Category: Lectures

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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Dr. James Keller, recently retired Curators’ Distinguished Professor in the EE/CS department at University of Missouri, Columbia, will present his lecture, “Soft Streaming Classification,” on Friday, October 30, 2020, at 3:00 p.m., via Zoom online meeting.

The talk is an Institute of Computing and Cybersystems’ (ICC) Distinguished Lecture Series event.

Join the meeting here.

A Life Fellow of the Institute of Electrical and Electronics Engineers (IEEE), Keller recently received the IEEE Frank Rosenblatt Award for his “fundamental work on fuzzy pattern recognition, fuzzy clustering, and fuzzy technologies in computer vision.” He holds a number of additional professional and academic honors and awards.

Lecture Abstract

As the volume and variety of temporally acquired data continues to grow, increased attention is being paid to streaming analysis of that data. Think of a drone flying over unknown terrain looking for specific objects which may present differently in different environments. Understanding the evolving environments is a critical component of a recognition system.

With the explosion of ubiquitous continuous sensing (something Lotfi Zadeh predicted as one of the pillars of Recognition Technology in the late 1990s), this on-line streaming analysis is normally cast as a clustering problem. However, examining most streaming clustering algorithms leads to the understanding that they are actually incremental classification models.

These approaches model existing and newly discovered structures via summary information that we call footprints. Incoming data is routinely assigned crisp labels (into one of the structures) and that structure’s footprints are incrementally updated; the data is not saved for iterative assignments.

The three underlying tenets of static clustering:

1. Do you believe there are any clusters in your data?
2. If so, can you come up with a technique to find the natural grouping of your data?
3. Are the clusters you found good groupings of the data?

These questions do not directly apply to the streaming case. What takes their place in this new frontier?

In this talk, I will provide some thoughts on what questions can substitute for the Big 3, but then focus on a new approach to streaming classification, directly acknowledging the real identity of this enterprise. Because the goal is truly classification, there is no reason that these assignments need to be crisp.

With my friends, I propose a new streaming classification algorithm, called StreamSoNG, that uses Neural Gas prototypes as footprints and produces a possibilistic label vector (typicalities) for each incoming vector. These typicalities are generated by a modified possibilistic k-nearest neighbor algorithm.

Our method is inspired by, and uses components of, a method that we introduced under the nomenclature of streaming clustering to discover underlying structures as they evolve. I will describe the various ingredients of StreamSoNG and demonstrate the resulting algorithm on synthetic and real datasets.

University Professor Timothy Schulz (ECE) will be featured at the Michigan Tech Research Forum (MTRF) at 4:30 p.m. Wednesday, Oct. 14.
Schulz’s presentation is titled “Direct Measurement of Coherent Fields.” Additional details can be found on the MTRF website.

The presentation will be available via Zoom and a limited number of people will be permitted to attend in person, dependent on university guidelines on the date of the event. If you wish to be considered for in-person attendance, complete this form by today (Oct. 9).

Schulz is a member of the ICC’s Center for Data Sciences.

The MTRF is presented by the Office of the Provost in coordination with the Office of the Vice President for Research. The forum showcases and celebrates the work of Michigan Tech researchers and aims to strengthen discussions in our community. All are welcome, including the general public.

Associate Professor Guy Hembroff, director of Michigan Tech’s Health Informatics graduate program, presented an invited virtual talk to physicians, residents, and medical students at the Bahiana Medical School, Salvador, Brazil, on September 25, 2020.

Hembroff spoke about, “The Challenges and Opportunities of Artificial Intelligence in Disease Prevention and Monitoring.”

BAHIANA (Bahia School of Medicine and Public Health) is a private, nonprofit, educational, cultural, scientific and healthcare institution. Its main purpose is “teaching, research and the spread of knowledge and special services in the fields of health, science and culture in general.” Learn more here.

Dr. Timothy Havens presented the lead talk at the Los Alamos National Laboratory’s ISR-2 Seminar Series on Advancing Toward Modern Detection and Estimation Techniques for Multi-Sensor Scenarios, presented online July 9, 2020.

Tim Havens is associate dean for research for the College of Computing, director of the Institute of Computing and Cybersystems (ICC), and the William and Gloria Jackson Associate Professor of Computer Systems.

The talk, “Explainable Deep Fusion,” described Havens’s sensor fusion systems research that seeks to combine cooperative and complementary sources to achieve optimal inference from pooled evidence.

Havens specifically discussed his innovations in non-linear aggregation learning with Choquet integrals and their applications in deep learning and Explainable AI.

To help faculty prepare for Flex Fall, IDEA Hub and the William G. Jackson Center for Teaching and Learning have organized a series of events, each including Flex Fall Q&A sessions and discussions about teaching.

Click the below links to register and receive a Google calendar invitation and the Zoom link. Questions? Email margaret@mtu.edu.

Session #2: Wednesday, June 17, 3:00 – 4:30 pm. Online Teaching Showcase
Teaching, Q&A: 3 – 3:30 pm; Teaching Showcase, Discussion: 3:30-4:30 pm)

Session #3, Wednesday, June 24, 3-5 pm: Develop Innovative Solutions
(Teaching, Q&A: 3 – 3:30 pm, Design Thinking Workshop: Develop Innovative Solutions: 3:30 – 5 pm)

Session #4: Wednesday, July 1, 3 to 5 pm: Prototype Your Innovative Solutions
(Teaching, Q&A: 3 to 3:30 pm; Design Thinking Workshop–Prototype Your Innovative Solutions: 3:30 – 5 pm)

Read the full story in Monday’s Tech Today.

