Dr. Snehamoy Chatterjee (GMES/ICC-DataS) is the principal investigator (PI) on a project that has received a $288,343 research and development contract from the Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health (NIOSH).
The project is titled “Mine Health and Safety Big Data Analysis and Text Mining by Machine Learning Algorithms.”
Aref Majdara (ECE/ICC) is a co-PI on this potential two-year project.
Timothy Schulz (ECE/ICC-DataS) is the principal investigator (PI) on a project that has received a $30,000 research and development grant from the University of Arizona.
The project is titled “Multiscale Phase Retrieval.”
Starting Monday, August 30, 2021, and during the fall semester Michigan Tech will offer free, walk-in COVID-19 testing on campus for students and employees. Testing takes place from 9:00 a.m. to noon on Mondays, Wednesdays, and Thursdays in the Datolite Room at the MUB.
No appointments are needed for walk-in testing, and tests are available to any student or employee, whether they have COVID-19 symptoms or not.
If you would like to be tested, come to the Datolite Room during testing hours and ask for a test. Your results should be available in approximately 24 hours, and will be emailed to you.
If you have a question about COVID-19 testing at Michigan Tech, check the MTU Flex Testing web page for up-to-date information and answers to frequently asked questions, or email mtuflex@mtu.edu.
Dr. Sidike Paheding, Applied Computing, is seeking a fall 2021 graduate student to assist with a research project. Details are below.
- Pay rate: $15/hour, 10 hours/week
- Employment dates: September 2021 through December 2021. An extension is possible.
- Basic requirements: Experience in Unity development platform and Virtual/Augmented Reality.
- Desired: Knowledge in machine learning/AI is a plus.
Sidike Paheding, Ph.D.
spahedin@mtu.edu
EERC 417
Assistant Professor, Dept. of Applied Computing
Affiliated Assistant Professor, Dept. of Computer Science
Affiliated Faculty, Center for Data Sciences, and Computational Science & Engineering
College of Computing, Michigan Technological University
by Office of Innovation and Commercialization
For several years, Michigan Tech has partnered with the State of Michigan and other stakeholders to create an entrepreneurial and innovation ecosystem. Members of the community at large can participate in this process at an event on the Michigan Tech campus.
Michigan Tech hosts one of five hubs that make up the Michigan Translational and Research Commercialization (MTRAC), funded by the state’s Michigan 21st Century Jobs fund through the Michigan Strategic Fund. MTRAC-supported projects have secured more than $315 million in follow-on funding.
Join us at noon on September 10, 2021 in GLRC 202 to hear directly from the program directors of each hub to learn about program requirements and what makes for a competitive proposal. Directors will have a few appointments on a first come, first serve availability following the seminar for one-on-one meetings with prospective principal investigators.
MTRAC provides matching funds for researchers to accelerate the transfer of new technologies from universities, hospital systems, and nonprofit research centers into the commercial market. Funding is available under any of the five statewide hub programs organized around the following technology areas:
- Ag Bio Innovation Hub (managed by Michigan State University)
- Life Sciences Innovation Hub (managed by the University of Michigan)
- Advanced Transportation Innovation Hub (managed by University of Michigan)
- Advanced Materials Innovation Hub (managed by Michigan Tech)
- Advanced Computing Innovation Hub (managed by Wayne State University)
Prospective entrepreneurs will learn about moving technology from lab to market. Program objectives, goals and scope will be discussed by representatives from the five MTRAC hubs and representatives from the Michigan Economic Development Corporation (MEDC).
August 2-6, 2021. PinT 2021 will be offered in a virtual-format.
Register online on the Registration Page.
Computer models and simulations play a central role in the study of complex systems in engineering, life sciences, medicine, chemistry, and physics. Utilizing modern supercomputers to run models and simulations allows for experimentation in virtual laboratories, thus saving both time and resources. Although the next generation of supercomputers will contain an unprecedented number of processors, this will not automatically increase the speed of running simulations. New mathematical algorithms are needed that can fully harness the processing potential of these new systems. Parallel-in-time methods, the subject of this workshop, are timely and necessary, as they extend existing computer models to these next generation machines by adding a new dimension of scalability. Thus, the use of parallel-in-time methods will provide dramatically faster simulations in many important areas, such as biomedical applications (e.g., heart modeling), computational fluid dynamics (e.g., aerodynamics and weather prediction), and machine learning. Computational and applied mathematics plays a foundational role in this projected advancement.
The primary focus of the proposed parallel-in-time workshop is to disseminate cutting-edge research and facilitate scientific discussions on the field of parallel time integration methods. This workshop aligns with the National Strategic Computing Initiative (NSCI) objective: “increase coherence between technology for modeling/simulation and data analytics”. The need for parallel time integration is being driven by microprocessor trends, where future speedups for computational simulations will come through using increasing numbers of cores and not through faster clock speeds. Thus as spatial parallelism techniques saturate, parallelization in the time direction offers the best avenue for leveraging next generation supercomputers with billions of processors. Regarding the mathematical treatment of parallel time integrators, one must use advanced methodologies from the theory of partial differential equations in a functional analytic setting, numerical discretization and integration, convergence analyses of iterative methods, and the development and implementation of new parallel algorithms. Thus, the workshop will bring together an interdisciplinary group of experts spanning these areas.