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GRIPS

GRIPS (13)

G-RIPS Projects 2017 - Project - Satalia

Hosting Lab

A major aim of the Research Campus MODAL is the development and use of mathematical synergies between the individual labs of the network.  In this context, MODAL SynLab, the Synergy Laboratory, aims to generalize problem-specific solution algorithms developed in each laboratory and implement it in a structure-specific way.  At the core of these activities is the development of the SCIP Optimization Suite, a software package for solving constraint and mixed-integer nonlinear optimization problems to global optimality. Learning from the problem-specific successes in the application-specific labs, our main goal is to accelerate the existing solution algorithms and extend the class of problems that can be handled.  This way we create an optimization tool that can form a stronger basis for future research projects.

Sponsor

Satalia is an optimization solutions company. Based in London, they develop SolveEngine, a platform that aims to make optimization technologies more accessible to practitioners. The company also produces stand-alone optimization tools, which it uses in some of its consulting projects for customers worldwide. Across the company, they use a diverse set of exact and heuristic algorithms such as satisfiability solving, mixed-integer linear programming, and constraint programming, both in general and problem-specific implementations.  It is their declared mission to transfer cutting-edge optimization technology developed in academia to practice.  The G-RIPS project will be accompanied by their Berlin-based consultants.

Project

Satalia's SolveEngine interfaces to a variety of optimization algorithms.  A given optimization problem can often be solved by many of these algorithms, but their performance can vary widely in practice. Predicting the best-performing solver on-the-fly and under limited response time is an unsolved question.  The aim of this project is to investigate and compare different machine learning techniques in order to select well-performing algorithms from instance features that can be collected with limited computational effort. This will involve understanding various machine learning algorithms, the design of experiments, and the use of existing machine learning packages and own implementations. Practitioners from Satalia and mixed-integer programming solver developers from MODAL SynLab will provide support. Students will gain experience in the implementation and use of machine learning as a research tool and gain insight into the world of mixed-integer programming algorithms.

 

Requirements

We welcome applications from highly motivated team-players who ideally

  • have a background in mathematical optimization, computer science, and/or machine learning,
  • have experience in working in a Linux/Unix environment and collaborative work on source code (e.g. working with revision control systems),
  • have experience with the scripting language Python and a high-level programming language (e.g. C++),
  • are fluent in English (written and spoken),
  • and have a general curiosity to learn new research skills along the way.

 

 

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G-RIPS Projects 2017 - Project - JCMwave

Hosting Lab

The computational nanooptics group at ZIB investigates advanced numerical techniques for simulating the interaction of light and nanoscale objects. The numerical methods developed and investigated in the group include finite-element methods with h-, p-, and hp-adaptivity, reduced basis methods, discontinuous Galerkin methods, and others. Applications range from fundamental research in physics to device design in the optical and semiconductor industries. This includes topics like optimization of photovoltaic devices for improved conversion efficiency, optimization of nanophotonic devices for quantum optics applications like quantum cryptography, design of plasmonic nano-antennas for optical near-field sensing, computational lithography for the design of state-of-the-art photolithography masks for computer chip manufacturers, 3D metamaterial design, and other topics.

Sponsor

JCMwave GmbH is a ZIB spin-off which develops and provides state-of-the-art finite element software. Within the JCMwave infrastructures the students of this project will have the opportunity to work with the newest development versions of finite-element software, to discuss with the development team in regular meetings, and to get an insight to industrial nanotechnology design challenges.

