Month: November 2020

Sidike Paheding Lecture is Dec. 11, 3 pm

Assistant Professor Sidike Paheding, Applied Computing, will present his lecture, “Deep Neural Networks for UAV and Satellite Remote Sensing Image Analysis,” on Dec. 11, 2020, at 3:00 p.m. via online meeting.

Paheding’s research focuses on the areas of computer vision, machine learning, deep learning, image/video processing, and remote sensing.

The lecture is presented by the Department of Computer Science.

Lecture Abstract

Remote sensing data can provide non-destructive and instantaneous estimates of the earth’s surface over a large area, and has been accepted as a valuable tool for agriculture, weather, forestry, defense, biodiversity, etc. In recent years, deep neural networks (DNN), as a subset of machine learning. for remote sensing has gained significant interest due to advances in algorithm development, computing power, and sensor systems.

This talk will start with remote sensing image enhancement framework, and then primarily focuses on DNN architectures for crop yield prediction and heterogeneous agricultural landscape mapping using UAV and satellite imagery.

Speaker Biography

Paheding is an associate editor of the Springer journal Signal, Image, and Video Processing, ASPRS Journal Photogrammetric Engineering & Remote Sensing, and serves as a guest editor/reviewer for a number of reputed journals. He has advised students at undergraduate, M.S., and Ph.D. levels, and authored/coauthored close to 100 research articles.


Bo Chen, CS, Wins REF Grant for Decentralized Cloud Storage Project

Bo Chen, Computer Science, has been awarded a Fall 2020 REF Research Seed Grant (REF-RS) for his project, “Towards Secure and Reliable Decentralized Cloud Storage.” Funding for the 12-month, $25,800 award begins on January 1, 2021.

“This grant will provide significant help to advance my current research,” says Chen. “This is really exciting news for me.”

Bo Chen is a researcher with the ICC’s Cybersecurity and Computing Education research groups.

As a recipient of the REF seed grant, which is awarded by the Michigan Tech Office of the Vice President for Research, Chen will participate in review and feedback for the next round of REF proposals. View the full list of Fall 2020 REF award recipients here.

Abstract

A decentralized cloud storage system eliminates the need of dedicated computing infrastructures by allowing peers which have spare storage space to join the network and to provide storage service. Compared to the conventional centralized cloud storage system, it can bring significant benefits including cheaper storage cost, better fault tolerance, greater scalability, as well as more efficient data storing and retrieval, making it well fit the emerging Internet of things (IoT) applications.

While bringing immense benefits, the decentralized cloud storage system also raises significant security concerns, since the storage peers are much less reputable than the traditional data centers and may more likely misbehave.

This project thus aims to build a secure and reliable decentralized cloud storage system which can serve as the cloud infrastructure for future IoT applications. The project will actively investigate two fundamental security issues faced by the decentralized cloud storage system: 1) How can we prevent the malicious storage peers from stealing the data? 2) How can we ensure that once the data are stored into the system, they are always retrievable even if the storage peers misbehave?

To address the aforementioned issues in an untrusted p2p environment, the PI will integrate efficient integrity checking with the blockchain, as well as the broadly equipped secure hardware like Intel SGX. The PI will also broaden the educational impact of the proposed project by actively involving both graduate and undergraduate students from the MTU cybersecurity programs.


Research Excellence Fund Awards Announced

by Vice President for Research Office

The Vice President for Research Office announces the Fall 2020 REF awards. Thanks to the individual REF reviewers and the REF review panelists, as well as the deans and department chairs, for their time spent on this important internal research award process.

Research Seed Grants:

  • Sajjad Bigham, Mechanical Engineering-Engineering Mechanics
  • Bo Chen, Computer Science
  • Daniel Dowden, Civil and Environmental Engineering
  • Ana Dyreson, Mechanical Engineering-Engineering Mechanics
  • Hassan Masoud, Mechanical Engineering-Engineering Mechanics
  • Xinyu Ye, Civil and Environmental Engineering

Master’s Defense: Rukayat Bukola Adeosun, Health Informatics

Rukayat Bukola Adeosun, Health Informatics, will present their master’s defense, “Hierarchical Clustering to Predict the Response of Cardiac Resynchronization Therapy in Patients with Heart Failure,” on Monday, November 30, 2020, at 12:00 to 1:00 p.m. Adeosun is advised by Weihua Zhou.


