(INCORP W/RIPS LA 2020) Graduate-level Research in Industrial Projects for Students (G-RIPS) – Berlin 2020

June 22 - August 14, 2020

Sponsors and Projects

The sponsors and projects for 2020 include:


Project 1: Cray Germany GmbH

SPONSOR: Cray Germany GmbH
TITLE: Code Analysis for high performance computing (HPC)
SUB-TITLE: Automatic extraction of Polyhedral Static Control Parts (SCoPs) for the F18 compiler

Polyhedral compilation encompasses the compilation techniques that rely on the representation of programs, especially those involving nested loops and arrays, thanks to parametric polyhedra or Presburger relations, and that exploit combinatorial and geometrical optimizations on these objects to analyze and optimize the programs. Initially proposed in the context of compilers-parallelizers, it is now used for a wide range of applications, including automatic parallelization, data locality optimizations, memory management optimizations, program verification, communication optimizations, SIMDization, code generation for hardware accelerators, high-level synthesis, etc. There has been experience in using such techniques in static compilers, just-in-time compilers, as well as DSL compilers. The polyhedral research community has a strong academic background, but more and more industry users start to adapt such technologies as well.

Many scientific codes have a strong Fortran code base, and have core regions that are very well suited to polyhedral compilation techniques. However, automatic tools to extract these regions at the source level are lacking. This project will explore integration of the pet (http://pet.gforge.inria.fr/) polyhedral extraction tool into the F18 (https://github.com/flang-compiler/flang/wiki) Fortran compiler, the upcoming high performance Fortran compiler for the LLVM compiler family.


Project 2: Deloitte GmbH 

SPONSOR: Deloitte GmbH
TITLE: Eplainable Machine Learning

The goal of this project is to assess the interpretability of machine learning techniques in the context of time series forecasts. Based on real sales data, time series patterns need to be analyzed in order to achieve adequate forecasts. Based on both statistical and machine learning approaches, competing forecasting models will be investigated. As a result, the applied approaches will be compared with respect to accuracy, complexity and interpretability.

Additionally, the results will be visualized and current techniques of interpretable Machine Learning will be discussed. You will work together with experienced data scientists and you will learn in this project how to visualize and to communicate results within a heterogeneous team. Furthermore, you will get deep insights into the challenges that practitioners are facing, especially when the achieved solutions to such real-world problems need to be implemented into applied risk management processes.


Project 3: 1000shapes GmbH

SPONSOR: 1000shapes GmbH
TITLE: Towards an understanding of Anaphylaxis

An Anaphylactic shock is a severe allergic reaction which is in most cases caused by food, insect bites or drugs. Individuals experiencing an Anaphylactic shock usually collapse due to a dramatic drop of blood pressure which leads to oxygen under-supply in the cells. If not treated immediately, this can lead to death. Unfortunately, still many reasons for getting an Anaphylactic shock are unknown.

Within this project we will analyze a medical database containing information about Anaphylaxis episodes. The goal is to work towards a better understanding of individual risk factor that can be used for personalized risk estimation of an Anaphylactic shock and the medical conditions that might lead to it. We will use, adjust and extend state-of-the-art machine learning algorithms to extract and understand possible correlations from the given database and will compare to and enrich them with data from literature and other available medical sources.