Mentor: Andre Schleife
Computational materials science research produces large amounts of static and time-dependent data for atomic positions and electron densities that is rich in information. Determining underlying processes and mechanisms from this data, and visualizing it in a comprehensive way, constitutes an important scientific challenge. In this project we will visualize electron-density data sets using isosurfaces and volume plots on the LookingGlass holographic display. Interaction with the data will be implemented using the LeapMotion device. We will explore both static and time-dependent visualizations for immersive movies. Experience with Unity and Blender as well as data visualization experience are a plus for this project.
Mentor: Sever Tipei
The project centers on DISSCO, software for composition, sound design and music notation/printing developed at Illinois and Argonne National Laboratory. Written in C++, it includes a graphical user interface using gtkmm, a parallel version is being developed at the San Diego Supercomputer Center. DISSCO has a directed graph structure and uses stochastic distributions, sieves (part of number theory) and elements of information theory to produce musical compositions. Presently, efforts are directed toward refining a system for the notation of music as well as to the realization of an evolving entity, a composition whose aspects change when computed recursively over long periods of time thus mirroring the way living organisms are transformed in time (artificial life).
Another possible direction of research is sonification, the aural rendition of computer generated complex data.
Skills desired: Proficiency in C++ programming, familiarity with Linux operation system, familiarity with music notation preferred but not required.
Mentor: Volodymyr Kindratenko
The Innovative Systems Lab (ISL) at NCSA is looking for a student interested in deploying and operating private cloud infrastructure based on OpenStack, RHEL OpenShift, Kubernetes or similar technologies. The student with work with ISL system engineers and C3SR researchers to develop and deploy innovative solutions to support hybrid cloud infrastructure research. The student is expected to have knowledge of Linux system administration, CLI, and Python. Knowledge of any open source or commercial cloud platforms is desirable.
Mentor: Volodymyr Kindratenko
The NCSA Center for AI Innovation in collaboration with the Innovative Systems Lab (ISL) at NCSA is looking for students interested in acceleration of machine learning algorithms on FPGAs and other unconventional architectures. The students will work with a team of other undergraduate and graduate students and a postdoc on several aspects of FPGA-based computing, ranging from machine learning frameworks integration with FPGA-based inference models to the development of HLS-based FPGA codes. The students are expected to have taken ECE 385 or similar class as well as an applied machine learning class. Knowledge of TensorFlow, PyTorch or any other open source platform for deep learning is desirable; knowledge of HLS design methodology is a plus. The students will become affiliates of the NCSA Center for AI Innovation, and will have access to FPGA systems at ISL and Xilinx Center of Excellence for Adaptive Computing at the Coordinated Systems Lab.
Mentor: Volodymyr Kindratenko
The NCSA Center for AI Innovation is looking for a student interested in the development of optimization techniques for reducing complexity of deep learning models. Previously we have developed a technique for network pruning carried out simultaneously with model training. This current project seeks to advance this technique by implementing it on new NVIDIA GPUs that have hardware support for sparse matrix operations. The student is expected to have taken ECE 408 or similar class as well as an applied machine learning class. Proficiency with TensorFlow, PyTorch or any other open source platform for deep learning is required. The student will become an affiliate of the NCSA Center for AI Innovation and will have access to GPU systems at the Innovative Systems Lab at NCSA.
Mentor: Volodymyr Kindratenko
The NCSA Center for AI Innovation is looking for a student interested in the development and implementation of machine learning models for recognizing human actions. We previously have developed models for human fall detection and aggression detection, and have implemented human fall detection model on RaspberryPI platform. The selected student will work on improving these models and developing new models and their implementations on low-power edge devices. The student is expected to have a good working knowledge of Python and C++. Knowledge of TensorFlow, PyTorch or any other open source platforms for deep learning is required as well. The student will become an affiliate of the NCSA Center for AI Innovation and will have access to advanced GPU hardware for model training.