ACSHF Doctoral Defense: Lavanya Rajesh Kumar

by Cognitive and Learning Sciences

Applied Cognitive Science and Human Factors doctoral candidate, Lavanya Rajesh Kumar, will defend her dissertation at 10 a.m. Tuesday (Dec. 1) via Zoom.

The title of her defense is “To Examine the Effects of Exercise & Instructional Based Interventions on Executive Functioning, Motor Learning & Emotional Intelligence Abilities Among Older Adults”.

Kumar is advised by Kevin Trewartha (CLS). All are welcome to attend.

Kevin Trewartha is a researcher with the ICC’s Human-Centered Computing group.


ECE Doctoral Defense – Yongyu Wang

by Electrical and Computer Engineering

Computer Engineering doctoral candidate Yongyu Wang will defend at 10 a.m. Tuesday (Dec 1) via Zoom.

The title of his presentation is “High-Performance Spectral Methods for Graph-Based Machine Learning.” Co-advisors are Chee-Wooi Ten (ECE) and Zhuo Feng (ECE).

Chee-wooi Ten is a researcher with the ICC’s Cyber-Physical Systems group.


ME-EM’s Bo Chen is Co-PI of New DoE Grant

by Sponsored Programs

Darrell Robinette (ME-EM/APSRC) is the principal investigator on a project that has received a $1,348,109 research and development co-op/joint agreement from the Department of Energy.

The project is entitled, ” Energy Optimization of Light and Heavy Duty Vehicle Cohorts of Mixed Connectivity, Automation and Propulsion System Capabilities via Meshed V2V-V2I and Expanded Data.”

Jeff Naber (ME-EM/APSRC), Bo Chen (ME-EM/APSRC), Jung Yun Bae (ME-EM/APSRC) and Chris Morgan (PHC/APSRC) are Co-PI’s on this potential 2.3-year project. Bo Chen is a researcher with the ICC’s Cyber-Physical Systems research group.


You’ve got #tenacity. Tell us about it.

It has definitely been a memorable semester! You’re on the home stretch now and you will get it done. After all, you’re a Husky … and you’ve got #tenacity. Tell us about it.

Share by email.

  1. Email computing@mtu.edu
  2. Share a few words about your Fall 2020 Husky #tenacity
  3. Send us a photograph (or two)
  4. We’ll publish your #tenacity on our website and social media channels

Fill out a Goole form.

https://forms.gle/GLgUQHtCP69HkLfm8


MTRI Research Scientist Joel LeBlanc to Present Lecture Dec. 4, 3 pm

Senior Research Scientist Joel LeBlanc of Michigan Tech Research Institute (MTRI) will present his lecture, “Testing the Validity of Physical (Software) Models in Inverse Problems,” on Friday, December 4, 2020, at 3:00 p.m. via online meeting.

The lecture is presented by the Michigan Tech Department of Computer Science.

Lecturer Bio

LeBlanc has a Ph.D. in Statistical Signal Processing. His areas of expertise include statistical signal processing, applied nonconvex optimization, EO/IR imaging, and Synthetic Aperture Radar (SAR) imaging. His research interests are in information theoretic approaches to inverse-imaging, computational techniques for solving large inverse problems, and fundamental limits of sensing.

Lecture Abstract

Numerical simulations are the modern analog of the “physical system” referenced by Rosenblueth and Wiener in their 1945 paper “The Role of Models in Science.” This talk will introduce the inverse-problem approach for making inferences about the physical world and discuss how the Maximum Likelihood (ML) principle leads to both performant estimators and algorithm agnostic bounds on performance. The resulting estimators and associated bounds are only valid when global convergence is achieved, so I will present new results on global convergence testing that I believe are widely applicable. Finally, I will discuss some of my ongoing research interests: optimal resource allocation and testing for adversarial behavior through model relaxation.

Michigan Tech Research Institute focuses on technology development and research to sense and understand natural and human-made environments. Through innovation, education, and collaboration, the Institute supports meaningful solutions to critical global issues, from infrastructure to invasive species, national security to public health.