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  • Category: Grad Students

    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

    Volunteers Needed for Augmented Reality Study

    by Department of Computer Science

    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:

    • You must have access to an Android mobile phone
    • You must have access to a printer
    • You must be a fluent speaker of English
    • You must be 18 years of age or older
    • You must live in the United States

    If you would like to take part, please contact rhabibi@mtu.edu


    PhD Student Daniel Byrne, CS, Awarded Finishing Fellowship


    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.


    AI, Mobile Security Grad-level Research Assistant Needed

    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 (xyyuan@mtu.edu).


    RedTeam Achieves Breakthrough in NCL Cybersecurity Competition

    The 23 members of the Michigan Tech RedTeam achieved a historic breakthrough in the Spring 2021 National Cyber League (NCL) competition.

    The primary team finished the capture-the-flag (CTF) team competition 3rd Place in the overall ranking (tied for 1st Place in score). More than 900 teams from across the country participated in the CTF.

    Students on the primary team are: Trevor Hornsby, Dakoda Patterson, Stu Kernstock, Matthew Chau, Ryan Klemm, Shane Hoppe, and Joshua Stiebel.

    Further, of the 4,180 individual players competing in this spring’s NCL, four RedTeam players ranked in the Top 100: Trevor Hornsby (50th Place), Dakoda Patterson (59th), Stu Kernstock (75th), and Matthew Chau (100th).

    “Amazing achievements!” said Dr. Bo Chen, Computer Science. “We are proud of you guys!” Chen, along with Dr. Yu Cai, Applied Computing, are advisors to the student organization.

    The biannual NCL cybersecurity competition, for college and high school students, consists of a series of individual and team challenges, which present opportunities for students to prepare and test themselves against practical cybersecurity knowledge and skills, such as identifying hackers from forensic data, pentesting and auditing vulnerable websites, and recovering from ransomware attacks.

    RedTeam is a registered Michigan Tech student organization. The team works to promote a security-driven mindset among students, and provide a community and resource for those wishing to learn more about information security.

    Interested in cybersecurity? RedTeam meets every Thursday, 6:00-7:00 p.m., in Discord. Students with little or no background in cybersecurity are welcome. Contact the Red Team (redteam@mtu.edu) for more information.