Research Projects

Project 1: Computational Materials Science: Multi-Scale Simulations and Machine Learning

Co-mentors

Andre Schleife

Andre Schleife
Materials Science and Engineering

Andrew Ferguson

Andrew Ferguson
Materials Science and Engineering

Social impact

Provide sophisticated, yet intuitive and user-friendly, visualization for effective materials data analysis and data dissemination to a broad scientific audience and the general public.

Project description

Modern computational materials science uses sophisticated simulation techniques to study properties of advanced and complex materials, including biomolecules, condensed-matter crystals, and polymers. At the same time, bridging length and time scales from atomistic resolution to actual samples is an important challenge. In this project, we aim to combine atomistic simulations and Maxwell modeling techniques, to accurately describe nano- and meso-structured materials. These simulations are computationally challenging and, while they yield accurate results, their high computational cost renders it difficult to apply them for high-throughput materials design.

By extracting data from these simulations, collecting that data in well-structured databases using modern materials schemas, and establishing connections to underlying structural descriptors, we aim to leverage supervised machine-learning techniques to significantly accelerate the materials design process. Working towards this goal, students will use existing and generate new data for complex structures, using Maxwell modeling. They will develop an open-source tool that interfaces an external Maxwell solver with the scikit-learn Python-based machine-learning library to perform supervised machine learning and guided materials design and discovery. In order to disseminate results to a broad scientific audience and the general public, using accurate yet intuitive visualization, students will have the opportunity to develop codes based on the open-source ray-tracer Blender/LuxRender and the open-source yt framework to produce image files and movies that are compatible with virtual and mixed reality viewers such as Google Cardboard or Windows Mixed Reality. View examples for possible outcomes.

Project 2: Data Storage and Analysis Framework for Semiconductor Nanocrystals Used in Bioimaging

Co-mentors

Andre Schleife

Andre Schleife
Materials Science and Engineering

Michal Ondrejcek

Michal Ondrejcek
NCSA

Social impact

By exploring systematic exchange of data and workflows, this project provides insights and best practices for the future of collaborative research. Providing access to sample specific data and analysis to the international community will accelerate deployment of novel semiconductor nanocrystals for bioimaging.

Project description

Light-emitting molecules are a central technology in biology and medicine that provide the ability to optically tag proteins and nucleic acids that mediate human disease. In particular, fluorescent dyes are a key part of molecular diagnostics and optical imaging reagents. We recently made major breakthroughs in engineering fluorescent semiconductor nanocrystals to increase the number of distinct molecules that can be accurately measured, far beyond what is possible with such organic dye molecules. We aim to develop nanocrystals that are able to distinguish diseased from healthy tissue and determine how the complex genetics underlying cancer respond to therapy, using measurement techniques and microscopes that are already widely accessible.

In order to achieve this goal, we need to understand a complex design space, that includes size, shape, composition, and internal structure of the different nanocrystals. To this end, we have started implementing a database that stores and catalogs optical properties and other relevant data describing semiconductor nanocrystals. Students in this team will work with computational and experimental researchers in several departments in order to turn this data into descriptors that are useful and efficient in the context of machine-learning. Schemas will be extended accordingly and the web interface will be improved such that data and analysis workflows can be efficiently shared between multiple researchers.

Students will first test the current descriptors and then implement improvements based on these tests. The framework will be interfaced with Globus and the Materials Data Facility and their underlying work flows. Students will also develop code that automatically analyzes data stored in the facility, e.g. to verify and validate experimental and computational results against each other. This project is highly interdisciplinary and students will work with a team of researchers in bioengineering, materials science, mechanical engineering, and NCSA.

Project 3: Modeling and Detection of Black Hole Collisions with the Blue Waters Supercomputer

Co-mentors

Gabrielle Allen

Gabrielle Allen
Astronomy/Education

Roland Haas

Roland Haas
NCSA

Eliu Huerta

Eliu Huerta
NCSA

Social impact

By developing open source software to further scientific community efforts to detect gravitational waves, students will learn skills that they can use to tackle grand computational challenges across science domains, including those with broad social benefits.

