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From the ICC Director: Reflections and Goals

Dear ICC Members and Friends,

Happy New Year! As we begin the new year and the Spring 2020 semester, I wanted to offer some reflections about the 2019 and share some goals for 2020.

For the ICC, the past six months have been thrilling, to say the least. The number of new awards is far above last year, with over $2 million in new projects to-date. And ICC research expenditures are on track for a record year. Thank you to everyone for all your hard work in developing collaborations, writing proposals, winning awards, executing your exciting research, mentoring, advising, and so much more.

The launch of Michigan Tech’s new College of Computing is such a fantastic opportunity. With this shift, we boldly announce that computing is a major field of study and not just an underpinning to other disciplines. I see the new College as a place of opportunity to experiment, collaborate, develop new pedagogies, and become a model for other institutions of higher learning. Our team is strong and creative, and it’s fun working on this puzzle with them.”

As the ICC is the research arm of the College of Computing, we are very much a part of the strategic vision for research in the College. This integration allows us to best utilize the finite resources of both the College of Computing and the ICC to realize the greatest return on key investments in people and resources.

To further support our members, the ICC has secured donor funding  that will make it possible to hire two key personnel in 2020. First, a search for a full-time assistant director for research development is underway. This new position will support ICC researchers as they collectively work to create and implement activities aimed at the growth and development of ICC-affiliated research and graduate programs, including pre- and post-award support, assisting with the financial processes of the institute, and helping to lead the daily administrative functions of the institute. We will also be starting a search soon for the first full-time Research Scientist in the ICC. More details on these hires will made public soon.

I’m very much looking forward to working with all of you in the new year.

Timothy C. Havens
Director, Institute for Computing and Cybersystems

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ECE’s Tony Pinar Joins ICC

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.”

Pinar is looking forward to becoming more involved with research, and he is looking for new collaborations with other ICC and Michigan Tech researchers.

“The resources and network the ICC provides to new—and even established—researchers are set up in a way to cultivate its members’ talent and support career pathways. I am looking forward to being a part of this dynamic Michigan Tech research institute,” Pinar says.

Pinar’s recent publications include the following.

M. A. Islam, D. T. Anderson, A. Pinar, T. C. Havens, G. Scott and J. M. Keller. “Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks”. IEEE Transactions on Fuzzy Systems(2019).

U. Agrawal, A.J. Pinar, C. Wagner, T.C. Havens, D. Soria, J.M. Garibaldi. “Comparison of Fuzzy Integral-Fuzzy Measure Based Ensemble Algorithms with the State-of-the-Art Ensemble Algorithms”. International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU) (2018).

B. Murray, M.A. Islam, A.J. Pinar, T.C. Havens, D.T. Anderson, G. Scott. “Explainable AI for Understanding Decisions and Data-Driven Optimization of the Choquet Integral”. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (2018).

A.J. Pinar, D.T. Anderson, T.C. Havens, A. Zare, T. Adeyeba. “Measures of the Shapley Index for Learning Lower Complexity Fuzzy Integrals”. SpringerGranular Computing(2017).

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“Artificial UnIntelligence,” A Keynote Lecture from Meredith Broussard

Meredith Broussard

Artificial UnIntelligence Book Cover

The Institute for Policy, Ethics, and Culture’s Algorithmic Culture series continues with “Artificial UnIntelligence,” a keynote lecture from Meredith Broussard, on Thursday, Dec. 5 at 7 p.m. in Memorial Union Building Ballroom B, followed by a Q&A.

Collective enthusiasm for applying computer technology to every aspect of life has resulted in a vast number of poorly designed systems. We are so eager to do everything digitally—hiring, driving, paying bills, even choosing romantic partners—that we have stopped demanding our technology actually work.

In this talk, author and professor Meredith Broussard looks at the inner workings and outer limits of technology, and explains why we should never assume that computers always get things right. Making a case against technochauvinism—the belief that technology is always the solution—Broussard looks at whether self-driving cars really work and why social problems persist in every digital Utopia. If we understand the limits of what we can do with technology, Broussard tells us, we can make better choices about what we should do with it to make the world better for everyone.

Meredith Broussard is an associate professor at the Arthur L. Carter Journalism Institute of New York University and the author of Artificial Unintelligence: How Computers Misunderstand the World. Her research focuses on artificial intelligence in investigative reporting, with a particular interest in using data analysis for social good. You can follow her on Twitter @merbroussard or contact her via meredithbroussard.com.

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Benjamin Ong Is PI on NSF Grant that Supports June 2020 Parallel-in-Time Conference

Benjamin Ong

Benjamin Ong (Math/ICC-DataS) is the principal investigator on a one-year project that has received a $36,636 other sponsored activities grant from the Mathematical Sciences Infrastructure Program at National Science Foundation. The project is entitled, “CBMS Conference: Parallel Time Integration.”

