Levi D. McClenny

Graduate Research Fellow

Dept. of Electrical Engineering, Texas A&M University

Army Research Lab

Levi is a graduate student researcher in the Department of Electrical and Computer Engineering at Texas A&M University in College Station, TX. Additionally, he is a member of the United States Army Reserves, currently serving in an Aviation Battalion in Conroe, Texas as a Blackhawk pilot. He is a Research Assistant in the Department of Electrical Engineering at Texas A&M and is currently collaborating with the Army Research Lab on research areas of interest to the Department of Defense.

He holds a Bachelor and Master of Science in Electrical Engineering from Texas A&M, completed in 2015 and 2016 respectively. He was formerly a member the Corps of Cadets, and while pursuing his Masters served as the Executive Vice President of Texas A&M’s Student Government, sitting on numerous university advisory boards and committees and helping found the Texas A&M University Career Closet.

In 2019 he was appointed to by the Governor of Texas to sit as Student Regent to the Texas A&M Board of Regents and later as the student representative to the Texas Higher Education Coordinating Board.

He has attained numerous civilian and military awards, including the Buck Weirus Spirit Award for his service to the Texas A&M campus community and the distinction of Distinguished Honor Graduate of his Air Assault class.

Curriculum Vitae

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Levi’s Research Interests are in artificial intelligence with applications in material systems, and lie in computer vision applications and at the intersection of deep learning and physics. He has held many fellowships as an undergraduate and graduate student:

NSF Undergraduate Research Fellow, 2015
NSF Graduate Research Fellow, 2015 - 2016
NSF Data-Enabled Discovery of Energy Materials Fellow, 2017 - 2019
Oak Ridge Associated Universities Fellow, 2019 -
- Sponsored by the US Army CCDC Army Research Lab

Interests

Computer Vision
Microstructure Informatics
Generative Deep Learning
Physics-Informed Deep Learning
Scientific Machine Learning
Numerical Methods in ODE/PDEs

Education

PhD in Electrical Engineering, 2021
Texas A&M University
M.S in Electrical Engineering, 2016
Texas A&M University
B.S. in Electrical Engineering, 2015
Texas A&M University

Technical Skills

Skills involving a keyboard

Python

Extensive scripting experience in Python

Tensorflow

Experience building and training deep learning algorithms Tensorflow and Keras

Julia

Organizer of the Julia subgroup in the Packt Reviewer Committee

R

Wrote and published the open-source software package R:BoolFilter to CRAN

GPU Accelerated Design

Development skills specifically for multi-GPU training

Amazon Web Services

Development and model training experience with Nvidia GPU Support in deep learning AMIs

Other Skills

Skills not related to a keyboard

Security Clearance

DoD-Issued

Leadership

Experience running organizations of 5-50+ people

Military Experience

Member of the US Army Reserve Component for ~10 years

UH-60 Pilot

Hundreds of flight hours in the UH-60 and other DoD and civilian airframes

Executive Oversight

Sat on numerous advisory and executive-level boards

Higher-Ed Advocacy

Years of experience of advocacy in the higher education sector

Research Experience

Professional experience in the research and academic realms

Graduate Research Fellow

Us Army CCDC Army Research Lab

May 2019 – Present Aberdeen, MD / College Staton, TX

Research engineer in the Vehicle Technology Directorate (VTD) applying multi-GPU computer vision and deep-learning based generative models to microscale material images, bridging the gap between GPU-enabled deep learning and what we understand about material systems in the physical world

Additional projects include: bringing domain expertise into a large-scale project for the Joint Artificial Intelligence Center (JAIC) involving analysis of over 27 TB of health monitoring system data for the H-60 platform - utilizing Nvidia’s RAPIDS platform for bayesian methodologies as well as recursive deep learning

Graduate Research Assistant

Texas A&M University

May 2015 – May 2019 College Station, TX

Ph.D. Student at Texas A&M University studying machine learning, deep CNNs, adversarial networks, and materials informatics.

Currently has many publications in multiple journals and IEEE Conference Proceedings, and has also published and currently maintains the open source R software ‘BoolFilter’ on the worldwide R repository, CRAN.

Undergraduate Research Assistant

Texas A&M University

Jan 2015 – May 2015 College Station, TX

Undergraduate research in Partially-Observed Boolean Dynamical Systems

Military Experience

Note: In the US Army, pilots who are also commissioned officers hold leadership positions within their units, in addition to being a rated pilot and performing flight duties.

