On May 18, 2021, Dr. Guy Hembroff, Applied Computing, presented an invited talk at a meeting of Michigan’s Health Information Management Systems Society (HIMSS). Dr. Hembroff discussed his work developing a trusted framework architecture designed to improve population health management and patient engagement.
The talk demonstrated his team’s work in the development of accurate geo-tagged pandemic prediction algorithms, which are used to help coordinate medical supply chains to care for patients most vulnerable to COVID-19, an innovation that can be extended to help improve general population health management.
The framework of the pandemic prediction architecture, which aggregates longitudinal patient health data, including patient generated health data and social determinants of health, is a holistic and secure mHealth community model. The model can help Michigan residents overcome significant barriers in healthcare, while providing healthcare agencies with improved and coordinated population management and pandemic prediction.
The architecture’s machine learning algorithms strategically connect residents to community resources, providing customized health education aimed to increase the health literacy, empowerment and self-management of patients. The security of the architecture includes development of unique health identifiers and touch-less biometrics capable of large-scale identity management.
Dr. Guy Hembroff is an associate professor in the Applied Computing department of the Michigan Tech College of Computing, and director of the Health Informatics graduate program. His areas of expertise are network engineering, medical/health informatics, biometric development, intelligent medical devices, data analytics, and cybersecurity.
The event was sponsored by HIMSS and Blue Cross Blue Shield of Michigan (BCBSM).
A mission-driven non-profit, the Healthcare Information and Management Systems Society, Inc. (HIMSS) is a global advisor and thought leader supporting the transformation of the health ecosystem through information and technology, according to the organization’s website.
The Institute of Computing and Cybersystems (ICC) is pleased to welcome Tony Pinar as a member. Pinar’s primary research interests are in applied machine learning and data fusion.
A lecturer in Michigan Tech’s Electrical and Computer Engineering department, Pinar holds a Ph.D. and M.S. in Electrical Engineering from Michigan Tech. His previous positions include research engineer for Michigan Tech’s Advanced Power System Research Center and electrical design engineer for GE Aviation. He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and the IEEE Computational Intelligence Society.
Pinar’s teaching interests include machine learning, signal processing, and electronic design. Included among the classes he teaches are Electronics, Electronic Applications, Probability—Signal Analysis, and Control Systems I.
“Teaching is like a puzzle where one may have to take a difficult concept, reduce it to digestible pieces, and deliver them to fresh minds in a way to maximize understanding and insight,” Pinar says. “That challenge is what drives me to be a better teacher.”
Pinar believes that to be a good teacher one must understand the topics very well and he strives for the most effective delivery. “This keeps me on my toes, forces me to constantly identify holes in my knowledge, and drives me to continuously strive to learn new things,” he explains.
On research, Pinar says it is rewarding to work on open-ended and novel problems that are in their infancy and at the cutting edge of today’s technology.
“It is also exciting to me to watch the cutting edge move forward, see what sticks and what doesn’t, and observe how the direction(s) of the field evolve,” he adds. “I’m very new to this domain so I haven’t been able to observe it for long, but I am looking forward to witnessing the future of the field.”
by Mark Wilcox, University Marketing and Communications
Michigan Tech is celebrating the accomplishments of more than 1,000 undergraduate and graduate students who completed their degrees by the end of the spring 2021 semester. Despite the lack of a formal ceremony due to COVID-19 restrictions, more than 600 undergraduate and graduate students are expected to participate in a physically distanced celebration tomorrow (April 30) throughout the afternoon.
The Graduation Celebration website shines a spotlight on the graduating class and provides links for graduates, their families and friends, and the community. Graduates and alumni can share their favorite memories and view profiles created by the Class of 2021.
Among the undergraduates, 99 will graduate cum laude, 116 magna cum laude and 68 summa cum laude. In the Graduate School, there will be 31 doctorates awarded, along with 203 Master of Science degrees, 46 graduate certificates, 27 Master of Business Administration degrees, two Master of Forestry degrees, five Master of Geographic Information Science degrees, and 46 graduate certificates.
