Category: DataS

New NSF Project to Improve Great Lakes Flood Hazard Modeling

Thomas Oommen, Timothy C. Havens, Guy Meadows (GLRC), and Himanshu Grover (U. Washington) have been awarded funding in the NSF Civic Innovation Challenge for their project, “Helping Rural Counties to Enhance Flooding and Coastal Disaster Resilience and Adaptation.”

The six-month project award is $49,999.

Vision. The vision of the new project is to develop methods that use remote sensing data resources and citizen engagement (crowdsourcing) to address current data gaps for improved flood hazard modeling and visualization that is transferable to rural communities.

Objective. The objective of the Phase-1 project is to bring together community-university partners to understand the data gaps in addressing flooding and coastal disaster in three Northern Michigan counties.  

The Researchers

Thomas Oommen is a professor in the Geological and Mining Engineering and Sciences department. His research efforts focus on developing improved susceptibility characterization and documentation of geo-hazards (e.g. earthquakes, landslides) and spatial modeling of georesource (e.g. mineral deposits) over a range of spatial scales and data types. Oommen is a member of the ICC’s Center for Data Sciences.

Tim Havens is associate dean for research, College of Computing, the
William and Gloria Jackson Associate Professor of Computer Systems, and director of the Institute of Computing and Cybersystems. His research interests include mobile robotics, explosive hazard detection, heterogeneous and big data, fuzzy sets, sensor networks, and data fusion. Havens is a member of the ICC’s Center for Data Sciences.

Guy Meadows is director of the Marine Engineering Laboratory (Great Lakes Research Center), the Robbins Professor of Sustainable Marine Engineering, and a research professor in the Mechanical Engineering-Engineering Mechanics department. His research interests include large scale field experimentation in the Inland Seas of the Great Lakes and coastal oceans; nearshore hydrodynamics and prediction; autonomous and semi-autonomous environmental monitoring platforms (surface and sub-surface); underwater acoustic remote sensing; and marine engineering.

Himanshu Grover is an asssistant professor at University of Washington. His research focus is at the intersection of land use planning, community resilience, and climate change.

About the Civic Innovation Challenge

The NSF Civic Innovation Challenge is a research and action competition that aims to fund ready-to-implement, research-based pilot projects that have the potential for scalable, sustainable, and transferable impact on community-identified priorities.


Health Research Institute Panel Is January 25, 12 pm

Michigan Tech’s Health Research Institute (HRI) will host a panel discussion on Monday, January 25, 2021,, from 12:00 to 1:00 p.m.

Health research at Michigan Tech has been steadily growing for over 10 years. This growth has led to many practical uses for the technology developed.  Three researchers, Dr. Megan Frost (Kinesiology and Integrative Physiology), Dr. Bruce Lee (Biomedical Engineering), and Assistant Professor Dr. Weihua Zhou (College of Computing) will discuss their experiences with start-ups and applying their research to relevant health problems.

Registration

Register for the live Zoom session here: http://bit.ly/HRI_talk


Nathir Rawashdeh Presents, Publishes Research at Mechatronics Conference

A conference paper published in IEEE Xplore entitled, “Interfacing Computing Platforms for Dynamic Control and Identification of an Industrial KUKA Robot Arm” has been published by Assistant Professor Nathir Rawashdeh, Applied Computing.

In this work, a KUKA robotic arm controller was interfaced with a PC using open source Java tools to record the robot axis movements and implement a 2D printing/drawing feature.

The paper was presented at the 2020 21st International Conference on Research and Education in Mechatronics (REM). Details available at the IEEE Xplore database.


College of Computing Overview with Tim Havens Is Tues., Jan. 19, 7-8 pm

Please join the College of Computing’s Tim Havens at a College of Computing Undergraduate Overview on Tuesday, January 19, from 7:00 to 8:00 p.m. The virtual event is presented by Michigan Tech Admissions. The focus of the event is on prospective students.

