Institute of Computing and Cybersystems (ICC) Blog

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

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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

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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.

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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.

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Anna Wilbik to Present Seminar October 3

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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

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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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Oommen Quoted in EOS Earth and Space News on Flooding in India

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Thomas Oommen

Thomas Oommen (GMES) was quoted in the article, “Devastating Floods Hit India for the Second Year in a Row,” posted August 26, 2019, in EOS Earth and Space News. Oommen studies studies landslide hazards in Kerala, India. Link to the article here: https://eos.org/articles/devastating-floods-hit-india-for-the-second-year-in-a-row

 

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Weihua Zhou to Present Invited Talk at 2019 American Society of Nuclear Cardiology Conference

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Weihua Zhou

Weihua Zhou (DataS), assistant professor of health informatics, will present an invited talk and give a poster presentation at the 2019 American Society of Nuclear Cardiology conference (ASNC), September 12-15, in Chicago, IL.

His talk, “Machine Learning for Automatic LV Segmentation and Volume Quantification,” will discuss the results of his recent research for the American Heart Association, “A new image-guided approach for cardiac resynchronization therapy.” (Project Number: 17AIREA33700016, PI: Weihua Zhou).

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Benjamin Ong Awarded 25K for Parallel-in-time Integration Workshop

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Benjamin Ong

Benjamin Ong (Math/ICC-DataS) is Principal Investigator on a one-year project that has received a $25,185 other sponsored activities grant from the National Science Foundation. The project is titled “Ninth Workshop on Parallel-In-Time Integration.”

The Ninth Workshop on Parallel-in-time Integration will take place June 8 – 12, 2020, at Michigan Tech. Ong (chair) and Jacob Schroder, assistant professor in the Dept. of Mathematics and Statistics at University of New Mexico, are heading the organizing committee for the workshop. Travel funding for early career researchers will be available. Application details and deadlines will be posted shortly on the event’s website at conferences.math.mtu.

Contact information:
ongbw@mtu.edu
906-487-3367

Invited speakers:

  • Professor Matthias Bolten, Bergische Universität Wuppertal
  • Professor Laurence Halpern, Université Paris 13
  • Professor George Karniadakis, Brown University
  • Professor Ulrich Langer, Johannes Kepler University Linz
  • Dr. Carol Woodward, Lawrence Livermore National Laboratory

The workshop is supported by:

  • Michigan Technological University, Department of Mathematical Sciences
  • Michigan Technological University, College of Science and Arts
  • Lawrence Livermore National Laboratory
  • Jülich Supercomputing Centre
  • FoMICS: The Swiss Graduate School in Computational Science

About the Workshop on Parallel-in-time Integration (from https://parallel-in-time.org/ and https://parallel-in-time.org/events/9th-pint-workshop/)

Computer models and simulations play a central role in the study of complex systems in engineering, life sciences, medicine, chemistry, and physics. Utilizing modern supercomputers to run models and simulations allows for experimentation in virtual laboratories, thus saving both time and resources. Although the next generation of supercomputers will contain an unprecedented number of processors, this will not automatically increase the speed of running simulations. New mathematical algorithms are needed that can fully harness the processing potential of these new systems. Parallel-in-time methods, the subject of this workshop, are timely and necessary, as they extend existing computer models to these next generation machines by adding a new dimension of scalability. Thus, the use of parallel-in-time methods will provide dramatically faster simulations in many important areas, such as biomedical applications (e.g., heart modeling), computational fluid dynamics (e.g., aerodynamics and weather prediction), and machine learning. Computational and applied mathematics plays a foundational role in this projected advancement.

The primary focus of the proposed parallel-in-time workshop is to disseminate cutting-edge research and facilitate scientific discussions on the field of parallel time integration methods. This workshop aligns with the National Strategic Computing Initiative (NCSI) objective: “increase coherence between technology for modeling/simulation and data analytics”. The need for parallel time integration is being driven by microprocessor trends, where future speedups for computational simulations will come through using increasing numbers of cores and not through faster clock speeds. Thus as spatial parallelism techniques saturate, parallelization in the time direction offers the best avenue for leveraging next generation supercomputers with billions of processors. Regarding the mathematical treatment of parallel time integrators, one must use advanced methodologies from the theory of partial differential equations in a functional analytic setting, numerical discretization and integration, convergence analyses of iterative methods, and the development and implementation of new parallel algorithms. Thus, the workshop will bring together an interdisciplinary group of experts spanning these areas.

