Institute of Computing and Cybersystems (ICC) Blog

Category: Research

Biocomputing, Digital Health Focus of New Research Center

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The Institute of Computing and Cybersystems (ICC) and the Health Research Institute (HRI) have established the Joint Center of Biocomputing and Digital Health (BDH).

The new research institute was co-founded by HRI member Jingfeng Jiang (BME) and ICC member Jinshan Tang (CC).

The mission of Joint Center of Biocomputing and Digital Health (BDH) is to conduct research, develop innovative solutions, and provide educational opportunities in the area of biocomputing and digital health, thereby enhancing Michigan Tech’s ability to recruit and retain high-quality researchers and students, elevating Michigan Tech’s presence in developing technologies for healthcare delivery, and increasing knowledge sharing in the global community.

Jingfeng Jiang is a professor with the Department of Biomedical Engineering. His research interests are in biomechanics, automated control of ultrasound scanning including the use of 3D printing technology, image and signal processing, non-invasive assessment of biomechanical properties of soft tissues, and computer aided analyses of cardiovascular flow.

Jinshan Tang is a professor in the Department of Applied Computing. His research interests are in image processing and pattern recognition, biomedical imaging and medical image analysis, and medical informatics and intelligent medical diagnosis systems. Tang is a member of the ICC’s Center for Cyber-Physical Systems.

The Institute of Computing and Cybersystems (ICC) creates and supports an arena in which faculty and students work collaboratively across organizational boundaries in an environment that mirrors contemporary technological innovation.

The Health Research Institute (HRI) aims to establish and maintain a thriving environment that promotes translational, interdisciplinary, and increasingly convergent health-related research and inspires education and outreach activities.

Please contact Jingfeng Jiang (jjiang1@mtu.edu) with questions.

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Meryl Spencer to Present Lecture, Feb. 26, 3 pm

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The Department of Computer Science will present a lecture by Meryl Spencer, Michigan Tech Research Institute, on Friday, February 26, 2021, at 3:00 p.m.

Spencer’s lecture is titled, “Advancing Robotics through competition.”

Join the virtual lecture here.

Meryl Spencer is a research scientist with the Michigan Tech Research Institute (MTRI). Her research interests include Multi-Agent Teaming, Robotics Simulation, Applications of Graph Theory, Biomimicry For Robotics, Emergent Behavior, Reinforcement Learning, and Camouflage Detection in Machine Learning.

Lecture Title

“Advancing Robotics through competition ”

Lecture Abstract

Michigan Tech is a top competitor in the DARPA Subterranean challenge, which pits teams of fully autonomous vehicles against difficult underground environments to find artifacts hidden in caves and mines. In this talk, Dr. Spencer will give an explanation of the graph-based approach the Michigan Tech team is using to enable joint searching of gps-denied environments with a heterogeneous team of robots.

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Emily Zhang Is ME-EM Graduate Seminar Speaker

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by Mechanical Engineering – Engineering Mechanics

The next virtual Graduate Seminar Speaker will be held at 4 p.m. tomorrow (Feb. 25) via Zoom.

Lan (Emily) Zhang (ECE) will present “Augmenting Radio Environments for better Wireless Ecosystems.”

Zhang is a member of the Institute of Computing and Cybersystems’s (ICC) Center for Cyber-Physical Systems.

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Sidike Paheding Awarded MSGC Seed Grant

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Michigan Space Grant Consortium

Assistant Professor Sidike Paheding, Applied Computing, has been awarded a one-year MSGC Research Seed Grant for his project, “Monitoring Martian landslides using deep learning and data fusion.”

Professor Thomas Oommen, Geological and Mining Engineering and Sciences, is Co-PI of the project. The grant will support part-time employment of two students during the award period.

This grant is supported in part by funding provided by the National Aeronautics and Space Administration (NASA), under award number 80NSSC20M0124, Michigan Space Grant Consortium (MSGC).

The MSGC Research Seed Grant Program supports junior faculty and research scientists at MSGC affiliate institutions. The program also helps mid-career and senior faculty develop new research programs. The objective of this program is to allow award recipients to develop the research expertise necessary to propose research activities in new areas to other federal or nonfederal sources.

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Leo Ureel Receives 2020-21 CTL Award for Innovative Teaching

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The 2020-2021 CTL Instructional Award for Innovative or Out of Class Teaching is being presented to two instructors, and Assistant Professor Leo Ureel, Computer Science, and Libby Meyer, senior lecturer, Visual and Performing Arts.

Ureel was nominated in recognition of his “student-centric efforts which have increased retention and diversified the cohort of first-year computing students.”

Ureel’s presentation, “Three course innovations to support communication,” will be presented at 3:30 p.m. on Thursday, February 18, 2021, as part of the CTL Instructional Award Presentation Series.

Link here to register for the event.

Ureel is a member of the Institute of Computing and Cybersystems’s (ICC) Computing Education Center.

Meyer’s presentation, “Beyond Carrots and Sticks: Mastery Based Grading and Narrative Assessment” will also be presented on February 18.

