Pedram is a PhD Fellow in Sustainable Machine Learning at the University of Copenhagen. He holds both a BSc and an MSc in Computer Science from the University of Copenhagen, including a six-month exchange during his master’s at ETH Zurich. His research studies the role of algorithmic complexity and description length in modern learning systems, with an emphasis on model compression and quantization. Current work focuses on algorithmic simplification of neural networks, connecting theory from algorithmic information/complexity to practical parameterizations and training procedures that reduce memory and compute while maintaining performance.
He previously led the development of CarbonTracker, a tool for measuring the energy use and carbon footprint of ML workloads, which was recognized by the European Commission’s Innovation Radar. Before academia, he worked as a software engineer at Danske Bank, co-leading the end-to-end design and deployment of their mortgage refinancing system. He also engages in science communication on the growing compute demands of AI and sustainability in ML, including appearances on Danish national TV, such as DR and TV2.
From February to June 2026, Pedram is a visiting researcher at the University of Sydney under Professor Yiming Ying (Department of Mathematics and Statistics) to work at the intersection of optimization guarantees and algorithmic complexity.