Category Archives: Seminar

MTRI to Present Research Seminars October 14

The Institute of Computing and Cybersystems will present four brief seminars by researchers from the Michigan Tech Research Institute (MTRI) on Monday, October 14, 2019, 11:00 a.m. to 12:00 p.m., in EERC 122.  MTRI research and outreach focuses on the development of technology to sense and understand natural and manmade environments.

Sarah Kitchen is a mathematician with background in algebraic geometry and representation theory. Her recent research interests include algebraic structures underlying optimization problems and applications of emerging statistical tools to signal processing and source separation problems. Her talk, “Collaborative Autonomy,” will discuss some considerations in centralized, semi-centralized, and decentralized decision-making methods for autonomous systems.

Susan Janiszewski is a mathematician specializing in graph theory and combinatorics. Her research interests lie in applying concepts from discrete mathematics to machine learning, computer vision, and natural language processing. Her talk, “Combining Natural Language Processing and Scalable Graph Analytics,” takes up the fast-growing field of Natural Language Processing (NLP), i.e. the development of algorithms to process large amounts of textual data. Janiszewski will discuss ways to combine common NLP and graph theoretic algorithms in a scalable manner for the purpose of creating overarching computational systems such as recommendation engines or machine common sense capabilities.

Joel LeBlanc has 10 years of experience in statistical signal processing. His research interests include information theoretic approaches to inverse imaging, and computational techniques for solving large inverse problems. LeBlanc’s talk, “Testing for Local Minima of the Likelihood Using Reparameterized Embeddings,” addresses the question: “Given a local maximum of a non-linear and non-convex log-likelihood equation, how should one test for global convergence?” LeBlanc will discuss a new strategy for identifying globally optimal solutions using standard gradient-based optimization techniques.

Meryl Spencer is a physicist with a background in complex systems and network theory. Her research interests include machine learning for image processing, applications of graph algorithms, and self-organization. Her talk, “Computational modeling of collaborative multiagent systems,” will discuss her previous work on modeling self organization in cellular networks, and some areas of interest for future work.

Download the event flyer.


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.


Dr. Timothy Wilkin to Present “Adventures of a Cyber-Physical Cow,” Mon., Oct. 7, 4 pm

Tim Wilkin

Dr. Timothy Wilkin, associate professor of computer science and associate head of school (student learning) within the School of Information Technology, Deakin University, Australia, will present a talk at Michigan Tech on Monday, October 7, from 4:00-5:00 p.m., in ME-EM 112. A reception and refreshments will follow.

Dr. Wilkin’s talk, “Adventures of a Cyber-Physical Cow,” will present findings from his recent industry-based research into the use of wearable technologies in livestock farming.

Talk Abstract: Fitness and activity trackers, and other wearable sensors have revolutionised both professional sports and the general health & wellbeing market. On the other hand, wearables to support precision livestock farming and general animal health and wellbeing tracking are virtually non-existent. There are significant opportunities to support and grow concepts such as “paddock to plate” food provenance, particularly in the meat and livestock sector, through the use of wearable technologies. In this talk I will present some recent industry-based research between Deakin University and Agersens Pty Ltd, an Australian manufacturer of a world-leading geofencing technology for livestock. Real-time behaviour classification and analytics were used to both improve the existing product, as well as to create new data products for farmers and a greatly enhanced marketability for their smart collar. I will also highlight how this industry-based research has led to several interesting and challenging research questions that have driven ongoing fundamental research in data science at Deakin.

Dr. Wilkin’s Bio: Dr Wilkin’s research interests cover problems in computational and artificial intelligence to support sensor and data analytics, with applications in intelligent control for robotics and autonomous systems, embedded/edge AI, and intelligent sensing. His research has been applied in diverse areas, from marine ecology to childhood health, farming, defence and commercial robotics. Dr Wilkin is also an innovative, award-winning teacher and academic leader. As Associate Head of School he overseas teaching and learning activities of over 100 full-time academic staff and 3500 students enrolled in 16 undergraduate and postgraduate programs.

Tim Wilkin Talk Flyer