Project

You will learn how to model and simulate nanophotonic setups. The underlying physical model is typically Maxwell’s wave equation in three spatial dimensions. A main challenge in such simulations is to obtain simulation results with upper bounds to numerical discretization errors within short computation times. Accurate and fast results are required e.g. for design optimizations in high-dimensional parameter spaces, and for parameter retrieval in optical metrology. For in-line applications in industrial quality control, speed and accuracy of parameter retrieval is currently a limiting factor to production speed. As shown in various benchmarks, the finite-element method is well suited to handle such computations, as its performance for highly accurate results can be orders of magnitude faster than competing methods. However, to further improve on its performance various properties of the method and of the models of interest can be exploited. These include higher-order vectorial finite elements, adaptive mesh refinement, hp-adaptivity, and automatic differentiation. This project will also concentrate on recent developments exploiting symmetries of the underlying models.

It is planned that the team members will specialize in the fields of mesh generation, finite element convergence and post-processing techniques, respectively. The team will then join the experiences from these fields to investigate methods for improved simulation efficiency, exploiting symmetries of modern nanophotonic devices. This includes validating results from automatic symmetry-detection methods.

The project should result in a comprehensive report to be presented at the end, and in a collection of Matlab- or Python-based automatic test routines. We expect that the report will meet high standards as we aim at a joint publication of the results in a peer-reviewed journal.

 

Requirements

We welcome applications from highly motivated team-players who ideally

  • have a background in physics, mathematics, computer science, or nano-technology,
  • have experience in high-level scripting languages like Matlab or Python,
  • have attended classes in optics, photonics, electromagnetism, or related topics,
  • are fluent in English (written and spoken).

 

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G-RIPS Projects 2017 - Project - 1000shapes

Hosting Lab

Within MedLab - and especially the Therapy Planning group at ZIB - we are dealing with a variety of medical and anthropometric data. To tackle the challenges of analyzing an always increasing amount of data and to provide software tools to automatically extract the relevant information out of it, we are investigating model based approaches (statistical shape and appearance models) as well as machine learning techniques (regression, classification, and semi-supervised learning), which can then be used in a number of applications such as scene recognition from photographs, object recognition in images, parameter estimation, classification, up to automatic diagnosis from medical image data.

Sponsor

The project is in close collaboration with 1000shapes GmbH, a ZIB spin-off that transfers research in life sciences into products for clinical applications. Within the project, algorithms are to be identified, developed/implemented, compared, and tested on data from various application domains. The successful applicant will have the opportunity to perform research in medical image computing and geometry processing within the ZIB research group Therapy Planning while obtaining professional support from 1000shapes in software development with insights into relevant applications. Within the project, students will have the opportunity to experience medical research under professional supervision in combination with industry-strength software development, with the goal of practical solutions and publication.

Project

Building on large medical image as well as a anthropometric 3D face databases, students will have the opportunity to investigate machine-learning approaches, i.e. deep learning, or the application of regression forests, to identify, analyze, and classify features or patterns based on medical images or geometric models.

Background:

Several databases are the foundation for possible investigations: (1) The OAI database of the Osteoarthritis Initiative (OAI), which is a multi-center, longitudinal, prospective observational study of knee osteoarthritis, providing clinical evaluation data, radiological (x-ray and magnetic resonance) images, and a bio-specimen repository from over 5000 patients. This information has great potential, both for developing a better understanding of disease onset and progression, as well as improving future therapeutic concepts; (2) A database of several hundreds of 3D face models from various individuals and with varying facial expressions, providing information for anthropometrical studies or psychological experiments; (3) A huge collection of dental 3D image data, where bony structures, nerves, and teeth are to be segmented, anatomical relationships are to be analyzed, and suitable shape and appearance models are to be developed; (4) A database of human spines giving the opportunity to study the morphology of single vertebrae up to the complete spine as well as the functional performance within the context of biomechanics and orthopedic research. The processing of such databases requires automated image and geometry processing as well as sophisticated data analysis approaches.

Challenges: For medical image processing, the challenge is to automatically extract anatomical shape and appearance information from image data, as well as to integrate this information in so called statistical 3D shape and appearance models to train and improve automated algorithms. For geometric data the challenge is to improve methods for determining correspondences, to analyze variation in shape, to establish suitable metrics for measuring similarities in various shape spaces, for clustering and population based analysis. To this end, machine learning combined with model-based approaches shall be employed and adopted.

