Category: ICC

Susanta Ghosh Publishes Paper in APS Physical Review B Journal

Assistant Professor Susanta Ghosh, ME-EM, has published the article, “Interpretable machine learning model for the deformation of multiwalled carbon nanotubes,” in the APS publication, Physical Review B.

Co-authors of the paper are Upendra Yadav and Shashank Pathrudkar. The article was published January 11, 2021.

Ghosh is a member of the Institute of Computing and Cybersystems’ Center for Data Sciences.

Article Abstract

In the paper, researchers present an interpretable machine learning model to predict accurately the complex rippling deformations of multiwalled carbon nanotubes made of millions of atoms. Atomistic-physics-based models are accurate but computationally prohibitive for such large systems. To overcome this bottleneck, we have developed a machine learning model. The proposed model accurately matches an atomistic-physics-based model whereas being orders of magnitude faster. It extracts universally dominant patterns of deformation in an unsupervised manner. These patterns are comprehensible and explain how the model predicts yielding interpretability. The proposed model can form a basis for an exploration of machine learning toward the mechanics of one- and two-dimensional materials.

APS Physics advances and diffuses the knowledge of physics for the benefit of humanity, promote physics, and serve the broader physics community.

Physical Review B (PRB) is the world’s largest dedicated physics journal, publishing approximately 100 new, high-quality papers each week. The most highly cited journal in condensed matter physics, PRB provides outstanding depth and breadth of coverage, combined with unrivaled context and background for ongoing research by scientists worldwide.

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.

Havens Appointed First IEEE CIS Conference Publication Editor

Timothy C. Havens, College of Computing, has been appointed as the first Conference Publication Editor of the IEEE Computational Intelligence Society (IEEE CIS).

Havens is associate dean for research, College of Computing, the William and Gloria Jackson Associate Professor of Computer Systems, director of the Institute of Computing and Cybersystems (ICC), and a member of the ICC’s Center for Data Sciences.

In this position, Dr. Havens will serve as the editor-in-chief for all publications of IEEE CIS conferences, including the flagship conferences IEEE International Joint Conference on Neural Networks (IJCNN), IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE Congress Evolutionary Computation (IEEE CEC), IEEE World Congress Computational Intelligence (WCCI), and IEEE Symposium Series on Computational Intelligence (SSCI).

Bob Mark Business Model Pitch Competition Is January 28

The virtual Bob Mark Business Model Pitch Competition takes place Thursday, January 28, 2021, from 5:30 to 7:30 p.m.

Graduate and undergraduate students from across campus disciplines are invited to compete. When registering, contestants can choose the competition category, as this year two pitch competition categories are available.

A tribute to the late Professor of Practice Bob Mark, College of Business, the Bob Mark Business Model Pitch Competition recognizes student entrepreneurial spirit.

Faculty, staff, students, alumni, and the community are invited to attend this energized virtual pitch competition.

Register to attend the Bob Mark Business Model Competition

Register to compete in the Bob Mark Business Model Competition

Category 1: Idea Pitch

A two-minute idea pitch that presents a creative solution to a problem. Pitches will be evaluated on their uniqueness and the potential impactfulness.

Category 2: Business Model Pitch

A four-minute business model pitch which touches on the innovation technology, emphasizes product-market fit and the potential value it brings to the market. Prizes will be awarded to the most scalable and actionable business model pitches.  Participants in the Business Model Pitch category are encouraged to sign up for the Business Model Boot Camp workshop on January 20, 2021 https://bit.ly/HuskyInnovateBootcamp

This event is hosted by Husky Innovate, a collaboration between Pavlis Honors College, the College of Business and the Office of Innovation and Commercialization. Prizes will be awarded by the College of Business, the MTEC SmartZone, and Husky Innovate.  

