Dr. Dukka KC, Electrical Engineering and Computer Science, Wichita State University, will present a talk on Wednesday, May 5, 2021, at 3:00 p.m.
Dr. KC will discuss some past and ongoing projects in his lab related to machine learning/deep learning-based approaches for an important problem in Bioinformatics: protein post-translational modification.
Bioinformatics as an emerging field of Data Science: Protein post-translation modification prediction using Deep Learning
In this talk, I will be presenting about some of the past and ongoing projects in my lab especially related to Machine Learning/Deep Learning based approaches for one of the important problems in Bioinformatics – protein post-translational modification.
Especially, I will focus on our endeavors to get away from manual feature extraction (hand-crafted feature extraction) from protein sequence, use of notion of transfer learning to solve problems where there is scarcity of labeled data in the field, and stacking/ensemble-based approaches.
I will also summarize our future plans for using multi-label, multi-task and multi-modal learning for the problem. I will highlight some of the ongoing preliminary works in disaster resiliency. Finally, I will provide my vision for strengthening data science related research, teaching, and service for MTU’s college of computing.
Dr. Dukka KC is the Director of Data Science Lab, Director of Data Science Efforts, Director of Disaster Resilience Analytics Center and Associate Professor of Electrical Engineering and Computer Science (EECS) in the Department of EECS at Wichita State University. His current efforts are focused on application of various computing/data science concepts including but not limited to Machine Learning, Deep Learning, HPC, etc. for elucidation of protein sequence, structure, function and evolution relationship among others.
He has received grant funds totaling $4.25M as PIs or Co-PIs, spanning 17 funded grants. He was the PI on the $499K NSF Excellence in Research project focused on developing Deep Learning based approaches for Protein Post-translational modification sites.
He received his B.E. in computer science in 2001, his M.Inf. in 2003 and his Ph.D. in Informatics (Bioinformatics) in 2006 from Kyoto University, Japan. Subsequently he did a postdoc at Georgia Institute of Technology working on refinement algorithms for protein structure prediction. He then moved to UNC-Charlotte and did another postdoc working on functional site predictions in proteins. He was a CRTA Fellow in National Cancer Institute at National Institutes of Health where he was working on intrinsically symmetric domains.
Prior to his arrival at WSU, he was associate professor and graduate program director in the Department of Computational Science and Engineering at North Carolina A&T State University.
Dr. KC has published more than 30 journal and 20 conference papers in the field and is associate editor for two leading journals (BMC Bioinformatics and Frontiers in Bioinformatics) in the field. He also dedicates much of his efforts to K-12 education, STEM workforce development, and increasing diversity in engineering and science.
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.
We are looking for volunteers to take part in a study exploring how people may interact with future Augmented Reality (AR) interfaces. During the study, you will record videos of yourself tapping on a printed keyboard. The study takes approximately one hour, and you will be paid $15 for your time. You will complete the study at your home.
To participate you must meet the following requirements:
Dr. Xiaoyong (Brian) Yuan and Dr. Bo Chen are seeking an hourly paid graduate research assistant to work in the areas of artificial intelligence and mobile security. The project is expected to begin Summer 2021 (5/10/2021).
Preferred Qualifications: 1. Passion for research in artificial intelligence and mobile security. 1. Familiar with Android OS and Android app development. 2. Basic knowledge of machine learning and deep learning. 3. Solid programming skills in Java, Python, or related programming languages. 4. Experience with popular deep learning frameworks, such as Pytorch and Tensorflow is a plus.
To Apply: Please send a resume and a transcript to Dr. Yuan (email@example.com).
This year’s Graduate Research Colloquium organized by the Graduate Student Government was hosted virtually due to COVID restrictions. There were in total 48 presentations — 17 poster presenters and 31 oral presenters.
Poster presentations took place in a pre-recorded video style and the oral sessions were hosted live via Zoom. You can watch all the poster videos and recordings for the oral sessions here. Each presentation was scored by two judges from the same field of research.