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NRI: Interactive Robotic Orchestration: Music-based Emotion and Social Interaction Therapy for Children with ASD

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Researchers: Myounghoon “Philart” Jeon, PI

Sponsor: National Institutes of Health through the National Robotics Initiative

Amount of Support: $258,362

Abstract: The purpose of the research is to design novel forms of musical interaction combined with physical activities for improving social interactions and emotional responses of children with autism spectrum disorder (ASD). It is well known that a subject with ASD shows deficiency in emotional and social interactions. We propose to address two aspects of this issue: physio-musical stimulus for initiating engagement and empathizing for deepening interaction and thus enhancing a child’s emotional and social interactions. People with or without ASD between the ages of 5 and 10 may join this study.

Summary: In the United States, the rapid increase in the population of children with autism spectrum disorder (ASD) has revealed the deficiency in the realm of therapeutic accessibility for children with ASD in the domain of emotion and social interaction. There have been a number of approaches including several robotic therapeutic systems, but most of these efforts have been centered on speech interaction and task-based turn-taking scenarios. Unfortunately, the spectral diversity of ASD is so vast that many current approaches are, as novel and intriguing as they are, still insufficient to provide parameterized therapeutic tools and approaches.

To overcome this challenge, state-of-the-art techniques must still be developed to facilitate the autonomous interaction methods for robots to effectively stimulate the emotional and social interactivity of children. We focus on the recent studies that reveal strong relevance in premotor cortex among neural domains for music, emotion, and motor behaviors. We propose that musical interaction and activities can provide a new therapeutic domain for effective development in the children’s emotion and social interaction. Of key importance within this proposed work are providing capabilities for the robotic system to monitor the emotional and interactive states of children and provide effective musical and behavioral stimuli with respect to the emotion and social interaction of children. Major research questions include (1) What kinds of music-based signals and music-coordinated activities can play effective roles in encouraging emotional interaction and social engagement?, (2) How can robotic learning of human behaviors during musical activities increase the interaction between human and robot and reinforce the emotional and social engagement?, and (3) What metrics can be designed to effectively measure and evaluate the changes in emotional and social engagement through musical interaction activities?

Intellectual Merits: Designing and evaluating core techniques to fuse music, emotion, and socio-physical interaction should be invaluable to advancing affective computing, robotics, and engineering psychology theory as well as providing guidelines in developing an effective therapeutic robot companion. With this research endeavor, we will identify the most crucial features of musical components in stimulating emotional and interactive intentions of children and the most effective way to correlate those musical components with motion behaviors to maximize the children’s social engagement and development. The findings of the proposed work will also contribute to the design of interactive scenarios for natural and creative therapy with an individualized and systematic approach.

Broader Impacts: The successful development of our framework with the music-based approach has the capability of creating a new domain of pediatric therapy that can tremendously increase the abilities of robots to interact with children in a safe and natural manner. The novelty and significance of this approach correlates to therapeutic design for children with ASD, but the foundation for our interactive and adaptive reinforcement scheme can be extended to other pediatric populations and developmental studies. We plan to incorporate these knowledge and approaches into courses designed for robotics and affective computing. Furthermore, we plan to encapsulate many of these ideas into an “outreach” workshop for underrepresented students. Undergraduate research projects and demonstrations are expected to inspire the next generation of engineers and scientist with a new robot-coexistent world.

Main Results: We have developed an emotion-based robotic motion platform that can encapsulate spatial components as well as emotional dimensions into robotic movement behaviors. The Romo robot and DARwin-OP are first used for our project (Figure 1). The robotic characters and interfaces are also newly designed to accommodate characteristics of children with ASD while satisfying software design specifications. We have also developed a multi-modal analysis system to monitor and quantify physical engagements and emotional reactions of children through facial expression recognition app (Figure 2), Kinect based movement analysis system, voice analysis app, and a music analysis software. All systems are designed for mobile computing environments, so our developed therapy sessions can be installed in any place with proper space and connectivity. We have implemented a sonification server (Figure 3) with 600 emotional sound cues for 30 emotional keywords and a real-time sonification generation platform. We have also carried out extensive user study with Americans and Koreans to validate our sounds and compare cultural differences. The results show that there are some commonalities in terms of emotional sound preferences between the two countries and we can narrow down our emotional sound cues for further research. We have created robotic scenarios for a pilot study (Typical day of Senses & Four Seasons), and will expand the scenarios with diverse genres of music and motion library sets.

Publications

Robotic Sonification for Promoting Emotional and Social Interactions of Children with ASD

Musical Robots for Children with ASD Using a Client-Server Architecture

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