Brian Jalaian

Brian Jalaian

Senior AI Research Scientist / Adjunct Assistant Professor

ARL / Virginia Tech

Biography

Dr. Brian Jalaian is an accomplished researcher and educator, currently serving as an Associate Professor of Computer Science at the University of West Florida (UWF) and a research scientist at IHMC (Institute for Human and Machine Cognition). With a strong commitment to advancing the field of artificial intelligence and machine learning, he joined IHMC and UWF to establish a multidisciplinary modern machine learning team that tackles complex challenges in AI and autonomy across the private and public sectors.

As a Computer Science professor, Dr. Jalaian specializes in robust machine learning, deep learning, and uncertainty quantification. His research delves into cutting-edge methodologies in AI assurance, safety, and security, while his expertise in optimization techniques and network science allows him to contribute to a wide range of research domains.

Alongside his research endeavors, Dr. Jalaian is dedicated to educating the next generation of AI and robotics scientists. As a faculty member in the joint Ph.D. program between IHMC and UWF ISR, he combines his passion for research with mentoring and guiding students. Previously, he served as a technical STEM leader in AI Test & Evaluation and as a machine learning research scientist in the public sector. Additionally, he held an adjunct faculty position in the Department of Electrical and Computer Engineering at Virginia Polytechnic Institute and State University (Virginia Tech). Dr. Jalaian holds a Ph.D. and master’s degrees in Electrical Engineering, as well as a master’s degree in Industrial and Systems Engineering with a focus on Operations Research, all from Virginia Tech.

Driven by the diverse research expertise at IHMC, Dr. Jalaian continues to advance the frontiers of deep learning and artificial intelligence. His collaborations with talented researchers and supportive colleagues fuel his enthusiasm for pushing the boundaries of AI. The captivating and sunny environment of Pensacola further enhances his work, providing an inspiring backdrop for his research endeavors.

For more information about Dr. Brian Jalaian’s research and accomplishments, please visit his website.

Interests
  • Safety and security of Artificial Intelligence
  • Uncertainty quantification for machine learning
  • Deep learning, Self-supervised learning, machine learning
  • Bayesian Deep Learning
  • Optimization
  • Complex Network Optimization
Education
  • PhD in Electrical Engineering - Network Optimization, 2016

    Virginia Tech

  • M.Sc. in Operation Research, 2014

    Virginia Tech

  • M.Sc in Electrical Engineering - Communication Systems, 2013

    Virginia Tech

Skills

Technical Leadership

100%

Team Building & Team work

100%

Deep/Machine Learning Researcher

100%

Quantitative Research

100%

Python, PyTorch & TensorFlow

100%

AI Assurance, Safety, & Security

100%

Experience

 
 
 
 
 
ARL
Senior Artificial Intelligence Researcher
ARL
Feb 2020 – Present Maryland

Responsibilities include:

  • Perform research on deep learning specifically on Bayesian Neural Network (BNN), adversarial machine learning, robust and resilient AI
  • Design novel framework for test and evaluation of machine learning for mission critical application
  • Technical advisor and subject matter expert on artificial intelligence assurance
 
 
 
 
 
ARL
Technical Research Lead & Research Scientist
ARL
Jan 2016 – Feb 2020 Maryland

Responsibilities include:

  • Perform basic research on deep learning, uncertainty quantification for machine learning, adversarial machine learning and Bayesian deep learning
  • Lead a research group focusing on uncertainty quantification for machine learning that Elevated the importance of risk-aware artificial intelligence at the laboratory (ARL) level in addition the DoD (OSD)
  • Technical Research Lead for a IoBT CRA: leading research on Safe, Robust and Resilient AI. Collaborating with academics from University of Illinois Urbana-Champaign, University of California Los Angeles, University of Southern California, University of Massachusetts Amherst, Purdue University, Carnegie Mellon University, Stanford and SRI International
 
 
 
 
 
Virginia Tech
Adjunct Assistant Professor
Jun 2018 – Present

Responsibilities include:

  • Perform basic research in complex network optimization
  • Advise Ph.D. students
  • Teach Graduate Courses in electrical and computer engineering department
 
 
 
 
 
Complex Network & Security Research Group, Virginia Tech
Research Assistant
Aug 2011 – Dec 2015 Blacksburg, Virginia

Responsibilities include:

  • Conduct basic research on mathematical modeling & optimization for complex networks
  • Perform basic research on modeling complex network dynamics via mathematical programming
  • Conduct basic research on wireless network optimization
 
 
 
 
 
National University of Singapore
Research Associate
Jan 2008 – Jul 2011 Singapore

Responsibilities include:

  • Conduct basic research on design & development of cognitive radio networks
  • Design novel wireless networking and communication protocols

Recent Publications

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(2021). Fountain Coding for Information Protection in Tactical Networks. ICMCIS.

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(2021). Machine learning raw network traffic detection. SPIE 2021.

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(2021). Minimizing AoI in a 5G-based IoT Network under Varying Channel Conditions. IEEE Internet of Things Journal.

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(2021). Maximize Spectrum Efficiency in Underlay Coexistence With Channel Uncertainty. IEEE/ACM ToN.

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(2020). Better call Surrogates: A hybrid Evolutionary Algorithm for Hyperparameter optimization. NeurIps Black Box Optimization Challenge.

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