The Air Force ROTC and Army ROTC will conduct a joint commissioning ceremony at 7:30 a.m. Saturday (May 1) at the Rozsa Center for the Performing Arts. Eleven cadets will be commissioned as officers in the Air Force and five cadets will be commissioned as officers in the Army. Due to limited seating, a livestream ceremony will be available via Zoom.
One of the highlights of Michigan Tech’s traditional commencement ceremony is an address by a member of the graduate class. Even without a formal ceremony, remarks by this year’s student speaker, Tanner Sheahan, can be viewed on the Celebration Messages page of the Graduation Celebration website.
Sheahan, a chemical engineering major from Bay City, Mich., is enthusiastic about the message he’ll share with his class. “I love writing, and I think I have a knack for telling stories. I had fun — this has been a great experience,” he said.
Because of COVID-19 restrictions, Sheahan’s internship last summer was conducted virtually, but it had a very positive outcome. “The situation really opened up extreme doors and led to a job as a sales operations manager at Nalco Water, an Ecolab Company in Naperville, Illinois,” he said.
University President Rick Koubek congratulated MTU’s newest alumni. “On behalf of Michigan Tech’s entire faculty and staff, we wish you the very best in your future endeavors and trust you will do great things with your degree from Michigan Tech,” said Koubek. “Congratulations again on this tremendous accomplishment.”
by Karen S. Johnson, Communications Director, College of Computing
Four Michigan Tech graduate students recently took 6th place in the U.S. Navy’s Artificial Intelligence (AI) Tracks at Sea Challenge, receiving a $6,000 prize.
The Challenge solicited software
solutions to automatically generate georeferenced tracks of maritime vessel
traffic based on data recorded from a single electro-optical camera imaging the
traffic from a moving platform.
Each Challenge team was presented
with a dataset of recorded camera imagery of vessel traffic, along with the
recorded GPS track of a vessel of interest that is seen in the imagery.
Graduate students involved in the challenge were Zach DeKraker and Nicholas Hamilton, both Computer Science majors advised by Tim Havens; Evan Lucas, Electrical Engineering, advised by Zhaohui Wang; and Steven Whitaker, Electrical Engineering.
Submitted solutions were evaluated
against additional camera data not included in the competition testing set in
order to verify generalization of the solutions. Judging was based on track
accuracy (70%) and overall processing time (30%).
“We never got our final score, but we were the “first runner up” team,” says Lucas. “Based on our testing before sending it, we think it worked well most of the time and occasionally tracked a seagull or the wrong boat.”
The total $200,000 prize was distributed among five winning teams, which submitted full working solutions, and three runners-up, which submitted partial working solutions.
The Challenge was sponsored by the Naval
Information Warfare Center (NIWC) Pacific and the Naval Science, Technology,
Engineering, and Mathematics (STEM) Coordination Office, and managed by the Office
of Naval Research. Its goal was to engage with the workforce of tomorrow on
challenging and relevant naval problems, with the immediate need to augment
unmanned surface vehicles’ (USVs’) maritime contact tracking capability.
problem presented was to find a particular boat in a video taken of a harbor,
and track its GPS coordinates.,” says Zach DeKraker. “We were provided with
samples of other videos along with the target boat’s GPS coordinates for that
video, which we were able to use to come up with a mapping from pixels to GPS
“Basically, we wanted to track boats with a video camera,” adds ECE graduate student Steven Whitaker. “Our team used machine learning and computer vision to do this. At weekly meetings we brainstormed approaches to tackling the problem, and at regular work sessions, together we programmed it all and produced a white paper with the technical details.”