Prizes include:

Idea Pitch Category

  • First Prize: $125
  • Second Prize: $75
  • Third Prize: $50
  • Social Impact Award: $100 (sponsored by Dr. Ellie Asgari – COB Gates Professor)

Business Model Category

  • First Prize: $2,000 (sponsored by Rick and Jo Berquist)
  • Second Prize: $1,000 
  • Third Prize: $500
  • Honorable Mention (2 prizes): $250 each Audience Favorite: $250
  • MTEC SmartZone Breakthrough Innovation Award: $1,000
  • Social Impact Award: $1,000 (sponsored by Dr. Ellie Asgari – COB Gates Professor)

Husky Innovate is Michigan Tech’s innovation and entrepreneurship resource hub. The unit hosts free workshops, competitions, NSF I-Corps lean startup workshops, innovation talks, internships, mentorship, and the Silicon Valley Experience.

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.

Shane Mueller to Present Lecture Jan. 22, 3 pm

The Department of Computer Science will present a lecture, by Dr. Shane Mueller on Friday, January 22, 2021, at 3:00 p.m.

Mueller is an associate professor in the Applied Cognitive Science and Human Factors program of the Cognitive and Learning Science department. His lecture is titled, “Explainable AI, and principles for building human-centered XAI systems.”

Mueller’s research focuses on human memory and the representational, perceptual, strategic, and decisional factors that support it. He employs applied and basic research methodologies, typically with a goal of implementing formal quantitative mathematical or computational models of cognition and behavior.

He is also the primary developer of the Psychology Experiment Building Language (PEBL), a software platform for creating psychology experiments.

Mueller has undergraduate degrees in mathematics and psychology from Drew University, and a Ph.D. in cognitive psychology from the University of Michigan. He was a senior scientist at Klein Associates Division of Applied Research Associates from 2006 to 2011. His research has been supported by NIH, DARPA, IARPA, the Air Force Research Laboratory, the Army Research Institute, the Defense Threat Reduction Agency, and others.

Lecture Title:

Explainable AI, and principles for building human-centered XAI systems

Lecture Abstract

In recent years, Explainable Artificial Intelligence (XAI) has re-emerged in response to the development of modern AI and ML systems. These systems are complex and sometimes biased, but they nevertheless make decisions that impact our lives. XAI systems are frequently algorithm-focused; starting and ending with an algorithm that implements a basic untested idea about explainability. These systems are often not tested to determine whether the algorithm helps users accomplish any goals, and so their explainability remains unproven. I will discuss some recent advances and approaches to developing XAI, and describe how many of these systems are likely to incorporate many of the lessons from past successes and failures to build explainable systems. I will then review some of the basic concepts that have been used for user-centered XAI systems over the past 40 years of research. Based on this, I will describe a set of empirically-grounded, human user-centered design principles that may guide developers to create successful explainable systems.

ICC Distinguished Lecture: James Bezdek, Jan 29, 3 pm

The Institute of Computing and Cybersystems will present a Distinguished Lecture by James C. Bezdek on Friday, January 29, 2021, at 3:00 p.m. via online meeting. Dr. Bezdek will present his lecture, “Streaming Data Analysis: Old Clothes Don’t Fit.”

Bezdek is a visiting research fellow at The University of Melbourne, Australia. His interests include clustering in big data, woodworking, optimization, data visualization, cigars, fishing, anomaly detection, blues music, poker. He retired in 2007, and will be coming to a university near you soon.

Bezdek received a Ph.D. in Applied Mathematics from Cornell University in 1973. He is past president of NAFIPS (North American Fuzzy Information Processing Society), IFSA (International Fuzzy Systems Association), and the IEEE CIS (Computational Intelligence Society). He is founding editor the international journals Approximate Reasoning and IEEE Transactions on Fuzzy Systems. He is life fellow of the IEEE and IFSA; and a recipient of the IEEE 3rd Millennium award, the IEEE CIS Fuzzy Systems Pioneer award, and the IEEE Rosenblatt and Kampe de Feriet award.

