About me

Hello! My name is Sílvia Casacuberta Puig and I come from Barcelona. This past May, I graduated summa cum laude from Harvard with a joint A.B. degree in Mathematics and Computer Science and a concurrent S.M. in Computer Science. Next fall, I will be continuing my Computer Science studies at the University of Oxford on a Global Rhodes Scholarship. After that, I will start my PhD in Computer Science at Stanford University, supported by a Stanford Graduate Fellowship.

I am broadly interested in theoretical computer science, including matrix algorithms, cryptography, data privacy, and algorithmic fairness. For my senior thesis, I worked with Salil Vadhan and Cynthia Dwork on complexity-theoretic implications of multicalibration. You can read my thesis here.

Email: scasacubertapuig [at] college.harvard.edu. Google scholar.


Differential Privacy
Widespread Underestimation of Sensitivity in Differentially Private Libraries and How to Fix It, with Michael Shoemate, Salil Vadhan, and Connor Wagaman. In ACM CCS 2022. Preliminary version at TPDP 2022 at ICML 2022. arXiv. Paper selected for one of the six spotlight talks at TPDP.

SoK: Oblivious Pseudorandom Functions, with Julia Hesse and Anja Lehmann. In IEEE EuroS&P 2022. IACR ePrint. EuroS&P 2022 Distinguished Paper Award Finalist.

Matrix Algorithms
Faster Matrix Inversion and Rank Computation in Finite Fields, with Rasmus Kyng. In ITCS 2022. arXiv.

Natural Language Processing
Evaluating Word Embeddings with Categorical Modularity, with Karina Halevy and Damián Blasi. In Findings of ACL, 2021. arXiv, GitHub.

Interpretable Machine Learning
PCACE: A Statistical Approach to Ranking Neurons for CNN Interpretability, with Esra Suel and Seth Flaxman. In Responsible AI and DeepSpatial workshops at KDD 2021. arXiv.

Number Theory
On the Divisibility of Binomial Coefficients. Ars Math. Contemp. 19 (2020), 297-309. arXiv.


Algorithmic Fairness & Complexity Theory
Finding Simple Models of Complex Objects: From Regularity Lemmas to Algorithmic Fairness. Senior thesis supervised by professors Salil Vadhan & Cynthia Dwork. PDF, 2023. Awarded the Thomas T. Hoopes Prize and the Captain Jonathan Fay Prize (awarded to the top three best theses of Harvard College’s graduating class).

Computational Social Choice
Obvious Independence of Clones, with Ratip Emin Berker, Christopher Ong, and Isaac Robinson. arXiv preprint, 2022.

Quantum and Classical Algorithms for Bounded Distance Decoding, with Richard Allen, Ratip Emin Berker, and Michael Gul. IACR ePrint, 2022.


Complexity-theoretic Implications of Multicalibration: Hardcore Sets, Dense Models, and Pseudoentropy, Simons Institute Workshop on Multigroup Fairness and the Validity of Statistical Judgment, Berkeley (US), April 2023.

Finding Simple Models of Complex Objects: From Regularity Lemmas to Algorithmic Fairness, Harvard Math Table, April 2023.

Widespread underestimation of sensitivities in DP libraries, Northeastern Network and Distributed Systems Security seminar, Boston (US), April 2023. Joint with Connor Wagaman.

Widespread underestimation of sensitivities in DP libraries, CCS’22, Los Angeles (US), November 2022. Joint with Connor Wagaman.

Oblivious pseudorandom functions, EuroS&P 2022, Genoa (Italy), June 2022.

Widespread underestimation of sensitivities in DP libraries: vulnerabilities and solutions, Privacy Tools DP seminar in Boston (US), May 2022. Joint with Connor Wagaman.

Faster sparse matrix inversion rank computation in finite fields, ITCS’22 (video), February 2022.

Faster sparse matrix inversion and rank computation in finite fields, Exact Computing Research Seminar at Université de Montpellier (France), January 2022.

Faster sparse matrix inversion and rank computation in finite fields, Google Zurich Algorithms & Optimization Group Seminar (virtual), December 2021.

Verification of differentially private algorithms, Harvard PRISE final presentation (virtual), August 2021.

PCACE: A Statistical Approach to Ranking Neurons for CNN Interpretability, Responsible AI workshop and DeepSpatial workshop at KDD 2021 (virtual), August 2021.

Oblivious pseudorandom functions (OPRFs): origins and modern applications, IBM Research Security Seminar, Zurich (Switzerland), May 2021.

Why are p-adic numbers useful for fast linear algebra algorithms?, Harvard Math Table (poster), December 2020.

On the divisibility of binomial coefficients, final presentation at the Research Science Institute at MIT (video), Boston (US), August 2017.



CS 226r: Differential Privacy, taught by Prof. Cynthia Dwork (Spring 2023).

AM 107: Graph Theory and Combinatorics, taught by Prof. Leslie Valiant (Spring 2022).

CS 120: Introduction to Algorithms and Their Limitations, taught by Prof. Salil Vadhan (Fall 2021, Fall 2022).

CS 124: Data Structures and Algorithms, taught by Prof. Michael Mitzenmacher (Spring 2020).

Math 1b: Calculus, Series, and Differential Equations (Fall 2019).


Co-president of Harvard Gender Inclusivity in Mathematics (Fall 2021-Spring 2022).

Co-academics director of Harvard Women in Computer Science (Fall 2020-Spring 2022).

Present and past affiliations

OpenDP (https://opendp.org/): June 2021 – present. Supervisor: Prof. Salil Vadhan.

IBM Research Zurich: January – May 2021. Supervisor: Dr. Julia Hesse.

ETH Zurich CS department: June – December 2020. Supervisor: Prof. Rasmus Kyng.

Harvard Radcliffe Institute: September 2019 – May 2020. Supervisor: Dr. Damián Blasi.

Imperial College Mathematics department: June – August 2019. Supervisor: Prof. Seth Flaxman.

Max Planck Institute for Quantum Optics: July 2018. Supervisor: Prof. Jordi Tura.

MIT Mathematics department: June – August 2017. Supervisor: Dr. Oscar Mickelin.