Francisca Vasconcelos

About Francisca Vasconcelos

In the early 1990s, Francisca Vasconcelos’ parents emigrated together from Portugal to the United States in pursuit of world-class scientific research opportunities. Francisca was born in Boston, while her parents were PhD students at MIT and Harvard University. When she was five, her parents began working at the University of California San Diego and Francisca was subsequently raised in San Diego.

Francisca graduated from MIT in 2020 with a BS in electrical engineering, computer science, and physics. As an undergraduate, she performed substantial research involving machine learning and data analysis for quantum computers in the MIT Engineering Quantum Systems Group, under the guidance of Professor William Oliver. Drawing upon her teaching and research experience at MIT, in 2019, Francisca became the founding academic director of The Coding School nonprofit’s Qubit x Qubit initiative. In the first year of the program, she taught over 8,000 students of all different backgrounds about the fundamentals of quantum computation. Since then, QxQ has introduced over 20,000 diverse students worldwide to the technology.

In 2020, Francisca received the Rhodes Scholarship, enabling her to pursue an MSc in statistical sciences and an MSt in philosophy of physics at the University of Oxford. Following from the MSc, Francisca performed substantial research on uncertainty quantification of machine learning models for medical imaging in the OxCSML group, under the supervision of Professor Yee Whye Teh. During her time at Oxford, Francisca also played for the Oxford University Women’s Blues Football team, receiving a Blues sporting award. 

Francisca is now a computer science PhD student and NSF Graduate Research Fellow at the University of California Berkeley. She is a member of both the Berkeley Artificial Intelligence Research (BAIR) Lab and CS Theory Group, advised by Professors Michael Jordan and Umesh Vazirani. Her research interests lie at the intersection of quantum computation and machine learning. She is especially interested in developing efficient classical algorithms to learn about quantum systems, as well as quantum algorithms to improve simulations of quantum processes. In doing so, Francisca hopes to find meaningful ways in which quantum computers can outperform classical computers, such as improving our understanding of physics and chemistry.