Example project: Establishing correspondences between shapes lies at the core of many operations in image analysis and geometry processing. The majority of existing methods formulate the matching problem as finding optimal pairings of points or regions on shapes. This representation, however, renders the matching intractable as the space of possible point correspondences grows exponentially and does not naturally support constraints such as map continuity or global consistency. Within a possible project, we will investigate a recent alternative approach that generalizes the notion of correspondences to mappings between real-valued functions on the shapes rather than the standard point-to-point maps. One challenge that we will address is the adaptation of this functional maps framework to the matching of volumetric geometries from 3D medical image data. Based on this, we will further derive a scheme for inferring group-wise correspondences that takes advantage of the context provided by the collection of shapes.

Based on the aforementioned background several topics in medical image and geometry processing are conceivable. Results of our research may become a basis for improving existing segmentation, classification, and diagnosis algorithms that are currently under development by 1000shapes.

Requirements

The prospective participant should:

  • have a background in computer science, bio-engineering, mathematics or physics,
  • preferably have experience with collaborative code development in C++ on Windows or Linux,
  • have attended classes in the area of machine learning, computer vision, image processing or statistics,
  • preferably be acquainted with software for processing large medical image datasets,
  • have fun working in teams, both with colleagues from academia and industry,

be able to communicate fluently in English

 

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G-RIPS Projects 2017

The sponsors and projects for 2017 include:

1. JCMwave GMBH (Nanotechnology)

Company: JCMwave GmbH is a ZIB spin-off which develops and provides state-of-the-art finite element software. Within the JCMwave infrastructures the students of this project will have the opportunity to work with the newest development versions of finite-element software, to discuss with the development team in regular meetings, and to get an insight to industrial nanotechnology design challenges.

Project: You will learn how to model and simulate nanophotonic setups. The underlying physical model is typically Maxwell’s wave equation in three spatial dimensions. A main challenge in such simulations is to obtain simulation results with upper bounds to numerical discretization errors within short computation times.

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2. Satalia / NPcomplete Ltd. (Data Science):

Company: Satalia is an optimization solutions company develping SolveEngine, a platform that aims to make optimization technologies more accessible to practitioners. The company also produces stand-alone optimization tools, which it uses in some of its consulting projects for customers worldwide. Across the company, Satalia uses a diverse set of exact and heuristic algorithms such as satisfiability solving, mixed-integer linear programming, and constraint programming, both in general and problem-specific implementations. It is their declared mission to transfer cutting-edge optimization technology developed in academia to practice.

Project: Satalia's SolveEngine interfaces to a variety of optimization algorithms.  A given optimization problem can often be solved by many of these algorithms, but their performance can vary widely in practice. Predicting the best-performing solver on-the-fly and under limited response time is an unsolved question.  The aim of this project is to investigate and compare different machine learning techniques in order to select well-performing algorithms from instance features that can be collected with limited computational effort.

Read more...

3. 1000shapes (Therapy Planning):

Company: The project is in close collaboration with 1000shapes GmbH, a ZIB spin-off that transfers research in life sciences into products for clinical applications. Within the project, algorithms are to be identified, developed/implemented, compared, and tested on data from various application domains. The successful applicant will have the opportunity to perform research in medical image computing and geometry processing within the ZIB research group Therapy Planning while obtaining professional support from 1000shapes in software development with insights into relevant applications. Within the project, students will have the opportunity to experience medical research under professional supervision in combination with industry-strength software development, with the goal of practical solutions and publication.

Project: Building on large medical image as well as a anthropometric 3D face databases, students will have the opportunity to investigate machine-learning approaches, i.e. deep learning, or the application of regression forests, to identify, analyze, and classify features or patterns based on medical images or geometric models.

Read more...