Lecture Title

Streaming Data Analysis: Old Clothes Don’t Fit

Lecture Abstract

This talk concerns models and algorithms that are generally described as “streaming clustering.” Some of the semantics and methods that are used in this field are co-opted from static clustering. But often, they don’t serve their purposes for streaming data very well. A review of “state of the art” methods such as sequential k-means, Birch, CluStream, DenStream, etc. shows that methods borrowed from classical batch techniques don’t transfer well to the streaming data case. Most of these models fail to acknowledge that the data are seen but once in real streaming analysis (e.g., intrusion detection, quality control). When the data are not saved, batch clustering ideas such as pre-clustering assessment, partitioning, and cluster validity are not relevant. I do not argue that current approaches to streaming clustering are wrong: but they are described wrong. This class of algorithms comprises transitional methods for an intermediate case that lies between static and (near real time) dynamic analysis which will eventually lead to a new and useful paradigm for this type of computation. I call these methods start and stop streaming data analysis.

Five models are briefly reviewed and illustrated (albeit poorly, with small labeled data sets!). Then I will discuss four new incremental Stream Monitoring Functions and a new approach for visual assessment of streaming data. The conclusions? Useful analysis of real streaming data is in its infancy. We need to carefully define the objectives of streaming analysis, and then choose terminology and methods that suit this evolving paradigm.

Bezdek says his views on this topic are a bit controversial. You can read them here:

Bezdek, J. C. and Keller, J. M. (2021). Streaming data analysis: Clustering or Classification?, IEEE Trans. SMC, DOI: 10.1109/TSMC.2020.3035957 

Lan Zhang, ECE, to Present Lecture Jan. 15, 3 pm

Assistant Professor Lan “Emily” Zhang, Electrical and Computer Engineering, will present her lecture, “Augmenting Radio Environments for Better Wireless Ecosystems,” on Friday, January 15, 2021, at 3:00 p.m., via online meeting.

The lecture is hosted by the Michigan Tech Department of Computer Science. Zhang is a member of the Cyber-Physical Systems (CPS) research group of the Institute of Computing and Cybersystems (ICC).

Zhang’s research interest span the fields of cyber-physical systems, distributed machine learning, wireless communications, and cybersecurity. In her talk, she will discuss a series of studies leveraging smart-surfaces, e.g., meta-surfaces or reconfigurable intelligent surfaces (RISs), to augment radio environments for various purposes.

Lecture Abstract

In the last several decades, wireless technologies have become well-established to fight against propagation obstacles. Most conventional efforts are focused on optimizing end devices, such as transmitters and receivers, in order to adapt to the given transmission environment for better communications. However, the recent rapid convergence of the cyber and physical worlds (Cyber-Physical Systems or CPSs) presents unprecedented challenges to the wisdom of conventional design. Given ever-growing service demands, as well as the diverse wireless application scenarios, it is critical to adaptively augment the radio environments in a cost-effective way, while maintaining the aesthetic nature of living environments.

In her talk, Zhang will discuss a series of studies leveraging smart-surfaces–e.g., meta-surfaces or reconfigurable intelligent surfaces (RISs)–to augment radio environments for various purposes. Specifically, she will focus on three promising areas for enhancing the throughput and reliability of wireless communications, mitigating the physical-layer security threats, and facilitating wireless sensing activities. Both model-based and learning-based methods will be used for theoretical and practical analysis.

Biography

Dr. Lan Zhang is an assistant professor in the Department of Electrical and Computer Engineering at Michigan Tech. She received a Ph.D. degree in computer engineering from the University of Florida in 2020, and M.S. and B.Eng. degrees in telecommunication engineering from the University of Electronic Science and Technology of China in 2016 and 2013, respectively.

Zhang has served as a technical program committee member for several respected conferences, such as NeurIPS-SpicyFL 2020 and the 2020 IEEE IFOCOM poster/demo section. She has also served as reviewer for leading journals, such as IEEE Transactions on Communications, IEEE Transactions on Vehicular Technology, IEEE Transactions on Mobile Computing, and IEEE Transactions on Wireless Computing.

Lan Zhang, ECE

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.