Global Technology Connection is looking for a dynamic Nuclear Engineer to join our forward-thinking team, someone who is passionate about nuclear engineering and has a solid grounding in Bayesian inference, ideally with particle filtering. If you’re someone who dabbles in machine learning and is excited about applying these skills in the nuclear engineering field, we want to hear from you!
What we’re looking for:
· Expertise in Nuclear Engineering, 🛠️ preferably holding a PhD degree in Nuclear Engineering or a related field. 🎓
· Proficiency in Bayesian Inference, particularly Particle Filtering 📊
· Knowledge of Machine Learning techniques is a significant plus 🤖
· Preferably a U.S. citizen or Green Card holder
· Python Proficiency: Strong coding skills in Python
This is an excellent opportunity for individuals looking to substantially impact their field, working on cutting-edge projects that blend traditional engineering with modern computational techniques. We’re offering a vibrant working environment, a culture of innovation, and the chance to work with a talented team dedicated to pioneering advancements in the industry.
If you’re ready to take the next step in your career and meet the qualifications mentioned, we’d love to discuss how you can significantly contribute to our team.
Apply Now: Please send your resume and a brief cover letter outlining your experience and why you’re a good fit for this role to admin@globaltechinc.com.
Author: Yekanth Ram Chalumuri
Title of dissertation: TOWARDS AUTOMATION OF HEMORRHAGE DIAGNOSTICS AND THERAPEUTICS
Date/time: April 10th, 2024 at 12pm
Location: ERF 1207 (IREAP Building)
Committee members:
Dr. Jin-Oh Hahn, Advisor and Chair
Dr. Yang Tao, Dean’s Representative
Dr. Hosam Fathy
Dr. Mark Fuge
Dr. Yancy Diaz-Mercado
Dr. Andrew Reisner
Abstract:
This thesis primarily focuses on developing algorithms and methodologies to achieve the broader aim of advancing the care in hemorrhage diagnostics and therapeutics, especially in low resource settings. The first goal of this thesis is to develop algorithms that can detect hemorrhage using non-invasive physiological signals. Our results showed that our machine learning algorithm can successfully classify various types of hypovolemia. We also developed a physics-based approach to detect and estimate internal hemorrhage even when the body is being compensated by fluid resuscitation using continuous non-invasive hemoglobin measurements. We evaluated the model in silico and determined the maximum acceptable measurement noise that can make these algorithms effective.
The second objective is to advance the regulatory aspects of physiological closed-loop control systems in maintaining blood pressure at a desired value during hemorrhage. Physiological closed-loop control systems offer an exciting opportunity to treat hemorrhage in low resource settings but often face regulatory challenges due to safety concerns. A physics-based model with rigorous validation can improve regulatory aspects of such systems but current validation techniques are very naïve. We developed a physics-based model that can predict blood pressure, heart rate, cardiac output, and hematocrit during hemorrhage and resuscitation and validated using a validation framework that uses sampled digital twins for validation along with more rigorous validation metrics. Overall, the goal of this research is to improve the care and outcomes of patients with hemorrhagic shock by utilizing machine learning, inference, and modeling approaches for early detection and treatment.
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UPCOMING THESIS DEFENSE: SIDNEY MOLNAR
Author: Sidney Molnar
Title of dissertation: Metareasoning Strategies to Correct Navigation Failures of Autonomous Ground Robots
Date/time: April 8th, 2024 at 10:00am
Zoom: https://umd.zoom.us/j/9892794999?omn=94673990602
Location: 2164 DeWALT Conference Room, Glenn L. Martin Hall.
Committee members:
- Dr. Jeffrey Herrmann, Advisor and Chair
- Dr. Michael Otte
- Dr. Shapour Azarm
Abstract: Due to the complexity of autonomous systems, theoretically perfect path planning algorithms sometimes fail because of unexpected behaviors that occur when these systems interact with various sub-processes like perception, mapping, and goal planning. These failures can prevent the success of a mission, especially in complex and unexplored environments. Metareasoning, or “thinking about thinking,” is one approach to mitigate these planning failures. This project introduces a novel metareasoning approach that employs various methods of monitoring and control to identify and address path planning irregularities that lead to failures. All methods were integrated into the ARL ground autonomy stack, which utilizes both global and local path planning ROS nodes. The monitoring methods proposed include listening to messages from the planning algorithms, assessing the environmental context of the robot, expected progress methods that evaluate the robot’s progress based on its movement capabilities from a milestone checkpoint, and fixed radius methods that use parameters selected based on mission objectives to assess progress from a milestone checkpoint. The control policies introduced are metric-based sequential policies which select new planner combinations based on benchmark robot performance metrics, context-based pairs policies that assess the effects of switching between two planner combinations, and a restart policy that relaunches a new instance of the same planner combination. The study evaluated which combinations of monitoring and control policies improved or degraded navigation performance by assessing how close the robot could get to the final mission goal. Additionally, this thesis suggests areas for further research to determine the conditions under which metareasoning can improve navigation.
