Category: Events

Faculty Candidate Lecture: Sidike Paheding

Flyer announcing faculty candidate lecture

The College of Computing’s Department of Applied Computing invites the campus community to lecture by MERET faculty candidate Dr. Sidike Paheding, Friday, April 10, 2020, at 3:30 p.m., via an online Zoom meeting. The title of Paheding’s lecture is, “Machine Learning in Multiscale and Multimodal Remote Sensing: From Ground to UAV with a stop at Satellite through Different Sensors.”

Link to the Zoom meeting here.

Paheding is currently a visiting assistant professor in the ECE department at Purdue University Northwest. His research interests cover a variety of topics in image/video processing, machine learning, deep learning, computer vision, and remote sensing.

 Abstract: Remote sensing data provide timely, non-destructive, instantaneous estimates of the earth’s surface over a large area, and has been accepted as a valuable tool for agriculture, weather, forestry, defense, biodiversity, etc. In recent years, machine learning for remote sensing has gained significant momentum due to advances in algorithm development, computing power, sensor systems, and data availability.

In his talk, Paheding will discuss the potential applications of machine learning in remote sensing from the aspects of different scales and modalities. Research topics such as multimodal data fusion and machine learning for yield prediction, plant phenotyping, augmented reality and heterogeneous agricultural landscape mapping will be covered.

Paheding earned his M.S. and Ph.D. degrees in electrical engineering at the University of South Alabama, Mobile, and University of Dayton, Ohio, respectively. He was a postdoctoral research associate and and assistant research professor in the Remote Sensing Lab at Saint Louis University from 2017 to 2019, prior to joining Purdue University Northwest.

He has advised students at the undergraduate, master’s, and doctoral levels, and authored or co-authored close to 100 research articles, including in several top peer-review journal papers.

He is an associate editor of the Springer journal Signal, Image, and Video Processing, a guest editor/reviewer for a number of reputed journals, and he has served on international conference committees. He is an invited member of Tau Beta Pi (Engineering Honor Society). 


Faculty Candidate Muhammad Fahad to Present Lecture April 9

The College of Computing’s Department of Applied Computing invites the campus community to a lecture by MERET faculty candidate Muhammad Fahad on Thursday, April 9, 2020, at 3:30 p.m., via an online Zoom meeting. His talk is titled, “Motion Planning and Control of Autonomous Mobile using Model Free Method.”

Link to the Zoom meeting here.

Dr. Fahad currently works as a robotics engineer at National Oil Well Varco. He received his M.S. and Ph.D. in electrical engineering from Stevens Institute of Technology, Hoboken, NJ, and his B.S in EE at University of Engineering and Technology, Lahore, Pakistan.

Fahad has extensive experience designing control and automation systems for the process industry using traditional control methods and robots. His research interests include cooperative distributed localization, human robot interaction (HRI), deep reinforcement learning (DRL), deep inverse reinforcement learning (DIRL) and generative adversarial imitation learning (GAIL), simulation tools design, parallel simulation frameworks and multi-agent learning.

Lecture Abstract. Robots are playing an increasingly important part in our daily lives. This increasing involvement of robots in our everyday lives has highlighted the importance of human-robot interaction, specifically, robot navigation of environments occupied by humans, such as offices, malls and airports. Navigation in complex environments is an important research topic in robotics.

The human motion model consists of several complex behaviors that are difficult to capture using analytical models. Existing analytical models, such as the social force model, although commonly used, are unable to generate realistic human motion and do not fully capture behaviors exhibited by humans. These models are also dependent on various parameters that are required to be identified and customized for each new simulation environment. 

Artificial intelligence has received booming research interest in recent years. Solving problems that are easy for people to perform but difficult to describe formally is one of the main challenges for artificial intelligence. The human navigation problem falls directly in this category, where it is hard to define a universal set of rules to navigate in an environment with other humans and static obstacles.

Reinforcement learning has been used to learn model-free navigation, but it requires a reward function that captures the behaviors intended to be inculcated in the learned navigation policy. Designing such a reward function for human like navigation is not possible due to complex nature of human navigation behaviors. The speaker proposes to use measured human trajectories to learn both the reward function and navigation policy that drives the human behavior.

Using a database of real-world human trajectories–collected over a period of 90 days inside a mall–we have developed a deep inverse reinforcement learning approach that learns the reward function capturing human motion behaviors. Further, this dataset was visualized in a robot simulator to generate 3D sensor measurement using a simulated LIDAR sensor onboard the robot. A generative adversarial imitation learning based method is developed to learn the human navigation policy using these human trajectories as expert demonstration. The learned navigation policy is shown to be able to replicate human trajectories both quantitatively, for similarity in traversed trajectories, and qualitatively, in the ability to capture complex human navigation behaviors. These navigation behaviors include leader follower behavior, collision avoidance behavior, and group behavior. 


Faculty Candidate Kahlid Miah to Present Lecture April 3

The College of Computing’s Department of Applied Computing invites the campus community to a lecture by faculty candidate Kahlid Miah on Friday, April 3, 2020, at 3:30 p.m., via an online Zoom meeting. The title of Miah’s lecture is, “Fiber-Optic Distributed Sensing Technology: Applications and Challenges.”