Whitaker says the competition tied in pretty closely to work the students have already done. “We had a good majority of the code already written. We just needed to fit everything together and add in a few more details and specialize it for the AI Tracks at Sea research,” he explains.
like this one often connect directly or indirectly with a student’s academic
and career goals.
good to not be pigeon-holed, and to use our knowledge in a different scenario,”
Steven Whitaker says of these opportunities. “This helps us remember that there
are other things in the world other than our small section of research.”
team knew that there were two primary issues at hand. First, how can the pixel
coordinates be translated into GPS coordinates? And second, how can the boat be
located so that GPS pixel coordinates can be determined?
we broke it down into these two subproblems, it became pretty clear how to
solve each half,” DeKraker says. “Steven had already done a significant amount
of work mapping pixel coordinates into GPS coordinates, so we had a pretty
quick answer to subproblem one.”
team met weekly to discuss their ideas for the project and compare and contrast
how effective they would be as solutions to the problem at hand. Then, they got
together on Fridays or during the weekends to work together on the project.
Havens would come in to our weekly meetings and nudge us in the right direction
or give tips on what we should do and what we should avoid,” Whitaker adds.
subproblem two, after some discussion the group decided it was probably best to
use a machine learning approach, as that promised the most significant gains
for the least amount of effort, which was important given the tight schedule.
tried some different sub-projects independently and then worked together to
combine the parts we thought worked best,” Evan Lucas says.
identify the boat and track its movement, the team used a simple neural network
and a computer vision technique called optical flow, which made the analysis
much faster and cleaner. They used a pre-built algorithm, adding a bit of
optical flow so that the boat’s position didn’t have to be verified every time.
two tools allowed us to find the pixel coordinates of the boat and turn them
into GPS coordinates,” DeKraker says, whose primary role in the project was
integrating the two tools and packaging it for testing.
of my PhD is to map out a snowmobile’s GPS coordinates with a camera,” Whitaker
says. “This is extremely similar to mapping out a boat’s GPS coordinates. I
could even say that it was exactly the same. I don’t believe I’ll add anything
new, but I’ve tweaked it to work for my research.”
sums up the team’s division of responsibilities like this: “Evan detects all
the boats in the picture; Nik detects which of those boats is our boat; Steven
takes our boat position and converts it to GPS coordinates, Zach glued all of
our pieces together.”
says, “One of the things the judges stressed was the ease of implementing the
solution. Since that falls under what I would consider user experience (UX) or
user interface (UI), it was pretty natural for me to take these tasks on,
having studied software engineering for my undergrad,” DeKraker says.
primary focus was speed. “Using machine learning for object detection tends to
be slow, so to mitigate that we used the boat detector only once every 5
seconds,” DeKraker explains.
of the tracking was done using a very fast technique called optical flow, which
looks at the difference between two consecutive frames of a video to track
motion,” DeKraker says. “It tended to drift from the target though, so we
decided on running the boat detector every 5 seconds to keep optical flow on
end result is that our solution could run nearly in real-time,” he says. “The
accuracy wasn’t the best, but given a little bit more time and more training
data, the neural network could be significantly improved.”
graduate studies focus heavily on various machine learning techniques, He says
that this opportunity to integrate machine learning into our solution was a
“First, it sounded like an interesting challenge. I don’t get to do a lot of software design these days, and this challenge sounded like a great opportunity to do just that,” he explains.
“Second, it looked like a great opportunity to build up my resume a little bit. Saying that you won thousands of dollars for your university in a nationwide competition sounds really good. And finally, I really wanted the chance to see a practical application of machine learning in action.”
completed a BS in Software Engineering at Michigan Tech in 2018. He returned to
Michigan Tech the next year to complete his master’s degree. He says the
biggest reason he did so was to learn more about machine learning.
embarking on this journey, I really didn’t know anything about it,” he says of
machine learning. “Having this chance to actually solve a problem, to integrate
a neural network into a fully realized boat tracker using nothing but a video
helped me see how machine learning can be used practically, rather than merely
understanding how it works.”
although it was a fascinating exploration into the practical side of machine
learning and computer vision, DeKraker says it’s rather tangential to his main research
focus right now, which is on comparing different network architectures to evaluate
which one performs best given particular data and the problem being solved.
believes that the culture is the most magnetizing thing about Tech. “Everybody
here is cut from the same cloth. We’re all nerds and proud of it,” he explains.
