Summary
Ivoline Ngong is a CS Ph.D. student at the University of Vermont and a research scientist at OpenMined with a decade of hands-on experience spanning privacy-preserving ML, federated learning, differential privacy, and provable fairness. Her work bridges theory and practice: she develops and implements privacy accounting and prediction-sensitivity techniques in PySyft and has led study groups and programs on medical federated learning. Past projects include deep learning for medical imaging, generative models, and compiler optimization research at Microsoft Research using reinforcement learning. She has collaborated with industry leaders (IBM, Google, DeepLearning.AI) and secured an Amazon Research Award supporting her PLAID lab research. Known for organizing communities and translating complex research into usable tools, she combines rigorous academic inquiry with open-source and educational impact. Based in Vermont, she often pairs formal privacy guarantees with practical evaluation to make ML systems both trustworthy and usable.
10 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of Vermont
Deep Learning Nanodegree, Deep Learning Nanodegree at Udacity
Master of Engineering - MEng, Computer Engineering, Master of Engineering - MEng, Computer Engineering at Konya Technical University
Bachelor's degree, Computer Software Engineering, Bachelor's degree, Computer Software Engineering at University of Buea
English, French, Turkish, creoles and pidgins, english-based