Rami Ariss

About Rami Ariss

Hello, I am Rami! I build AI/ML-powered software to optimize energy and transportation systems. In 2019, I led the launch of an AI-powered energy bidding platform that optimized a large wind farm in South Australia. This experience inspired me to deepen my technical knowledge by pursuing a Ph.D. at Carnegie Mellon University, focusing on applying AI/ML to impactful energy and transportation problems. My research focuses on sequential decision making, reinforcement learning, and optimization methods for coordinated transportation systems.As a product manager, I combined technical domain-knowledge in the economic value of behind-the-meter distributed energy resources and front-of-the-meter solar, wind, and energy storage assets with design thinking and an eye for UI/UX. I successfully launched an intelligent AI-powered energy bidding product (now Fluence Mosaic) for the Australian Energy market, and led a data-driven energy management SaaS product from ideation to launch.Driven to deepen my AI/ML knowledge, I pursued a Ph.D. at Carnegie Mellon, the top ranked university for AI. I leveraged my industry-acquired subject-matter expertise to design impactful energy and transportation problems where coordinating complex decisions required state-of-the-art optimization and AI/ML methods to solve. In May 2024, I completed my Civil Engineering Ph.D. by solving novel problems and developing methods for making electric delivery trucks economically competitive via coordinated grid-savvy routing and for optimal control of a demand-responsive vehicle coordinated with shared transportation for equitable ride-pooling.I am excited to return to industry with my Ph.D. to build decision intelligence into the heart of user-centric software. I aim to innovate at the bleeding edge of AI/ML applications for robotaxis, self-driving cars, electric vehicles, energy trading, and beyond. In my leisure, you will find me paddling out for a surf session, producing music, recharging with a home cooked Lebanese meal, and hanging out at the local darkroom developing and printing film. I look forward to connecting!

Experience

1. Staff Ai Machine Learning Engineer, Data Intelligence Group

   General Motors

   Jun 2023 — Present

   Leading development of a fleetwide managed charging simulation and MILP model that optimally determines charging schedules that minimize costs and satisfy delivery requirements for multiple ZEVOs with uncertain dwell times and SoCs.• Implement an electricity tariff engine using factory design methods and integrate a third-party tariff application (Genability) to achieve a tariff engine calculator accuracy with <1% error of actual costs for use in customer-facing Insights features.

Education

• University of California, Berkeley

  Bachelor of Science (B.S.), Chemical Engineering (major), Energy Engineering (minor), Conservation and Resources (minor)

• University of California, Berkeley

  Master of Science (M.S.), Civil and Environmental Engineering, Energy, Civil Infrastructure, and Climate (ECIC)

• Carnegie Mellon University

  Doctor of Philosophy - PhD, Civil and Environmental Engineering, Advanced Infrastructure Systems (AIS)