Please join the College of Computing’s Tim Havens at a College of Computing Undergraduate Overview on Tuesday, January 19, from 7:00 to 8:00 p.m. The virtual event is presented by Michigan Tech Admissions. The focus of the event is on prospective students.
Event Details: Check out our diverse selection of majors, including Computer Network and System Administration, Computer Science, Cybersecurity, Electrical Engineering Technology, Mechatronics, Software Engineering, and our first-year computing undecided program, General Computing.
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.
You are invited to spend one-zero-one-zero—that is, ten—minutes with Dr. Timothy Havens on Wednesday, January 20, from 5:30 to 5:40 p.m. EST.
Havens is the Associate Dean for Research in the College of Computing, Director of the Institute of Computing and Cybersystems, and the William and Gloria Jackson Associate Professor of Computer Systems at Michigan Tech.
In this informal discussion, Havens will talk about undergraduate research opportunities at Michigan Tech, his research in AI and machine learning, and answer your questions about the College of Computing.
We look forward to spending 1010 minutes with you!
Did you miss the December 16, 1010 with Nathir Rawashdeh? Watch the video below.
The 1010 with … series continues on Wednesday January 27 … with more to come!
Associate Professor Charles Wallace, Computer Science, was quoted in the story “Older adults struggle to access COVID-19 vaccine appointment websites,” in a January 12, 2021, article in The Verge.
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.”
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 |
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.
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!
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.