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G-RIPS Projects 2016 - Project - 1000shapes

Hosting Lab

Within the therapy planning group at ZIB we are dealing with a variety of medical data. To tackle the challenges of analyzing an always increasing amount of data and to provide software tools to automatically extract the relevant information out of it, we are investigating model based approaches (statistical shape and appearance models) as well as machine learning techniques (regression, classification, and semi-supervised learning), which can then be used in a number of applications such as scene recognition from photographs, object recognition in images, or automatic diagnosis from medical image data.

Sponsor

The project is in close collaboration with 1000shapes GmbH, a ZIB spin-off that transfers research in life sciences into products for clinical applications. Within the project, algorithms are to be developed within an existing software framework and tested on clinical image data. The successful applicant will have the opportunity to perform research in medical image computing within the ZIB research group therapy planning while obtaining professional support from 1000shapes in software development and implementing algorithms within existing software frameworks. Within the project, students will have the opportunity to experience medical research in combination with industry-strength software development.

Project

Building on a large medical image database, students will investigate new machine-learning techniques, i.e. the application of regression forests, to analyze and classify features or disease patterns in medical image data.

Background:

Osteoarthritis (OA) is the most common form of arthritis and the major cause of activity limitation and physical disability in older people. Today, 35 million people (13 percent of the U.S. population) are 65 and older, and more than half of them have radiological evidence of osteoarthritis in at least one joint. By 2030, 20 percent of Americans (about 70 million people) will have passed their 65th birthday and will be at risk for OA. The Osteoarthritis Initiative (OAI) is a multi-center, longitudinal, prospective observational study of knee osteoarthritis (KOA), providing a database for osteoarthritis that includes clinical evaluation data, radiological (x-ray and magnetic resonance) images, and a bio-specimen repository from over 5000 patients. Radiological images are a rich source of information of both anatomical and physiological properties of the human body. This information has great potential both for developing a better understanding of disease onset and progression, as well as improving future therapeutic concepts. However, such vast amounts of data require automated image processing approaches.

Challenges: The overall goal is to automatically extract anatomical shape and appearance (image intensity) information such as bone, cartilage, tendons and muscle tissues from the radiological images. To this end, machine learning combined with model-based approaches shall be adopted. In particular, the random forest regression voting method has been shown to be very successful in localizing landmarks in 2D x-ray images. However, this approach is computationally quite expensive both in terms of CPU time and particularly memory consumption. Furthermore, 3D magnetic resonance images have a significantly larger range of variation in intensity due to their acquisition process.

Aim: The goal of this project is to evaluate existing implementations of the random forest regression voting methods and to develop and evaluate own implementations of it for 3D shape detection in magnetic resonance tomography data. Results of this work may become a basis for improving existing segmentation, classification, and diagnosis algorithms that are currently under development or even in use by 1000shapes.

Requirements

The prospective participant should:

  • have a background in computer science, bio-engineering, mathematics or physics,
  • have experience with collaborative code development in C++ on Windows or Linux,
  • have attended classes in the area of machine learning, computer vision, image processing or statistics,
  • be acquainted with software for processing large medical image datasets,
  • have fun working in teams, both with colleagues from academia and industry,
  • be able to communicate fluently in English
Read more...

G-RIPS Projects 2016 - Project - BAHN

Hosting Lab

The MODAL:RailLab cooperates with DB Fernverkehr to develop an optimization core that helps to operate the Intercity-Express (ICE), Germany's fastest and most prestigious train, in the most efficient way. This is achieved by determining how the ICEs should rotate within Germany and, thereby, reducing the number of empty trips. The software has now been deployed in production at DB Fernverkehr for two years.

Sponsor

Deutsche Bahn (DB) is Germany's major railway company. It transports on average 5.4 million customers every day over a rail network that consists of 33,500 km of track, and 5,645 train stations. DB operates in over 130 countries world-wide. It provides its customers with mobility and logistical services, and operates and controls the related rail, road, ocean and air traffic networks.

