Category: Events

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.


Faculty Candidate Teseo Schneider to Present Lecture March 23

The College of Computing invites the campus community to a lecture by faculty candidate Teseo Schneider on Monday, March 23, 2020, at 3:00 p.m. The title of Schneider’s lecture is, “Robust Black-box Analysis.”

Link to the online Zoom meeting here.

Schneider is an assistant professor and faculty fellow in computer science at the Courant Institute of Mathematical Sciences at New York University. He holds a Ph.D. in computer science from the Universita della Svizzera Italiana (2017). His research interests are in finite element simulations, mathematics, discrete differential geometry, and geometry processing. 

Numerical solutions of partial differential equations (PDEs) are ubiquitous in many different applications, ranging from simulations of elastic deformations for manufacturing to flow simulations to reduce drag in airplanes, and to organs’ physiology simulations to anticipate and prevent diseases.

The finite element method (FEM) is the most commonly used discretization of PDEs due to its generality and rich selection of off-the-shelf commercial implementations. Ideally, a PDE solver should be a “black-box”: the user provides as input the domain’s boundary, the boundary conditions, and the governing equations, and the code returns an evaluator that can compute the value of the solution at any point of the input domain. This is surprisingly far from being the case for all existing open-source or commercial software, despite the many research efforts in this direction and the sustained interest from academia and industry.

To a large extent, this issues from treating meshing (and geometry more in general) and FEM basis construction as two disjoint problems. The FEM basis construction may make a seemingly innocuous assumption (e.g., on the geometry of elements), leading to exceedingly difficult requirements for meshing software.

This state of matters presents a fundamental problem for all applications, and is even more problematic in applications that require fully automatic, robust processing of large collections of meshes of varying sizes, which have become increasingly common as large collections of geometric data become available. Most importantly, this situation arises in the context of machine learning on geometric and physical data, where one needs to run large numbers of simulations to learn from, as well as solve problems of shape optimization, which require solving PDEs in the inner optimization loop on a constantly changing domain.

Schneider’s research presents recent advancements towards an integrated pipeline, considering meshing and element design as a unique challenge, leading thus to a black-box pipeline that can solve simulations on 10,000 in the wild meshes, without any parameter tuning.

Schneider earned a Postdoc.Mobility fellowship from the Swiss National Science Foundation (SNSF) to pursue his research aiming to bridge physical simulations and geometry.Teseo is also the main developer of Polyfem (https://polyfem.github.io/), a flexible and easy to use Finite Element Library. He is one of the maintainers of libigl (https://github.com/libigl/libigl), and a contributor to wild meshing (https://github.com/wildmeshing), a 2D and 3D robust meshing library.

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Faculty Candidate Junqiao Qiu to Present Lecture March 30

The College of Computing invites the campus community to a lecture by faculty candidate Junqiao Qiu on Monday, March 30, 2020, at 3:00 p.m. The title of Qui’s talk is, “Model-Centric Speculative Parallelization for Scalable Data Processing.”

Link to the Zoom meeting here.

Junqiao Qiu is a Ph.D. candidate in the computer science and engineering department at University of California Riverside, advised by Prof. Zhijia Zhao. He received his bachelor’s degree in electronics and communications engineering from Sun Yat-sen University in 2015. His research interests are in the areas of programming systems and runtime support for parallel computing and scalable data processing. 

Exploiting parallelism is key to designing and implementing high-performance data processing on modern processors. However, there are many data processing routines running in serial, due to the sequential nature of their underlying computation models, such as finite-state machines (FSMs), a classic but inherently sequential computational model with applications in data decoding, parsing, and pattern matching.

In his talk, Qui will present techniques using speculation to “break” the inherent data dependencies, thus enabling scalable data-parallel processing. First, he will introduce a basic speculative parallelization scheme that breaks the state transition dependencies in FSM computations. Then, more interestingly, he will show how a broader range of applications, known as bitstream processing, can benefit from FSM-based speculative parallelization techniques. 

The key idea is to extract from programs the variable bits that cause dependencies and model their value-changing patterns with FSMs. Such techniques, for the first time, offer a principled approach to addressing the parallelization challenges in bitstream programs. With this approach, Qui’s research demonstrates that a rich set of performance-critical bitstream kernels can be effectively parallelized, with up to linear speedups on parallel processors. Finally, Qui will briefly discuss the major challenges in designing effective speculative parallelization frameworks for FSM-based computations, and present some of his forward-looking research ideas. 

Qui is a recipient of the UC-Riverside Dissertation Year Program (DYP) Award and Dean’s Distinguished Fellowship.

