Summary
Golnoosh Farnadi is an Assistant Professor and researcher with a decade of experience at the intersection of machine learning, user modeling, and fairness in AI, currently based in Canada and affiliated with McGill and Mila. She holds a PhD from KU Leuven and Ghent University for work that leveraged social media profiles to infer latent user attributes for personalization and ad retrieval. Her trajectory spans top research labs and universities worldwide—including postdocs at UC Santa Cruz, Université de Montréal, and industry collaborations at Microsoft Research and Google—demonstrating a balance of theoretical work in probabilistic graphical models and applied causal and fairness-focused research. She is known for translating complex probabilistic and relational learning methods into practical systems for user profiling and bias discovery, and for applying deep fusion techniques to noisy social media data. Colleagues would note her uncommon combination of long-term academic rigor with hands-on engineering experience dating back to mobile and systems development roles.
10 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy (PhD) User modeling and advertisement retrieval, Doctor of Philosophy (PhD) User modeling and advertisement retrieval at KU Leuven
Doctor of Philosophy (PhD) User modeling and advertisement retrieval, Doctor of Philosophy (PhD) User modeling and advertisement retrieval at Ghent University
Bachelor of Science (BSc) Computer Science, Bachelor of Science (BSc) Computer Science at Shahid Beheshti University
Master of Science (MSc) Computer Science, Master of Science (MSc) Computer Science at Delft University of Technology
English, Persian, Dutch