Category: Seminars

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 Saleem Ashraf

The College of Computing Department of Applied Computing invites the campus community to a lecture by faculty candidate Saleem Ashraf on, April 7, 2020, at p.m., via an online Zoom meeting.

Dr. Ashraf is currently an assistant professor of mechatronics engineering in the ECE department at Sultan Qaboos University, Oman. He received his Ph.D. and MSc. degrees in mechatronics engineering from DeMontfort University, UK, in 2006 and 2003, respectively, and his BSc. in electrical and computer engineering from Philadelphia University, Pa., in 2000.

Ashraf’s research interests are unified under the theme, “developing real-time smart controllers for different engineering systems,” and his research investigates electromechanical, electro-pneumatic, and piezoelectric based systems. 

Advancements in field of unmanned vehicle system, artificial intelligence, and computer vision have empowered the integration of solutions that would potentially automate many processes. 

Ashraf’s seminar presents his research experience in the field of smart and vision-based unmanned vehicle systems, and how this technology has been employed to solve real-life problems in Oman.

The talk will present a selection of Ashraf’s fundamental research work focused on the modeling and control of long-stroke piezoelectric actuators, which are being used widely in micro positioning systems. He will also share his experience in the establishment of the “Embedded & Interconnected Vision Systems” (EIVS) lab. 

The second part of Ashraf’s talk will cover his teaching experience, including philosophy, courses, new courses, extracurricular activities, and practical projects. He will present his methodology in supervising multi-disciplinary final year projects with some examples of completed projects. Finally, Ashraf will discuss his ideas about how he can contribute to the Michigan Tech curriculum at all levels, undergraduate and graduate.

Ashraf has been awarded external research grants totaling more than $450K, and three internal grants totaling $58K; he attributes his success in this regard to his development of excellent relations with local industry and the Omani research council (TRC). The common aim of these research projects is to develop vision-based unmanned vehicles to solve real life problems such as oil spill in seawater. 

He has published more than 45 peer-reviewed papers in reputable journals and at international conferences. He is one of the founders of the “Embedded & Interconnected Vision Systems” (EIVS) lab at Sultan Qaboos University, which was inaugurated this March and funded by BP Oman. The lab hosts equipment for Embedded Vision Systems, Artificial Intelligence (UVS / Robotics), and IoT.


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.


ROTC Cybersecurity Training for Tomorrow’s Officers

The U.S. Department of Defense, Office of Naval Research, has awarded Michigan Tech faculty researchers a $249,000 grant that supports the creation of an ROTC undergraduate science and engineering research program at Michigan Tech. The primary goal of the program is to supply prepared cadets to all military branches to serve as officers in Cyber commands.

The principal investigator (PI) of the project is Andrew Barnard, Mechanical Engineering-Engineering Mechanics. Co-PIs are Timothy Havens, College of Computing; Laura Brown , Computer Science, and Yu Cai, Applied Computing. The title of the project is, “Defending the Nation’s Digital Frontier: Cybersecurity Training for Tomorrow’s Officers.”

The curriculum will be developed over the summer, and instruction associated with the award will begin in the fall 2020 semester. Cadets interested in joining the new program are urged to contact Andrew Barnard.

Initially, the program will focus on topics in cybersecurity, machine learning and artificial intelligence, data science, and remote sensing systems, all critical to the The Naval Science and Technology (S&T) Strategic Plan and the Navy’s Force of the Future, and with equal relevance in all branches of the armed forces.

The plan of work focuses on on engaging ROTC students in current and on-going Cyber research, and supports recruitment of young ROTC engineers and scientists to serve in Navy cybersecurity and cyber-systems commands. The program will compel cadets to seek positions within Cyber commands upon graduation, or pursue graduate research in Cyber fields.

“Our approach develops paid, research-based instruction for ROTC students through the existing Michigan Tech Strategic Education Naval Systems Experiences (SENSE) program,” said principal investigator Andrew Barnard, “ROTC students will receive one academic year of instruction in four Cyber domains: cybersecurity, machine learning and artificial intelligence (ML/AI), data science, and remote sensing systems.”

Barnard says the cohort-based program will enrich student learning through deep shared research experiences. He says the program will be designed with flexibility and agility in mind to quickly adapt to new and emerging Navy science and technology needs in the Cyber domain.

Placement of officers in Cyber commands is of critical long-term importance to the Navy (and other DoD branches) in maintaining technological superiority, says the award abstract, noting that technological superiority directly influences the capability and safety of the warfighter.

Also closely involved in the project are Michigan Tech Air Force and Army ROTC officers Lt. Col. John O’Kane and LTC Christian Thompson, respectively.

“Unfortunately, many ROTC cadets are either unaware of Cyber related careers, or are unprepared for problems facing Cyber officers,” said Lt. Col. O’Kane. “This proposal aims to provide a steady flow of highly motivated and trained uniformed officers to the armed-services, capable of supporting the warfighter on day-one.”

Andrew Barnard is director of Michigan Tech’s Great Lakes Research Center, an associate professor of Mechanical Engineering-Engineering Mechanics, and faculty advisor to the SENSE Enterprise.

Tim Havens is director of the Institute of Computing and Cybersystems, associate dean for research, College of Computing, and the William and Gloria Jackson Associate Professor of Computer Systems.

Laura Brown is an associate professor, Computer Science, director of the Data Science graduate program, and a member of the ICC’s Center for Data Sciences.

Yu Cai is a professor of Applied Computing, an affiliated professor of Computational Science and Engineering, a member of the ICC’s Center for Cybersecurity, and faculty advisor for the Red Team, which competes in the National Cyber League (NCL).

The Great Lakes Research Center (GLRC) provides state-of-the-art laboratories to support research on a broad array of topics. Faculty members from many departments across Michigan Technological University’s campus collaborate on interdisciplinary research, ranging from air–water interactions to biogeochemistry to food web relationships.

The Army and Air Force have active ROTC programs on Michigan Tech’s campus.

The Office of Naval Research (ONR) coordinates, executes, and promotes the science and technology programs of the United States Navy and Marine Corps.


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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