Category: ICC

Summer Youth Programs (SYP): Topics in Computing

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.

Explore the SYP website and see all SYP classes here.

Computing Programs
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
Engineering Programs
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

CS Lecture: Kelly Steelman, CLS, March 19, 3 pm

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.”

Join the virtual lecture here.

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.


CS Dept. Lecture: Hongyu An, ECE, Friday, March 5

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.

Join the virtual lecture here.

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).

Spend 1010 with Dean Dennis Livesay, Feb. 17, 5:30 pm

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.

ICC Distinguished Lecture: Alina Zare, Univ. of Florida

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.

Beth Veinott to Present Lecture February 12, 3 pm

The Department of Computer Science will present a lecture by Dr. Elizabeth Veinott on Friday, February 12, 2021, at 3:00 p.m.

Veinott is an associate professor in the Cognitive and Learning Sciences department. She will present, “Beyond the system interface: Using human-centered design to support better collaborative forecasting.”


Speaker Biography

Elizabeth Veinott is a cognitive psychologist working in technology-mediated environments to improve decision making, problem solving and collaboration. She directs Michigan Tech’s Games, Learning and Decision Lab and is the lead for the Human-Centered Computing group of Michigan Tech’s Institute of Computing and Cybersystems (ICC).

She has been active in the ACM’s SIGCHI and on the conference organizing committees for CHI Play and CSCW. Prior to joining Michigan Tech in 2016, she worked as a principal scientist in an industry research and development lab and as a contractor at NASA Ames Research Center. Her research has been funded by NIH, Army Research Institute, Army Research Lab, Air Force Research Laboratory, and IARPA.

Lecture Abstract

Teams use technology to help them make judgments in a variety of operational environments. Collaborative forecasting is one type of judgment performed by analyst teams in weather, business, epidemiology, and intelligence analysis. Research related to collaborative forecasting has produced mixed results.

In her talk, Veinott will describe a case of using cognitive task analysis to develop and evaluate a new forecast process and tool. The method captured analysts’ mental models of game-based forecasting problems, and allowed the process to co-evolve with the system design. The tool was tested in a simulation environment with expert teams conducting analyses over the course of hours and compared to a control group. Challenges and lessons learned will be discussed, including implications for human-centered design of collaborative tools.

Sidike Paheding Wins MDPI Electronics Best Paper Award

A scholarly paper co-authored by Assistant Professor Sidike Paheding, Applied Computing, is one of two papers to receive the 2020 Best Paper Award from the open-access journal Electronics, published by MDPI.

The paper presents a brief survey on the advances that have occurred in the area of Deep Learning.

Paheding is a member of the Institute of Computing and Cybersystems’ (ICC) Center for Data Sciences (DataS).

Co-authors of the article, “A State-of-the-Art Survey on Deep Learning Theory and Architectures,” are Md Zahangir Alom, Tarek M. Taha, Chris Yakopcic, Stefan Westberg, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul A. S. Awwal, and Vijayan K. Asari. The paper was published March 5, 2019, appearing in volume 8, issue 3, page 292, of the journal.

View and download the paper here.

Papers were evaluated for originality and significance, citations, and downloads. The authors receive a monetary award , a certificate, and an opportunity to publish one paper free of charge before December 31, 2021, after the normal peer review procedure.

Electronics is an international peer-reviewed open access journal on the science of electronics and its applications. It is published online semimonthly by MDPI.

MDPI, a scholarly open access publishing venue founded in 1996, publishes 310 diverse, peer-reviewed, open access journals.

Paper Abstract

In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, cybersecurity, and many others.

This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network (DNN). The survey goes on to cover Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). Additionally, we have discussed recent developments, such as advanced variant DL techniques based on these DL approaches. This work considers most of the papers published after 2012 from when the history of deep learning began.

Furthermore, DL approaches that have been explored and evaluated in different application domains are also included in this survey. We also included recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches. There are some surveys that have been published on DL using neural networks and a survey on Reinforcement Learning (RL). However, those papers have not discussed individual advanced techniques for training large-scale deep learning models and the recently developed method of generative models.

Sidike Paheding

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).