Author: Christina Nikiforidou
Title of dissertation: Locomotion on Granular Media: Reduced-Order Models
Date/time: April 3rd, 2024 at 9:30am
Location: 2164 DeWALT Conference Room, Glenn L. Martin Hall.
Committee members:
Dr. Balakumar Balachandran, Advisor and Chair
Dr. Peter Chung
Dr. Teng Li
Abstract: (attached)
Author: Gurtajbir Herr
Title of dissertation: ON DATA-BASED MAPPING AND NAVIGATION OF UNMANNED GROUND VEHICLES
Date/time: April 3rd, 2024 at 12:00pm
Location: AVW 1146 (ISR seminar room)
Committee members:
Dr. Nikhil Chopra, Advisor and Chair
Dr. Anubhav Datta, Dean’s Representative
Dr. Balakumar Balachandran
Dr. Miao Yu
Dr. Yancy Diaz-Mercado
Abstract:
Unmanned ground vehicles (UGVs) have seen tremendous advancement in their capabilities and applications in the past two decades. With several key algorithmic and hardware breakthroughs and advancements in deep learning, UGVs are quickly becoming ubiquitous, finding applications as self-driving cars, in remote site inspections, in hospitals and shopping malls, among several others. Motivated by their large-scale adoption, this dissertation aims to enable the navigation of UGVs in complex environments. In this dissertation, we develop a supervised learning-based navigation algorithm that utilizes model predictive control (MPC) for providing training data. Improving MPC performance by data-based modelling of complex vehicle dynamics is then addressed. Finally, this dissertation deals with detecting and registering transparent objects that may deteriorate navigation performance.
Navigation in dynamic environments poses unique challenges, particularly due to the limited knowledge of the decisions made by other agents and their objectives. In this dissertation, we propose a solution that utilizes an MPC-based planner as an expert to generate high-quality motion commands for a car-like robot operating in a simulated dynamic environment. These commands are then used to train a deep neural network, which learns to navigate. The deep learning-based planner is further enhanced with safety margins to improve its effectiveness in collision avoidance. We then evaluate the performance of our method through simulations and real-world experiments, demonstrating its superior performance in terms of obstacle avoidance and successful mission completion. This research has practical implications for the development of safer and more efficient autonomous vehicles.
Many real-world applications rely on MPC to control UGVs due to its safety guarantees and constraint satisfaction properties. However, the performance of such MPC-based solutions is heavily reliant on the accuracy of the motion model. This dissertation addresses this challenge by exploring a data-based approach to discovering vehicle dynamics. Unlike existing physics-based models that require extensive testing setups and manual tuning for new platforms and driving surfaces, our approach leverages the universal differential equations (UDEs) framework to identify unknown dynamics from vehicle data. This innovative approach, which does not make assumptions about the unknown dynamics terms and directly models the vector field, is then deployed to showcase its efficacy. This research opens up new possibilities for more accurate and adaptable motion models for UGVs.
With the increasing adoption of glass and other transparent materials, UGVs must be able to detect and register them for reliable navigation. Unfortunately, such objects are not easily detected by LiDARs and cameras. In this dissertation, we study algorithms for detecting and including glass objects in a Graph SLAM framework. We use a simple and computationally inexpensive glass detection scheme to detect glass objects. We present the methodology to incorporate the identified objects into the occupancy grid maintained by such a framework. We also address the issue of drift accumulation that can affect mapping performance when operating in large environments.
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UPCOMING DISSERTATION DEFENSE: WEIDI YIN
Author: Weidi Yin
Title of dissertation: De-conflicting Management of Fluid Resuscitation and Intravenous Medication Infusion
Date/time: April 4th, 2024 at 12:15pm
Location: 2164 DeWALT Conference Room, Glenn L. Martin Hall.
