William Curran's Location
Arlington, Virginia, United States, United States
William Curran's Contact Details
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About William Curran
Research Scientist with over 10 years of experience in the field of AI and Autonomy. Throughout my career I have focused on solving the challenges associated with resilient autonomy for long duration deployments. These have included such applications as space habitats, mars rovers, underwater vehicles, and minimally manned/unmanned surface vessels. I have applied such techniques as platform introspection and reasoning, physics-based modeling, introspective decision making, fault management, and holistic platform performance modeling to these domains. I am excited to find a position working with and leading motivated teams solving hard autonomy problems.
William Curran Work Experience Details
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Research ScientistMetron, Inc.Arlington, Va, Us -
Ai & Autonomy LeadMitre Jul 2023 - PresentMclean, Va, Us -
Research ScientistMetron, Inc. Nov 2020 - May 2023Reston, Va, Us -
Research ScientistNavatek Llc Sep 2018 - Oct 2020I was the technical lead for Navatek’s efforts related to learning systems, with expertise in reinforcement learning techniques. I frequently wrote white papers and competitive proposals for the DoD and NASA, as well as act as the Principal Investigator and Project Manager for won proposals totaling $2.5 million from 2019-2021. These proposal topics primarily relates to the design of future robotic systems and sensors, autonomous control logic, simulation-based performance evaluation, and new theoretical developments in machine learning and intelligent systems. My research focused on applying novel artificial intelligence techniques in the certification of autonomous behaviors before deployment and then fault identification and sustainment analytics after deployment.I was the Principal Investigator and Program Manager for the following projects:• Reinforcement Learned Adversarial Agents for Active Fault Detection in Space Habitats (NASA). Reinforcement learning techniques to develop a fault prediction and detection solution that improves NASA's ability to reveal latent, unknown conditions that would lead to failure of life sustainment systems. https://www.sbir.gov/sbirsearch/detail/1881977• AI-based MDP Uncertainty Reduction Tool (Navy). Certification and testing of artificial intelligence and machine learning technologies to increase trust, understanding, and reliability.
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Karle'S FellowU.S. Naval Research Laboratory Sep 2017 - Aug 2018Washington, Dc, UsWorked on the Meso-scale Robotic Locomotion Initiative (MERLIN) to develop a small robot that a marine could carry in a backpack and deploy to conduct intelligence, surveillance, and reconnaissance missions. Due to the small size requirements, MERLIN uses hydraulics that have much more energy density, but are a harder engineering and modeling challenge. I developed Control Theoretic/Deep Reinforcement Learning hybrid techniques with the goal of robust, adaptive, and model-free control. -
Graduate Research AssistantOregon State University Aug 2013 - Jul 2017Corvallis, Or, UsMy research interests lie in developing learning algorithms for real-world robot control systems. In particular, my work focuses on scalable model-free reinforcement learning for adaptive robot control in systems where a mathematical model is not easily understood or computable. Specifically, I worked on:Dimensionality Reduced Reinforcement Learning (2015-2017)The complexity of state-of-the-art personal robots lead to large dimensional state spaces, which are difficult to learn in. To alleviate this issue, we developed a technique called Dimensionality Reduced Reinforcement Learning. This technique leverages concepts from dimensionality reduction, transfer learning and reinforcement learning to learn high-dimensional policies quickly.Project Chiron (2014-2016)Project Chiron is a self-driving wheelchair project at Oregon State University. We worked with individuals with ALS and the ALS foundation to develop a small package that can be mounted on a powered wheelchair to provide self-driving capabilities. In 2016, I was a part of a 4-man team sent to the Robots for Good competition in Dubai. We placed 7th out of over 700 entries.As Autonomous As Possible (2014-2015)In this work we introduce a risk-aware task-level reinforcement learning algorithm that adapts an end-user's risk tolerance. A3P learns a task-level policy where states are tasks and actions are approaches in accomplishing that task. -
Nreip Asee Student ContractorU.S. Naval Research Laboratory Jun 2016 - Sep 2016Washington, Dc, UsI implemented a Memory for Goals cognitive model for human error prediction and embodied this model in a robot perceptual system for online error prediction in human-robot collaborations.Dr. Gregory Trafton, Navy Research Lab, Navy Center for Applied Research in Artificial Intelligence