“You can have a half-hour conversation with a complete stranger about
singularities, the economics of fielding a fleet of star destroyers, or how
Sting was forged.”
the most appealing thing about Michigan Tech was its size. DeKraker says. “When
I looked at a ranking of the top universities in Michigan, Tech was number 3,
but still extremely small. It was a perfect blend of being a small but very
he says the second-best thing about Tech is the location. “The Keweenaw is one
of the most beautiful places on earth.”
has many ideas about where he’d like to take his career. For instance, he’d
love the chance to work for DARPA, Los Alamos National Laboratory, or NASIC. He
also intends to commission into the Air Force in the next couple of years, “if
they have a place for programmers like me.”
Evan Lucas is a PhD candidate in the Electrical Engineering department., advised by Zhaohui Wang. Lucas completed both a bachelor’s and master’s in Mechanical Engineering at Tech in 2012 and 2014,
Lucas, whose research interests are in applying machine learning methods to underwater acoustic communication systems, worked on developing a classifier to separate the boat of interest from the many other boats in the image. Although the subject of the competition is tangential to Lucas’s graduate studies, as computer vision isn’t his area, there was some overlap in general machine learning concepts. respectively.
sounded like a fun challenge to put together an entry and learn more about
computer vision,” Lucas says. “Working with the rest of the team was a really
good opportunity to learn from people who have experience making software that is
used by other people.”
completion of his doctoral degree, hopefully in spring 2023, Lucas plans to return
to industry in a research focused role that applies some of the work he did in his
Steven Whitaker’s research interests are in machine learning and acoustics. He tracks and locates the position of on-ice vehicles, like snowmobiles, based on acoustics. He says he has used some of the results from this competition project in his PhD research.
Whitaker’s machine learning research is experiment-based., and that’s why he chose Michigan Tech. “There aren’t many opportunities in academia to do experiment-based research,” he says. “Most machine learning is very software-focused using pre-made datasets. I love doing the experiments myself. Research is fun. I enjoy getting paid to do what I normally would do in my free time.”
In 2019, Whitaker completed his BS in Electrical Engineering at Michigan Tech. He expects to complete his master’s degree in Electrical Engineering at the end of the summer 2021 semester, and his PhD in summer 2022. His advisors are Tim Havens and Andrew Barnard.
would love to be a university professor one day, but first he wants to work in
Timothy 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 (ICC). His research interests are in pattern recognition and machine learning, signal and image processing, sensor and data fusion, heterogeneous data mining, and explosive hazard detection.
Michael Roggeman is a professor in the Electrical and Computer Engineering department. His research interests include optics, image reconstruction and processing, pattern recognition, and adaptive and atmospheric optics.
Zhaohui Wang is an associate professor in the Electrical and Computer Engineering department. Her research interests are in communications, signal processing, communication networks, and network security, with an emphasis on underwater acoustic applications.
The Naval Information Warfare Center (NIWC) Pacific and the Naval Science, Technology, Engineering, and Mathematics (STEM) Coordination Office, managed by the Office of Naval Research are conducting the Artificial Intelligence (AI) Tracks at Sea challenge.
Challenge.gov is a web platform that assists federal agencies with inviting ideas and solutions directly from the public, or “crowd.” This is called crowdsourcing, and it’s a tenet of the Challenge.gov program. The website enables the U.S. government to engage citizen-solvers in prize competitions for top ideas and concepts as well as breakthrough software, scientific and technology solutions that help achieve their agency missions.
This site also provides a comprehensive
toolkit, a robust repository of considerations, best practices, and case
studies on running public-sector prize competitions as developed with insights
from prize experts across government.
The Department of Computer Science will present a lecture by Dr. Qun Li on Friday, April 23, 2021, at 3:00 p.m. Dr. Li is a professor in the computer science department at William and Mary university. The title of his lecture is, “Byzantine Fault Tolerant Distributed Machine Learning.”
Training a deep learning network requires a large amount of data and a lot of computational resources. As a result, more and more deep neural network training implementations in industry have been distributed on many machines. They can also preserve the privacy of the data collected and stored locally, as in Federated Deep Learning.
