Cecilia Ferrando

Research Assistant in Machine Learning and Artificial Intelligence

University of Massachusetts, Amherst

I am a PhD student in the Manning College of Information and Computer Sciences at the University of Massachusetts, Amherst. My current research is on statistical inference under differential privacy. I'm broadly interested in statistical machine learning and privacy-preserving machine learning. I am fortunate to be advised by professor Daniel Sheldon.

Before starting my PhD, I obtained a MS in Computational Design at Carnegie Mellon University, where I was supported by the Fulbright Scholarship. Upon graduating from Carnegie Mellon, I worked as a Machine Learning Software Engineer at Cadence, Pittsburgh. In the past couple of years, I have worked as a Summer research intern at Google (2021) and Meta (2022).

Besides research and courses, I serve as Co-Chair of the PhD Applicant Support Program. In 2021, I also co-lead the organizing committee of Voices of Data Science at UMass Amherst 2021.


  • Statistical machine learning
  • Privacy-preserving machine learning
  • Differential Privacy


  • PhD in Computer Science, 2025 (exp.)

    University of Massachusetts, Amherst

  • MS in Computational Design, focus on Machine Learning, 2018

    Carnegie Mellon University

  • BA+MA in Economics and Statistics, 2016

    Collegio Carlo Alberto

  • BSc+MSc in Architecture, 2015

    Politecnico di Torino


Combining Public and Private Data

Differential privacy is widely adopted to provide provable privacy guarantees in data analysis. We consider the problem of combining …

Parametric Bootstrap for Differentially Private Confidence Intervals

One of the most common statistical goals is to estimate a population parameter and quantify uncertainty by constructing a confidence …

Architectural Distant Reading Using Machine Learning to Identify Typological Traits Across Multiple Buildings

This paper introduces an approach to architectural “distant reading”: the use of computational methods to analyze architectural data in …

A Machine Learning Framework for Spatial Analysis

Can Machine Learning algorithms become a useful tool in the analysis of architectural space? Motivated by this question, in this poster …



Research Engineer Intern


May 2022 – Jul 2022 New York, NY
Differential Privacy applied research with James Honaker

Research Intern

Google Research NY

May 2021 – Aug 2021 New York, NY
Differential Privacy research with Alex Kulesza and Jenny Gillenwater, Modeling and Data Science team, NY

Research Assistant

College of Information and Computer Sciences, University of Massachusetts, Amherst

Sep 2019 – Present Amherst, MA
Private Machine Learning research with prof. Daniel Sheldon

Machine Learning Software Engineer

Cadence Design Systems

Jun 2018 – May 2019 Pittsburgh, PA
Applied research in deep learning, GANs and unsupervised learning

Quantitative Research Intern

Procore Technologies

May 2017 – Jul 2017 Santa Barbara, CA
Statistical data analysis for UX

Research Assistant

CodeLab, Carnegie Mellon University

Apr 2017 – Sep 2017 Pittsburgh, PA
Computational design research with prof. Daniel Cardoso Llach

Service and Leadership

  • (2022-) JMLR reviewer

  • (2020-2022) PhD Applicant Support Program (PASP). University of Massachusetts Amherst CICS, Co-Founder and Co-Chair. A new mentorship program for prospective PhD students, with a focus on supporting underrepresented candidates. Received Dean’s Outstanding Anti-Racism Leadership Award.

  • (2020-2022) Graduate mentor. Mentored 8 CS undergraduate students. Honors thesis mentor to Adi Geva (now at NVIDIA).

  • (2019-2020) Voices of Data Science. Co-Chair. Lead the committee organizing the inaugural Voices of Data Science at UMass Amherst conference. The 2021 edition highlighted work by women (cis and trans) and non-binary data scientists

  • (2020) UMass Graduate CS Women group. Social Co-Chair. Organized networking events for CS women graduate students and faculty




Machine Learning

Deep Learning


Data Analysis

Differential Privacy

Probabilistic Graphical Models

Reinforcement Learning