Category: College of Computing

Software Engineering Program Ranked Among the Best

Michigan Tech’s BS in Software Engineering is in the top 10 nationwide according to College Rank. The website ranked the 35 Best Bachelor’s in Software Engineering.

Michigan Tech, which appears at number nine on the list, was one of only two Michigan colleges to make the ranking. The University of Michigan – Dearborn was ranked 15th.

“It’s great to see our program get this well-deserved recognition,” says Professor and Chair Linda Ott, Computer Science. “We consistently hear from industries that hire our graduates that our alumni are well-prepared and quickly become productive developers in their organizations.”

“Our students gain a solid theoretical framework, which provides the foundation for life-long career growth and success, as well as extensive practical, hands-on experience through class projects, internships and the Michigan Tech Enterprise program,” Ott explains.

College Rank uses a ranking methodology based on three aspects — Potential Salary After Graduation (40%), Individual Program Accreditation (30%) and Overall Affordability (30%).

“This program will help you to secure your position in a well-regarded profession,” says the College Rank website about Michigan Tech’s Software Engineering program. “You’ll be able to work with teams in your classes as well as labs and in the Senior Enterprise or Design programs. The Enterprise Program is a unique opportunity that brings together students of all majors to work on real projects with real clients in a business-like environment. You’ll receive guidance and coaching from faculty mentors throughout every step of your journey here.”

Computing Majors on Team that Takes 3rd in Lockheed CTF Competition

Two College of Computing RedTeam students are part of a five-member team that finished 3rd in last weekend’s invitation-only Lockheed Martin Advanced Technologies Laboratories (ATL) Capture the Flag cybersecurity competition.

The multi-day virtual event involved 200 students on 40 teams. It opened for answer submission Friday, January 8, at 8:00 p.m., and closed Sunday, January 10, at 8 p.m.

The 3rd Place team, GoBlue!, trailed the 2nd Place team by only 14 points. RedTeam members are Michigan Tech undergraduates Dakoda Patterson, Computer Science, and Trevor Hornsby, Cybersecurity, and three University of Michigan students from the RedTeam’s partnership with that institution.

Michigan Tech RedTeam faculty advisors are Professor Yu Cai, Applied Computing, and Assistant Professor Bo Chen, Computer Science.

“We were lucky to be one of the 40 teams invited,” said Cai. “This was no small task, as the CTF included a large number of points in Reversing and “pwning” challenges, which proved to be fairly difficult. Other challenges were Cryptography, Stegonography, Web Exploitation, and miscellaneous challenges.”

CTF competitions place hidden “flags” in various computer systems, programs, images, messages, network traffic and other computing environments. Each individual or team is tasked with finding these flags. Participants win prizes while learning how to defend against cybersecurity attacks in a competitive and safe arena.

Top Three Teams

Placement Team Name Institution Total Points
1st Place nullbytes George Mason University 3697
2nd Place ChrisSucks George Mason University 3330
3rd Place GoBlue! Michigan Tech and University of Michigan 3316

Master’s Defense: Taylor Morris, CS, Tues., Jan. 5

Computer Science graduate student Taylor Morris will present a Master’s Defense on Tuesday, January 5, from 6:00 to 7:00 p.m.

Presentation Title: “Using Text Mining and Machine Learning Classifiers to Analyze Stack Overflow.”

Advisor: Associate Professor Laura Brown, Computer Science

Link to the Michigan Tech Events Calendar entry here.

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!

Did you miss last week’s 1010 with Chuck Wallace? Watch the video below.

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. EST Kakula is advised by Dr. Timothy Havens, College of Computing.

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.

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 ’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, 10 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. Wallace is a researcher with the ICC’s Human-Centered Computing and Computing Education research groups.

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

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 Dr. 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.

Sidike Paheding Lecture is Dec. 11, 3 pm

Assistant Professor Sidike Paheding, Applied Computing, will present his lecture, “Deep Neural Networks for UAV and Satellite Remote Sensing Image Analysis,” on Dec. 11, 2020, at 3:00 p.m. via online meeting.

Paheding’s research focuses on the areas of computer vision, machine learning, deep learning, image/video processing, and remote sensing.

The lecture is presented by the Department of Computer Science.

Lecture Abstract

Remote sensing data can provide non-destructive and instantaneous estimates of the earth’s surface over a large area, and has been accepted as a valuable tool for agriculture, weather, forestry, defense, biodiversity, etc. In recent years, deep neural networks (DNN), as a subset of machine learning. for remote sensing has gained significant interest due to advances in algorithm development, computing power, and sensor systems.

This talk will start with remote sensing image enhancement framework, and then primarily focuses on DNN architectures for crop yield prediction and heterogeneous agricultural landscape mapping using UAV and satellite imagery.

Speaker Biography

Paheding is an associate editor of the Springer journal Signal, Image, and Video Processing, ASPRS Journal Photogrammetric Engineering & Remote Sensing, and serves as a guest editor/reviewer for a number of reputed journals. He has advised students at undergraduate, M.S., and Ph.D. levels, and authored/coauthored close to 100 research articles.

Bo Chen, CS, Wins REF Grant for Decentralized Cloud Storage Project

Bo Chen, Computer Science, has been awarded a Fall 2020 REF Research Seed Grant (REF-RS) for his project, “Towards Secure and Reliable Decentralized Cloud Storage.” Funding for the 12-month, $25,800 award begins on January 1, 2021.

Bo Chen, Computer Science

“This grant will provide significant help to advance my current research,” says Chen. “This is really exciting news for me.”

Bo Chen is a researcher with the ICC’s Cybersecurity and Computing Education research groups.

As a recipient of the REF seed grant, which is awarded by the Michigan Tech Office of the Vice President for Research, Chen will participate in review and feedback for the next round of REF proposals. View the full list of Fall 2020 REF award recipients here.


A decentralized cloud storage system eliminates the need of dedicated computing infrastructures by allowing peers which have spare storage space to join the network and to provide storage service. Compared to the conventional centralized cloud storage system, it can bring significant benefits including cheaper storage cost, better fault tolerance, greater scalability, as well as more efficient data storing and retrieval, making it well fit the emerging Internet of things (IoT) applications.

While bringing immense benefits, the decentralized cloud storage system also raises significant security concerns, since the storage peers are much less reputable than the traditional data centers and may more likely misbehave.

This project thus aims to build a secure and reliable decentralized cloud storage system which can serve as the cloud infrastructure for future IoT applications. The project will actively investigate two fundamental security issues faced by the decentralized cloud storage system: 1) How can we prevent the malicious storage peers from stealing the data? 2) How can we ensure that once the data are stored into the system, they are always retrievable even if the storage peers misbehave?

To address the aforementioned issues in an untrusted p2p environment, the PI will integrate efficient integrity checking with the blockchain, as well as the broadly equipped secure hardware like Intel SGX. The PI will also broaden the educational impact of the proposed project by actively involving both graduate and undergraduate students from the MTU cybersecurity programs.