Also In This Section
  • Topics

  • Day: February 2, 2021

    College of Computing Invites Applications for Two Faculty Positions

    Are you interested in a faculty position with the new Michigan Tech College of Computing? Do you know someone who is?

    Michigan Technological University’s College of Computing invites applications for two (2) assistant, associate, or full professor positions to start in August 2021.

    Areas of particular interest include cybersecurity, artificial intelligence/machine learning, and data science; exceptional candidates in other areas of computing will also be considered.

    Successful candidates will demonstrate a passion for their research, an enthusiasm for undergraduate and graduate education, and a strong commitment to cultivating diverse and inclusive learning environments.

    View the positions description and apply here:

    Review of applications will begin immediately and continue until the position is filled. To learn more about this opportunity, please visit or contact the search chair, Dr. Timothy Havens, at Applications received by March 1, 2020 will receive full consideration.

    Michigan Tech is building a culturally diverse faculty committed to teaching and working in a multicultural environment and strongly encourages applications from all individuals. We are an ADVANCE Institution having received three National Science Foundation grants in support of efforts to increase diversity, inclusion, and the participation and advancement of women and underrepresented individuals in STEM.

    Michigan Tech actively supports dual-career partners to retain a quality workforce; we offer career exploration advice and assistance finding positions at the University and in the local community. Please visit for more information.

    An applicant must have earned a Ph.D. degree in Computer Science, Computer Engineering, Computing, or a closely related area. Michigan Tech places a strong emphasis on balancing cutting-edge research with effective teaching, outreach, and service. Candidates for these positions are expected to demonstrate potential for excellence in independent research, excellence in teaching, and the ability to contribute service to their department and profession. Salary is negotiable depending upon qualifications.

    Michigan Tech is an internationally renowned doctoral research university with 7,100 students and 400 faculty located in Houghton, Michigan, in the scenic Upper Peninsula on the south shore of Lake Superior. The area provides a unique setting where natural beauty, culture, education, and a diversity of residents from around the world come together to share superb living and learning experiences.

    The College of Computing has 36 faculty members, 650 undergraduate students in five degree programs (Computer Science, Computer Network and System Administration, Cybersecurity, Electrical Engineering Technology, Mechatronics, and Software Engineering) and 90 graduate students in four MS degree programs (Computer Science, Cybersecurity, Data Science, Health Informatics, and Mechatronics) and Ph.D. degree programs in Computer Science and Computational Science and Engineering.

    Sidike Paheding Wins MDPI Electronics Best Paper Award

    A scholarly paper co-authored by Assistant Professor Sidike Paheding, Applied Computing, is one of two papers to receive the 2020 Best Paper Award from the open-access journal Electronics, published by MDPI.

    The paper presents a brief survey on the advances that have occurred in the area of Deep Learning.

    Paheding is a member of the Institute of Computing and Cybersystems’ (ICC) Center for Data Sciences (DataS).

    Co-authors of the article, “A State-of-the-Art Survey on Deep Learning Theory and Architectures,” are Md Zahangir Alom, Tarek M. Taha, Chris Yakopcic, Stefan Westberg, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul A. S. Awwal, and Vijayan K. Asari. The paper was published March 5, 2019, appearing in volume 8, issue 3, page 292, of the journal.

    View and download the paper here.

    Papers were evaluated for originality and significance, citations, and downloads. The authors receive a monetary award , a certificate, and an opportunity to publish one paper free of charge before December 31, 2021, after the normal peer review procedure.

    Electronics is an international peer-reviewed open access journal on the science of electronics and its applications. It is published online semimonthly by MDPI.

    MDPI, a scholarly open access publishing venue founded in 1996, publishes 310 diverse, peer-reviewed, open access journals.

    Paper Abstract

    In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, cybersecurity, and many others.

    This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network (DNN). The survey goes on to cover Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). Additionally, we have discussed recent developments, such as advanced variant DL techniques based on these DL approaches. This work considers most of the papers published after 2012 from when the history of deep learning began.

    Furthermore, DL approaches that have been explored and evaluated in different application domains are also included in this survey. We also included recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches. There are some surveys that have been published on DL using neural networks and a survey on Reinforcement Learning (RL). However, those papers have not discussed individual advanced techniques for training large-scale deep learning models and the recently developed method of generative models.

    Sidike Paheding

    Susanta Ghosh Publishes Paper in APS Physical Review B Journal

    Assistant Professor Susanta Ghosh, ME-EM, has published the article, “Interpretable machine learning model for the deformation of multiwalled carbon nanotubes,” in the APS publication, Physical Review B.

    Co-authors of the paper are Upendra Yadav and Shashank Pathrudkar. The article was published January 11, 2021.

    Ghosh is a member of the Institute of Computing and Cybersystems’ Center for Data Sciences.

    Article Abstract

    In the paper, researchers present an interpretable machine learning model to predict accurately the complex rippling deformations of multiwalled carbon nanotubes made of millions of atoms. Atomistic-physics-based models are accurate but computationally prohibitive for such large systems. To overcome this bottleneck, we have developed a machine learning model. The proposed model accurately matches an atomistic-physics-based model whereas being orders of magnitude faster. It extracts universally dominant patterns of deformation in an unsupervised manner. These patterns are comprehensible and explain how the model predicts yielding interpretability. The proposed model can form a basis for an exploration of machine learning toward the mechanics of one- and two-dimensional materials.

    APS Physics advances and diffuses the knowledge of physics for the benefit of humanity, promote physics, and serve the broader physics community.

    Physical Review B (PRB) is the world’s largest dedicated physics journal, publishing approximately 100 new, high-quality papers each week. The most highly cited journal in condensed matter physics, PRB provides outstanding depth and breadth of coverage, combined with unrivaled context and background for ongoing research by scientists worldwide.