Event Details: Check out our diverse selection of majors, including Computer Network and System Administration, Computer Science, Cybersecurity, Electrical Engineering Technology, Mechatronics, Software Engineering, and our first-year computing undecided program, General Computing.

Register for the live Zoom session here.

View the University Events Calendar listing here.


1010 with … Tim Havens, Weds., Jan. 20, 5:30-5:40 pm

You are invited to spend one-zero-one-zero—that is, ten—minutes with Dr. Timothy Havens on Wednesday, January 20, from 5:30 to 5:40 p.m. EST.

Havens is the Associate Dean for Research in the College of Computing, Director of the Institute of Computing and Cybersystems, and the William and Gloria Jackson Associate Professor of Computer Systems at Michigan Tech.

In this informal discussion, Havens will talk about undergraduate research opportunities at Michigan Tech, his research in AI and machine learning, and answer your questions about the College of Computing.

We look forward to spending 1010 minutes with you!

Join 1010 with Tim Havens here.

Did you miss the December 16, 1010 with Nathir Rawashdeh? Watch the video below.

1010 with … Nathir Rawashdeh, December 16, 2020

The 1010 with … series continues on Wednesday January 27 … with more to come!


Master’s Defense: Taylor Morris, CS, Tues., Jan. 5

Computer Science graduate student Taylor Morris will present a Master’s Defense on Tuesday, January 5, from 6:00 to 7:00 p.m.

Presentation Title: “Using Text Mining and Machine Learning Classifiers to Analyze Stack Overflow.”

Advisor: Associate Professor Laura Brown, Computer Science

Link to the Michigan Tech Events Calendar entry here.

Join the Zoom meeting here. (michigantech.zoom.us/j/83033288850)


Panel Discussion Jan. 5: Mobility at Michigan Tech: “Where are we?”

Mobility is an increasingly used word today in conjunction with the advent of automated vehicle technologies, but what else is covered under this term that is often defined as“the ability to move or be moved freely and easily“? Even more importantly, what is happening at Michigan Tech related to Mobility? Dr. Pasi Lautala (CEE) is working as a Faculty Fellow sponsored by the Vice President for Research Office toward building a collaborative environment for Mobility-related development and research and expanding Michigan Tech’s role as a leader in the field. 

As a kickoff event for these efforts, Dr. Lautala will be hosting a virtual panel discussion on Tuesday, January 5th, from 3:00-4:30 p.m. (EST).  This virtual event will bring together leading Mobility experts from our Michigan Tech community to discuss the wide range of issues addressed under the umbrella of Mobility. The panelists will start the event by briefly introducing how they and their teams are involved in Mobility, followed by an hour-long open discussion on Mobility and related issues. We encourage all university and local community members interested in Mobility to tune in and participate in the discussion. 
The panelists will include:

  • Bill Buller,  Senior Research Scientist, Michigan Tech Research Institute (MTRI) 
  • Timothy Havens, William and Gloria Jackson Associate Professor of Computer Systems
  • Don LaFreniere, Associate Professor of Geography and GIS
  • Jeff Naber,  Ron and Elaine Starr Professor in Energy Systems, Mechanical Engineering—Engineering Mechanics
  • Chelsea Schelly, Associate Professor of Sociology, Social Sciences
  • Roman Sidortsov,  Assistant Professor, Energy Policy, Social Sciences

This panel discussion is the first in a series of events related to Mobility planned for the spring semester, and will largely focus on the current state of Mobility at Michigan Tech.  Following events will seek to bring in external experts to share their insights and begin to develop building blocks that will lay the foundation for specific Mobility-related collaborative research proposals.

To participate in the event, use the Zoom link provided below. For more information, please contact Pasi Lautala at ptlautal@mtu.edu.

https://michigantech.zoom.us/s/85775632314 (Dial-in, US : +1 312 626 6799 or +1 301 715 8592 or +1 646 876 9923)


1010 with … Nathir Rawashdeh, Weds., Dec. 16

Nathir Rawashdeh (right) and Dan Fuhrmann, Interim Dean, Dept. of Applied Computing

You are invited to spend one-zero-one-zero—that is, ten—minutes with Dr. Nathir Rawashdeh on Wednesday, December 16, from 5:30 to 5:40 p.m.