Lead Intelligence Officer (S2)

1-158 Assault Helicopter Battalion

May 2020 – Present Conroe, TX

Lead Intelligence officer for aviation operations. Responsible for examining and aggregating enemy and friendly courses of action (COAs) and advising battalion staff for timely, effective, and thorough mission execution

Platoon Leader

B-Company, 1-158 Assault Helicopter Battalion

Jul 2017 – May 2020 Conroe, TX

Awards: Distinguished Honor Graduate, Air Assault Course, #1 of 220

Directly responsible for almost 30 personnel and 5 aircraft, totaling approx. $110 million in Army assets.

Job requirements consist of maintaining flight minimums, training requirements, and professional military education while enabling other personnel in the platoon to do the same.

Flight School Candidate, UH-60 Track

D-Company, 1-145 Aviation Regiment

May 2016 – Aug 2017 Ft. Rucker, AL

Flight school student in the IERW UH-60M track at Ft. Rucker, AL.

Graduated as the top ranked reserve component officer and second overall in the class of over 25 commissioned and warrant officers.

Advocacy

Volunteer experience and notable student involvement

Student Representative

Texas Higher Education Coordinating Board

Jun 2020 – Present Austin, TX

The mission of the Texas Higher Education Coordinating Board (THECB) is to provide leadership and coordination for Texas higher education and to promote access, affordability, quality, success, and cost efficiency through 60x30TX, resulting in a globally competitive workforce that positions Texas as an international leader.

Appointed by the Governor of Texas to advocate on behalf of the ~1.5m students in institutions of higher education in the state of Texas. Responsible for advocating for affordability, experience, and quality of higher education institutions and organizations.

Student Regent

Texas A&M University System Board of Regents

Jun 2019 – May 2020 College Station, TX

Appointed by the Governor of Texas to serve as the sole student member to the Texas A&M Board of Regents - responsible for advocating for ~167,000 students across 11 campuses

Directly provide oversight to the Texas A&M University System’s budget of ~$5 billion in revenue and expenditures, reviewing proposals as fit for the individual campuses in the system

Executive Vice President

Texas A&M University Student Government Association

May 2015 – May 2016 College Station, TX

Responsible for stepping in for the Student Body President at a moment’s notice and coordinating the efforts of the Student Government Executive Cabinet to align with the SBPs vision.

Sat on numerous university committees and boards, including Recreational Sports, Student Affairs Fee Advisory Board, Student Organization Funding Assistance Board, Campus Concealed Carry Task force, and others.

Press

Student regent pilot analyzes materials through interdisciplinary program

Texas A&M Engineering Student Spotlight Jun 2019

See Article

Governor Greg Abbott Appoints University Student Regents And Student Representative To The Texas Higher Education Coordinating Board

Texas.gov May 2019

Student Regent Appointment Press Release

See Article

Machine Learning For Stronger Military Vehicles

Texas A&M Today Feb 2019

See Article

Black Hawk helicopter pilot interns with Army Researchers

US Army Public Affairs Dec 2018

See Article

Featured Publications

Levi D. McClenny, Mulugeta Haile, Vahid Attari, Brian Sadler, Ulisses Braga-Neto, Raymundo Arroyave

June 2020 Deep Multimodal Transfer-Learned Regression in Data-Poor Domains Materials Informatics

Deep Multimodal Transfer-Learned Regression in Data-Poor Domains

In many real-world applications of deep learning, estimation of a target may rely on various types of input data modes, such as audio-video, image-text, etc. This task can be further complicated by a lack of sufficient data. Here we propose a Deep Multimodal Transfer-Learned Regressor (DMTL-R) for multimodal learning of image and feature data in a deep regression architecture effective at predicting target parameters in data-poor domains. Our model is capable of fine-tuning a given set of pre-trained CNN weights on a small amount of training image data, while simultaneously conditioning on feature information from a complimentary data mode during network training, yielding more accurate single-target or multi-target regression than can be achieved using the images or the features alone. We present results using phase-field simulation microstructure images with an accompanying set of physical features, using pre-trained weights from various well-known CNN architectures, which demonstrate the efficacy of the proposed multimodal approach.

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