The Graduate Student Government (GSG) Professional Development Committee has organized a free webinar series in Computer Programming, which begins Tuesday, July 14, 2020.

July 14: “Introduction to Machine Learning with Python,” by Timothy Havens (CC)

July 15: “Managing Data” (Data Mining)” by MS Data Science candidate Sneha Nimmagadda

July 16: “Introduction to Deep Learning,” by Timothy Havens (CC)

Seats are not limited, but participants are asked to register so webinar organizers know how many attendees to expect.

Find more information, including links to register and join Zoom meetings, visit the GSG website.

The Institute of Computing and Cybersystems and the Department of Electrical and Computer Engineering will present a lecture by Dr. Kun Zhu on Monday, March 2, 2020, at 3:00 p.m., in EERC 501. The title of Dr. Zhu’s talk is “Power Grid Operations – Beyond Physics.

Dr. Zhu holds a Ph.D. in electrical engineering from Iowa State University. He has 20 years’ experience in the power industry, including 17 years at MISO, an independent, not-for-profit organization that delivers safe, cost-effective electric power across 15 U.S. states and the Canadian province of Manitoba.

Dr. Zhu’s presentation will provide a high level introduction to how regional operators manage the power grid in the U.S. He will discuss how energy markets and balancing authorities (those responsible for maintaining the electricity balance within their respective regions) manage their regions and interact with each other; differences in how energy and transmission assets are managed; and the function of Regional Transmission Organizations (RTO).

At MISO, Dr. Zhu’s experience expands across planning, operations, and tariff administration. Currently, he is the manager of generator interconnection and chair of the SPIDER Working Group (SPIDER), a working unit of North America Electric Reliability Cooperation (NERC).  

MISO operates one of the world’s largest energy markets with more than $29 billion in annual gross market energy transactions. 

Members of the Michigan Tech Transportation Institute (MTTI) were active at the

Among the many Michigan Tech students and faculty who attended and presented at the 2020 Transportation Research Board (TRB) Annual Meeting held recently in Washington, DC. was Thomas Oommen (GMES), who gave a lecture on “Remote terrain Strength for Mobility Characterization” at the meeting’s lectern Session 1384: Integration of Remote Sensing Techniques and Classical Instrumentation. Oommen is a member of the ICC’s Center for Data Sciences.

The Transportation Research Board (TRB) 99th Annual Meeting was held January 12–16, 2020, in Washington, D.C. More than 13,000 transportation professionals from around the world were expected to attendd.

The meeting program covered all transportation modes, with more than 5,000 presentations in nearly 800 sessions and workshops, addressing topics of interest to policy makers, administrators, practitioners, researchers, and representatives of government, industry, and academic institutions. A number of sessions and workshops focused on the spotlight theme for the 2020 meeting: A Century of Progress: Foundation for the Future.

Learn more about the TRB.

Read the full Tech Today On the Road article.

Homin Song, a postdoctoral researcher at Argonne National Laboratory, will present a lecture on Thursday, January 23, 2020, at 4:00 p.m., in EERC 103.

The lecture is part of the Mechanical Engineering-Engineering Mechanics Graduate Seminar Speaker Series. It is presented in part by the Tomorrow Needs Faculty and Scientist Seminar Series sponsored by the Michigan Tech colleges of Computing, Engineering, and Sciences and Arts, Great Lakes Research Center, and Institute of Computing and Cybersystems. Learn more at mtu.edu/icc/seminars.

Song completed a Ph.D. in civil engineering at University of Illinois at Urbana-Champaign in 2019. He holds an M.S. degree from Korea Advanced Institute of Science and Technology (KAIST) and a B.S. from Hanyang University, also in civil engineering.

Homin’s research interests lie in nondestructive evaluation (NDE) and structural health monitoring (SHM) based on ultrasonic wave motion. His broad spectrum of expertise encompasses the topical areas of NDE/SHM, such as advanced ultrasound sensing technology, signal/data processing, numerical modeling, and experimental solid mechanics. His current postdoctoral research aims at developing a super-resolution non-contact ultrasonic array imaging technique via deep learning.

Homin was awarded the Student Best Paper Award at the 2017 International Workshop on Structural Health Monitoring, the Student Award for Research on NDT from American Concrete Institute, and the Outstanding Paper Award from the Korean Society of Civil Engineers. 

Abstract: Nondestructive evaluation (NDE) and structural health monitoring (SHM) systems are essential for today’s modern structures to ensure their long-term performance and reduced maintenance cost. The talk will present two full-field high-resolution ultrasonic imaging approaches to detect, image, and characterize internal damage in various materials and structural elements. The first approach is a near-field imaging technique via noncontact ultrasonic scanning measurements. Development of novel ultrasonic scanning hardware, numerical and experimental wave mechanics study to understand complicated wave scattering, and wavefield data processing are presented. A unique application of the developed approach to large-scale concrete structures under realistic damage-promoting environments is also presented. The second approach is a far-field imaging technique based on deep learning. A novel hierarchical multi-scale deep learning approach designed to image subtle structural defects is presented. The results are compared with those obtained by a widely accepted high-resolution imaging technique, Time-reversal MUSIC.