It is possible for an adversary to launch Byzantine attacks to a distributed or federated deep neural network training. That is, some participating machines may behave arbitrarily or maliciously to deflect the training process. In this talk, I will discuss our recent results on how to make distributed and federated neural network training resilient to Byzantine attacks. I will first show how to defend against Byzantine attacks in a distributed stochastic gradient descent (SGD) algorithm, which is the core of distributed neural network training. Then I will show how we can defend against Byzantine attacks in Federated Learning, which is quite different from distributed training.
An article by Dr. Sidike Paheding, Applied Computing, has been accepted for publication in the Elsevier journal, Remote Sensing of Environment, a top journal with an impact factor of 9.085. The journal is ranked #1 in the field of remote sensing, according to Google Scholar.
The paper, “Estimation of root zone soil moisture from ground and remotely sensed soil information with multisensor data fusion and automated machine learning,” will be published in Volume 260, July 2021 of the journal. Read and download the article here.
A machine learning approach to estimation of root zone soil moisture is introduced.
Remotely sensed optical reflectance is fused with physical soil properties.
The machine learning models well capture in situ measured root zone soil moisture.
Model estimates improve when measured near-surface soil moisture is used as input.
Paheding’s co-authors are:
Ebrahim Babaeian, Assistant Research Professor, Environmental Science, University of Arizona, Tucson
Vijay K. Devabhaktuni, Professor of Electrical Engineering, Department Chair, Purdue University Northwest, Hammond, IN
Nahian Siddique, Graduate Student, Purdue University Northwest
Markus Tuller, Professor, Environmental Science, University of Arizona
Root zone soil moisture (RZSM) estimation and monitoring based on high spatial resolution remote sensing information such as obtained with an Unmanned Aerial System (UAS) is of significant interest for field-scale precision irrigation management, particularly in water-limited regions of the world. To date, there is no accurate and widely accepted model that relies on UAS optical surface reflectance observations for RZSM estimation at high spatial resolution. This study is aimed at the development of a new approach for RZSM estimation based on the fusion of high spatial resolution optical reflectance UAS observations with physical and hydraulic soil information integrated into Automated Machine Learning (AutoML). The H2O AutoML platform includes a number of advanced machine learning algorithms that efficiently perform feature selection and automatically identify complex relationships between inputs and outputs. Twelve models combining UAS optical observations with various soil properties were developed in a hierarchical manner and fed into AutoML to estimate surface, near-surface, and root zone soil moisture. The addition of independently measured surface and near-surface soil moisture information to the hierarchical models to improve RZSM estimation was investigated. The accuracy of soil moisture estimates was evaluated based on a comparison with Time Domain Reflectometry (TDR) sensors that were deployed to monitor surface, near-surface and root zone soil moisture dynamics. The obtained results indicate that the consideration of physical and hydraulic soil properties together with UAS optical observations improves soil moisture estimation, especially for the root zone with a RMSE of about 0.04 cm3 cm−3. Accurate RZSM estimates were obtained when measured surface and near-surface soil moisture data was added to the hierarchical models, yielding RMSE values below 0.02 cm3 cm−3 and R and NSE values above 0.90. The generated high spatial resolution RZSM maps clearly capture the spatial variability of soil moisture at the field scale. The presented framework can aid farm scale precision irrigation management via improving the crop water use efficiency and reducing the risk of groundwater contamination.
Remote Sensing of Environment (RSE) serves the Earth observation community with the publication of results on the theory, science, applications, and technology of remote sensing studies. Thoroughly interdisciplinary, RSE publishes on terrestrial, oceanic and atmospheric sensing. The emphasis of the journal is on biophysical and quantitative approaches to remote sensing at local to global scales.
