Dr. Sidike Paheding, Applied Computing, is seeking a fall 2021 graduate student to assist with a research project. Details are below.
- Pay rate: $15/hour, 10 hours/week
- Employment dates: September 2021 through December 2021. An extension is possible.
- Basic requirements: Experience in Unity development platform and Virtual/Augmented Reality.
- Desired: Knowledge in machine learning/AI is a plus.
Sidike Paheding, Ph.D.
Assistant Professor, Dept. of Applied Computing
Affiliated Assistant Professor, Dept. of Computer Science
Affiliated Faculty, Center for Data Sciences, and Computational Science & Engineering
College of Computing, Michigan Technological University
Graduate student Chen Zhao will defend his dissertation proposal for the PhD in Computational Science and Engineering Monday, July 26, 2021, at 2:00 p.m. via online meeting.
Zhao’s dissertation advisor is Dr. Weihua Zhao, Applied Computing; his research topic is Deep Learning for Medical Image Segmentation using Prior Knowledge and Topology.
Join the Zoom meeting here: https://michigantech.zoom.us/j/85266979905
by Pasi Lautala
Thomas Oommen (GMES, ICC), Ricardo Eiris, (CEGE, ICC), and Beth Veinott (CLS, ICC) are among eight Michigan Tech researchers who have submitted a a record number of eight concept papers for proposed research projects with the Federal Railroad Administration.
The Federal Railroad Administration (FRA) requested that Michigan Tech submit a record number of eight concept papers for proposed research projects as part of their 2021 Broad Agency Announcement.
In addition, Tech is a subcontractor for two more concept paper proposals. The paper submittal was coordinated by the Rail Transportation Program and the range of topics speaks to the diversity of Michigan Tech’s expertise applicable to the rail transportation. The PIs are looking forward to FRA decisions on how many of these papers advance to full proposals.
Each of the 10 projects had a different principal investigator (PI), representing six university departments/institutes and several more co-PIs.
The project titles and their PIs include:
- Hyper- and Multi-spectral Sensing and Deep Learning for Automated Identification of Roadbed Condition, (PI, Thomas Oommen, GMES).
- Wire Arc Additive Manufacturing (WAAM) for Weld Enhanced Cast Steel Coupler Knuckles (PI, Paul Sanders, MSE).
- IoT Assisted Data-analytics Framework Enables Assessment of Location Based Ride Quality (LBRQ) (PI, Sriram Malladi, MEEM).
- RailStory: Using Web-based Immersive Storytelling to Attract the Next Generation of Young Women in Rail (PI, Ricardo Eiris, CEGE).
- A Risk Informed Decision-Making Framework for Coastal Railroad System Subjected to Storm Hazards and Sea Level Rise (PI, Yousef Darestani, CEGE).
- Rail Corridor Life-Cycle Assessment (LCA) Framework, Factors and Models to Support Project Evaluation and Multi-Modal Comparisons (PI, Pasi Lautala, CEGE).
- Development of Infrared Thermography for Effective Rail Weld Inspection (PI, Qingli Dai, CEGE).
- Enabling Longer-distance, AI-enabled Drone-based Grade Crossing Assessment in Potentially GPS Denied Environments (PI, Colin Brooks).
- Multi-Site Simulation to Examine Driver Behavior Impact of Integrated Rail Crossing Violation Warning (RCVW) and In-Vehicle Auditory/Visual Alert (IVAA) System (PI, Elizabeth Veinott, subcontract with Virginia Tech).
- Evaluation of Non-traditional Methods of Reducing Emissions in Short Line Railroad Operations (PI, Jeremy Worm, subcontract with ASLRRA).
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.”