eLife, a prestigious journal in cell biology, has published a paper co-written by Sangyoon Han, “Pre-complexation of talin and vinculin without tension is required for efficient nascent adhesion maturation.”
Dr. Han is an assistant professor in the Biomedical Engineering department, and a member of the Data Sciences research group of the Institute of Computing and Cybersystems (ICC).
eLife is a non-profit organization created by funders and led by researchers. Their mission is to accelerate discovery by operating a platform for research communication that encourages and recognizes the most responsible behaviors.
A paper co-authored by Sidike Paheding, Applied Computing, has been published in the journal, IEEE Access. “Trends in Deep Learning for Medical Hyperspectral Image Analysis,” was available for early access on March 24, 2021.
The paper discusses the implementation of deep learning for medical hyperspectral imaging.
Co-authors of the paper are Uzair Khan, Colin Elkin, and Vijay Devabhaktuni, all with the Department of Electrical and Computer Engineering, Purdue University Northwest.
Abstract
Deep learning algorithms have seen acute growth of interest in their applications throughout several fields of interest in the last decade, with medical hyperspectral imaging being a particularly promising domain. So far, to the best of our knowledge, there is no review paper that discusses the implementation of deep learning for medical hyperspectral imaging, which is what this work aims to accomplish by examining publications that currently utilize deep learning to perform effective analysis of medical hyperspectral imagery.
This paper discusses deep learning concepts that are relevant and applicable to medical hyperspectral imaging analysis, several of which have been implemented since the boom in deep learning. This will comprise of reviewing the use of deep learning for classification, segmentation, and detection in order to investigate the analysis of medical hyperspectral imaging. Lastly, we discuss the current and future challenges pertaining to this discipline and the possible efforts to overcome such trials.
DOI: 10.1109/ACCESS.2021.3068392
IEEE Access is a multidisciplinary, applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE’s fields of interest. Supported by article processing charges, its hallmarks are a rapid peer review and publication process with open access to all readers.
This is part of a series of short introductions about College students, faculty, and staff that we would like to include in the Weekly Download. Would you like to be featured? Send a photo and some background info about yourself to computing@mtu.edu.
Dr. Robert Pastel, Associate Professor of Computer Science
- Advisor to Humane Interface Design Enterprise (HIDE)
- Has been teaching at Michigan Tech for about 20 years, and teaching for 30 years.
- Researcher with the Human-Centered Computing group of the Institute of Computing and Cybersystems (ICC)
Education
- PhD, University of New Mexico, Physics
- MS, Computer Science, Michigan Tech
Classes Dr. Pastel teaches:
o CS5760 – Human-Computer Interaction – Usability Evaluation and Testing
o CS4791 and CS4792 – Senior Design
o ENT1960 – ENT5960 – Humane Interface Design Enterprise
The “coolest” class you teach, and why: All my classes are “cool” because they all involve making applications that will be used by people. The “coolest” class is CS4760 – User Interface – Design and Implementation where students work with scientists across the world to make citizen science applications.
The importance of your class topics to the overall understanding of Computing and your discipline: In all my classes, students learn to design and implement usable applications for people.
Your teaching philosophy: My teaching philosophy is that students learn best by experience and working with others. Consequently students work in teams on project for clients.
Research projects in which students are assisting:
- StreamCLIMES – Large collaborative project studying bio diversity of intermittent streams. I’m responsible for developing a web applications monitoring the stream.
- FloodAware – Large collaborative project recording and modelling flooding in urban areas. I’m responsible for developing the citizen science effort.
- KeTT – Keweenaw Time Traveler – Historical geospatial information citizen science website for user to record region’s history and explore the maps and stories.
Interests beyond teaching and research: The outdoors: skiing, biking and hiking. Every summer, he takes a one-month backpacking trip.
Dr. Livesay’s open office hours are discontinued for the summer and will return in August. Have a fantastic summer!
College of Computing Dean Dennis Livesay holds open drop-in office hours every Friday from 3:00 to 4:00 p.m., when classes are in session.
And starting Friday, March 19, you can meet with Dean Livesay in person!
Drop-in office hours are now both virtual and in-person. Stop by Rekhi Hall Room 217.
