Team
Our Mission
Due to a fundamental value misalignment, installing private energy resources in hopes of contributing to energy sustainability and reducing bills increases overall energy prices.
TREX fixes this by combining AI technology and peer-to-peer energy markets.
The Vision
A financially and ecologically sustainable energy future, that fairly distributes value and gives everyone an opportunity to pitch in.
Peter Atrazhev
CFO, Co-Founder
Education & Expertise:
- 7 years of experience on applying multi agent reinforcement learning
- MSc Candidate, Software Engineering and Intelligent Systems, University of Alberta
- BSc., Electrical Engineering, University of Alberta
- Expert in multi-agent reinforcement learning
- Knowledge Management
- Company finances, bookkeeping
- Grants
Daniel May
COO, Co-Founder
Education & Expertise:
- 10 years of R&D on autonomous energy management systems in academic and industrial setting
- PhD Candidate, Software Engineering and Intelligent Systems, University of Alberta
- BSc., Electrical and computer Engineering, Technische Universität München
- MSc., Electrical and computer Engineering, Technische Universität München
- Working Student, automotive energy system predevelopment, BMW Group
- Expert for on applying AI based methods to system control tasks
- Engineering Management
- Philosophy of Innovation
- First Principles Thinking
Steven Zhang
CTO, Co-Founder
Education & Expertise:
- 11 years of experience on energy transition and distributed energy resources in academia and industry
- PHD Candidate, Software Engineering and Intelligent Systems, University of Alberta
- BSc., Electrical Engineering, University of Alberta
- Design Lead, Atco Electric
- Expert for transactive energy and local energy markets
- Rapid prototyping
- Microelectronics and embedded systems
T-Rex
CEO, Mascot
Our Mentors
Matt Taylor
Associate Professor
Faculty of Computing Science
Director of the Intelligent Robot Learning Lab
Fellow-in-Residence at Amii
Established expert in applying machine learning and reinforcement learning to real-world problems