Han recently joined the Institute of Computing and Cybersystems and its Data Sciences research group. His primary research interests are in mechanobiology, cell migration, and image data modeling. His research goals include applying computer vision to microscopic images to capture meaningful information, and he’s looking for collaborators.
with a good machine learning background is encouraged to contact me to discuss
potential research,” he says. “Also, students who learned assignment problems
or particle tracking are encouraged to contact me to discuss potential
Teaching and Mentoring
teaching interests include computer vision for microscopic images, fluid mechanics,
cell biomechanics and mechanobiology, and soft tissue mechanics. This academic
year, he instructed Computer Vision for Microscopic Images in the Fall
semester, and Fluid Mechanics this Spring.
enjoys teaching and interacting with students, “and feel their energy, too.” He
says he makes a deliberate effort in his classes to pause from time to time so
that his students can ask questions.
advises two Biomedical Engineering Ph.D. students, Nikhil Mittal and Mohanish
is working on finding myosin-independent mechanosensitivity mechanism for
stiffness sensing, and Mohanish works on the project finding mechano-transmission
for fluid shear stress sensing by endothelial cells,” he says.
Mechanobiology Lab is interested in finding fundamental mechanisms governing
mechanotransduction, and how cells sense mechanical forces and convert them into
image cells and associated forces using high-resolution live imaging, which we
analyze to obtain statistically meaningful quantity of data,” Han explains. “We
apply force-measuring and molecular-imaging/analysis technologies for stiffness
sensing, shear flow sensing, adhesion assembly, and cancer mechanobiology.”
is working to gain a thorough understanding of the mechano-chemical interaction
between cancer cells and their microenvironment, and develop a an effective
mechano-therapeutic strategy to stop the progression of cancer, and breast
cancer in particular. Ultimately, he wants to apply that knowledge to cancer
is principal investigator of a three-year NIH/NIGMS
research project, “Nascent Adhesion-Based Mechano-transmission for
Extracellular Matrix Stiffness Sensing.” The research aims to determine whether
newly-born adhesions can sense tissue stiffness through the accurate
measurement of the mechanical force and molecular recruitment of early adhesion
2012, Han received his Ph.D. in Mechanical Engineering from the University of
Washington in the areas of cell mechanics, multiphysics modeling, and bioMEMS.
his postdoctoral training, he joined the Computational Cell Biology lab led by
Dr. Gaudenz Danuser in the Cell Biology department of Harvard Medical School.
In 2014, he joined the UT Southwestern (University of Texas) Department of Cell
Biology and Bioinformatics. Han received his B.S and M.S. degrees in mechanical
engineering at Seoul National University, Korea, in 2002 and 2004,
holds several patents and in 2015, he developed an open-source TFM (Traction
Force Microscopy) Package, which is shared via his lab’s website: hanlab.biomed.mtu.edu/software.
Beyond Research and Teaching
loves science and discovering something new in his research investigations. Beyond
his work as a professor and scientist, he describes himself as a husband to
Sunny, and a dad to his son, Caleb.
am just a normal Korean who likes singing and dancing,” he says. “Unfortunately,
my voice is still recovering from surgery, but I hope to get back to it soon. I
also like to listen to all kinds of music, including hip-hop, classics, and
appreciates a good sense of humor, but he says that being humorous in American
English is something he continues to learn.
says he tries to be “normal” and not too nerd-like when he’s not pursuing his
research, but “there are times when I am making my own hypothesis about some
phenomena I observe in my daily life.”
enjoys life at Michigan Tech and in the Cooper Country. He likes getting to
know his energetic students and he finds Michigan Tech faculty members very
strong and collegial. He also enjoys the snow, hockey, and the mountains.
really like the snow here. I am already sad that the weather is becoming too
mild!” he confirms. “It’s also a safe environment to raise kids, which is a big
And he likes his academic department. “Everyone is so nice in the Biomedical Engineering program, they have been so welcoming and appreciative my research,” Han says. “It’s a family-like environment.”
1R15GM135806-01 (09/16/2019 – 08/31/2022)
Funding Agency: NIH/NIGMS
Nascent Adhesion-Based Mechano-transmission for
Extracellular Matrix Stiffness Sensing
Project Goals: To determine whether newly-born
adhesions can sense tissue stiffness by accurate measurement of mechanical
force and of molecular recruitment of early adhesion proteins using traction
force microscopy and computer vision techniques.