The award provides support for the NSF-CBMS Conference on Parallel Time Integration, to be held June 1-5, 2020, at Michigan Tech. The focus of the conference is to educate and inspire researchers and students in new and innovative numerical techniques for the parallel-in-time solution of large-scale evolution problems on modern supercomputing architectures, and to stimulate further studies in their analysis and applications.

Co-organizing the conference with Ong is Jacob B. Schroder, assistant professor in the Dept. of Mathematics and Statistics at University of New Mexico.

The conference will feature ten lectures by Professor Martin Gander, an expert in parallel time integration and a professor at the University of Geneva, Switzerland. Using appropriate mathematical methodologies from the theory of partial differential equations in a functional analytic setting, numerical discretizations, integration techniques, and convergence analyses of these iterative methods, conference participants will be exposed to the numerical analysis of parallel-in-time methodologies and their implementations. The proposed topics include multiple shooting type methods, waveform relaxation methods, time-multigrid methods, and direct time-parallel methods. These lectures will be accessible to a wide audience from a broad range of disciplines, including mathematics, computer science and engineering.

Visit the conference website at http://conferences.math.mtu.edu/cbms2020/.

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Benjamin Ong Awarded 2019-2020 Kliakhandler Fellowship

Benjamin Ong

Benjamin Ong (Math/ICC-DataS) has been awarded the Michigan Tech Department of Mathematics 2019-2020 Kliakhandler Fellowship, which will support two parallel-in-time conferences being held at Michigan Tech in June 2020.  As Kliakhandler Fellow, Ong will receive a $5,000 stipend, plus $10,000 to organize a workshop or conference at Michigan Tech.

The purpose of the Kliakhandler Fellowship is to stimulate research activity in the Michigan Tech Department of Mathematics. Awarded annually, the Kliakhandler Fellow is chosen based on a record of excellence in research and the potential of the proposed workshop to stimulate further research achievements and bring visibility to Michigan Tech and the Department of Mathematical Sciences.

Ong, along with Jacob B. Schroder, assistant professor in the Dept. of Mathematics and Statistics at University of New Mexico, is organizing two conferences to take place at Michigan Tech in June 2020. The first, “The CBMS Conference – Parallel Time Integration,” will take place June 1-5, 2020. The focus of this parallel-in-time workshop is to educate and inspire researchers and students in new and innovative numerical techniques for the parallel-in-time solution of large-scale evolution problems on modern supercomputing architectures, and to stimulate further studies in their analysis and applications.  The conference will feature ten lectures by Professor Martin Gander, an expert in parallel time integration and a professor at the University of Geneva, Switzerland.

The second conference, 9th Workshop on Parallel-in-Time Integration,” takes place June 8-12, 2020. The workshop the workshop will bring together an interdisciplinary group of experts to disseminate cutting-edge research and facilitate scientific discussions on the field of parallel time integration methods.
Igor Kliakhandler, a former Michigan Technological University mathematics faculty member, was born in Moscow, Russia in 1966. He graduated from the Moscow Oil and Gas Institute in 1983 and started his PhD studies there in 1988. He then emigrated with his family to Israel in 1991 and began his PhD at Tel-Aviv University under the guidance of Gregory Sivashinsky. He received his PhD in Applied Mathematics in 1997 from Tel-Aviv University. He held positions at Universidad Complutense de Madrid, Lawrence Berkeley National Laboratory and Northwestern University before joining Michigan Tech’s Department of Mathematical Sciences as an assistant professor in 2001. He was promoted to associate professor in 2005 and left the University in 2007 to work in the energy sector in Houston. He manages a group of companies that trade electric power across US, and is involved in a few start-up projects. Igor remained fond of Michigan Tech and its Math Department. Kliakhandler provides a generous gift to host the Kliakhandler Conference, an annual event at Michigan Tech to stimulate research activity in the mathematical sciences.

Link to more information about the two conferences at:

http://conferences.math.mtu.edu/cbms2020, June 1-5 2020

http://conferences.math.mtu.edu, June 8 – 12, 2020

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Anna Little to Present Talk October 18, 1 p.m.

Anna Little

Dr. Anna Little, a postdoc in the Department of Computational Mathematics, Science, and Engineering at Michigan State University, will present her talk, “Robust Statistical Procedures for Clustering in High Dimensions,” on Friday, October 18, 2019, at 1:00 p.m., in Fisher Hall Room 327B.

Dr. Little completed a PhD in mathematics at Duke University in 2011. She has been at Michigan State since 2018.  Visit her website at www.anna-little.com.

Lecture Abstract: This talk addresses multiple topics related to robust statistical procedures for clustering in high dimensions, including path-based spectral clustering (a new method), classical multidimensional scaling (an old method), and clustering in signal processing. Path-based spectral clustering is a novel approach which combines a data driven metric with graph-based clustering. Using a data driven metric allows for fast algorithms and strong theoretical guarantees when clusters concentrate around low-dimensional sets.

