Pursue a Cybersecurity Career with this Generous NSF CyberCorps Scholarship


Apply now for Michigan Tech’s 2021-22 cohort of Cybersecurity Scholars and jumpstart your cybersecurity career!

The deadline to apply is June 1, 2021.

This generous scholarship opportunity provides up to three years of tuition and annual stipend.

Then, following completion of your degree, you’ll work in a cybersecurity-related position for a federal, state, local, or tribal agency for up to three years– a period equal to the length of your scholarship.

See full guidelines, requirements, and application information on the SFS website: mtu.edu/sfs.


Eligible Degree Programs

  1. BS in Cybersecurity (CyS)
  2. BS in Computer Network and System Administration (CNSA)
  3. BS in Computer Science (CS)
  4. BS in Software Engineering (SE)
  5. BS in Computer Engineering (CpE)
  6. BS in Electrical Engineering (EE)
  7. BS in Management Information Systems (MIS)
  8. MS in Cybersecurity

Ready to apply? Visit mtu.edu/sfs

Questions? Email sfs@mtu.edu


The Michigan Tech SFS Program

The SFS program at Michigan Tech involves multiple programs and departments, including the College of Computing and its departments of Applied Computing and Computer Science; the College of Engineering’s Department of  Electrical and Computer Engineering; and the College of Business’s Management Information Systems B.S. program. 

“The U.S. is facing a significant shortage of well-trained and well-prepared cybersecurity professionals,” said Dr. Yu Cai, professor of applied computing and the principal investigator of the grant. “This new scholarship will continue to develop Michigan Tech’s national and international reputation as a leader and innovator in cybersecurity education, research and outreach activities.”

The five-year, $3.3 million NSF grant provides up to three years of full scholarship support for 20 Michigan Tech undergraduate and graduate students.


About the NSF Scholarship

Protecting worldwide digital infrastructure has become an urgent focus of industry and government. And employment in this sector is expected to grow exponentially in the coming years.

In response, the National Science Foundation CyberCorps: Scholarship for Service (SFS) program was introduced as a nationwide program to recruit and train the next generation of information technology professionals, industrial control system security professionals, and security managers.

Congratulations Class of 2021!

It has been a challenging academic year, to say the least. As part of the Class of 2021, you are an exceptional group of graduates. Your final academic year presented you with unforeseen and unprecedented challenges, yet you persevered.

We are all proud to have mentored, instructed, and supported you on your educational journey. We know you’ll do well. You are a Husky, after all!

Please stay in touch!

Students Place in ICPC Programming Championships


A team of Michigan Tech students competed last week in the International Collegiate Programming Contest (ICPC) North America Division Championships, placing 28th out of 42 teams in the Central Division.

To qualify for the Championships, a Michigan Tech student team placed 14th out of more than 80 teams in the regional ICPC contest this February. Students on that team were Alex Gougeon (Software Engineering), Ben Wireman (Mathematics), and Dominika Bobik.

Students interested in the programming competitions are encouraged to contact Dr. Laura Brown, Computer Science. Additional programming contests and events take place throughout the year.

The International Collegiate Programming Contest is the premier world-wide, algorithmic programming contest for college students.

In ICPC competitions, teams of three students work to solve the most real-world problems efficiently and correctly. Teams represent their university in multiple levels of competition: regionals, divisionals, championships, and world finals.

EET Senior Design Project on TV 6


The Senior Design project completed this semester by four senior-level Electrical Engineering Technology (EET) is the topic of a news story that was aired on WLUC-TV6 (Marquette) on Friday, April 23, 2021. The project was to design and produce a motorized swing set that will help a disabled child enjoy herself and sleep comfortably.

Read an article about the EET students’ project here.

View the TV6 news story here.

The students are Joe Barbercheck, Seth Cherry, Heather Harris, and Cole Kubick.

Tackling the project top to bottom, the students designed the electrical system, control and drive systems, and portions of the mechanical design. Their top priority was making sure the systems and mechanical structure are safe.

Specifications for the swing include that it be lightweight, reliable, and portable. The unit is battery-operated with a rechargeable lithium-ion battery. The swing will both rock the child to sleep and serve as a play toy for three to four years, although the actual lifetime of the swing will be much longer.

Professor Alex Sergeyev and Lecturer Paniz Hazaveh are co-advisors to the team. “The students are very excited about the project,” Sergeyev says. “It’s very meaningful to them.”

“The skills that we are teaching in the EET and Mechatronics undergraduate programs makes students able to just jump on these kinds of projects,” Sergeyev says. “It’s great to see that their learning can be applied to a project as complex as this one.”