All faculty, staff, and students who wish to chat with Dr. Livesay are invited to “stop in” to this weekly meeting. Appointments are not needed.
With extensive safety planning and health precautions underway, Michigan Tech Summer Youth Programs plans to offer in-person programs for summer 2021. Programs run weekly from June 21-August 7, 2021.
Registration is now open for 2021 Summer Youth Programs. Many classes are already full, but there are plenty more to choose from
Interested in computing-related classes? Below are SYP programs of particular interest.
| Class Number | Title | Additional Cost Required | Seats Available | Grades | Week |
| 51400 | App and Web Development: Designing for Humans | 12 | 9 – 11 | July 18 – July 24 | |
| 51890 | Coding for the Internet of Things | See Course Details | 12 | 9 – 11 | July 11 – July 17 |
| 51678 | Coding for the Internet of Things | See Course Details | 12 | 9 – 11 | June 20 – June 26 |
| 52422 | Introduction to Computational Physics | 15 | 9 – 11 | June 20 – June 26 | |
| 51204 | Introduction to Video Game Programming | 12 | 6 – 8 | June 27 – July 03 | |
| 51541 | Video Game Programming | 7 | 9 – 11 | July 18 – July 24 |
| Class No. | Class Title | Add’l Costs | Seats Avail. | Grade Level | Dates of Class |
| 52409 | AI & Machine Learning | None | 8 | 9-11 | July 18 – July 24 |
| 52199 | The Gaming Industry Wants You! | None | 6 | 9-11 | June 27 – July 3 |
| 52410 | Intro to the Perfect Machine | None | 7 | 6-8 | July 18 – July 24 |
| 52412 | The Perfect Machine | None | 20 | 9-11 | July 11 – July 17 |
| 51909 | Electrical and Computer Engineering | See Course Details | 7 | 9-11 | June 27 – July 3 |
| 52092 | Electrical and Computer Engineering | See Course Details | 11 | 9-11 | June 20 – June 26 |
| 51190 | Electrical and Computer Engineering | See Course Details | 5 | 9-11 | July 11 – July 17 |
| Scholarship Programs | |||||
| 51435 | Women in Computer Science (WICS) | None | 17 | 9-11 | June 27 – July 3 |
| Science and Technology Programs | |||||
| 52199 | The Gaming Industry Wants You! | None | 6 | 9-11 | June 27 – July 3 |
The Department of Computer Science will present a lecture by Dr. Kelly Steelman, Cognitive and Learning Sciences, on Friday, March 19, 2021, at 3:00 p.m.
The title of the lecture is, “Keeping Up with Tech.”
Steelman is interim department chair and associate professor in the Department of Cognitive and Learning Sciences. Her research interests include basic and applied attention, models of attention, human performance in aviation, display design, tech adoption, and technology training.
Lecture Title
“Keeping Up with Tech”
Lecture Abstract
COVID has revealed much in the past year, including our dependence on technology and the challenges that many of us experience trying to keep up with it. Dr. Kelly Steelman has spent the past 15 years studying human attention and applying it to support the introduction of new technologies in contexts ranging from aviation to education.
In her presentation, Steelman will provide an overview of her research, using examples from Next Gen Aviation and the BASIC Digital Literacy Training Program to illustrate how understanding human attention can help us predict the consequences of introducing new technology, improve the design of technology, and support training to help people keep up with the rapid pace of technological change.
The Department of Computer Science will present a lecture by Assistant Professor Hongyu An, ECE, on Friday, March 5, 2021, at 3:00 p.m.
An’s lecture is titled, “Designing an Energy-Efficient Neuromorphic System through Two-Layer Memristive Synapses.”
An will introduce Brain-inspired Computing, an emerging approach for an energy-efficient artificial intelligent system through hardware and software co-design.
Lecture Title
Designing an Energy-Efficient Neuromorphic System through Two-Layer Memristive Synapses
Lecture Abstract
Recently, deep learning is suffering from the excessive-high power consumption issue, which cannot be resolved alone by software/algorithm optimization. In this talk, An will introduce an emerging concept named Brain-inspired Computing, which is an emerging approach for an energy-efficient artificial intelligent system through hardware and software co-design.
