache Spark, Dask and Numba. He obtained BSc in Mathematics from the University of Naples, and MSc in Mathematics from the Universities of Padua and Leiden. In 2014, he was awarded a PhD in Pure Mathematics by the University of Southampton with a thesis in group theory.