During spring 2017, academic deans were asked to begin recognizing instructors making contributions in these areas as part of the Deans’ Teaching Showcase, effectively nominating them for instructional awards.

CTL and Provost’s office members along with previous awardees then select one individual in each category from a pool composed of the Showcase and those nominated to the Academy of Teaching Excellence.

Ureel Lecture Abstract

Three course innovations to support communication Introductory courses present many communication challenges between faculty and first year students. In this context, we discuss three innovations used in our introductory computer science courses.

The first is the use of Snap, a high-level, visual programming language, as a form of pseudocode during the first five weeks of the course to build student vocabulary and problem solving skills before tackling programming in Java.

The second is a Code Critiquer developed as a Canvas plugin to provide immediate guidance and feedback to students when they submit their programming assignments.

The third is a grade visualization tool that helps students understand their current performance in the course and project a range that will contain their final grade. While not everyone teaches introductory computer science, we discuss how these or similar innovations and tools might apply to your course.

Leo Ureel, Computer Science
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Vijay Garg, UT Austin, to Present Lecture Feb. 19, 3 pm

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This lecture has been canceled.

Dr. Vijay Garg, University of Texas Austin, will present a lecture on February 19, 2021, at 3:00 p.m. The lecture is hosted by the Department of Computer Science.

Vijay Garg Bio

Vijay Garg is a Cullen Trust Endowed Professor in the Department of Electrical & Computer Engineering at The University of Texas at Austin. He received his Ph.D. in computer science at the University of California at Berkeley and B. Tech. in computer science at IIT, Kanpur.

His research interests are in distributed computing, discrete event systems and lattice theory. He is the author of “Elements of Distributed Computing” (Wiley, 2002), “Introduction to Lattice Theory with Computer Science Applications” (Wiley, 2015), and “Modeling and Control of Logical Discrete Event Systems” (Springer, 2012). He is an IEEE Fellow.

Lecture Title

Applying Predicate Detection to Discrete Optimization Problems

Lecture Abstract

We present a method to design parallel algorithms for the constrained combinatorial optimization problems. Our method solves and generalizes many classical combinatorial optimization problems including the stable marriage problem, the shortest path problem and the market clearing price problem.

These three problems are solved in the literature using Gale-Shapley algorithm, Dijkstra’s algorithm, and Demange, Gale, Sotomayor algorithm. Our method solves all these problems by casting them as searching for an element that satisfies an appropriate predicate in a distributive lattice. Moreover, it solves generalizations of all these problems — namely finding the optimal solution satisfying additional constraints called lattice-linear predicates.

For stable marriage problems, an example of such a constraint is that Peter’s regret is less than that of Paul. Our algorithm, called Lattice-Linear Predicate Detection (LLP) can be implemented in parallel with without any locks or compare-and-set instructions. It just assumes atomicity of reads and writes.

In addition to finding the optimal solution, our method is useful in enumerating all constrained stable matchings, and all constrained market clearing price vectors. The talk is an extended version of a paper that appeared in ACM SPAA’20.

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Yakov Nekrich Paper Accepted for Top Computing Conference

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A publication by Associate Professor Yakov Nekrich, Computer Science, has been accepted to the 53rd Annual ACM Symposium on Theory of Computing (STOC).

The paper, “Optimal-Time Dynamic Planar Point Location in Connected Subdivisions,” describes an optimal-time solution for the dynamic point location problem and answers an open problem in computational geometry. 

The data structure described in the paper supports queries and updates in logarithmic time. This result is optimal in some models of computation.  Nekrich is the sole author of the publication.

The annual ACM Symposium on Theory of Computing (STOC), is the flagship
conference of SIGACT, the Special Interest Group on Algorithms and
Computation Theory, a special interest group of the Association for
Computing Machinery (ACM).

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Registration Open for Graduate Research Colloquium

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by Graduate Student Government

Registration for this year’s virtual Graduate Research Colloquium (GRC) is open. Due to the continuation of the SARS-CoV-19 pandemic, the GRC will be held virtually on Thursday and Friday, April 1and 2.

The GRC is a great opportunity to work on your presentation skills and prepare for upcoming conferences. Students are free to give an oral presentation, a poster talk, or both. All talks will be scored by judges from the same field as the presenter.

Cash prizes are available for the top three places in both oral and poster presentations (1st – $300, 2nd – $200, and 3rd – $100). Registration closes Tuesday March 2, at 11:59 PM. Register today.

Poster presentations will take place in a pre-recorded video style. The deadline for video submission is Monday, March 22. A short Q&A session will take place with judges between 4-6 p.m. on April 1. Oral presentations are limited to 12 minutes plus a Q&A session.

The GRC will be capped off with a virtual awards ceremony. All participants and judges are invited to attend. The ceremony will be held on April 2, from 5-7 pm. Full information can be found on our website.

Feel free to contact Sarvada Chipkar if you have any questions or concerns.

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ICC Distinguished Lecture: Alina Zare, Univ. of Florida

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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.

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