Payam Mousavi

Payam Mousavi

Research Scientist / ML Scientist

I am a research scientist with 12+ years of experience bridging physics, engineering, and machine learning. I develop production-grade solutions in agentic systems, reinforcement learning, computer vision, and control systems across gaming, defense, energy, and logistics. I am experienced in leading R&D teams from research through deployment.

Location
Vancouver, British Columbia, Canada
Website
https://payam-mousavi.com
Twitter
PayamMousavi4
LinkedIn
pmousavi

Experience

– present

Senior Machine Learning Scientist at Electronic Arts

Technical lead on research projects for autonomous agents in 3D games within Quality Verification Services (QVS)

Highlights

  • Developing reinforcement learning technologies for autonomous agent navigation and decision-making in 3D ARPG and FPS game environments
  • Architecting agentic systems that integrate System I (fast/reactive) and System II (slow/deliberative) reasoning for complex 3D game scenarios
  • Building tools to augment LLM-based agents with 3D spatial awareness and reasoning capabilities
  • Extending theoretical foundations of state-space coverage to enhance and optimize automated game testing

– present

Applied Research Scientist at Alberta Machine Intelligence Institute (Amii)

Technical lead on multi-domain research projects with industrial clients in the Advanced Technology group

Highlights

  • Led the Advanced Technology group, delivering ML solutions to industrial clients
  • Created mathematical models and optimization software for plunger lift systems in oil & gas, reducing operational downtime and environmental impact
  • Developed physics-informed neural network models for chemical process control in industrial settings
  • Applied RL to a wide range of domains: industrial process control, multi-robot warehouse planning, active noise cancellation, VLSI routing, and analog circuit design
  • Developed and deployed real-time control tools for the Drayton Valley Water Treatment Plant, directly interfacing with SCADA systems to optimize membrane cleaning

–

Staff R&D Scientist (Team Lead) at MDA Systems

Leading the AI Group in optics, ML for natural/satellite imagery, Earth Observation, and Command & Control

Highlights

  • Designed and implemented GAN and VAE architectures to synthesize, manipulate, and perform anomaly detection on satellite imagery
  • Developed software for detection, classification, and tracking of vessels in high-resolution optical satellite imagery
  • Explored over-the-horizon communication concepts using laser-induced sodium layer scattering mechanisms
  • Worked with Synthetic Aperture Radar (SAR) and high-resolution hyperspectral imaging data

–

Research Scientist at Phase Technology

Optical analyzers for cold-flow property measurement of hydrocarbons

Highlights

  • Designed and optimized optical imaging systems using TracePro, COMSOL, OpenCV, and MATLAB
  • Created automated tools to image and measure pour, cloud, and freezing points of liquids (e.g., jet fuel)
  • Developed robotic arm control software (MATLAB, LabView, Python) for automated sample loading
  • Applied machine learning techniques for liquid sample classification from optical measurements

–

PhD Candidate at Honeywell Automated Control Solutions (ACS)

Industrial terahertz imaging & spectroscopy for material composition analysis

Highlights

  • Designed and built a terahertz spectrometer for non-destructive composition analysis of dielectric materials
  • Developed mathematical models of the electromagnetic response of flat complex dielectrics in the terahertz spectrum
  • Created an inference algorithm to determine material composition from optical measurements
  • Developed a comprehensive statistical noise model that improved system measurement precision
  • Contributed to the design and construction of the industrial alpha-prototype terahertz sensor

Education

–

PhD in Applied Physics from Simon Fraser University

–

MASc in Mechanical Engineering from University of British Columbia

–

BASc in Engineering Physics (Electrical Major) from University of British Columbia

Publications

MaskRenderer: 3D-Infused Multi-Mask Realistic Face Re-enactment , Pattern Recognition, vol. 157

A Novel Framework for Automated Warehouse Layout Generation , Frontiers in Artificial Intelligence, vol. 7

Human-in-the-Loop Reinforcement Learning: A Survey and Position on Requirements, Challenges, and Opportunities , Journal of Artificial Intelligence Research (JAIR), vol. 79, pp. 359-415

Applying Reinforcement Learning to Learn Best Net to Rip and Re-route in Global Routing , ACM Trans. Design Automation of Electronic Systems (TODAES), vol. 29(4), pp. 1-21

Multi-Robot Warehouse Optimization: Leveraging Machine Learning for Improved Performance , AAMAS 2023, pp. 3047-3049

RL-Ripper: A Framework for Global Routing Using Reinforcement Learning and Smart Net Ripping Techniques , ACM Great Lakes Symposium on VLSI (GLVLSI), pp. 197-201

A Real-time Bayesian Decision-Support System for Predicting Suspect Vehicle’s Intended Target Using a Sparse Camera Network , International Conference on Defense, Intelligence, and Security (ICDIS)

Maximum Likelihood Parameter Estimation in Terahertz Time-Domain Spectroscopy , Optics Express, vol. 29(4), pp. 4912-4926

Deep Learning for Vessel Detection and Identification from Spaceborne Optical Imagery , Ann. Photogramm. Remote Sens. Spatial Inf. Sci. (ISPRS), V-3-2021, pp. 303-310

Onboard Artificial Intelligence for Space Situational Awareness with Low-Power GPUs , 21st Advanced Maui Optical and Space Surveillance Technologies (AMOS)

Simultaneous Composition and Thickness Measurement of Paper Using Terahertz Time-Domain Spectroscopy , Applied Optics

A Novel Flow Reactor for Studying Reactions on Liquid Surfaces Coated by Organic Monolayers , The Journal of Physical Chemistry A, vol. 111(43)

Time Domain Spectroscopy (TDS)-Based Method and System for Obtaining Coincident Sheet Material Parameters , U.S. Patent 8,187,424

Continuous Referencing for Increasing Measurement Precision in Time-Domain Spectroscopy , U.S. Patent 8,378,304

Languages

English
Fluency: Native speaker
Farsi
Fluency: Native speaker

Skills

Machine Learning
Level: Master
Keywords:
  • Reinforcement Learning
  • Deep Learning
  • Generative Models (GANs, VAEs, Diffusion models)
  • Physics-Informed Neural Networks
  • Computer Vision
  • LLM Agents
Domains
Level: Master
Keywords:
  • Autonomous Game Agents
  • Communication Theory
  • Industrial Process Control
  • Satellite Imagery / Earth Observation
  • Optical Imaging & Spectroscopy
  • Multi-Robot Planning
  • VLSI Routing
Languages & Tools
Level: Master
Keywords:
  • Python
  • PyTorch
  • Mathematica
  • OpenCV
  • MATLAB
  • LabView
  • C/C++
  • COMSOL
  • TracePro
Leadership
Level: Master
Keywords:
  • Technical roadmap development
  • Cross-functional team leadership
  • Client-facing research delivery
  • Strategic planning

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