Participants were able to gain valuable feedback from these judges before presenting their research at an actual conference. It was stiff competition amongst all presenters. Following are the winners for each of these sessions.
Of the many presentations were the following by two graduate students affiliated with the College of Computing.
Simulating the Spread of Infectious Diseases Meara Pellar-Kosbar, Data Science
This simulation is designed to show how a fictional viral illness could spread among people in a virtual room. Over the course of the virtual simulation, a number of automatic simulated people called subjects will move about an adjustable virtual grid. During this time, subjects will come into contact with each other and with item cells in the virtual room. Subjects will be exposed to this fictional virus via contact with other subjects, items, and via the air when within a certain distance of a contagious subject. The viral counts of each subject will be tracked and shown as the simulation runs, showing how the actions of the subjects’ affects their viral counts.
Cultural Competence Effects of Repeated Implicit Bias Training Karen Colbert, Social Sciences Karen Colbert is a PhD student in the Computational Sciences and Engineering department.
Abstract: Diversity training literature suggests that mandatory and recurrent sessions should maximize training efficacy, but research has primarily focused on single, brief training sessions that are often voluntary. Michigan Tech is one of few universities to implement required and repeated diversity training for all faculty who serve on search, tenure, and promotion committees. The goal of this study is to evaluate the training’s effectiveness, as well as to fill the gap in research on mandatory recurring diversity training. To do this, we anonymously surveyed faculty members on their knowledge, attitudes, and skills related to content from the Diversity Literacy program and scored responses to create a single composite score for each participant. We hypothesized that composite Cultural Competency Score (CCS) would be higher for faculty who 1) have taken more refresher trainings, and 2) completed training more recently. This study included 130 total respondents (large sample), 69 of whom provided their Diversity Literacy completion information anonymously through Human Resources (small sample). Composite CCS did not differ significantly by frequency of training, H(2)=3.78, p=.151. CCS did differ significantly by years since last training, F(2,63)=4.436, p=.016. Results from both large and small groups showed no statistical significant relationship between CCS and faculty committee service. CCS was negatively correlated with years employed at Tech in both the large (r=-0.363, p=0.002) and small (r = -0.258, p=0.01) samples. This relationship between low CCS and longer employment at Tech may additionally be related to the Diversity Literacy program’s implementation in 2010. Qualitative responses were also collected regarding training material that faculty found most memorable (N=102) and most confident to put into practice (N=93).
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.
State replication on edge devices under the scarcity of resources.
Fault tolerance mechanism on the edge and in the cloud.
Models for the predication of service latency and costs in distributed fault-tolerant protocols on the edge and in the cloud.
Fault-tolerant distributed protocols for resource management of edge devices.
Fault-tolerant edge/cloud computing.
Fault-tolerant computing on low-end devices.
Load balancing (on the edge and in the cloud) in the presence of failures.
Fault-tolerant data intensive applications on the edge and the cloud.
Metrics and benchmarks for the evaluation of fault tolerance mechanisms in cloud/edge computing.
The Internet of Things (IoT) has brought a new era of computing that permeates in almost every aspect of our lives. Low-end IoT devices (e.g., smart sensors) are almost everywhere, monitoring and controlling the private and public infrastructure (e.g., home appliances, urban transportation, water management system) of our modern life. Low-end IoT devices communicate enormous amount of data to the cloud computing centers through intermediate devices, a.k.a. edge devices, that benefit from stronger computational resources (e.g., memory, processing power).
To enhance the throughput and resiliency of such a three-tier architecture (i.e., low-end devices, edge devices and the cloud), it is desirable to perform some tasks (e.g., storing shared objects) on edge devices instead of delegating everything to the cloud. Moreover, any sort of failure in this three-tier architecture would undermine the quality of service and the reliability of services provided to the end users.
Theoretical and experimental methods that incorporate fault tolerance in cloud and edge computing, which have the potential to improve the overall robustness of services in three-tier architectures.
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