Role: Principal Investigator
The Mechanobiology Labstudies mechanobiology, particularly how adherent cells can sense and respond to mechanical stiffness of the extracellular matrix. To investigate this, the lab has established experimental and computational frameworks for force measurement and adhesion dynamics quantification. Researchers apply these frameworks, with cutting edge computer vision technique, on live-cell microscope images to investigate the fundamental mechanism underlying mechanosensation in normal cells, and the biomechanical signature of the diseased cells whose signaling has gone awry.
The Institute of Computing and Cybersystems (ICC) creates and supports an arena in which faculty and students work collaboratively across organizational boundaries in an environment that mirrors contemporary technological innovation. The ICC’s 60+ members, in six research centers, represent more than 20 academic disciplines at Michigan Tech. https://www.mtu.edu/icc/
The ICC Center for Data Sciences (DataS) focuses on the research of data sciences education, algorithms, mathematics, and applications. https://www.mtu.edu/icc/centers/data-sciences/
The National Institutes of Health (NIH), a part of the U.S. Department of Health and Human Services, is the nation’s medical research agency — making important discoveries that improve health and save lives. https://www.nih.gov/
The National Institute of General Medical Sciences (NIGMS) supports basic research that increases understanding of biological processes and lays the foundation for advances in disease diagnosis, treatment, and prevention. https://www.nigms.nih.gov/
Han, S. J.; Azarova, E. V.; Whitewood, A. J.; Bachir, A.; Guttierrez, E.; Groisman, A.; Horwitz, A. R.; Goult, B. T.; Dean, K. M.; Danuser, G. Pre-Complexation of Talin and Vinculin without Tension Is Required for Efficient Nascent Adhesion Maturation. eLife 2021, 10, e66151. https://doi.org/10.7554/eLife.66151.
Schäfer, C., Ju, Y., Tak, Y., Han, S.J., Tan, E., Shay, J.W., Danuser, G., Holmqvist, M., Bubley, G. (2020) TRA-1-60-positive cells found in the peripheral blood of prostate cancer patients correlate with metastatic disease. Heliyon 6(1), e03263.
Isogai, T., Dean, K.M., Roudot, P., Shao, Q., Cillay, J.D., Welf, E.S., Driscoll, M.K., Royer, S.P., Mittal, N., Chang, B., Han, S.J., Fiolka, R., Danuser, G., Direct Arp2/3-vinculin binding is essential for cell spreading, but only on compliant substrates and in 3D, BioRxiv, 2019
Mohan, A.S., Dean, K.M., Isogai, T., Kasitinon, S.Y., Murali, V.S., Roudot, P., Groisman, A., Reed, D.K., Welf, E.S., Han, S.J., Noh, J., and Danuser, G. (2019). Enhanced Dendritic Actin Network Formation in Extended Lamellipodia Drives Proliferation in Growth-Challenged Rac1P29S Melanoma Cells. Developmental Cell, 49(3), pp.444-460.
Manifacier I., Milan, J., Beussman, K., Han, S.J., Sniadecki, N.J., About, I (2019) The consequence of large-scale rigidity on actin network tension. In press. Comp Meth Biomech Biomed Eng, 2019 Oct;22(13):1073-1082.
Costigliola, N., Ding, L., Burckhardt, C.J., Han, S.J., Gutierrez, E., Mota, A., Groisman, A., Mitchison, T.J., and Danuser, G. (2017) Vimentin directs traction stress. PNAS. 2017 114 (20) 5195-5200.
Han, S.J., Rodriguez M.L., Al-Rekabi, Z., Sniadecki, N.J. (2016) Spatial and Temporal Coordination of Traction Forces in One-Dimensional Cell Migration, Cell Adhesion & Migration. 10(5): 529-539.