New Course: Applied Machine Learning


Summary

  • Course Number: 84859, EET 4996-01
  • Class Times: T/R, 9:30-10:45 am
  • Location: EERC 0723
  • Instructor: Dr. Sidike Paheding
  • Course Levels: Graduate, Undergraduate
  • Prerequisite: Python Programming and basic knowledge of statistics.
  • Preferred knowledge: Artificial Intelligence (CS 4811) or Data Mining (CS4821) or Intro to Data Sciences (UN 5550)

Course Description/Overview

Rapid growth and remarkable success of machine learning can be witnessed by tremendous advances in technology, contributing to the fields of healthcare, finance, agriculture, energy, education, transportation and more. This course will emphasize on intuition and real-world applications of Machine Learning (ML) rather than statistics behind it. Key concepts of some popular ML techniques, including deep learning, along with hands-on exercises will be provided to students. By the end of this course, students will be able to apply a variety of ML algorithms to practical

Instructor

Applications Covered

  • Object Detection
  • Digital Recognition
  • Face Recognition
  • Self-Driving Cars
  • Medical Image Segmentation
  • Covid-19 Prediction
  • Spam Email Detection
  • Spectral Signal Categorization

Tools Covered

  • Python
  • scikit learn
  • TensorFlow
  • Keras
  • Open CV
  • pandas
  • matplotlib
  • NumPy
  • seaborn
  • ANACONDA
  • jupyter
  • SPYDER

Download the course description flyer:

Download

Husky Games Takes 1st in Design Expo ’21 Enterprise Awards

As we’ve come to expect, the judging for Design Expo 2021 was VERY CLOSE, but the official results are in. The College of Engineering and the Pavlis Honors College have announced the award winners.

The Husky Game Development Enterprise (Team 115) has come out on top in Enterprise Awards category of Design Expo 2021. The student organization is advised by Scott Kuhl, Computer Science.

Husky Games is led by students Gabe Oetjens, Computer Science, and Keira Houston, Civil Engineering. The group is sponsored by Sponsored by: the Pavlis Honors College’s Enterprise Program.

Read more about Husky Games, view their Design Expo video submission, and explore all 2021 entries here: mtu.edu/expo.

More than 1,000 students in Enterprise and Senior Design showcased their hard work April 15 at Michigan Tech’s second-ever fully virtual Design Expo.

Teams competed for cash awards totaling nearly $4,000. Judges for the event included corporate representatives, community members and Michigan Tech staff and faculty.

Download and play the game here: www.huskygames.com.

Watch the video below:

115 Husky Game Development

ENTERPRISE AWARDS (Based on video submissions)

First Place
Husky Game Development (Team 115)
Advisor Scott Kuhl, College of Computing
Video

Second Place
Aerospace Enterprise (Team 106)
Advisor L. Brad King, Mechanical Engineering-Engineering Mechanics
Video

Third Place: 
Innovative Global Solutions (Team 116)
Advisors Radheshyam Tewari, ME-EM and Nathan Manser, Geological and Mining Engineering and Sciences
Video

Honorable Mention: 
Consumer Product Manufacturing (Team 111)
Advisor Tony Rogers, Chemical Engineering
Video

See all categories and awards here: mtu.edu/expo.

PhD Student Daniel Byrne, CS, Awarded Finishing Fellowship


by Karen S. Johnson, Communications Director, College of Computing

The Graduate Dean Awards Advisory Panel and dean have awarded a Summer 2021 Finishing Fellowship to PhD student Daniel Byrne, Computer Science. Byrne will receive full support for the semester, which includes three research credit hours and a stipend.

“The panel was impressed with your research, publication record, and contribution to the mission of Michigan Tech,” says the award letter. “The intent of this fellowship is to allow you to focus your time on your dissertation so that you can complete your degree requirements during the fellowship period.”

Byrne’s research centers around the modeling and optimization of memory systems, which are found in today’s datacenters. He explains that data caching helps improve the speed and efficiency of front-end cloud applications, such as websites and video streaming.


In collaboration with researchers at the University of Rochester, Byrne has developed a new data caching system. “Our system uses intelligent data replication and allocation across multiple memory devices to maximize performance while reducing overall operating costs,” Byrne says.

“Specifically, we focus on utilizing new memory technologies to lower operational costs while meeting performance targets,” Byrne adds. “Even small increases in performance and energy savings have significant impact over an entire deployment of servers.”

