Category: Havens

ECE Doctoral Defense – Adam Webb

by Electrical and Computer Engineering Department

Electrical Engineering doctoral candidate Adam Webb will present his PhD defence at 3:30 p.m. Thursday (April 15) via Zoom.

The title of his presentation is “Novel Methods in Computational Imaging with Applications in Remote Sensing.”

Webb’s co-advisors are Timothy Schulz (ECE) and Timothy Havens (CC).


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.

Join the virtual lecture here.

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.


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.


College of Computing Overview with Tim Havens Is Tues., Jan. 19, 7-8 pm

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.

Register for the live Zoom session here.

View the University Events Calendar listing here.


1010 with … Tim Havens, Weds., Jan. 20, 5:30-5:40 pm

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.

1010 with … Nathir Rawashdeh, December 16, 2020

The 1010 with … series continues on Wednesday January 27 … with more to come!


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.


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.


Tim Havens: Warm and Fuzzy Machine Learning

What are you doing for supper this Monday night at 6? Grab a bite with Dean Janet Callahan and Associate Professor Tim Havens, director of the Michigan Tech’s Institute of Computing and Cybersystems and associate dean for research in the College of Computing. Get the full scoop and register at mtu.edu/huskybites.

“Nearly everyone has heard the term ‘Deep Learning’ at this point, whether to describe the latest artificial intelligence feat like AlphaGo, autonomous cars, facial recognition, or numerous other latest-and-greatest gadgets and gizmos,” says Havens. “But what is Deep Learning? How does it work? What can it really do—and how are Michigan Tech students advancing the state-of-the-art?”

In this session of Husky Bites, Prof. Havens will talk about everyday uses of machine learning—including the machine learning research going on in his lab: explosive hazards detection, under-ice acoustics detection and classification, social network analysis, connected vehicle distributed sensing, and other stuff.

Joining in will be one of Havens’ former students, Hanieh Deilamsalehy, who earned her PhD in electrical engineering at Michigan Tech. She’s now a machine learning researcher at Adobe. Dr. Deilamsalehy graduated from Michigan Tech in 2017 and headed to Palo Alto to work for Ford as an autonomous vehicle researcher. She left the Bay Area for Seattle to take a job at Microsoft, first as a software engineer, and then as a machine learning scientist. In April she accepted a new machine learning position at Adobe, “in the middle of the pandemic!”

Havens is a Michigan Tech alum, too. He earned his BS in ‘99 and MS in Electrical Engineering in ‘00, then went to the MIT Lincoln Laboratory, where he worked on simulation and modeling of the Airborne Laser System, among other defense-related projects. From there it was the University of Missouri for a PhD in Electrical and Computer Engineering, researching machine learning in ontologies and relational data.

Nowadays, Havens is the William and Gloria Jackson Associate Professor and Associate Dean for Research in the College of Computing. In addition to serving as director of Michigan Tech’s ICC, he also heads up the ICC Center for Data Sciences and runs his own PRIME Lab, too (short for Pattern Recognition and Intelligent Machines Engineering).

“An important goal for many mobile platforms—terrestrial, aquatic, or airborne—is reliable, accurate, and on-time sensing of the world around them.”Tim Havens

Havens has spent the past 12 years developing methods to find explosive hazards, working with the US Army and a research team in his lab. According to a United Nations report, more than 10,000 civilians were killed or injured in armed conflict in Afghanistan in 2019, with improvised explosive devices used in 42 percent of the casualties. Havens is working to help reduce the numbers.

“Our algorithms detect and locate explosive hazards using two different systems: a vehicle-mounted multi-band ground-penetrating radar system and a handheld multimodal sensor system,” Havens explains. “Each of these systems employs multiple sensors, including different frequencies of ground penetrating radar, magnetometers and visible-spectrum cameras. We’ve created methods of integrating the sensor information to automatically find the explosive hazards.” 