Committee members:
Dr. Jin-Oh Hahn, Advisor and Chair
Dr. Robert M. Sanner, Dean’s Representative
Dr. Hosam Fathy
Dr. Nikhil Chopra
Dr. Yancy Diaz-Mercado
Abstract: (attached)
Author: Sangeeth Balakrishnan
Title of dissertation: MACHINE LEARNING IN SCARCE DATA REGIME FOR DESIGN AND DISCOVERY OF MATERIALS
Date/time: March 29th, 2024 at 10am
Location: 2164 DeWALT Conference Room, Glenn L. Martin Hall.
Committee members:
Dr. Peter Chung, Advisor and Chair
Dr. Mark Fuge, Co-chair
Dr. Yifei Mo, Dean’s Representative
Dr. Nikhil Chopra
Dr. Hugh Bruck
Dr. Brian Barnes
Abstract:
In recent years, data-driven approaches based on machine learning have emerged as a promising method for rapid and efficient estimation of the structure-property-performance relationships, leading to the discovery of advanced materials. However, the cost and time required to obtain relevant data have limited application of these methods to only a few classes of materials where extensive property data are available. Moreover, the material property prediction poses its own unique set of challenges, in part, due to: 1) the complex non-linear response of materials in different space and time domains, 2) inherent variability in material in terms of composition and processing conditions from the atomic to the macroscopic scales, and 3) the need for accurate, rapid and less expensive predictive models for accelerated material discovery. This dissertation aims to develop three novel machine learning frameworks for constructing targeted learning frameworks and discovering novel materials when dealing with limited available data. The dissertation also highlights the future directions and challenges of such approaches.
In the first approach, we develop data-driven methods to estimate the material properties under shock compression. A novel featurization approach combining synthetic and physical features was developed showing substantial improvements in the machine learning model performance. The effects of feature engineering, model choices, and uncertainty in the experimental data were investigated. In the second approach, we develop a novel joint embedding framework that enables transfer learning, with the target of locally optimizing the shock wave properties of nitrogen-rich molecules. This work is motivated by a need to overcome challenges associated with the translation of machine learning approaches to domains where there is a relative lack of domain-specific data. However, the properties studied in the second approach do not consider factors needed to assemble a complete material system. Therefore, in the third and final approach, we investigate material systems whose properties at system level are determined by various upstream design factors, such as the composition of raw materials, manufacturing variability, and considerations involved while assembling the system. We propose a stacked ensemble learning framework to make statistical inferences about the system properties.
Title of dissertation: ANALYSIS OF THE LIFE-CYCLE COST AND CAPABILITY TRADEOFFS ASSOCIATED WITH THE PROCUREMENT AND SUSTAINMENT OF OPEN SYSTEMS
Date/time: March 19th at 9:00 AM
Location: 2164 DeWALT Conference Room, Glenn L. Martin Hall.
Zoom link: https://umd.zoom.us/j/6254630014?omn=99524160244
Committee members:
Professor Peter A. Sandborn, Chair
Professor Bilal M. Ayyub, Dean’s Representative
Professor Katrina Groth
Professor Jeffrey Herrmann
Professor William Lucyshyn
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UPCOMING THESIS DEFENSE : SAMUEL GIGIOLI
Author: Samuel Gigioli
Date: November 13th, 2023 at 10:00AM
Location: IREAP 1207
Committee Members:
Dr. Ashwani Gupta, Chair
Dr. Bao Yang
Dr. Kenneth
Title: DISSECTION AND MODELING OF AEDC WIND TUNNEL 9 CONTROL LAW AND FACILITY DURING BLOW PHASE
Abstract:
This work presents the progress towards a mathematical modeling of the Arnold
Engineering Development Complex (AEDC) Wind Tunnel 9 control law during the blow
phase of a given tunnel run, composing of electrical analog physics, ideal gas control
volume physics, incompressible fluid mechanics, and force balance kinematics. Tunnel 9
does not currently have a well-defined process for developing control law parameters for a
new tunnel condition. The modeled control law is developed based on electrical schematics
and theoretical analog circuitry. The model is further refined by analyzing historical data
through a two-factor interaction analysis of several run-condition factors. The primary goal
of this work is to provide enhanced support to the Tunnel 9 engineers with the ability to
model different run conditions. Key facility measurements can be estimated, aiding in the
determination if proposed non-standard run conditions will meet or maintain the facility
capabilities, and if the facility can be operated under safe operating limits. The secondary
goal of this model is to progress toward a digitally controlled valve system to replace the
current analog system. Such changes may enable considerably more advantages in facility
(1) performance, (2) health monitoring, (3) maintainability, and (4) sustainment.