His improvements to caching systems have already been adopted outside the lab, into a widely-used open-source caching system called “memcached.”

“Daniel’s research focuses on modeling and designing a hybrid memory system where the conventional DRAM (faster, but more expensive) and the emerging non-volatile memory (NVM, cheaper but slower) are combined to host a key-value store,” says Dr. Zhenlin Wang, Computer Science, Byrne’s faculty advisor, along with Dr. Nilufer Onder, associate professor in the CS department.

Wang expects that Byrne’s research will have a long term impact on design and implementation of a hybrid key-value store. “His work explores the theoretical properties of and interactions between inclusive and exclusive caches, a design space which has never been investigated before,” Wang says.

Byrne began his Michigan Tech PhD studies in computer science in Fall 2016. “I am grateful for the amount of support from my advisors, the Computer Science department, and the Graduate School during my PhD program,” he says.

“I am also incredibly grateful for my PhD committee’s support as I finish my dissertation over the summer. It has been a wonderful journey, and I have greatly enjoyed my time as a graduate student, especially my tenure as GSG vice president.”

“I extend my sincere gratitude to the Graduate School for this support during the final period of completing and defending my dissertation,” he adds.

“I also would like to thank the College of Computing for its efforts in creating a strong research environment and a supportive community of graduate students and faculty.”

Recipients of the fellowship are expected to finish during the semester for which funding is provided, maintain good academic and conduct standing, publish their work in internationally recognized peer review journals, among other requirements.

Byrne served as vice president of the Michigan Tech Graduate Student Government from Summer 2019 to Spring 2020. He says he is happy to have had the opportunity to advocate for graduate students and achieve increased support for health care, an initiative he championed during his tenure.

In Spring 2019 he received a Graduate Student Service Award, which is awarded by the Graduate Student Government Executive Board. The Service Award recognizes outstanding contributions to the graduate community at Michigan Tech. See the April 5, 2019, announcement in Tech Today here.

View Byrne’s Github page here.

Grad Students Take 6th Place in Navy’s AI Tracks at Sea Challenge

by Karen S. Johnson, Communications Director, College of Computing

The Challenge

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.

The Problem

“The 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 coordinates.”

“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.

Competitions like this one often connect directly or indirectly with a student’s academic and career goals.

“It’s 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.”

Dividing Responsibilities

The 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?

“Once 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.”

The 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.

“Dr. 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.

For 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.

“We tried some different sub-projects independently and then worked together to combine the parts we thought worked best,” Evan Lucas says.

The Solution

To 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.

“These 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.

“Part 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.”

Whitaker 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.”

DeKraker 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.

A 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.

“Most 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 target. “

“The 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.”


Zach DeKraker

DeKraker’s graduate studies focus heavily on various machine learning techniques, He says that this opportunity to integrate machine learning into our solution was a fantastic experience.

“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.”

DeKraker 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.

“Before 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.”

And 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.

DeKraker 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.”

And 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 good school.”

And he says the second-best thing about Tech is the location. “The Keweenaw is one of the most beautiful places on earth.”

DeKraker 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

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.

“It 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.”

Following 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 PhD.


Steven Whitaker

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.

Whitaker would love to be a university professor one day, but first he wants to work in industry.


Background Info

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.

View more details about the Challenge competition here: https://www.challenge.gov/challenge/AI-tracks-at-sea/

Watch a Navy webinar about the Challenge here: https://www.youtube.com/watch?v=MjZwvCX4Tx0.

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.

EET Senior Design Project to Help Child Rest Easy

By Karen S. Johnson, Communications Director, College of Computing

Read Part II of this article, “EET Motorized Swing Senior Project: The Students.”

Professor Alex Sergeyev, Applied Computing, was reading an American Society of Engineering Education (ASEE) Engineering Technology Division magazine last summer when he saw an article that prompted an idea for an Electrical Engineering Technology (EET) undergraduate Senior Design project.

Sergeyev and Applied Computing Lecturer Paniz Hazaveh are advisors to the team.

The story begins with a mother’s wish to help her child get a good night’s sleep. Her three-year-old daughter has a spinal disorder that leaves the child unable to walk, sit up, or lie down comfortably, especially for long periods of time. Children or adults with this type of disability find it very difficult to fall asleep, and the motion of a swing helps bring restful sleep. Mom was getting tired, too.

“We have to help!”

The mother had reached out to a community college in her area, asking faculty there if their students might be interested in designing and producing a motorized swing set that would allow her daughter to enjoy herself and sleep comfortably without being limited by her parents’ stamina. Unfortunately, the community college was unable to take on the project, and that may have been the end of the story

But when Sergeyev saw the ASEE article, he said to himself, “We have to help!”