Link to the Zoom meeting here.

Dr. Miah is currently a visiting faculty member in the ECE department at Indiana University – Purdue University Indianapolis (IUPUI). He received his Ph.D. and M.S. in electrical engineering from University of Texas at Austin, and a B.S. in aerospace engineering, also from Indiana University. His research interests are in computational geophysics, signal and image processing, instrumentation, and fiber-optic distributed sensing system development.

Lecture Abstract: In distributed fiber-optic sensing systems, a fiber-optic cable itself acts as an array of sensors, allowing users to detect and monitor multiple physical parameters such as temperature, vibration and strain with fine spatial resolution over a long sensing distance. There are many applications, especially in geophysical, geotechnical, and mining engineering where simultaneous multiparameter measurements are essential. Data deluge, difficulty in multicomponent measurements, and poor sensor-medium coupling are key challenges, and thus provide opportunities for future research and development.  

Dr. Miah’s past teaching and research experience includes a faculty position in the Geophysical Engineering department at Montana Technological University. He has held a postdoctoral research position at University of Alberta and a visiting fellowship position at the Geological Survey of Canada. He has also worked as a process engineer for a semiconductor equipment manufacturer in Austin, Tex.

Note: The College of Computing Department of Applied Computing is a new administrative unit replacing the CMH Division; its official start date is July 1, 2020. Applied Computing academic programs include Computer Network and System Administration (CNSA), Cybersecurity, Electrical Engineering Technology (EET), Health Informatics, and Mechatronics.


Welcome to Spring 2020 Preview Day!

Welcome prospective students and friends and families! The Michigan Tech College of Computing is pleased to welcome you to Spring 2020 Preview Day.

Since you’re at home instead of on campus, we’ve prepared a special video to share with you today. Well, actually our academic advisor Kay Oliver produced the video. Thanks, Kay! (Scroll down to play the video.)

In the video, Kay will tell you about our undergraduate and graduate degree programs, and show you lots of photos of Michigan Tech students, faculty, labs, and classrooms.

Kay, along with Denise Landsberg, our second academic advisor, are standing by to answer your questions. You can email Kay and Denise at csadvisor@mtu.edu.

Please read more below the video.

College of Computing Preview Day: Spring 2020

On the virtual tour, you’ll also hear from Dr. Linda Ott, chair of the Computer Science department, who’ll fill you in on the Computer Science and Software Engineering degree programs, concentrations, and minors and go over some of the first-year Computing courses.

And you’ll learn a little bit about our Applied Computing degrees:

Computer Network and System Administration (CNSA)
Cybersecurity
Electrical Engineering Technology (EET)
Mechatronics

And if you’re still exploring which Computing degree is the right one for you, check out our General Computing major, which gives you a little time and space to make this important decision.

Finally, Computer Science prof Dr. Chuck Wallace will tell you about Michigan Tech’s unique student Enterprise program, where Computing students are working on real computing solutions for real clients. The Computing-focused student Enterprises are:

Husky Games
HIDE (Human Interface Development Enterprise)
IT Oxygen Enterprise.

Please enjoy the video. Contact us anytime with your questions, large or small, and be sure to visit our website (mtu.edu/computing), our news blog, and visit, share, connect, and like us on social media.

We hope to see you on campus this fall!


Faculty Candidate Interviews and Lectures to Take Place Online

The Strategic Faculty Hiring Initiative (SFHI) candidates affected by this change are:

Briana Bettin, March 16-17, 2020 | View blog post
Zoom Meeting: https://michigantech.zoom.us/j/468935183

Leo Ureel, March 24-26 | View blog post
Zoom Meeting: https://michigantech.zoom.us/j/696407720

The Computer Science faculty candidates affected by this change are:

Junqiao Qiu, March 30-31, 2020 | View blog post
Zoom Meeting: https://michigantech.zoom.us/j/842795573

Teseo Schneider, March 23-24  | View blog post
Zoom Meeting: https://michigantech.zoom.us/j/519255087

Vidhyashree Nagaraju, March 20-21 | View blog post
Zoom Meeting: https://michigantech.zoom.us/j/636248962

Please note, two faculty candidates who requested that their time on campus not be publicized on this blog are not included here. Please contact Vicky Roy, director of administration, if you have questions about these candidates.

Instructions on how to use Zoom can be found here.

More information about Michigan Tech’s response to COVID-2019 can be found here.


2020 Undergraduate Research Symposium is March 27

Undergraduate researchers and scholars from all colleges—first-year students to soon-to-graduate seniors—will present a record 76 posters at the 2020 Undergraduate Research Symposium, Friday, March 27, 2020, in the lobby of the Rozsa Center. Two sessions will take place, from 11:00 a.m. to 1:00 p.m., and from 2:00 to 4:00 p.m.

The Symposium, hosted by the Pavlis Honors College, highlights the amazing cutting-edge research being conducted on Michigan Tech’s campus by some of our best and brightest undergraduate students.

All faculty, staff and students are encourage to attend and support our excellent undergraduate researchers. Faculty members who would like to serve as distinguished judges at this year’s symposium may complete this short form

Learn more about the Symposium here.