Another approach to high-dimensional clustering is classical multidimensional scaling (CMDS), a dimension reduction technique widely popular across disciplines due to its simplicity and generality. CMDS followed by a simple clustering algorithm can exactly recover all cluster labels with high probability when the signal to noise ratio is high enough. However, scaling conditions become increasingly restrictive as the ambient dimension increases, illustrating the need for robust unbiasing procedures in high dimensions.  Clustering in signal
processing is the final topic; in this context each data point corresponds to a corrupted signal. The classic multireference alignment problem is generalized to include random dilation in addition to random translation and additive noise, and a wavelet based approach is used to define an unbiased representation of the target signal(s) which is robust to high frequency perturbations.

Download the event flyer.

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Anna Wilbik to Present Seminar October 3

The Institute of Computing and Cybersystems (ICC) and the Michigan Tech Visiting Professor Program will present a seminar by Dr. Anna Wilbik on Thursday, October 3, starting at 3:00 p.m., in ME-EM 112 . A reception will follow and refreshments will be served.  The title of Dr. Wilbik’s seminar is, “The explainability challenge in descriptive analytics: do we understand the data?”
The seminar is presented by the Institute of Computing and Cybersystems and the Michigan Tech Visiting Professor Program, which is funded by a grant to the Michigan Tech Provost Office from the State of Michigan’s King-Chavez-Parks Initiative.
Dr. Anna Wilbik is an assistant professor in the Information Systems Group of the Department of Industrial Engineering and Innovation Sciences at Eindhoven University of Technology (TU/e) in the Netherlands. She received her PhD in Computer Science from the Systems Research Institute, Polish Academy of Science, Warsaw, Poland, in 2010. In 2011, she was a Postdoctoral Fellow with the Department of Electrical and Computer Engineering at the University of Missouri, Columbia, USA. Her research interests are in business intelligence, especially focused on linguistic summaries and computing with words. With her research she tries to bridge the gap between the fuzzy sets theory and industrial applications. She makes this connection in research projects collaborating with industry both on the national and the European level. She has published over 80 papers in international journals and conferences.
Seminar Abstract: Nowadays, ever more data are collected, for instance in the healthcare domain. The amount of patients’ data has doubled in the previous two years. This exponential growth creates a data flood that is hard to handle by decision makers. In many domains, humans are collaborating with machines for decision making purposes to cope with the resulting data complexity and size. This collaboration can be realized through machine learning, visual analytics, or online analytical processing, where a machine is just a tool – but often used to make important decisions. The question now is: do we really understand the data by using the tool this way?
Explainability is a great challenge in data analytics, with the aim to explain to the user why certain decisions have been recommended or made. This challenge is especially important in predictive and prescriptive analytics. Less attention in this respect is payed to less mature analytics levels, descriptive and diagnostics, although they are the first steps for understanding data.
Data analysis methods use numbers, figures, or mathematical equations to show data, decision recommendations, and patterns. Yet for a human, the natural way of communication is natural language: words, not numbers or figures. This causes a gap between the meaning of data and human understanding. The challenge is: How to make data more understandable for humans?
Fuzzy techniques, or the application of the computing with words paradigm, have the potential to close the gap by using natural language as the communication means. In this talk, I will focus on descriptive analytics and show with a set of examples how fuzzy techniques can provide better insight of data to the user. I pay special attention to the technique of linguistic summaries.

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Laura Monroe to Speak About High-performance Computing, Tues. Sept. 24

The Department of Mathematical Sciences and the College of Computing will present a lecture on high-performance computing by Dr. Laura Monroe from the Ultrascale Systems Research Center (USRC) at Los Alamos National Laboratory on Tuesday, September 24, from 5:00 to 6:00 p.m., in Fisher Hall, Room 133. The lecture is titled “The Mathematical Analysis of Faults and the Resilience of Applications.” Discussion will follow the lecture, and pizza and refreshments will be served.

Abstract: As the post-Moore’s-Law era advances, faults are expected to increase in number and in complexity on emerging novel devices. This will happen on exascale and post-exascale architectures due to smaller feature sizes, and also on new devices with unusual fault models. Attention to error-correction and resilience will thus be needed in order to use such devices effectively. Known mathematical error-correction methods may not suffice under these conditions, and an ad hoc approach will not cover the cases likely to emerge, so mathematical approaches will be essential. We will discuss the mathematical underpinnings behind such approaches, illustrate with examples, and emphasize the interdisciplinary approaches that combine experimentation, simulation, mathematical theory and applications that will be needed for success.

Dr. Monroe has spent most of her career focused on unconventional approaches to difficult computing problems, specifically researching new technologies to enable better performance as processor-manufacturing techniques reach the limits of the atomic scale, also known as the end of Moore’s Law. Dr. Monroe received her PhD in the theory of error-correcting codes, working with Dr.Vera Pless. She worked at NASA Glenn, then joined Los Alamos National Laboratory in 2000. She has contributed on the design teams on the LANL Cielo and Trinity supercomputers, and originated and leads the Laboratory’s inexact computing project that is meant to address Moore’s Law challenges in a unique way. She also provides mathematical and theoretical support to LANL’s HPC Resilience project.

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