News & Press Releases

Please attend one required informational meeting for students who are interested in applying for the RA position for the 2024-2025 academic year. 20 opportunities to attend in November are available. LOCATION: VIA Zoom https://umassd.zoom.us/j/95552332558?pwd=WktucDZGZjEzWkgrbER4WWZGMzVQdz09 For More information contact Michelle Black at mblack2@umassd.edu

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Please attend one required informational meeting for students who are interested in applying for the RA position for the 2024-2025 academic year. 20 opportunities to attend in November are available. LOCATION: Balsam Hall first floor Lounge For More information contact Michelle Black at mblack2@umassd.edu

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Please attend one required informational meeting for students who are interested in applying for the RA position for the 2024-2025 academic year. 20 opportunities to attend in November are available. LOCATION: Spruce Hall first floor Lounge For More information contact Michelle Black at mblack2@umassd.edu

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Topic: Terrain Aware Autonomous Ground Navigation in Unstructured Environments Informed by Human Demonstrations Location: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A Zoom Conference Link: https://umassd.zoom.us/j/3753158123 Meeting ID: 375 315 8123 Passcode: 495427 Abstract: Mobile robots equipped with the capability to perform autonomous waypoint navigation can replace humans for applications such as humanitarian assistance, nuclear cleanup, and reconnaissance. In such tasks, the robot must be able to accurately and reliably perform complex behaviors such as the ability to navigate over unstructured terrain and respond to unseen situations similar to how a human would. However, to implement complex behaviors beyond obstacle avoidance, many current approaches employ machine learning methods requiring large amounts of labeled data. While simulations can quickly generate large amounts of labeled data, the same cannot be said for real world environments, limiting adoption of mobile robots to complete the aforementioned applications. Moreover, solutions are often brittle, exhibiting poor performance when operating outside of environments beyond where they were designed or trained. Therefore, there is a need for methods which can quickly learn navigation behaviors from limited data while being able to adapt to dynamic scenarios. To communicate both positive and negative environmental scenarios, roboticists assign rewards (or inversely, costs) to all the relevant environmental features expected. For robots that need to quickly transition between varying unstructured environments, defining rewards prior to understanding all future features the robot will encounter is often unachievable as either: (i) the robot is unable to perceive the new feature, and (ii) the numerical reward for each feature is unknown. In such scenarios, it is more effective for a human supervisor to provide examples of desired behavior than for an engineer to explicitly define it. Therefore, this dissertation focuses on a ground robot's ability to incorporate human demonstrations as a weak supervisory signal to solve each respective preceding problem by (i) obtaining a semantic perception model capable of classifying terrains present in the current environment given a sequence of unlabeled (unsupervised) images and (ii) using Bayesian inverse reinforcement learning to learn rewards associated with the terrains identified to build cost-maps online for autonomous waypoint traversal. Advisor(s): Dr. Lance Fiondella, Associate Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth Committee Members: Dr. Hong Liu, Commonwealth Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Jiawei Yuan, Associate Professor, Department of Computer Information Science, UMASS Dartmouth; Dr. Craig T. Lennon and Dr. Maggie B. Wigness, United States Army Research Laboratory NOTE: All ECE Graduate Students are ENCOURAGED to attend. All interested parties are invited to attend. Open to the public. *For further information, please contact Dr. Lance Fiondella at 508.999.8596 or via email at lfiondella@umassd.edu.

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EAS Doctoral Proposal Defense by David Gillcrest Date: Tuesday, December 12, 2023 Time: 11:00 a.m. Topic: Inverse Analysis and Calibration of Physical Models: An Approached Based on Greedy Kaczmarz Multi-Fidelity Surrogates and Aggregated Directional Statistics Location: CSCDR - TXT 105 Abstract: Advances in surrogate modeling have allowed for highly accurate Polynomial Chaos (PC) expansions to be constructed for physical models ranging from mild to major complexity in their respective parameter spaces. Multi-fidelity techniques in the field of uncertainty analysis have been employed to efficiently capture the variations in the outputs of models given reasonable perturbations in these parameters. Recently, a greedy Kaczmarz algorithm (GKA) has been used to cheaply construct surrogate models in a way that greatly outperforms the previous least square approaches that demand samples about 2-3 times the number of coefficients in the PC expansion. In this research we look at the utility of GKA-produced surrogate models in solving parameter estimation problems - inverse problems typically solved using statistical least squares methods - for partial differential equations (PDEs). We present a generalized approach to solving two-dimensional parameter estimations for PDEs and demonstrate its potential using two Advection-Diffusion-Reaction equations: one for the source localization of a river contaminant, and the other for the estimations of both the area source contamination magnitude as well as ambient pollution concentration in the context of an urban heat island under the influence of mesoscale wind. The physical domain of each problem is discretized in such a way as to accommodate the hypothetical usage of detection apparatuses. An array of surrogate models is constructed to capture the variation in outputs at these detectors with respect to the PDEs' parameter spaces. Using directional statistical techniques we examine the optimal combination of the surrogate models, specifically by looking at the local interactions of each model's slope field. The quality of the combination of surrogates can then be quantified and aggregated in order to achieve a cost value for a grouping of surrogates. ADVISOR(S): Dr. Mazdak Tootkaboni, Dept of Civil and Environmental Engineering (mtootkaboni@umassd.edu) Dr. Yanlai Chen, Dept. of Mathematics (ychen@umassd.edu) COMMITTEE MEMBERS: Dr. Sigal Gottlieb, Department of Mathematics Dr. Zheng Chen, Department of Mathematics NOTE: All EAS Students are ENCOURAGED to attend.

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Please join the Faculty Senate for its December meeting where we discuss issues concerned with academic matters and matters relating to the intellectual life of the University of Massachusetts Dartmouth. The Faculty Senate meeting is open to the public; however, only elected Senators may vote on issues presented to the Senate. For your information, Senate members for AY 2023/2024 can be found at www.umassd.edu/faculty/senate/roster. If you missed any of our meetings or want to refresh your memory, the Faculty Senate has been recording its meetings, which are posted to the Senate website (www.umassd.edu/faculty/senate/minutes/).

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