The minds behind Engram

Imperial Business School

Zihan leads Engram's business model, go-to-market, and investor relations. Before founding Engram, he worked in strategy consulting at Kearney and investment analysis at V-Capital, where he developed deep expertise in financial analysis and quantitative research. He is completing his undergraduate studies and is an incoming FinTech MS student at Imperial College Business School, bringing rigorous commercial thinking to how Engram approaches the market. He believes the center of entrepreneurship is shifting from producing products to producing consensus, and that cognitive alignment is the infrastructure that makes that possible.

Imperial · MIT

Yuhan leads Engram's research and engineering. He holds a MEng in Mathematics and Computer Science from Imperial College London and is a researcher at the MIT Media Lab, where he co-authored research on 4D perception and dynamic scene understanding. As an AI Engineer at Millennium, he brings production-grade systems experience to Engram's cognitive alignment engine. He is driven by the conviction that the best interface between human and machine cognition is one that neither side notices.

Harvard · MIT

Grace leads Engram's technical strategy and feasibility validation, combining data science, computer science, and mathematics. She is pursuing a Master's in Data Science at Harvard and conducts research at the MIT Media Lab on computer vision and AI perception systems. As an AI Engineer at Millennium, she brings hands-on experience with production-grade AI systems. At Engram, she stress-tests architectural decisions, evaluates whether cognitive alignment approaches can scale in practice, and bridges research ambition with engineering reality.

Imperial Business School

Stephen contributes to Engram's quantitative research. He is a Mathematics BSc at Imperial College London with depth in probability, statistics, stochastic processes, and numerical methods, and an incoming FinTech MS at Imperial College Business School. Previously a Research Intern at the World Resources Institute, where he built China-EU steel emissions datasets and shipped a Python + OpenAI API document-analysis pipeline, and an analyst intern at Tianjin Jia Hong PE, screening 30+ renewable-energy companies. At Engram, he combines mathematical training with applied tooling (Python simulations for queueing models, numerical methods for non-local PDEs) to turn complex problems into decision-useful outputs.