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Nrl Student ContractorU.S. Naval Research Laboratory Jun 2015 - Sep 2015Washington, Dc, UsContinued dimensionality reduced reinforcement learning with PCA research. Applied this approach to Mountain Car 3D, Mario, and worked on applying it to an autonomous underwater vehicle.Dr. David Aha, Navy Research Lab, Navy Center for Applied Research in Artificial Intelligence
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Research InternNasa Ames Research Center Jun 2013 - Sep 2013Moffett Field, California, UsDeveloped a new simulation approach leveraging the concept of a multi-fidelity simulation. Computationally complex simulation is used only when agents are in a potential separation violation, while a low fidelity simulation is used for navigation. The key research focus is when to change from one fidelity to another, as small nuanced issues can easily occur that impedes agent learning.Dr. Adrian Agogino, NASA AMES, Moffett Field -
Graduate Research AssistantOregon State University Jul 2011 - Jun 2013Corvallis, Or, UsFor AADI I worked on Air Traffic Control with a focus on multiagent reinforcement learning. This work involved removing congestion and optimizing delay in the national airspace by controlling ground delay imposed upon aircraft. This approach is typically computationally intractable due to the extremely large size of the environment (40,000 aircraft per day). Developed an approach using domain-based agent clustering, essentially clustering agents into semi-independent groups and treating each cluster of agents as an independent problem. I then extended this work to become domain independent, removing the need for domain experts when performing agent clustering.Dr. Kagan Tumer, Oregon State University, MIME Department
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Undergraduate Research AssistantOregon State University Jun 2010 - Aug 2012Corvallis, Or, UsApplied Machine Learning techniques on the image processing of multiple radar images. The goal is to design software to autonomously find rare bird roosts using radar images.Dr. Dan Sheldon, Oregon State University, EECS DepartmentDr. Tom Dietterich, Oregon State University, EECS Department
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Undergraduate Research AssistantOregon State University Mar 2011 - Aug 2011Corvallis, Or, UsLead a study exploring how users give feedback to machine learning vision algorithms. Lack of trust is a large issue in the Vision domain of Machine Learning. This study is designed to help develop techniques to alleviate this problem.Dr. Weng-Keen Wong, Oregon State University, EECS DepartmentDr. Sinisa Todorovic, Oregon State University, EECS Department
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Undergraduate Research AssistantOregon State University Aug 2010 - Jul 2011Corvallis, Or, UsExplored the juncture between machine learning, human-computer interaction, and testing.Dr. Margaret Burnett, Oregon State University, EECS DepartmentDr. Alex Groce, Oregon State University, EECS Department
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Undergraduate Research AssistantOregon State University Apr 2008 - Sep 2010Corvallis, Or, UsWorked with fellow students and the physics department at Oregon State University to launch OSU’s first CubeSat into space. I was the lead programmer and Vice-President of the OSUPSP. I also co-wrote proposals, recruited members, helped obtain funding and co-wrote programs for this project in Python.Dr. William Hetherington, Oregon State, Physics Department
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Research InternNasa Ames Research Center Jun 2012 - Sep 2012Moffett Field, California, UsExtended my previous Air Traffic Control work by simulating the entire National Airspace (40,000 planes) rather than a small subset, and developed an automated partitioning system with real world data.Dr. Adrian Agogino, NASA AMES, Moffett Field
William Curran Skills
Python
C++
Machine Learning
Artificial Intelligence
Robotics
Neural Networks
Ros
Reinforcement Learning
Evolutionary Algorithms
Multi Agent Systems
Robot Operating System
Computer Science
Latex
Matlab
Computer Vision
William Curran Education Details
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Oregon State UniversityRobotics -
Oregon State UniversityComputer Science
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Oregon State UniversityComputer Science
Frequently Asked Questions about William Curran
What company does William Curran work for?
William Curran works for Metron, Inc.
What is William Curran's role at the current company?
William Curran's current role is Research Scientist.
What is William Curran's email address?
William Curran's email address is cu****@****sci.com
What is William Curran's direct phone number?
William Curran's direct phone number is +154167*****
What schools did William Curran attend?
William Curran attended Oregon State University, Oregon State University, Oregon State University.
What skills is William Curran known for?
William Curran has skills like Python, C++, Machine Learning, Artificial Intelligence, Robotics, Neural Networks, Ros, Reinforcement Learning, Evolutionary Algorithms, Multi Agent Systems, Robot Operating System, Computer Science.
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