Dr Efstratios Rappos is currently a post-doc researcher at HEIG-VD, Switzerland working in the area of combinatorial optimization, algorithm design and optimization for data science and Machine Learning.

He obtained his Ph.D. in Combinatorial Optimization (Operations Research) at the Business School of Imperial College London. 

He studied Mathematics at the University of Cambridge (BA/MA) and obtained a  Masters in Mathematics (CASM/Part III/now called MMath)  from the same University. He also holds a MSc in Finance from Birkbeck College, University of London.

Previously he has worked as a principal analyst responsible for leading a team of data scientists in the UK Department for Work and Pensions (DWP) developing mathematical models for large-scale data mining, automated data cleaning, forecasting and economic modelling of social security and demographic data. He has also worked as a post-doc at Imperial College London and taught statistics at LSE. 

Corporate websites

HEIG-VD is an engineering school located in Yverdon-les-Bains. It is part of the University of Applied Sciences of Western Switzerland (HES-SO). 

 

 LinkedIn           Google Scholar       OCRID 0009-0005-4918-1046 

Contact

HEIG-VD
Route de Cheseaux 1
Case Postale
1401 Yverdon-les-Bains
Switzerland

email: efstratios [.] rappos [@] heig-vd.ch

tel : +41 24 557 71 89

 

Competencies by category

  • Data analytics
    • Data cleaning and preprocessing datasets produced by many commercial tools (major db exports, system logs, website logs, social network data
    • Java data structures for mapping, sorting, and apache commons tools
  • Predictive analytics
    • Forecasting, prediction
    • Real-time analytics
  • Combinatorial optimization
    • Mathematical modelling of decision problems
    • Mixed integer programming and constraint programming approaches
    • Algorithms based on IBM CPLEX, Gurobi and Google OR Tools libraries
  • Big Data
    • Hadoop, hive, hbase, Presto, Storm, Spark
    • Extensive use of the above tools, including optimizing configuration and programmatically manipulating the above in Java, Python and Linux script
  • Machine Learning
    • Research on decision trees and feature generation (for time series data)
    • Large experience on online methods for very large datasets (vowpal wabbit)
    • Scikit-learn tooklit and the like
    • Integration scripts (connection to remote machines for data retrieval and analysis via APIs, mysql, posgresql, hbase, redis, mongodb, elasticsearch)
    • Outlier detection methods (for risk scoring and fraud detection)
    • Regression-based methods
    • Handling of imbalanced data
    • Part-of-speech tagging and information extraction (via Hidden Markov Chains)
    • Textual analysis of Social network data (e.g., ~100 million messages of Twitter)
  • Technical skills
    • Expert knowledge of Java, Python and C++, very good knowledge of R
    • Practical knowledge of efficient data structures and memory-reducing designs
    • Research work on the parallelization of algorithms (eg CUDA C++) and/or multithreading approaches in Java and C++
  • I am a modeller :