Project

You will learn to think about the railway network at DB from a planner's perspective. Making up ICE rotations sounds easy at first, but you will soon find out that a lot of constraints have to be taken into account and do not forget about the size of Germany's rail network! This makes finding and understanding suitable mathematical programming models a difficulty of its own. It will be your daily business to deal with huge data sets. You will write scripts to process the data and extract useful information. At your option you can come up with your own ideas and propose and implement extensions for our optimization core. The past project assignments included to find out how robust optimization methodology can be incorporated in the optimization process and to develop a rotation plan for the situation that a restricted amount of train conductors is available, e.g. in a strike scenario.

The program lasts for 8 weeks and should result in a comprehensive report which has to be presented at the end. It requires a high commitment, even more so if knowledge gaps have to be closed along the way.

Requirements

The prospective participant should:

  • have a good command of a high-level programming language (preferably C++) and experience in writing scripts, e.g. in Python or Shell,
  • have attended classes in the area of combinatorial optimization, linear and integer programming or acquired the foundations of this field by some other means
  • be prepared to work with huge datasets from industry partners (which involves cleaning and preprocessing to overcome inconsistencies and incompleteness).

Ideally he or she

  • is familiar with procedures in the area of rail traffic and/or logistics,
  • has experience in working in a Linux/Unix environment and
  • collaborative work on source code (e.g. working with revision control systems).
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G-RIPS Projects 2016 - Project - MODAL AG

Hosting Lab

Changes in cells while they are undergoing transformation from "normal" to malignant cells (e.g. during infections) happen on many biological levels, such as genome, transcriptome, proteome and metabolome. Following the central dogma of molecular biology and its extensions these levels are highly interconnected and depend on each other. Within the MODAL:MedLab, we develop new mathematical methods that allow (1) identification of multivariate disease signatures that describe changes in multiple data-sources and (2) development of multi-level models that embeds these findings into the actual biological context. Both parts combined will eventually lead to a thorough understanding of the modeled process and open up the opportunity to use the respective model for diagnostic purposes for individuals, thus allowing high-throughput classification of biological samples. These techniques can then be adjusted to an individual by using its -omics data and thus allows to derive information about the individual's state, for example, as a diagnostic tool for a certain disease that is captured by the data and the model. All algorithms will be implemented using state-of-the art software frameworks that can cope with the very large data volumes.

Sponsor

The MODAL AG (MAG) is a ZIB spin-off that works as a bridge between research and industry. MAG offers the students in this project access to real world data and expertise from leading hospitals and companies working in this field. Within the MAG infrastructure, students will have the opportunity to experience creation of industry-strength technology and software solutions.

Project

Building on state-of-the-art database technology, students will develop new machine-learning techniques to analyze medical massive data sets. First, students will learn the necessary biological foundation needed to successfully complete the project. They will then use data from a large clinical trial to model medical phenomena based on ideas from the areas of compressed sensing, machine learning, and network-of-networks theory.

Background: Tumor diseases rank among the most frequent causes of death in Western countries coinciding with an incomplete understanding of the underlying pathogenic mechanisms and a lack of individual treatment options. Hence, early diagnosis of the disease and early relapse monitoring are currently the best available options to improve patient survival. This calls for two things: (1) identification of disease specific sets of biological signals that reliably indicate a disease outbreak (or status) in an individual. We call these sets of disease specific signals fingerprints of a disease. And (2), development of new classification methods allowing for robust identification of these fingerprints in an individual's biological sample. In this project we will use -omics data sources, such as proteomics or genomics. The advantage of -omics data over classical (e.g. blood) markers is that for example a proteomics data set contains a snapshot of almost all proteins that are currently active in an individual, opposed to just about 30 values analyzed in a classical blood test. Thus, it contains orders of magnitudes more potential information and describes the medical state of an individual much more precisely. However, to date there is no gold-standard of how to reliably and reproducible analyze these huge data sets and find robust fingerprints that could be used for the ultimate task: (early) diagnostics of cancer.