So, he sent a message to the publication, who put him in touch with the community college. And eventually, Sergeyev talked with the mother of the child, collected the needed data, and discussed her design preferences, such as the direction, amplitude, and frequency of the swing’s arc. The project is funded entirely by University friends and donors.

A meaningful, complex project.

Four senior-level EET students are working on the project. Sergeyev and Applied Computing Lecturer Paniz Hazaveh are co-advisors to the team. “The students are very excited about the project,” says Sergeyev. “It’s very meaningful to them.”

“The skills that we are teaching in the EET and Mechatronics undergraduate programs makes students able to just jump on these kinds of projects,” Sergeyev says. “It’s great to see that their learning can be applied to a project as complex as this one.”

Specifications for the swing include that it be lightweight, reliable, and portable. The unit is battery-operated with a rechargeable lithium-ion battery. The swing will both rock the child to sleep and serve as a play toy for three to four years, although the actual lifetime of the swing will be much longer.

The students are tackling the project top to bottom, designing the electrical system, control and drive systems, and portions of the mechanical design. Their top priority is making sure the systems and mechanical structure are safe. With the assistance of mechanical engineering graduate student Pratik Korgaonkar, complex structural and stress analysis was successfully completed, confirming the design’s feasibility and structural stability.

Keeping things balanced.

Each student on the team has assigned roles to keep the workload balanced. Heather Harris manages the project, writes reports, and keeps everyone organized and on deadline. Joe Barbercheck leads the mechanical design and 3D CAD modeling. Seth Cherry works on sourcing parts and assists with CAD design. And Cole Kubick has been tasked with finding a motor within spec and figuring out a way to drive the motor electrically using pulse width modulation, a method of reducing the average power delivered by an electrical signal, by essentially splitting the power into discrete parts.

“The challenge is to create a swing that is rigid, because she is not capable of pumping a swing on her own, and because the mother does not have the energy to swing her for hours at a time,” explains Heather Harris. “We have designed it so that the car seat the child normally sits in will be attached to the swing, and a handheld controller will determine the height and speed of the swing.”

The motorized swing is also designed for ease of disassembly so that it can be moved from one location to another. Seth Cherry says that initially the frame was designed to be collapsible, but the team moved away from that to a more rigid, structurally sound design, one that can be easily disassembled with the removal of a few key parts.

The Senior Design team is working to build their first prototype by late February or early March. They would like to deliver the finished swing to the mother and child as soon as possible.

Sergeyev says that a scholarly paper will be published with the results of the project. “It’s not a rare disease, so the design could be replicated for other children and perhaps used in playgrounds.”

Dr. Junqiao Qiu Awarded $175K NSF pre-CAREER grant


Dr. Junqiao Qiu, Computer Science, has been awarded a two-year, $174,797 NSF pre-CAREER grant, which supports research independence among early-career academicians

The project is titled, “CRII: SHF: GPU-accelerated FSM computations with advanced speculation.”

Dr. Qiu’s research focuses on parallel computing, programming systems, and compiler optimization.

View the grant on the NSF website.


Abstract

Finite State Machine (FSM)-based computations have played critical roles in a variety of important applications, ranging from cyber security and data analytics to software engineering and hardware design. Due to the growing data volumes and limitations on computer processing power, nowadays FSM efficiency is greatly constrained, and a new dimension of efficiency optimization is desired. This project proposes a novel framework to enhance the computing efficiency of FSM-based computations on GPUs. The combination of GPU acceleration and speculative parallelization developed in the proposed framework shows promise for boosting performance of FSM computations and presents the potential to optimize even more general non-FSM computations.

This research investigates how to build up the synergy between highly-parallel GPU architectures and FSM computations. The key idea is exploring multiple dimensions of parallelism for increasing compute utilization as well as reducing data-movement overheads. Additionally, this research designs and integrates advanced speculative parallelization into FSM computations. The advanced speculative parallelization not only enables more effective predictors on different FSMs, it also provides efficient speculative-thread scheduling. All these optimizations will be built into a framework that further explores the trade-offs between different objectives and automatically optimizes application configurations based on the given objectives. Finally, this research seeks to enlarge the applicability of the envisioned results, and it brings the preliminary exploration about a new dimension of computing efficiency for irregular computations as well as applications associated with speculative parallelization.


