# Francisca Vasconcelos

CS PhD @ UC Berkeley Theory Group and BAIR Lab.

francisca @ berkeley.edu

I am a second-year PhD student and NSF Graduate Research Fellow in the UC Berkeley Department of Electrical Engineering and Computer Science. I am co-advised by Profs Michael Jordan and Umesh Vazirani. My research interests lie at the intersection of quantum computation and machine learning theory.

In 2020, I received a BS in EECS and Physics from MIT, where I was fortunate to do substantial undergraduate research advised by Prof William Oliver in the MIT Engineering Quantum Systems group. As an undergraduate, I also interned under Dr. Marcus da Silva at Rigetti Computing and Microsoft Research Quantum. Supported by a Rhodes Scholarship, I received two masters from the University of Oxford: an MSc in Statistical Sciences and MSt in Philosophy of Physics. Following from the MSc, I performed statistical ML research in the OxCSML group, advised by Prof Yee Whye Teh.

I am also the Founding Academic Director of the Qubit x Qubit (QxQ) initiative of The Coding School (TCS) non-profit. Since 2019, we have taught 20,000+ diverse K-12 students, undergraduates, and members of the workforce worldwide about the fundamentals of quantum computing and QISE.

## selected publications

- A Quadratic Speedup in Finding Nash Equilibria of Quantum Zero-Sum Games
*2023*⭐ Long talk at(CERN).**QTML**2023 - On the Pauli Spectrum of QAC
^{0}*In*, 2024**56th Annual ACM Symposium on Theory of Computing (****STOC**)⭐ Talk at(Taiwan)**QIP**2024

I also presented at: Simons Quantum Pod, Berkeley CS Theory Lunch. - UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography
, 2023**Transactions on Machine Learning Research (****TMLR**)🎓 Oxford MSc in Statistical Sciences Thesis

Early version of work (Abstract) also presented at NeurIPS 2021 workshops:

Med-NeurIPS (Oral - 6.66% Acceptance Rate) & Bayesian Deep Learning (Poster)