Problems and (some) hope: -omics data is ultra high-dimensional and very noisy - but only sparsely filled with information: Biological -omics data (e.g. proteomics or genomics data) is typically very large (millions of dimensions), which increases the complexity of algorithms for analyzing the parameter space significantly or makes them even infeasible. At the same time, this data exhibits a very particular structure, in the sense that it is highly sparse. Thus the information content of this data is much lower than its actual dimension seems to suggest, which is the requirement for any dimension reduction with small loss of information.

However, the sparsity structure of this data is highly complex, since not only do the large entries exhibit a particular clustering with the amplitudes forming Gaussian-like shapes, but also the noise affecting the signal is by no means Gaussian noise -- a customarily assumed property. In addition, considering different sample sets, those clusters also slightly differ in the locations from sample set to sample set, hence also do not coincide with normal patterns such as joint sparsity. This means, although the data is highly sparse, the sparsity structure as well as the noise distribution is non-standard. However, specifically adapted automatic -- without cumbersome by-hand-identification of significant values -- dimension reduction strategies such as compressed sensing have actually never been developed for instance for proteomics data. In our project, such a dimension reduction step will be a crucial ingredient and shall precede the analysis of parameter space, thereby then enabling low complexity algorithms.

The major challenge in these applications is to extract a set of features, as small as possible, that accurately classifies the learning examples.

The goal: In this project we aim to develop a new method that can be used to solve this task: the identification of minimal, yet robust fingerprints from very high-dimensional, noisy -omics data. Our method will be based on ideas from the areas of compressed sensing and machine learning.

Requirements

The prospective participant should:

  • have a background in mathematics, bioinformatics or computer science,
  • have experience with a high-level programming language (e.g. C++ or Java) and a statistical software package such as SPSS or R, have attended classes in the area of data mining or acquired the foundations of this field by some other means be prepared to work with very large datasets from industry partners (which involves preprocessing, e.g. to overcome inconsistencies and incompleteness).
  • Ideally he or she is familiar with the biological background and has already worked with biological data-sets, has experience in working in a Linux/Unix environment and collaborative work on source code (e.g. working with revision control systems).
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G-RIPS Projects 2016

The sponsors and projects for 2016 include:

1. MODAL AG (Biotechnology)

Company: The MODAL AG (MAG) is a ZIB spin-off that works as a bridge between research and industry. MAG offers the students in this project access to real world data and expertise from leading hospitals and companies working in this field. Within the MAG infrastructure, students will have the opportunity to experience creation of industry-strength technology and software solutions.

Project: Building on state-of-the-art database technology, students will develop new machine-learning techniques to analyze medical massive data sets. First, students will learn the necessary biological foundation needed to successfully complete the project. They will then use data from a large clinical trial to model medical phenomena based on ideas from the areas of compressed sensing, machine learning, and network-of-networks theory.

Read more...

2. Deutsche Bahn (Public Transport):

Company: Deutsche Bahn (DB) is the main German railway company. It transports on average 5.4 million customers every day over a rail network that consists of 33,500 km of track, and 5,645 train stations. DB operates in over 130 countries world-wide. It provides its customers with mobility and logistical services, and operates and controls the related rail, road, ocean and air traffic networks.

Project: You will learn to think about the railway network at DB from a planner's perspective. Making up ICE rotations sounds easy at first, but you will soon find out that a lot of constraints have to be taken into account and do not forget about the size of Germany's rail network! This makes finding and understanding suitable mathematical programming models a difficulty of its own. It will be your daily business to deal with huge data sets.

Read more...