More specifically, An will introduce and discuss applying Three-dimensional Integrated Circuits (3D-ICs), Spiking Neural Networks (SNNs), and memristors to achieving a high-speed and energy-efficient system with the smallest design area. Our memristive synapses are utilized for storing the exported weights of the SNNs that have threshold function as the activation function. The simulation results demonstrate the significant improvement of memristive synapses on design area, power consumption, and latency.
Speaker Bio
Hongyu An is an assistant professor in the Department of Electrical and Computer Engineering at Michigan Technological University. He obtained his doctoral degree in electrical engineering at Virginia Tech. He received an M.S. degree and B.S. in electrical engineering at Missouri University of Science and Technology and Shenyang University of Technology, respectively.
He is the recipient of the 2021 Bill and LaRue Blackwell Graduate Research Ph.D. Dissertation/Paper Award and he was a DAC Young Fellow in 2020. His research areas include neuromorphic computing, energy-efficient neuromorphic electronic circuit design for Artificial Intelligence, spiking neural networks, and machine learning for medical applications.
An is a member of the Institute of Computing and Cybersystems’s (ICC) Center for Scalable Architectures and Systems (SAS).
You are invited to spend one-zero-one-zero—that is, ten—minutes with Dr. Dennis Livesay on Wednesday, February 17, from 5:30 to 5:40 p.m. EST.
Dr. Livesay is the Dave House Dean of Computing and a professor in the Department of Applied Computing.
In this informal discussion, Dean Livesay will talk about his journey from chemist to engineer to informatician, with computing being the common thread.
He will also answer any questions you might have about the College of Computing at Michigan Tech.
We look forward to spending 1010 minutes with you!
Visit the 1010 with … webpage here.
Did you miss the January 20 “1010 with Tim Havens?” Watch the video below.
The Institute of Computing and Cybersystems will present a Distinguished Lecture by Dr. Alina Zare on Friday, April 16, 2021, at 3:00 p.m.
Her talk is titled, “Multiple Instance Learning for Plant Root Phenotyping.”
Dr. Zare is a professor in the Electrical and Computer Engineering department at University of Florida. She teaches and conducts research in the areas of pattern recognition and machine learning.
Lecture Title
Multiple Instance Learning for Plant Root Phenotyping
Lecture Abstract
In order to understand how to increase crop yields, breed drought tolerant plants, investigate relationships between root architecture and soil organic matter, and explore how roots can play in a role in greenhouse gas mitigation, we need to be able to study plant root systems effectively. However, we are lacking high-throughput, high-quality sensors, instruments and techniques for plant root analysis. Techniques available for analyzing root systems in field conditions are generally very labor intensive, allow for the collection of only a limited amount of data and are often destructive to the plant. Once root data and imagery have been collected using current root imaging technology, analysis is often further hampered by the challenges associated with generating accurate training data.
Most supervised machine learning algorithms assume that each training data point is paired with an accurate training label. Obtaining accurate training label information is often time consuming and expensive, making it infeasible for large plant root image data sets. Furthermore, human annotators may be inconsistent when labeling a data set, providing inherently imprecise label information. Given this, often one has access only to inaccurately labeled training data. To overcome the lack of accurately labeled training, an approach that can learn from uncertain training labels, such as Multiple Instance Learning (MIL) methods, is required. In this talk, I will discuss our team’s approaches to characterizing and understanding plant roots using methods that focus on alleviating the labor intensive, expensive and time consuming aspects of algorithm training and testing.
Speaker Bio
Dr. Zare earned her Ph.D. in December 2008 from the University of Florida. Prior to joining the faculty at the University of Florida in 2016, she was a faculty member at the University of Missouri.
Zare’s research has focused primarily on developing machine learning and pattern recognition algorithms to autonomously understand and process non-visual imagery. Her research work has included automated plant root phenotyping using visual and X-ray imagery, 3D reconstruction and analysis of X-ray micro-CT imagery, sub-pixel hyperspectral image analysis, target detection and underwater scene understanding using synthetic aperture sonar, LIDAR data analysis, Ground Penetrating Radar analysis, and buried landmine and explosive hazard detection.
