Category: Events

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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Preview Day Is Sat., Mar. 28

The College of Computing will conduct tours of its Computing labs and classrooms on Saturday, March 28, 2020, from 2:30 to 3:15 p.m. in Rekhi Hall. Tours will begin in Room 201. Refreshments will be served.

The tours are offered in conjunction with Preview Day, also March 28, hosted by the Admissions Office. The event is an opportunity for admitted students and their families to experience campus and learn more about Michigan Tech as they consider their final college choice.

Also on Preview Day, the College of Computing will participate in the Michigan Tech Showcase, which takes place from 11:30 a.m. to 1:30 p.m. in the Memorial Union Building Alumni Lounge. The showcase gives students and their families a chance to learn about all of the great opportunities and resources available to them at Michigan Tech.

Learn more and register for Preview Day here.