3. 1000shapes (Therapy Planning):

Company: The project is in close collaboration with 1000shapes GmbH, a ZIB spin-off that transfers research in life sciences into products for clinical applications. Within the project, algorithms are to be developed within an existing software framework and tested on clinical image data. The successful applicant will have the opportunity to perform research in medical image computing within the ZIB research group therapy planning while obtaining professional support from 1000shapes in software development and implementing algorithms within existing software frameworks. Within the project, students will have the opportunity to experience medical research in combination with industry-strength software development.

Project: Building on a large medical image database, students will investigate new machine-learning techniques, i.e. the application of regression forests, to analyze and classify features or disease patterns in medical image data.

Read more...

Read more...

G-RIPS Projects 2015 - SAP Project

Background

Tumor diseases rank among the most frequent causes of death in Western countries coinciding with an incomplete understanding of the underlying pathogenic mechanisms and a lack of individual treatment options. Hence, early diagnosis of the disease and early relapse monitoring are currently the best available options to improve patient survival. This calls for two things: (1) identification of disease specific sets of biological signals that reliably indicate a disease outbreak (or status) in an individual. We call these sets of disease specific signals fingerprints of a disease. And (2), development of new classification methods allowing for robust identification of these fingerprints in an individual's biological sample. In this project we will use -omics data sources, such as proteomics or genomics. The advantage of -omics data over classical (e.g. blood) markers is that for example a proteomics data set contains a snapshot of almost all proteins that are currently active in an individual, opposed to just about 30 values analyzed in a classical blood test. Thus, it contains orders of magnitudes more potential information and describes the medical state of an individual much more precisely. However, to date there is no gold-standard of how to reliably and reproducible analyze these huge data sets and find robust fingerprints that could be used for the ultimate task: (early) diagnostics of cancer.

Problems and (some) hope: -omics data is ultra high-dimensional and very noisy - but only sparsely filled with information

Biological -omics data (e.g. proteomics or genomics data) is typically very large (millions of dimensions), which increases the complexity of algorithms for analyzing the parameter space significantly or makes them even infeasible. At the same time, this data exhibits a very particular structure, in the sense that it is highly sparse. Thus the information content of this data is much lower than its actual dimension seems to suggest, which is the requirement for any dimension reduction with small loss of information.

However, the sparsity structure of this data is highly complex, since not only do the large entries exhibit a particular clustering with the amplitudes forming Gaussian-like shapes, but also the noise affecting the signal is by no means Gaussian noise -- a customarily assumed property. In addition, considering different sample sets, those clusters also slightly differ in the locations from sample set to sample set, hence also do not coincide with normal patterns such as joint sparsity. This means, although the data is highly sparse, the sparsity structure as well as the noise distribution is non-standard. However, specifically adapted automatic -- without cumbersome by-hand-identification of significant values -- dimension reduction strategies such as compressed sensing have actually never been developed for instance for proteomics data. In our project, such a dimension reduction step will be a crucial ingredient and shall precede the analysis of parameter space, thereby then enabling low complexity algorithms.

The major challenge in these applications is to extract a set of features, as small as possible, that accurately classifies the learning examples.

The goal

In this project we aim to develop a new method that can be used to solve this task: the identification of minimal, yet robust fingerprints from very high-dimensional, noisy -omics data. Our method will be based on ideas from the areas of compressed sensing and machine learning.

Read more...

G-RIPS Application

Who can apply?

Eligible applicants include master and PhD students from the areas of mathematics, computer or natural sciences who are currently enrolled at an European university. Due to the large number of applications we typically receive, we do not accept applications from previous RIPS or G-RIPS students.

How to apply

Please use our application platform which can be found here: http://math-jobs-berlin.de/job/35-g-rips (use the "Apply for the job" button at the bottom of the page)

You will need the following documents :

  1. motivation letter,
  2. resume,
  3. an academic record or transcript (unofficial).

Further, one or two letter(s) of recommendation needs to be send to this email address directly by the person who wrote that letter: grips[at)forschungscampus-berlin(dot]de 
 

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