As a PhD student at Michigan Tech, Deilamsalehy worked alongside Havens as a research assistant in the ECE department’s Intelligent Robotics Lab (IRLab). “My research was focused on sensor fusion, machine learning and computer vision, fusing the data from IMU, LiDAR, and a vision camera for 3D localization and mapping purposes,” she says. “I used data from a sensor platform in the IRLab, mounted on an unmanned aerial vehicle (UAV), to evaluate my proposed fusion algorithm.”

Havens is also co-advisor to students in the SENSE (Strategic Education through Naval Systems Experience) Enterprise team at Michigan Tech, along with ME-EM Professor Andrew Barnard. Students in SENSE design, build, and test engineering systems in all domains: space, air, land, sea, and undersea. Like all Enterprise teams, SENSE is open to students in any major. 

Prof. Havens, when did you first get into engineering? What sparked your interest?

I first became an engineer at Michigan Tech in the late 90s. What really sparked my interest in what-I-do-now was my introductory signal processing courses. The material in these courses was the first stuff that really ‘spoke’ to me. I have always been a serious musician and the mathematics of waves and filters was so intuitive because of my music knowledge. I loved that this field of study joined together the two things that I really loved: music and math. And I’ve always been a computer geek. I was doing programming work in high school to make extra money; so that side of me has always led me to want to solve problems with computers.

Hometown, Hobbies, Family?

I grew up in Traverse City, Michigan, and came to Tech as a student in the late 90s. I’ve always wanted to come back to the Copper Country; so, it’s great that I was able to return to the institution that gave me the jump start in my career. I live (and currently work from home) in Hancock with my partner, Dr. Stephanie Carpenter (an author and MTU professor), and our two fur children, Rick Slade, the cutest ginger in the entire world, and Jaco, the smartest cat in the entire world. I have a grown son, Sage, who enjoys a fast-paced life in Traverse City. Steph and I enjoy exploring the greater Keweenaw and long discussions about reality television, and I enjoy playing music with all the local talent, fishing (though catching is a challenge), and gradually working through the lumber pile in my garage.

Dr. Deilamsalehy, how did you find engineering? What sparked your interest?

I was born and raised in Tehran, Iran. I have always been into robotics. I was a member of our robotics team in high school and that led me to engineering. I decided to apply to Michigan Tech sort of by chance when a friend of mine told me about it. I looked at the programs in the ECE department, and felt they aligned with my interests. Then soon after I first learned about Michigan Tech, I found out that one of my undergraduate classmates went there. I talked to him, and he also encouraged me to apply. And that’s how I was able to join Michigan Tech for my PhD program. My degree is in electrical engineering but my focus at Michigan Tech involved computer science and designing Machine Learning solutions.

Hobbies and Interests?

I now live in Seattle, famous for outdoor activities—kind of like the UP, but without the cold—so I do lots of mountaineering, biking, rock climbing, and in the winter, skiing. I learned how to ski at Michigan Tech, up on Mont Ripley. It’s steep, and it’s cold! Once you learn skiing on Ripley, you’re good. You can ski just about anywhere.
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College of Computing, CNSA Program Focus of HostingAdvice Article

The College of Computing and the Institute of Computing and Cybersystems (ICC) are the subjects of an article published today (Sept. 2, 2020) on HostingAdvice.com, a website and blog that educates visitors to the site about the world of web hosting.

The article, for which College of Computing Dean Adrienne Minerick was interviewed, provides a close look at the new College, its well-established Computer Science and Software Engineering degree programs (B.S., M.S., and Ph.D.), new Cybersecurity and Mechatronics undergraduate programs, as well as faculty research and the ICC.

Special emphasis is placed on the Computer Network and Systems Administration undergraduate degree program, in which students prepare for careers as network and computer systems administrators, commonly referred to as a “sysadmins.”

Read the full article here.

“Our readers know that a lot goes into finding the best providers of shared, dedicated, and virtual private servers,” said Sean Garrity, managing editor at HostingAdvice.com. “The article provides information about how to prepare if you want to to break into the industry as a professional, not just a consumer.”