Oudin, M.J., Barbier, L., Schäfer, C, Kosciuk, T., Miller, M.A., Han, S.J., Jonas, O., Lauffenburger, D.A., Gertler, F.B. (2016) Mena confers resistance to Paclitaxel in triple-negative breast cancer. Mol Cancer Ther.DOI: 10.1158/1535-7163. MCT-16-0413.
Milan,J., Manifacier, I., Beussman, K.M., Han, S.J., Sniadecki, N.J., About, I., Chabrand, P. (2016) In silico CDM model sheds light on force transmission in cell from focal adhesions to nucleus. J Biomechanics. 49(13):2625-2634.
Lomakin. A.J., Lee, K.C., Han, S.J., Bui, A., Davidson, M., Mogilner, A., Danuser G. (2015) Competition for molecular resources among two structurally distinct actin networks defines a bistable switch for cell polarization, Nature Cell Biology. 17, 1435–1445
Han, S.J., Oak, Y., Groisman, A., Danuser, G. (2015) Traction Microscopy to Identify Force Modulation in Sub-resolution Adhesions, Nature Methods. 12(7): 653–656
Dr. Sergeyev will discuss his research, the Applied Computing department, and the Mechatronics BS and MS programs. He will answer questions following his presentation.
Michigan Tech is a pioneer in Mechatronics education, having introduced a graduate degree program in 20xx, and a bachelor’s program in Fall 2019.
“Mechatronics is an industry buzzword synonymous with robotics, controls, automation, and electromechanical engineering,” Sergeyev says.
In his presentation, he will discuss Mechatronics in general, explain what the degree has to offer, job opportunities in Mechatronics, and some of the research he is conducting in this field.
In Spring 2021, a Mechatronics Playground was opened on campus. The hands-on learning lab and industry-grade equipment was funded by alumnus Mark Gauthier of Donald Engineering, Grand Rapids, MI, and other major companies.
A common degree in Europe, China, Japan, Russia, and India, advanced study in Mechatronics is an underdeveloped academic discipline in the United States, even though the industrial demand for these professionals is enormous, and continues to grow.
Sergeyev’s areas of expertise are in electrical and computer engineering, physics, and adaptive optics, and his professional interests include robotics. He is principal investigator for research grants totaling more that $1 million. He received both his MS and PhD degrees at Michigan Tech, in physics and electrical and computer engineering, respectively.
eLife, a prestigious journal in cell biology, has published a paper co-written by Sangyoon Han, “Pre-complexation of talin and vinculin without tension is required for efficient nascent adhesion maturation.”
eLife is a non-profit organization created by funders and led by researchers. Their mission is to accelerate discovery by operating a platform for research communication that encourages and recognizes the most responsible behaviors.
A paper co-authored by Sidike Paheding, Applied Computing, has been published in the journal, IEEE Access. “Trends in Deep Learning for Medical Hyperspectral Image Analysis,” was available for early access on March 24, 2021.
The paper discusses the implementation of deep learning for medical hyperspectral imaging.
Co-authors of the paper are Uzair Khan, Colin Elkin, and Vijay Devabhaktuni, all with the Department of Electrical and Computer Engineering, Purdue University Northwest.
Deep learning algorithms have seen acute growth of interest in their applications throughout several fields of interest in the last decade, with medical hyperspectral imaging being a particularly promising domain. So far, to the best of our knowledge, there is no review paper that discusses the implementation of deep learning for medical hyperspectral imaging, which is what this work aims to accomplish by examining publications that currently utilize deep learning to perform effective analysis of medical hyperspectral imagery.
This paper discusses deep learning concepts that are relevant and applicable to medical hyperspectral imaging analysis, several of which have been implemented since the boom in deep learning. This will comprise of reviewing the use of deep learning for classification, segmentation, and detection in order to investigate the analysis of medical hyperspectral imaging. Lastly, we discuss the current and future challenges pertaining to this discipline and the possible efforts to overcome such trials.
IEEE Access is a multidisciplinary, applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE’s fields of interest. Supported by article processing charges, its hallmarks are a rapid peer review and publication process with open access to all readers.