Francisca Vasconcelos

CS PhD @ UC Berkeley Theory Group and BAIR Lab.


francisca @

I am a second-year PhD student in the UC Berkeley Department of Electrical Engineering and Computer Science. My research is supported by the NSF Graduate Research Fellowship and Paul & Daisy Soros Fellowship for New Americans. 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

  1. qzsg.png
    A Quadratic Speedup in Finding Nash Equilibria of Quantum Zero-Sum Games
    Francisca Vasconcelos*, Emmanouil-Vasileios Vlatakis-Gkaragkounis*, Panayotis Mertikopoulos, Giorgios Piliouras, and Michael I. Jordan
    ⭐ Long talk at QTML 2023 (CERN).
  2. qac0.png
    On the Pauli Spectrum of QAC0
    Shivam Nadimpalli*, Natalie Parham*, Francisca Vasconcelos*, and Henry Yuen*
    In 56th Annual ACM Symposium on Theory of Computing (STOC), 2024
    ⭐ Talk at QIP 2024 (Taiwan)
    I also presented at: Simons Quantum Pod, Berkeley CS Theory Lunch, CWI QuSoft Seminar.
  3. uncertainr.png
    UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography
    Francisca Vasconcelos*, Bobby He*, Nalini Singh, and Yee Whye Teh
    Transactions on Machine Learning Research (TMLR), 2023
    🎓 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)