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Faculty Candidate Leo Ureel to Present Lecture March 24

The Colleges of Computing and Engineering invite the campus community to a lecture by faculty candidate Leo C. Ureel II on Tuesday, March 24, 2020, at 3:00 p.m. The title of Ureel’s lecture is, “Critiquing Student Code by Identifying Novice Anti-patterns.”

Join the online Zoom meeting here.

Ureel is a senior lecturer and PhD candidate in the Computer Science department at Michigan Tech. He has been teaching at the college level for 10 years, and he has over 20 years of industry experience in developing software for engineering, artificial intelligence, and education.

Ureel’s research focuses on a constructionist approach to introductory computer science that leverages code critiquers to motivate students to learn computer programming, with less cognitive overhead than is usually associated with learning programming and computation. In particular, he is developing critiques tools designed to provide students with feedback on programming assignments in ways that are similar to human instructors. Critiquer systems can be used to engage students in test-driven agile development methods through small cycles of teaching, coding integrated with testing, and immediate feedback.

Ureel’s work has provided him the opportunity to develop rich collaborations with researchers across the U.S. and in the U.K., Europe, and Africa, and he recently led an ITICSE working group of international researchers. Ureel teaches CS1 and CS2 courses, primarily to first year students, in which he works to broaden students’ views of computing, ground them in a programming language, and teach them problem solving skills. His research has has been supported by NSF, Google, and NCWIT.

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Faculty Candidate Vidhya Nagaraju to Present Lecture March 20

The College of Computing invites the campus community to a lecture by faculty candidate Vidhyashree Nagaraju on Friday, March 20, 2020, at 3:00 p.m. The title of Nagaraju’s talk is “Software Reliability Engineering: Algorithms and Tools.”

The lecture will be presented online through a Zoom meeting. Link to the meeting here.

Vidhyashree Nagaraju is a Ph.D. candidate in the Department of Electrical and Computer Engineering at the University of Massachusetts Dartmouth (UMassD), where she received a M.S. in Computer Engineering in 2015. She received a B.E. in electronics and communication engineering from Visvesvaraya Technological University, India, in 2011.

While there are many software reliability models, there are relatively few tools to automatically apply these models. Moreover, these tools are over two decades old and are difficult or impossible to configure on modern operating systems, even with a virtual machine. To overcome this technology gap, Nagaraju is developing an open source software reliability tool for the software and system engineering community. 

A key challenge posed by such a project is the stability of the underlying model fitting algorithms, which must ensure that the parameter estimates of a model are indeed those that best characterize the data. If such model fitting is not achieved, users who lack knowledge of the underlying mathematics may inadvertently use inaccurate predictions. This is potentially dangerous if the model underestimates important measures such as the number of faults remaining or overestimates the mean time to failure (MTTF).

To improve the robustness of the model fitting process, expectation conditional maximization (ECM) algorithms have been developed to compute the maximum likelihood estimates of nonhomogeneous Poisson process (NHPP) software reliability models. Nagaraju ‘s talk will present an implicit ECM algorithm, which eliminates computationally intensive integration from the update rules of the ECM algorithm, thereby achieving a speedup of between 200 and 400 times that of explicit ECM algorithms. The enhanced performance and stability of these algorithms will ultimately benefit the software.

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Faculty Candidate Briana Bettin to Present Lecture March 16

The Colleges of Computing and Engineering invite the campus community to a lecture by faculty candidate Briana Bettin on Monday, March 16, 2020, at 3:00 p.m. The title of Bettin’s talk is, “Understanding and Enhancing Novice Mental Models of Computing.”

The lecture will be presented online through a Zoom meeting. Link here to join the Zoom meeting.

Bettin is a PhD candidate and King-Chavez-Parks Future Faculty Fellowship recipient in Michigan Tech’s Department of Computer Science. Her research blends user experience methodologies with education research to better understand programming students and the impacts of the classroom environment.

Bettin’s research interests span education, experiential design, and human factors. She has a B.S. in computer science from Michigan Tech and an M.S. in human-computer interaction from Iowa State University.

The need for computer science coursework has exploded worldwide, and now more than ever students need coding and problem solving skills for the future. Students in the computing classroom come from a variety of majors, and students within the major are increasingly diverse in background and career interests.

Bettin’s presentation explores how students acquire and understand programming concepts, and how their development of foundational knowledge can be better facilitated. Her talk discusses work from several studies exploring questions such as, How can we relate topical material to such a wide variety of students? How are they interpreting these concepts and retaining them? And How does the classroom environment impact our students’ learning? 

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