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  • Month: December 2020

    Michigan Tech Ranked as Best ROI Among Public Schools in Michigan

    Michigan Tech was ranked as having the best return on investment (ROI) of any public college in Michigan by Stacker.com.

    In an article published Saturday, “Public College in Every State with the Best ROI,” Stacker listed the public school with the “best bang for the buck,” in each state.

    Michigan Tech came out on top of the 15 public colleges or universities in Michigan considered for the ranking. The article’s author John Harrington said of MTU, “Students at the school develop printable 3D prosthetic hands created from recycled plastic to help kids in Nicaragua, create quieter snowmobiles and launch orbiting nanosatellites.”

    Stacker considered public colleges that primarily issue bachelor’s degrees. “The college with the highest 40-year ROI in every state was included. The study incorporated net present value, that calculates future earnings based on income ten and forty years, respectively, after starting college.”


    Celebrate Husky Innovation January 25-29

    Husky Innovate is organizing Innovation Week, a series of innovation themed events the week of January 25 to 29, 2020. Our goal is to provide opportunities for students, faculty and alumni to meet virtually to engage around the topic of innovation.

    We will host panel discussions, alumni office hours and the Bob Mark Business Model Pitch Competition from 5:30 to 7:30 p.m. on Thursday, January 28.

    We will celebrate entrepreneurship, innovative research and projects on campus and within our extended MTU community.

    If you are interested in hosting an innovation tour, participating in a panel discussion, leading a workshop or something else, sign-up here.

    Faculty and staff are invited to celebrate innovation week with an innovation themed learning module or student activity.


    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. 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.


    College of Computing Convocation is December 18, 3:30 pm

    Congratulations Class of 2020!

    We are looking forward to celebrating the accomplishments of our graduates at a Class of 2020 virtual Convocation program on Friday, December 18, 2020, at 3:30 p.m. EST.

    Join the virtual event here.

    The celebration will include special well-wishes from CC faculty and staff, and many will be sporting their graduation regalia. It is our privilege to welcome Ms. Dianne Marsh, 86, ’92, as our Convocation speaker. Dianne is Director of Device and Content Security for Netflix, and a member of the new College of Computing External Advisory Board.

    We may be spread across the country and world this December, but we can still celebrate with some style. We look forward to sharing our best wishes with the Class of 2020 and wishing them continued success as they embark on the next phase of their lives!

    This December, 40 students are expected to graduate with College of Computing degrees, joining 92 additional Class of 2020 PhD, MS, and BS alumni.

    Dianne Marsh

    Dianne Marsh ’86, ’92 is Director of Device and Content Security for Netflix. Her team is responsible for securing the Netflix streaming client ecosystem and advancing the platform security of Netflix-enabled devices. Dianne has a BS (’86) and MS (’92) in Computer Science from Michigan Tech.

    Visit the Class of 2020 Webpage

    Congratulations Graduates. We’re proud of you.


    1010 Minutes with … Chuck Wallace

    Chuck Wallace, center, at a BASIC computer tutoring session at Portage Lake District Library, Houghton.

    You are invited to spend one-zero-one-zero—that is, ten—minutes with Dr. Charles Wallace on Wednesday, December 9 from 5:30 to 5:40 p.m.

    Wallace is associate dean for curriculum and instruction and associate professor of computer science in the College of Computing at Michigan Tech.

    In his informal discussion, Wallace will talk about computing at Michigan Tech, his research on how humans can better understand, build, and use software, and answer any questions you might have.

    We look forward to spending 1010 minutes with you!

    Join 1010 with Chuck Wallace here.

    Next week, on Wednesday, December 15, at 5:30 p.m., Assistant Professor Nathir Rawashdeh, Applied Computing, will present his current research work, including his use of artificial intelligence for autonomous driving on snow covered roads, and a mobile robot using ultraviolet light to disinfect indoor spaces.

    Did you miss 1010 with Chuck Wallace on December 9? Watch the video below.

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