Dr. Qiu’s lab has openings (RA/TA support) for self-motivated students who are interested in doing system research. For more information, please email Dr. Qiu at junqiaoq@mtu.edu.


Undergraduate Summer Lab Positions: Autonomous Driving Research


Dr. Xiaoyong (Brian) Yuan, Applied Computing and Computer Science, is seeking several hourly paid undergraduate students to work in the areas of autonomous driving.

This project is funded by MTU Research Excellence Fund (REF) and expected to begin in summer 2021 (7/1/2021).

Preferred Qualifications

  • Passion for research in autonomous driving and machine learning
  • Solid programming skills in C, Python, Java, or related programming languages
  • Familiar with Linux OS

To Apply

To apply, please send a resume and a transcript to Dr. Yuan (xyyuan@mtu.edu).


Conference on Applied Cryptography: Call for Participation


The 2021 EAI International Conference on Applied Cryptography in Computer and Communications (AC3 2021) takes place May 15-16, 2021.

Register for the virtual conference here.

Dr. Bo Chen, Computer Science, founding general chair of the new EAI conference, says the conference has brought together researchers, developers and practitioners from around the world to discuss and explore the area of applied cryptography in computer and communication systems.

Conference Topics

Conference topics include all aspects of applied cryptography, including symmetric cryptography, public-key cryptography, cryptographic protocols, cryptographic implementations, cryptographic standards and practices, as well as using cryptography to solve real-world problems.

Technical Program

The AC3 2021 technical program includes four main conference tracks at which 11 papers will be presented virtually in oral presentations.

  • Track 1 – Blockchain
  • Track 2 – Authentication
  • Track 3 – Secure Computation
  • 4 – Practical Crypto Application. Aside from the high-quality technical paper presentations, the technical program also features two keynote speeches, and one technical workshop.

Keynotes

The two keynote speeches will be delivered by Prof. Kui Ren (ACM Fellow, IEEE Fellow), Zhejiang University, China; and IEEE Fellow Prof. Robert Deng, Singapore Management University.

Workshop

A workshop, the First International Workshop on Security for Internet of Things (IOTS 2021), includes four technical papers which aim to develop cryptographic techniques for ensuring the IoT security. The conference, originally planned to be held in Xiamen China, was moved it online for the health and safety of participants.

Register to participate in the virtual conference here. Use the “Sign up for free access to the livestream” option.

European Alliance for Innovation (EAI) is an international professional community and a nonprofit organization. The goal of EAI is to empower the global ICT research and innovation community, and to promote cooperation between European and International ICT communities.

EAI Conferences span the globe with opportunities to meet, explore, and contribute to the world of ICT research. With 100+ annual events (including MobiQuitous, SecureComm, etc.), EAI is one of the world’s most prolific scientific communities.

EAI Conferences are published via Springer’s LNICST and EAI’s EUDL, and they are indexed in all leading indexing services, including EI, ISI, Scopus, CrossRef, Google Scholar, dblp, MAS, EBSCO, Microsoft Academic Search, CiteSeerX, and more.


New Fall ’21 Course: IoT Application and Design

This fall, Assistant Professor Lan Zhang (ECE/CS) will instruct the new class, “IoT Application and Design.”

The hands-on, multi-disciplinary, project-oriented course covers the application areas, revolution, and fundamental building blocks of the Internet of Things (IoT).

  • Course Number: EE4370
  • Class Time: MWF, 10:00 am – 10:50 am
  • Location: EERC 0227
  • Instructor: Lan Zhang, Ph.D., Asst. Prof., ECE, Affiliate Asst. Prof., CS
  • Office: EERC 623
  • E-mail: lanzhang@mtu.edu
  • Faculty Website: https://www.mtu.edu/ece/department/faculty/zhang
  • Course Levels: Graduates, Undergraduates
  • Prerequisite: Students may come from College of Engineering and College of Computing, provided that s/he has practical knowledge of basic internet technologies (TCP/IP, packet-switched networks, etc.) and basic machine learning concepts.

Course Description

This course consists of the application areas, revolution, and fundamental building blocks (data collection, connectivity, and analysis) in Internet of Things. A hands-on, multi-discipline project-oriented course.

Learning Objectives

Upon successful completion of this course, students will be able to:

  • Compare the major factors that led to the IoT development and revolution.
  • Characterize the major building blocks of IoT systems.
  • Select appropriate building blocks suites for various IoT applications.
  • Code the IoT problems in Matlab or Python, run the code, and evaluate the
    solution.
  • Communicate results to a group of peers in verbal presentation format successfully.