Project description

The Laser Interferometer Gravitational-Wave Observatory's (LIGO) detection of gravitational waves from merging black holes in September 2014 inaugurated a new era in astronomy and astrophysics, opening a window to observe the Universe through gravitational radiation. Occurring 100 years after Einstein's announcement of his theory of general relativity, the detection spurred world-wide interest in physics and science in general, making headline news around the world. The recent Nobel Prize awarded for this detection and the announcement of the detection of the double binary neutron star system by LIGO/Virgo underline the importance of these efforts and the interest that the wider society has in it.

In this project, a pair of REU-INCLUSION students will write Python/C libraries to extract information from numerical relativity simulations that describe mergers of black holes and neutron stars. Through this work the students will become familiar with one of the more exciting research topics in contemporary astronomy, and this work will provide them with new tools to study phenomena across science domains that require high performance environments. These simulations will also be used to create scientific visualizations for outreach purposes.

Project 4: Visualizing and Preserving Environmental Data for Improved Governance

Co-mentors

Anita Say Chan

Anita Say Chan
Media and Cinema Studies

Ben Grosser

Ben Grosser
School of Art & Design

Social impact

Supporting current environmental data justice initiatives that hold governments and companies accountable for the environmental damage their policies and actions cause, and attend to how these oversights impact marginalized communities disproportionately.

Project description

Project 5: Intelligent Synthesis: Statistical Learning to Optimize Graphene Synthesis Parameters for Nanomanufacturing

Co-mentors

Elif Ertekin

Elif Ertekin
Mechanical Science and Engineering

Sameh Tawfick

Sameh Tawfick
Mechanical Science and Engineering

Placid Ferreira

Placid Ferreira
Mechanical Science and Engineering

Social impact

Provide sophisticated data driven approaches to enable high quality, reproducible synthesis of nanomaterials for use as manufactured components in nanoelectronics.

Project description

Material synthesis is a primary bottleneck in emerging nanoelectronic devices. The promise of nanoelectronics will not become a reality unless the synthesis process is scalable and leads to high quality, reproducible materials. Although there exists a tremendous amount of academic and industry research in synthesis, most advancements in synthesis science are achieved by expensive and tedious trial and error approach.

In this project we will go beyond the traditional trial and error approach by adopting a data-driven methodology to rapidly optimize the chemical vapor deposition synthesis of graphene and other emerging 2D nanomaterials. They key aspects include building and populating a large database of synthesis parameters and results, and implementing a system for automated data capture and extraction from actual growth experiments in real time. The database will be populated by both experiments carried out at Illinois and via crowd-sourcing from research groups around the world.

Students will develop and populate the 2D materials synthesis database, and will implement tools that allow users to access and analyze the contents of the database. They will also explore the optimization of growth parameters by implementing python-based libraries for supervised machine learning. Students will also develop a configurable system for automated data collection from nanofabrication tools during growth experiments in real time. Experimental parameters will be pushed to a cloud server so that they can be curated and served to computational models. Educational video tutorials on the synthesis database and the machine learning approach will be developed.

Project 6: Optimization of Open-Source Software for Deep Learning

Co-mentors

Volodymyr Kindratenko

Volodymyr Kindratenko
Electrical and Computer Engineering

William Gropp

William Gropp
Computer Science

Social impact

Contributing to the advancement of machine learning, which is at the core of many modern approaches to solve real-world problems in the fields ranging from education to healthcare to engineering to core sciences.

Project description

Deep neural networks are at the core of artificial intelligence, machine learning, computer vision, and other advanced applications across many disciplines. Such networks allow computers to "learn and infer" rather than "compute," which is essential for many problems in which models that describe the data are multi-dimensional, non-linear, and generally are too complex for traditional mathematical techniques. Many deep learning frameworks have been developed over the course of past decade providing advanced neural network construction, training, and inference functionality. However, vast majority of these codes have been developed for a single compute node execution, which precludes them from training complex network models using large datasets in acceptable time. The challenge is to redesign existing or develop new frameworks that can take advantage of heterogeneous computing platforms to speed up the network training tasks while providing easy to use programming abstractions for domain scientists.

In this project, students will analyze open-source deep learning software frameworks and will work on optimizing them and removing bottlenecks in order to improve performance of the applications relying on these codes. Students will be expected to contribute their changes back to these codes, as well as making new codes open sourced. The project will contribute to the development of NSF-funded computer system for deep learning and will result in open-source software that will be deployed on this system. Students will learn about parallel programming systems such as MPI, OpenMP, and OpenCL, how to study their performance, and techniques for improving that performance.

 


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