Rawashdeh is assistant professor of applied computing in the College of Computing at Michigan Tech.

He will present his current research work, including the using artificial intelligence for autonomous driving on snow covered roads, and a mobile robot using ultraviolet light to disinfect indoor spaces. Following, Rawashdeh will field listener questions.

We look forward to spending 1010 minutes with you!

Join 1010 with Nathir Rawashdeh here.

Did you miss last week’s 1010 with Chuck Wallace? Watch the video below.

1010 with … Chuck Wallace, Assoc. Prof, Computer Science, December 9, 2020.

The 1010 with … series will continue on Wednesday afternoons in the new year on January 6, 13, 20, and 27 … with more to come!


Siva Kakula to Present PhD Defense Dec. 21, 3 pm

Graduate student Siva Krishna Kakula, Computer Science, will present his PhD defense, “Explainable Feature- and Decision-Level Fusion,” on Monday, December 21, 2020, from 3:00 to 5:00 p.m. EST Kakula is advised by Dr. Timothy Havens, College of Computing.

Link to the virtual lecture here.

Siva Kakula earned his master of science in computer engineering at Michigan Tech in 2014, and completed a bachelor of technology in civil engineering at IIT Guwahati in 2011. His research interests include machine learning, pattern recognition, and information fusion.

Download the informational flier below.

Lecture Abstract

Information fusion is the process of aggregating knowledge from multiple data sources to produce more consistent, accurate, and useful information than any one individual source can provide. In general, there are three primary sources of data/information: humans, algorithms, and sensors. Typically, objective data—e.g., measurements—arise from sensors. Using these data sources, applications such as computer vision and remote sensing have long been applying fusion at different “levels” (signal, feature, decision, etc.). Furthermore, the daily advancement in engineering technologies like smart cars, which operate in complex and dynamic environments using multiple sensors, are raising both the demand for and complexity of fusion. There is a great need to discover new theories to combine and analyze heterogeneous data arising from one or more sources.

The work collected in this dissertation addresses the problem of feature- and decision-level fusion. Specifically, this work focuses on Fuzzy Choquet Integral (ChI)-based data fusion methods. Most mathematical approaches for data fusion have focused on combining inputs relative to the assumption of independence between them. However, often there are rich interactions (e.g., correlations) between inputs that should be exploited. The ChI is a powerful aggregation tool that is capable modeling these interactions. Consider the fusion of N sources, where there are 2N unique subsets (interactions); the ChI is capable of learning the worth of each of these possible source subsets. However, the complexity of fuzzy integral-based methods grows quickly, as the fusion of N sources requires training 2N-2 parameters; hence, we require a large amount of training data to avoid the problem of over-fitting. This work addresses the over-fitting problem of ChI-based data fusion with novel regularization strategies. These regularization strategies alleviate the issue of over-fitting while training with limited data and also enable the user to consciously push the learned methods to take a predefined, or perhaps known, structure. Also, the existing methods for training the ChI for decision- and feature-level data fusion involve quadratic programming (QP)-based learning approaches that are exorbitant with their space complexity. This has limited the practical application of ChI-based data fusion methods to six or fewer input sources. This work introduces an online training algorithm for learning ChI. The online method is an iterative gradient descent approach that processes one observation at a time, enabling the applicability of ChI-based data fusion on higher dimensional data sets.

In many real-world data fusion applications, it is imperative to have an explanation or interpretation. This may include providing information on what was learned, what is the worth of individual sources, why a decision was reached, what evidence process(es) were used, and what confidence does the system have on its decision. However, most existing machine learning solutions for data fusion are “black boxes,” e.g., deep learning. In this work, we designed methods and metrics that help with answering these questions of interpretation, and we also developed visualization methods that help users better understand the machine learning solution and its behavior for different instances of data.


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. Join the Zoom lecture here.

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.